Tech Guides - Artificial Intelligence

The ethics of artificial intelligence seems to have found its way into just about every corner of public life. From law enforcement to justice, through to recruitment, artificial intelligence is both impacting both the work we do and the way we think. But if you really want to get into the ethics of artificial intelligence you need to go further than the public realm and move into the bedroom. Sex robots have quietly been a topic of conversation for a number of years, but with the rise of artificial intelligence they appear to have found their way into the mainstream - or at least the edges of the mainstream. There’s potentially some squeamishness when thinking about sex robots, but, in fact, if we want to think seriously about the consequences of artificial intelligence - from how it is built to how it impacts the way we interact with each other and other things - sex robots are a great place to begin. Read next: Introducing Deon, a tool for data scientists to add an ethics checklist Sexualizing artificial intelligence It’s easy to get caught up in the image of a sex doll, plastic skinned, impossible breasts and empty eyes, sad and uncanny, but sexualized artificial intelligence can come in many other forms too. Let’s start with sex chatbots. These are, fundamentally, a robotic intelligence that is able to respond to and stimulate a human’s desires. But what’s significant is that they treat the data of sex and sexuality as primarily linguistic - the language people use to describe themselves, their wants, their needs their feelings. The movie Her is a great example of a sexualised chatbot. Of course, the digital assistant doesn’t begin sexualised, but Joaquin Phoenix ends up falling in love with his female-voiced digital assistant through conversation and intimate interaction. The physical aspect of sex is something that only comes later. https://youtu.be/3n5muEWaE_Q Ai Furuse - the Japanese sex chatbot But they exist in real life too. The best example out these is Ai Furuse, a virtual girlfriend that interacts with you in an almost human-like manner. Ai Furuse is programmed with a dictionary of more than 30,000 words, and is able to respond to conversational cues. But more importantly, AI Furuse is able to learn from conversations. She can gather information about her interlocutor and, apparently, even identify changes in their mood. The more you converse with the chatbot, the more intimate and closer your relationship should be (in theory). Immediately, we can begin to see some big engineering questions. These are primarily about design, but remember - wherever you begin to think about design we’re starting to move towards the domain of ethics as well. The very process of learning through interaction requires the AI to be programmed in certain ways. It's a big challenge for engineers to determine what’s really important in these interactions. The need to make judgements on how users behave. The information that’s passed to the chatbot needs to be codified and presented in a way that can be understood and processed. That requires some work in itself. The models of desire on which Ai Furuse are necessarily limited. They bear the marks of the engineers that helped to create 'her'. It becomes a question of ethics once we start to ask if these models might be normative in some way. Do they limit or encourage certain ways of interacting? Desire algorithms In the context of one chatbot that might not seem like a big deal. But if (or as) the trend moves into the mainstream, we start to enter a world where the very fact of engineering chatbots inadvertently engineers the desires and sexualities that are expressed towards them. In this instance, not only do we shape the algorithms (which is what’s meant to happen), we also allow these ‘desire algorithms’ to shape our desires and wants too. Storing sexuality on the cloud But there’s another more practical issue as well. If the data on which sex chatbots or virtual lovers runs on the cloud, we’re in a situation where the most private aspects of our lives are stored somewhere that could easily be accessed by malicious actors. This a real risk of Ai Furuse, where cloud space is required for your ‘virtual girlfriend’ to ‘evolve’ further. You pay for additional cloud space. It’s not hard to see how this could become a problem in the future. Thousands of sexual and romantic conversations could be easily harvested for nefarious purposes. Sex robots, artificial intelligence and the problem of consent Language, then, is the kernel of sexualised artificial intelligence. Algorithms, when made well, should respond, process, adapt to and then stimulate further desire. But that's only half the picture. The physical reality of sex robots - both as literal objects, but also the physical effects of what they do - only adds a further complication into the mix. Questions about what desire is - why we have it, what we should do with it - are at the forefront of this debate. If, for example, a paedophile can use a child-like sex robot as a surrogate object of his desires, is that, in fact, an ethical use of artificial intelligence? Here the debate isn’t just about the algorithm, but how it should be deployed. Is the algorithm performing a therapeutic purpose, or is it actually encouraging a form of sexuality that fails to understand the concept of harm and consent? This is an important question in the context of sex robots, but it’s also an important question for the broader ethics of AI. If we can build an AI that is able to do something (ie. automate billions of jobs) should we do it? Who’s responsibility is it to deal with the consequences? The campaign against sex robots These are some of the considerations that inform the perspective of the Campaign Against Sex Robots. On their website, they write: "Over the last decades, an increasing effort from both academia and industry has gone into the development of sex robots – that is, machines in the form of women or children for use as sex objects, substitutes for human partners or prostituted persons. The Campaign Against Sex Robots highlights that these kinds of robots are potentially harmful and will contribute to inequalities in society. We believe that an organized approach against the development of sex robots is necessary in response the numerous articles and campaigns that now promote their development without critically examining their potentially detrimental effect on society." For the campaign, sex robots pose a risk in that they perpetuate already existing inequalities and forms of exploitation in society. They prevent us from facing up to these inequalities. They argue that it will “reduce human empathy that can only be developed by an experience of mutual relationship.” Consent and context Consent is the crucial problem when it comes to artificial intelligence. And you could say that it points to one of the limitations of artificial intelligence that we often miss - context. Algorithms can’t ever properly understand context. There will, undoubtedly be people who disagree with this. Algorithms can, for example, understand the context of certain words and sentences, right? Well yes, that may be true, but that’s not strictly understanding context. Artificial intelligence algorithms are set a context, one from which they cannot deviate. They can’t, for example, decide that actually encouraging a pedophile to act out their fantasies is wrong. It is programmed to do just that. But the problem isn’t simply with robot consent. There’s also an issue with how we consent to an algorithm in this scenario. As journalist Adam Rogers writes in this article for Wired, published at the start of 2018: "It’s hard to consent if you don’t know to whom or what you’re consenting. The corporation? The other people on the network? The programmer?" Rogers doesn’t go into detail on this insight, but it gets to the crux of the matter when discussing artificial intelligence and sex robots. If sex is typically built on a relationship between people, with established forms of communication that establish both consent and desire, what happens when this becomes literally codified? What happens when these additional layers of engineering and commerce get added on top of basic sexual interaction? Is the problem that we want artificial intelligence to be human? Towards the end of the same piece, Rogers finds a possible solutions from privacy researcher Sarah Jamie Lewis. Lewis wonders whether one of the main problems with sex robots is this need to think in humanoid terms. “We’re already in the realm of devices that look like alien tech. I looked at all the vibrators I own. They’re bright colors. None of them look like a penis that you’d associate with a human. They’re curves and soft shapes.” Of course, this isn’t an immediate solution - sex robots are meant to stimulate sex in its traditional (arguably heteronormative) sense. What Lewis suggests, and Rogers seems to agree with, is really just AI-assisted masturbation. But their insight is still useful. On reflection, there is a very real and urgent question about the way in which we deploy artificial intelligence. We need to think carefully about what we want it to replicate and what we want it to encourage. Sex robots are the starting point for thinking seriously about artificial intelligence It’s worth noting that when discussing algorithms we end up looping back onto ourselves. Sex robots, algorithms, artificial intelligence - they’re a problem insofar as they pose questions about what we really value as humans. They make us ask what we want to do with our time, and how we want to interact with other people. This is perhaps a way forward for anyone that builds or interacts with algorithms. Whether they help you get off, or find your next purchase. Consider what you’re algorithm is doing - what’s it encouraging, storing , processing, substituting. We can’t prepare for a future with artificial intelligence without seriously considering these things.

“It’s not who has the best algorithm that wins. It’s who has the most data” ~ Andrew Ng If you would look at the way algorithms were trained in Machine Learning, five or ten years ago, you would notice one huge difference. Training algorithms in Machine Learning are much better and efficient today than it used to be a few years ago. All credit goes to the hefty amount of data that is available to us today. But, how does Machine Learning make use of this data? Let’s have a look at the definition of Machine Learning. “Machine Learning provides computers or machines the ability to automatically learn from experience without being explicitly programmed”. Machines “learn from experience” when they’re trained, this is where data comes into the picture. How’re they trained? Datasets!   This is why it is so crucial that you feed these machines with the right data for whatever problem it is that you want these machines to solve. Why datasets matter in Machine Learning? The simple answer is because Machines too like humans are capable of learning once they see relevant data. But where they vary from humans is the amount of data they need to learn from. You need to feed your machines with enough data in order for them to do anything useful for you. This why Machines are trained using massive datasets. We can think of machine learning data like a survey data, meaning the larger and more complete your sample data size is, the more reliable your conclusions will be. If the data sample isn’t large enough then it won’t be able to capture all the variations making your machine reach inaccurate conclusions, learn patterns that don’t really exist, or not recognize patterns that do. Datasets help bring the data to you. Datasets train the model for performing various actions. They model the algorithms to uncover relationships, detect patterns, understand complex problems as well as make decisions. Apart from using datasets, it is equally important to make sure that you are using the right dataset, which is in a useful format and comprises all the meaningful features, and variations. After all, the system will ultimately do what it learns from the data. Feeding right data into your machines also assures that the machine will work effectively and produce accurate results without any human interference required. For instance, training a speech recognition system with a textbook English dataset will result in your machine struggling to understand anything but textbook English. So, any loose grammar, foreign accents, or speech disorders would get missed out. For such a system, using a dataset comprising all the infinite variations in a spoken language among speakers of different genders, ages, and dialects would be a right option. So keep in mind that it is important that the quality, variety, and quantity of your training data is not compromised as all these factors help determine the success of your machine learning models. Top Machine Learning Datasets for Beginners Now, there are a lot of datasets available today for use in your ML applications. It can be confusing, especially for a beginner to determine which dataset is the right one for your project. It is better to use a dataset which can be downloaded quickly and doesn’t take much to adapt to the models. Further, always use standard datasets that are well understood and widely used. This lets you compare your results with others who have used the same dataset to see if you are making progress. You can pick the dataset you want to use depending on the type of your Machine Learning application. Here’s a rundown of easy and the most commonly used datasets available for training Machine Learning applications across popular problem areas from image processing to video analysis to text recognition to autonomous systems. Image Processing There are many image datasets to choose from depending on what it is that you want your application to do. Image processing in Machine Learning is used to train the Machine to process the images to extract useful information from it. For instance, if you’re working on a basic facial recognition application then you can train it using a dataset that has thousands of images of human faces. This is how Facebook knows people in group pictures. This is also how image search works in Google and in other visual search based product sites. Dataset Name Brief Description 10k US Adult Faces Database This database consists of 10,168 natural face photographs and several measures for 2,222 of the faces, including memorability scores, computer vision, and psychological attributes. The face images are JPEGs with 72 pixels/in resolution and 256-pixel height. Google's Open Images Open Images is a dataset of 9 million URLs to images which have been annotated with labels spanning over 6000 categories. These labels cover more real-life entities and the images are listed as having a Creative Commons Attribution license. Visual Genome This is a dataset of over 100k images densely annotated with numerous region descriptions ( girl feeding elephant), objects (elephants), attributes(large), and relationships (feeding). Labeled Faces in the Wild This database comprises more than 13,000 images of faces collected from the web. Each face is labeled with the name of the person pictured.   Fun and easy ML application ideas for beginners using image datasets: Cat vs Dogs: Using Cat and Stanford Dogs dataset to classify whether an image contains a dog or a cat. Iris Flower classification: You can build an ML project using Iris flower dataset where you classify the flowers in any of the three species. What you learn from this toy project will help you learn to classify physical attributes based content to build some fun real-world projects like fraud detection, criminal identification, pain management ( eg; ePAT which detects facial hints of pain using facial recognition technology), and so on. Hot dog - Not hot dog: Use the Food 101 dataset, to distinguish different food types as a hot dog or not. Who knows, you could end up becoming the next Emmy award nominee! Sentiment Analysis As a beginner, you can create some really fun applications using Sentiment Analysis dataset. Sentiment Analysis in Machine Learning applications is used to train machines to analyze and predict the emotion or sentiment associated with a sentence, word, or a piece of text. This is used in movie or product reviews often. If you are creative enough, you could even identify topics that will generate the most discussions using sentiment analysis as a key tool. Dataset Name Brief Description Sentiment140 A popular dataset, which uses 160,000 tweets with emoticons pre-removed Yelp Reviews An open dataset released by Yelp, contains more than 5 million reviews on Restaurants, Shopping, Nightlife, Food, Entertainment, etc. Twitter US Airline Sentiment Twitter data on US airlines starting from February 2015, labeled as positive, negative, and neutral tweets. Amazon reviews This dataset contains over 35 million reviews from Amazon spanning 18 years. Data include information on products, user ratings, and the plaintext review.   Easy and Fun Application ideas using Sentiment Analysis Dataset: Positive or Negative: Using Sentiment140 dataset in a model to classify whether given tweets are negative or positive. Happy or unhappy: Using Yelp Reviews dataset in your project to help machine figure out whether the person posting the review is happy or unhappy.   Good or Bad: Using Amazon Reviews dataset, you can train a machine to figure out whether a given review is good or bad. Natural Language Processing Natural language processing deals with training machines to process and analyze large amounts of natural language data. This is how search engines like Google know what you are looking for when you type in your search query. Use these datasets to make a basic and fun NLP application in Machine Learning: Dataset Name Brief Description Speech Accent Archive This dataset comprises 2140 speech samples from different talkers reading the same reading passage. These Talkers come from 177 countries and have 214 different native languages. Each talker is speaking in English. Wikipedia Links data This dataset consists of almost 1.9 billion words from more than 4 million articles. Search is possible by word, phrase or part of a paragraph itself. Blogger Corpus A dataset comprising 681,288 blog posts gathered from blogger.com. Each blog consists of minimum 200 occurrences of commonly used English words.   Fun Application ideas using NLP datasets: Spam or not: Using Spambase dataset, you can enable your application to figure out whether a given email is spam or not. Video Processing Video Processing datasets are used to teach machines to analyze and detect different settings, objects, emotions, or actions and interactions in videos. You’ll have to feed your machine with a lot of data on different actions, objects, and activities. Dataset Name Brief Description UCF101 - Action Recognition Data Set This dataset comes with 13,320 videos from 101 action categories. Youtube 8M YouTube-8M is a large-scale labeled video dataset. It contains millions of YouTube video IDs, with high-quality machine-generated annotations from a diverse vocabulary of 3,800+ visual entities.   Fun Application ideas using video processing dataset: Action detection: Using UCF101 - Action Recognition DataSet, or Youtube 8M, you can train your application to detect the actions such as walking, running etc, in a video. Speech Recognition Speech recognition is the ability of a machine to analyze or identify words and phrases in a spoken language. Feed your machine with the right and good amount of data, and it will help it in the process of recognizing speech. Combine speech recognition with natural language processing, and get Alexa who knows what you need. Dataset Name Brief Description Gender Recognition by Voice and speech analysis This database identifies a voice as male or female, depending on the acoustic properties of voice and speech. The dataset contains 3,168 recorded voice samples, collected from male and female speakers. Human Activity Recognition w/Smartphone Human Activity Recognition database consists of recordings of 30 subjects performing activities of daily living (ADL) while carrying a smartphone ( Samsung Galaxy S2 ) on the waist. TIMIT TIMIT provides speech data for acoustic-phonetic studies and for the development of automatic speech recognition systems. It comprises broadband recordings of 630 speakers of eight major dialects of American English, each reading ten phonetically rich sentences, phonetic and word transcriptions. Speech Accent Archive This dataset contains 2140 speech samples, each from a different talker reading the same reading passage. Talkers come from 177 countries and have 214 different native languages. Each talker is speaking in English.   Fun Application ideas using Speech Recognition dataset: Accent detection: Use Speech Accent Archive dataset, to make your application identify different accents from a given sample of accents. Identify the activity: Use Human Activity Recognition w/Smartphone dataset to help your application detect the human activity. Natural Language Generation Natural Language generation refers to the ability of machines to simulate the human speech. It can be used to translate written information into aural information or assist the vision-impaired by reading out aloud the contents of a display screen. This is how Alexa or Siri respond to you. Dataset Name Brief Description Common Voice by Mozilla Common Voice dataset contains speech data read by users on the Common Voice website from a number of public sources like user-submitted blog posts, old books, movies, etc. LibriSpeech This dataset consists of nearly 500 hours of clean speech of various audiobooks read by multiple speakers, organized by chapters of the book with both the text and the speech.   Fun Application ideas using Natural Language Generation dataset: Converting text into Audio: Using Blogger Corpus dataset, you can train your application to read out loud the posts on blogger. Autonomous Driving Build some basic self-driving Machine Learning Applications. These Self-driving datasets will help you train your machine to sense its environment and navigate accordingly without any human interference. Autonomous cars, drones, warehouse robots, and others use these algorithms to navigate correctly and safely in the real world. Datasets are even more important here as the stakes are higher and the cost of a mistake could be a human life. Dataset Name Brief Description Berkeley DeepDrive BDD100k This is one of the largest datasets for self-driving AI currently. It comprises over 100,000 videos of over 1,100-hour driving experiences across different times of the day and weather conditions. Baidu Apolloscapes Large dataset consisting of 26 different semantic items such as cars, bicycles, pedestrians, buildings, street lights, etc. Comma.ai This dataset consists of more than 7 hours of highway driving. It includes details on car’s speed, acceleration, steering angle, and GPS coordinates. Cityscape Dataset This is a large dataset that contains recordings of urban street scenes in 50 different cities. nuScenes This dataset consists of more than 1000 scenes with around 1.4 million image, 400,000 sweeps of lidars (laser-based systems that detect the distance between objects), and 1.1 million 3D bounding boxes ( detects objects with a combination of RGB cameras, radar, and lidar).   Fun Application ideas using Autonomous Driving dataset: A basic self-driving application: Use any of the self-driving datasets mentioned above to train your application with different driving experiences for different times and weather conditions.   IoT Machine Learning in building IoT applications is on the rise these days. Now, as a beginner in Machine Learning, you may not have advanced knowledge on how to build these high-performance IoT applications using Machine Learning, but you certainly can start off with some basic datasets to explore this exciting space. Dataset Name Brief Description Wayfinding, Path Planning, and Navigation Dataset This dataset consists of samples of trajectories in an indoor building (Waldo Library at Western Michigan University) for navigation and wayfinding applications. ARAS Human Activity Dataset This dataset is a Human activity recognition Dataset collected from two real houses. It involves over 26 millions of sensor readings and over 3000 activity occurrences.   Fun Application ideas using IoT dataset: Wearable device to track human activity: Use the ARAS Human Activity Dataset to train a wearable device to identify human activity. Read Also: 25 Datasets for Deep Learning in IoT Once you’re done going through this list, it’s important to not feel restricted. These are not the only datasets which you can use in your Machine Learning Applications. You can find a lot many online which might work best for the type of Machine Learning Project that you’re working on. Some popular sources of a wide range of datasets are Kaggle,  UCI Machine Learning Repository, KDnuggets, Awesome Public Datasets, and Reddit Datasets Subreddit. With all this information, it is now time to use these datasets in your project. In case you’re completely new to Machine Learning, you will find reading, ‘A nonprogrammer’s guide to learning Machine learning’quite helpful. Regardless of whether you’re a beginner or not, always remember to pick a dataset which is widely used, and can be downloaded quickly from a reliable source. How to create and prepare your first dataset in Salesforce Einstein Google launches a Dataset Search Engine for finding Datasets on the Internet Why learn machine learning as a non-techie?

PyTorch is a Python-based scientific computing package that uses the power of graphics processing units. It is also one of the preferred deep learning research platforms built to provide maximum flexibility and speed. It is known for providing two of the most high-level features; namely, tensor computations with strong GPU acceleration support and building deep neural networks on a tape-based autograd systems. There are many existing Python libraries which have the potential to change how deep learning and artificial intelligence are performed, and this is one such library. One of the key reasons behind PyTorch’s success is it is completely Pythonic and one can build neural network models effortlessly. It is still a young player when compared to its other competitors, however, it is gaining momentum fast. A brief history of PyTorch Since its release in January 2016, many researchers have continued to increasingly adopt PyTorch. It has quickly become a go-to library because of its ease in building extremely complex neural networks. It is giving a tough competition to TensorFlow especially when used for research work. However, there is still some time before it is adopted by the masses due to its still “new” and “under construction” tags. PyTorch creators envisioned this library to be highly imperative which can allow them to run all the numerical computations quickly. This is an ideal methodology which fits perfectly with the Python programming style. It has allowed deep learning scientists, machine learning developers, and neural network debuggers to run and test part of the code in real time. Thus they don’t have to wait for the entire code to be executed to check whether it works or not. You can always use your favorite Python packages such as NumPy, SciPy, and Cython to extend PyTorch functionalities and services when required. Now you might ask, why PyTorch? What’ so special in using it to build deep learning models? The answer is quite simple, PyTorch is a dynamic library (very flexible and you can use as per your requirements and changes) which is currently adopted by many of the researchers, students, and artificial intelligence developers. In the recent Kaggle competition, PyTorch library was used by nearly all of the top 10 finishers. Some of the key highlights of PyTorch includes: Simple Interface: It offers easy to use API, thus it is very simple to operate and run like Python. Pythonic in nature: This library, being Pythonic, smoothly integrates with the Python data science stack. Thus it can leverage all the services and functionalities offered by the Python environment. Computational graphs: In addition to this, PyTorch provides an excellent platform which offers dynamic computational graphs, thus you can change them during runtime. This is highly useful when you have no idea how much memory will be required for creating a neural network model. PyTorch Community PyTorch community is growing in numbers on a daily basis. In the just short year and a half, it has shown some great amount of developments that have led to its citations in many research papers and groups. More and more people are bringing PyTorch within their artificial intelligence research labs to provide quality driven deep learning models. The interesting fact is, PyTorch is still in early-release beta, but the way everyone is adopting this deep learning framework at a brisk pace shows its real potential and power in the community. Even though it is in the beta release, there are 741 contributors on the official GitHub repository working on enhancing and providing improvements to the existing PyTorch functionalities. PyTorch doesn’t limit to specific applications because of its flexibility and modular design. It has seen heavy use by leading tech giants such as Facebook, Twitter, NVIDIA, Uber and more in multiple research domains such as NLP, machine translation, image recognition, neural networks, and other key areas. Why use PyTorch in research? Anyone who is working in the field of deep learning and artificial intelligence has likely worked with TensorFlow before, Google’s most popular open source library. However, the latest deep learning framework - PyTorch solves major problems in terms of research work. Arguably PyTorch is TensorFlow’s biggest competitor to date, and it is currently a much favored deep learning and artificial intelligence library in the research community. Dynamic Computational graphs It avoids static graphs that are used in frameworks such as TensorFlow, thus allowing the developers and researchers to change how the network behaves on the fly. The early adopters are preferring PyTorch because it is more intuitive to learn when compared to TensorFlow. Different back-end support PyTorch uses different backends for CPU, GPU and for various functional features rather than using a single back-end. It uses tensor backend TH for CPU and THC for GPU. While neural network backends such as THNN and THCUNN for CPU and GPU respectively. Using separate backends makes it very easy to deploy PyTorch on constrained systems. Imperative style PyTorch library is specially designed to be intuitive and easy to use. When you execute a line of code, it gets executed thus allowing you to perform real-time tracking of how your neural network models are built. Because of its excellent imperative architecture and fast and lean approach it has increased overall PyTorch adoption in the community. Highly extensible PyTorch is deeply integrated with the C++ code, and it shares some C++ backend with the deep learning framework, Torch. Thus allowing users to program in C/C++ by using an extension API based on cFFI for Python and compiled for CPU for GPU operation. This feature has extended the PyTorch usage for new and experimental use cases thus making them a preferable choice for research use. Python-Approach PyTorch is a native Python package by design. Its functionalities are built as Python classes, hence all its code can seamlessly integrate with Python packages and modules. Similar to NumPy, this Python-based library enables GPU-accelerated tensor computations plus provides rich options of APIs for neural network applications. PyTorch provides a complete end-to-end research framework which comes with the most common building blocks for carrying out everyday deep learning research. It allows chaining of high-level neural network modules because it supports Keras-like API in its torch.nn package. PyTorch 1.0: The path from research to production We have been discussing all the strengths PyTorch offers, and how these make it a go-to library for research work. However, one of the biggest downsides is, it has been its poor production support. But this is expected to change soon. PyTorch 1.0 is expected to be a major release which will overcome the challenges developers face in production. This new iteration of the framework will merge Python-based PyTorch with Caffe2 allowing machine learning developers and deep learning researchers to move from research to production in a hassle-free way without the need to deal with any migration challenges. The new version 1.0 will unify research and production capabilities in one framework thus providing the required flexibility and performance optimization for research and production. This new version promises to handle tasks one has to deal with while running the deep learning models efficiently on a massive scale. Along with the production support, PyTorch 1.0 will have more usability and optimization improvements. With PyTorch 1.0, your existing code will continue to work as-is, there won’t be any changes to the existing API. If you want to stay updated with all the progress to PyTorch library, you can visit the Pull Requests page. The beta release of this long-awaited version is expected later this year. Major vendors like Microsoft and Amazon are expected to provide complete support to the framework across their cloud products. Summing up, PyTorch is a compelling player in the field of deep learning and artificial intelligence libraries, exploiting its unique niche of being a research-first library. It overcomes all the challenges and provides the necessary performance to get the job done. If you’re a mathematician, researcher, student who is inclined to learn how deep learning is performed, PyTorch is an excellent choice as your first deep learning framework to learn. Read more Can a production-ready Pytorch 1.0 give TensorFlow a tough time? A new geometric deep learning extension library for Pytorch releases! Top 5 tools for reinforcement learning

In the technological era, many industries are finding new ways to carve out space for themselves. From healthcare to hospitality, rapid developments in tech have radically changed the way we do business, and adaptation is a must. The information technology industry holds a unique position in the world. With the changing market, IT support companies must also adapt to new technology more quickly than anyone else to ensure competitiveness. Decreased Individual Support, Increased Organizational Support Individual, discrete tech users are requiring less and less IT support than ever before. Every day, the human race produces 2.5 quintillion bytes of data – a staggering amount of information that no individual could possibly consume within a lifetime. That rate is increasing every day. With such widespread access to information, individuals are now able to find solutions with unrivaled ease and speed and the need for live, person-to-person support has decreased. Adding to the figure is the growing presence of younger generations who have grown up in a world saturated with technology. Children born in the 2000’s and later have never seen a world without smartphones, Bluetooth, and the World Wide Web. Development alongside technology not only implants a level of comfort with using technology but also with adapting to its constant changes. For the newest cohort of young adults, troubleshooting is no longer a professional task, but a household one. Alternatively, businesses require just as much support as ever. The accelerating pace of software development has opened up a new world of opportunity for organizations to optimize data management, customer support, marketing, finance, and more. But it’s also created a new, highly-competitive market where late-adopters run the risk of falling hard. Adapting to and using new information technology systems takes a highly-organized and knowledgeable support team, and that role is increasingly being outsourced outside organization walls. Companies like CITC are stepping up to provide more intensive expertise to businesses that may have, in the past, utilized in-house teams to manage IT systems.Source:Unsplash Improving Customer Service While individual tech users may need increasingly less company-provided support, they continue to expect increasingly better service. Likewise, organizations expect more from their IT support companies – faster incident response, simplified solutions, and the opportunity to troubleshoot on-the-spot. In response to these demands, IT support organizations are experimenting with modern models of support. Automation and Prediction-Based Support with Artificial Intelligence Artificial intelligence has become a part of everyday life for much of the world. From Google Assistant and Siri to self-driving cars, AI has offered a myriad of tools for streamlining daily tasks and simplifying our lives. It’s no surprise that developers are looking for ways to integrate Artificial Intelligence into IT support as well. Automated responses and prediction-based support offer a quick, cost-effective option that allows IT professionals to spend their time on tasks that require a more nuanced approach. However, automation of IT support comes with its own set of problems. First, AI lacks a human touch. Trying to discuss a potentially imminent problem with a bot can be a recipe for frustration, and the unreliability of problem self-reporting poses the additional risk of ineffective or inappropriate solutions. Automation also poses security risks which can be especially hazardous to industries with valuable trade secrets. Community-Based Support A second shift that has begun to take place in the IT support industry is a move towards community-based support. Crowdsourced solutions, like AI, can help carry some of the burden of smaller problems by allowing staff to focus energy on more pressing tasks. Unlike automated solutions, however, community-based support allows for human-to-human interaction and more seamless, collaborative, feedback-based troubleshooting. However, community-based support has limited applications. Crowdsourced solutions, contrary to the name, are often only the work of a few highly-qualified individuals. The turnaround for answers from a qualified source can be extensive, which is unacceptable under many circumstances. Companies offering a community-based support platform must moderate contributions and fact-check solutions, which can end up being nearly as resource intensive as traditional support services. While automation and community-based support offer new alternatives to traditional IT support for individual tech users, organizational support requires a much different approach. Organizations that hire IT support companies expect expert solutions, and dedicated staffing are a must. The Shift: Management to Adoption Software is advancing at a rapid pace. In the first quarter of 2018, there were 3,800,000 apps available for Android users and 2,000,000 for Apple. The breadth of enterprise software is just as large, with daily development in all industry sectors. Businesses are no longer able to adopt a system and stick with it - they must constantly adapt to new, better technology and seek out the most innovative solutions to compete in their industries. Source: Unsplash IT support companies must also increasingly dedicate themselves to this role. IT support is no longer a matter of technology management, but of adaptation and adoption. New IT companies bring a lot to the table here. Much like the newer generations of individual users, new companies can offer a fresh perspective on software developments and a more fluid ability to adapt to changes in the market. Older IT companies also have plenty to offer, however. Years of traditional support experience can be a priceless asset that inspires confidence from clients. All IT support organizations should focus on being able to adapt to the future of technology. This can be done by increasingly making rapid changes in software, an increasing reliance on digital technology, and transitioning to digital resource management. Additionally, support companies must be able to provide innovative solutions that strike an effective balance between automation and interaction. Ultimately, companies must realize that the future is already here and that traditional methods of service are no longer adequate for the changing landscape of tech. In Conclusion The world of technology is changing and, with it, the world of IT support. Support needs are rapidly shifting from customer service to enterprise support, and IT support companies must adapt to serve the needs of all industry sectors. Companies will need to find innovative solutions to not only provide management of technology but allow companies to adapt seamlessly into new technological advancements. This article is written by a student of New York City College of Technology. New York City College of Technology is a baccalaureate and associate degree-granting institution committed to providing broad access to high quality technological and professional education for a diverse urban population.

The most common programming languages currently used for AI and machine learning development are Python, R, Scala, Go, among others with the latest addition being Julia. Functional languages as old as Lisp and Haskell were used to implement machine learning algorithms decades ago when AI was an obscure research area of interest. There wasn’t enough hardware and software advancements back them for implementations. Some commonalities in all of the above language options are that they are simple to understand and promote clarity. They use fewer lines of code and lend themselves well to the functional programming paradigm. What is Functional programming? Functional programming is a programming approach that uses logical functions or procedures within its programming structure. It means that the programming is done with expressions instead of statements. In a functional programming (FP) approach, computations are treated as evaluations of mathematical functions and it mostly deals with immutable data. In other words, the state of the program does not change, the functions or procedures are fixed and the output value of a function depends solely on the arguments passed to it. Let’s look at the characteristics of a functional programming approach before we see why they are well suited for developing artificial intelligence programs. Functional programming features Before we see functional programming in a machine learning context, let’s look at some of its characteristics. Immutable: If a variable x is declared and used in the program, the value of the variable is never changed later anywhere in the program. Each time the variable x is called, it will return the same value assigned originally. This makes it pretty straightforward, eliminating the need to think of state change throughout the program. Referential transparency: This means that an expression or computation always results in the same value in any part/context of the program. A referentially transparent programming language’s programs can be manipulated as algebraic equations. Lazy evaluation: Being referentially transparent, the computations yield the same result irrespective of when they are performed. This enables to postpone the computation of values until they are required/called. This means one could evaluate them lazily. Lazy evaluation helps avoids unnecessary computations and saves memory. Parallel programming: Since there is no state change due to immutable variables, the functions in a functional program can work in parallel as instructions. Parallel loops can be easily expressed with good reusability. Higher-order functions: A higher order function can take one or more functions as arguments. They may also be able to return a function as their result. Higher-order functions are useful for refactoring code and to reduce repetition. The map function found in many programming languages is an example of a higher-order function. What kind of programming is good for AI development? Machine learning is a sub-domain of artificial intelligence which deals with concepts of making predictions from data, take actions without being explicitly programmed, recommendation systems and so on. Any programming approach that focuses on logic and mathematical functions is good for artificial intelligence (AI). Once the data is collected and prepared it is time to build your machine learning model.. This typically entails choosing a model, then training and testing the model with the data. Once the desired accuracy/results are achieved, then the model is deployed. Training on the data requires data to be consistent and the code to be able to communicate directly with the data without much abstraction for least unexpected errors. For AI programs to work well, the language needs to have a low level implementation for faster communication with the processor. This is why many machine learning libraries are created in C++ to achieve fast performance. OOP with its mutable objects and object creation is better suited for high-level production software development, not very useful in AI programs which works with algorithms and data. As AI is heavily based on math, languages like Python and R are widely used languages in AI currently. R lies more towards statistical data analysis but does support machine learning and neural network packages. Python being faster for mathematical computations and with support for numerical packages is used more commonly in machine learning and artificial intelligence. Why is functional programming good for artificial intelligence? There are some benefits of functional programming that make it suitable for AI. It is closely aligned to mathematical thinking, and the expressions are in a format close to mathematical definitions. There are few or no side-effects of using a functional approach to coding, one function does not influence the other unless explicitly passed. This proves to be great for concurrency, parallelization and even debugging. Less code and more consistency The functional approach uses fewer lines of code, without sacrificing clarity. More time is spent in thinking out the functions than actually writing the lines of code. But the end result is more productivity with the created functions and easier maintenance since there are fewer lines of code. AI programs consist of lots of matrix multiplications. Functional programming is good at this just like GPUs. You work with datasets in AI with some algorithms to make changes in the data to get modified data. A function on a value to get a new value is similar to what functional programming does. It is important for the variables/data to remain the same when working through a program. Different algorithms may need to be run on the same data and the values need to be the same. Immutability is well-suited for that kind of job. Simple approach, fast computations The characteristics/features of functional programming make it a good approach to be used in artificial intelligence applications. AI can do without objects and classes of an object oriented programming (OOP) approach, it needs fast computations and expects the variables to be the same after computations so that the operations made on the data set are consistent. Some of the popular functional programming languages are R, Lisp, and Haskell. The latter two are pretty old languages and are not used very commonly. Python can be used as both, functional and object oriented. Currently, Python is the language most commonly used for AI and machine learning because of its simplicity and available libraries. Especially the scikit-learn library provides support for a lot of AI-related projects. FP is fault tolerant and important for AI Functional programming features make programs fault tolerant and fast for critical computations and rapid decision making. As of now, there may not be many such applications but think of the future, systems for self-driving cars, security, and defense systems. Any fault in such systems would have serious effects. Immutability makes the system more reliable, lazy evaluation helps conserve memory, parallel programming makes the system faster. The ability to pass a function as an argument saves a lot of time and enables more functionality. These features of functional programming make it a fitting choice for artificial intelligence. To further understand why use functional programming for machine learning, read the case made for using the functional programming language Haskell for AI in the Haskell Blog. Why functional programming in Python matters: Interview with best selling author, Steven Lott Grain: A new functional programming language that compiles to Webassembly 8 ways Artificial Intelligence can improve DevOps

According to Gartner analyst Dave Cappuccio, 80% of enterprises will have shut down their traditional data centers by 2025, compared to just 10% today. The figures are fitting considering the host of problems faced by traditional data centers. The solution to all these problems lies right in front of us- Incorporating Intelligence in traditional data centers. To support this claim, Gartner also predicts that by 2020, more than 30 percent of data centers that fail to implement AI and Machine Learning will cease to be operationally and economically viable. Across the globe, Data science and AI are influencing the design and development of the modern data centers. With the surge in the amount of data everyday, traditional data centers will eventually get slow and result in an inefficient output. Utilizing AI in ingenious ways, data center operators can drive efficiencies up and costs down. A fitting example of this is the tier-two automated control system implemented at Google to cool its data centers autonomously. The system makes all the cooling-plant tweaks on its own, continuously, in real-time- thus saving up to 30% of the plant’s energy annually. Source: DataCenter Knowledge AI has enabled data center operators to add more workloads on the same physical silicon architecture. They can aggregate and analyze data quickly and generate productive outputs, which is specifically beneficial to companies that deal with immense amounts of data like hospitals, genomic systems, airports, and media companies. How is AI facilitating data centers Let's look at some of the ways that Intelligent data centers will serve as a solution to issues faced by traditionally operated data centers. #1 Energy Efficiency The Delta Airlines data center outage in 2016, that was attributed to electrical-system failure over a three day period, cost the airlines around $150 million, grounding about 2,000 flights. This situation could have been easily averted had the data centers used Machine Learning for their workings. As data centers get ever bigger, more complex and increasingly connected to the cloud, artificial intelligence is becoming an essential tool for keeping things from overheating and saving power at the same time. According to the Energy Department’s U.S. Data Center Energy Usage Report, the power usage of data centers in the United States has grown at about a 4 percent rate annually since 2010 and is expected to hit 73 billion kilowatt-hours by 2020, more than 1.8 percent of the country’s total electricity use. Data centers also contribute about 2 percent of the world’s greenhouse gas emissions, AI techniques can do a lot to make processes more efficient, more secure and less expensive. One of the keys to better efficiency is keeping things cool, a necessity in any area of computing. Google and DeepMind (Alphabet Inc.’s AI division)use of AI to directly control its data center has reduced energy use for cooling by about 30 percent. #2 Server optimization Data centers have to maintain physical servers and storage equipment. AI-based predictive analysis can help data centers distribute workloads across the many servers in the firm. Data center loads can become more predictable and more easily manageable. Latest load balancing tools with built-in AI capabilities are able to learn from past data and run load distribution more efficiently. Companies will be able to better track server performance, disk utilization, and network congestions. Optimizing server storage systems, finding possible fault points in the system, improve processing times and reducing risk factors will become faster. These will in turn facilitate maximum possible server optimization. #3 Failure prediction / troubleshooting Unplanned downtime in a datacenter can lead to money loss. Datacenter operators need to quickly identify the root case of the failure, so they can prioritize troubleshooting and get the datacenter up and running before any data loss or business impact take place. Self managing datacenters make use of AI based deep learning (DL) applications to predict failures ahead of time. Using ML based recommendation systems, appropriate fixes can be inferred upon the system in time. Take for instance the HPE artificial intelligence predictive engine that identifies and solves trouble in the data center. Signatures are built to identify other users that might be affected. Rules are then developed to instigate a solution, which can be automated. The AI-machine learning solution, can quickly interject through the entire system and stop others from inheriting the same issue. #4 Intelligent Monitoring and storing of Data Incorporating machine learning, AI can take over the mundane job of monitoring huge amounts of data and make IT professionals more efficient in terms of the quality of tasks they handle. Litbit has developed the first AI-powered, data center operator, Dac. It uses a human-to-machine learning interface that combines existing human employee knowledge with real-time data. Incorporating over 11,000 pieces of innate knowledge, Dac has the potential to hear when a machine is close to failing, feel vibration patterns that are bad for HDD I/O, and spot intruders. Dac is proof of how AI can help monitor networks efficiently. Along with monitoring of data, it is also necessary to be able to store vast amounts of data securely. AI holds the potential to make more intelligent decisions on - storage optimization or tiering. This will help transform storage management by learning IO patterns and data lifecycles, helping storage solutions etc. Mixed views on the future of AI in data centers? Let’s face the truth, the complexity that comes with huge masses of data is often difficult to handle. Humans ar not as scalable as an automated solution to handle data with precision and efficiency. Take Cisco’s M5 Unified Computing or HPE’s InfoSight as examples. They are trying to alleviate the fact that humans are increasingly unable to deal with the complexity of a modern data center. One of the consequences of using automated systems is that there is always a possibility of humans losing their jobs and being replaced by machines at varying degrees depending on the nature of job roles. AI is predicted to open its doors to robots and automated machines that will soon perform repetitive tasks in the datacenters. On the bright side, organizations could allow employees, freed from repetitive and mundane tasks, to invest their time in more productive, and creative aspects of running a data center. In addition to new jobs, the capital involved in setting up and maintaining a data center is huge. Now add AI to the Datacenter and you have to invest double or maybe triple the amount of money to keep everything running smoothly. Managing and storing all of the operational log data for analysis also comes with its own set of issues. The log data that acts as the input to these ML systems becomes a larger data set than the application data itself. Hence firms need a proper plan in place to manage all of this data. Embracing AI in data centers would mean greater financial benefits from the outset while attracting more customers. It would be interesting to see the turnout of tech companies following Google’s footsteps and implementing AI in their data centers. Tech companies should definitely watch this space to take their data center operations up a notch. 5 ways artificial intelligence is upgrading software engineering Intelligent Edge Analytics: 7 ways machine learning is driving edge computing adoption in 2018 15 millions jobs in Britain at stake with Artificial Intelligence robots set to replace humans at workforce

AI has been around for quite some time, enabling developers to build intelligent apps that cater to the needs of their users. Not only app developers, businesses are also using AI to gain insights from their data such as their customers’ buying behaviours, the busiest time of the year, and so on. While AI is all cool and fascinating, developing an AI-powered app is not that easy. Developers and data scientists have to invest a lot of their time in collecting and preparing the data, building and training the model, and finally deploying it in production. Machine learning, which is a subset of AI, feels difficult because the traditional development process is complicated and slow. While creating machine learning models we need different tools for different functionalities, which means we should have knowledge of them all. This is certainly not practical. The following factors make the current situation even more difficult: Scaling the inferencing logic Addressing continuous development Making it highly available Deployment Testing Operation This is where serverless computing comes into picture. Let’s dive into what exactly serverless computing is and how it can help in easing AI development. What is serverless computing? Serverless computing is the concept of building and running applications in which the computing resources are provided as scalable cloud services. It is a deployment model where applications, as bundle of functions, are uploaded to a cloud platform and then executed. Serverless computing does not mean that servers are no longer required to host and run code. Of course we need servers, but server management for the applications is taken care of by the cloud provider. This also does not implies that operations engineers are no longer required. In fact, it means that with serverless computing, consumers no longer need to spend time and resources on server provisioning, maintenance, updates, scaling, and capacity planning. Instead, all of these tasks and capabilities are handled by a serverless platform and are completely abstracted away from the developers and IT/operations teams. This allows developers to focus on writing their business logic and operations engineers to elevate their focus to more business critical tasks. Serverless computing is the union of two ideas: Backend as a Service (BaaS): BaaS provides developers a way to link their application with third-party backend cloud storage. It includes services such as, authentication, access to database, and messaging, which are supplied through physical or virtual servers located in the cloud. Function as a Service (FaaS): FaaS allows users to run a specific task or function remotely and after the function is complete, the function results return back to the user. The applications run in stateless compute containers that are event-triggered and fully managed by a third party. AWS Lambda, Google Cloud Function, Azure Functions, and IBM Cloud Functions, are some of the serverless computing providers which enable us to upload a function and the rest is taken care for us automatically. Read also: Modern Cloud Native architectures: Microservices, Containers, and Serverless – Part 2 Why serverless is a good choice for AI development? Along with the obvious advantage of hassle free server management, let’s see what else it has to offer for your artificial intelligence project development: Focus on core tasks Managing servers and deploying a machine learning model is not a good skill match for a data scientist or even for a machine learning engineer. With serverless computing, servers will conveniently vanish from your development and deployment workflow. Auto-scalability This is one of the key benefits of using serverless computing. As long as your model is correctly deployed on the serverless platform, you don’t have to worry about making it scale when your workload raises. Serverless computing gives all businesses, big and small, the ability to use what they need and scale without worrying about complex and time-consuming data migrations. Never pay for idle In traditional application deployment models, users need to pay a fixed and recurring cost for compute resources, regardless of the amount of computing work that is actually being performed by the server. In serverless computing deployment, you only have to pay for service usage. You are only charged for the number of executions and the corresponding duration. Reduces interdependence You can think of machine learning models as functions in serverless, which can be invoked, updated, and deleted. You can do this any time without having any side effect on the rest of the system. Different teams can work independently to develop, deploy, and scale their microservices. This greatly simplifies the orchestration of timelines by Product and Dev Managers. Abstraction from the users Your machine learning model will be exposed as a service to the users with the help of API Gateway. This makes it easier to decentralize your backend, isolate failure on a per-model level, and hide every implementation details from the final user. High availability Serverless applications have built-in availability and fault tolerance. You don't need to architect for these capabilities since the services running the application provide them by default. Serverless computing can facilitate a simpler approach to artificial intelligence by removing the baggage of server maintenance from developers and data scientists. But nothing is perfect, right? It also comes with some drawbacks, number one being, vendor lock-in. Serverless features varies from one vendor to another, which makes it difficult to switch vendors. Another disadvantage is decreased transparency. Your infrastructure is managed by someone else, so understanding the entire system becomes a little bit difficult. Serverless is not an answer to every problem but it is definitely improving each day making AI development easier. What’s new in Google Cloud Functions serverless platform Serverless computing wars: AWS Lambdas vs Azure Functions Google’s event-driven serverless platform, Cloud Function, is now generally available

“..what we want is a machine that can learn from experience..” ~Alan Turing, 1947 Thanks to artificial intelligence, Turing’s vision is coming true. Machines are learning, from others’ experience (using training datasets) and from their own as well.  Machines can now play chess, Go, and other games, they can help predict cancer, manage your day, summarize today’s news for you, edit your essays, identify your face, and even mimic dance moves and facial expressions. Come to think of it, every job role and career demands that you learn from experience, improve over time and explore new ways to do things.  Yes, machines are very effective at the former two, but humans still have an edge when it comes to innovative thinking. Imagine what you could achieve if you put together your mind with that of an efficient learning algorithm! You might think that artificial intelligence and machine learning are a dense and impenetrable field limited to research labs and textbooks. Does that mean only software engineers and researchers can dream of making it into this fascinating field? Not quite. We’ll unpick machine learning in the following sections and present our case for why it makes sense for everyone to understand this field better. Machine learning is, potentially, a first-class ticket to an exciting career, whether you are starting off fresh from college or are considering a career switch. Beyond the artificial intelligence and machine learning hype Artificial intelligence is simply an area of computing that solves complex real-world problems. Yes, research still happens in universities, and yes, data scientists are still exploring the limits of artificial intelligence in forward-thinking businesses, but it's much more than that. AI is so pervasive - and mysterious - that its applications hide in plain sight. Look around you carefully. From Netflix recommending personalized content to its 130 million viewers, to Youtube’s video search and automatic captions in videos, to Amazon’s shopping recommendations, to Instagram hashtags, Snapchat filters, spam filters on your Gmail and virtual assistants like Siri on our smartphones, artificial intelligence, and machine learning techniques are in action everywhere. This means as a user you are at some level already impacted by algorithms every day. The question then is should you be the person who’s career is limited by algorithms or the one whose career is propelled by algorithms. Why get into artificial intelligence development as a non-programmer? Artificial Intelligence is a perfect blend of knowledge, high salary, and some really great opportunities. Your non-programming field does not have to deter your growth in the AI field. In fact, your background can give you an edge over the traditional software developers and data scientists in terms of domain awareness and better understanding what the system should do, what it should look for, and make the users feel. Below are some reasons proving why you should make the jump in AI. Machine learning can help you be better at your current job How? You may ask. Take a news reporter or editor’s job for example. They must possess a blend of research/analysis centric capabilities, a creative set of skills and speed to come up with timely, quality articles on topics of interest to their readers. A data journalist or a writer with machine learning experience could quickly find great topics to write on with the help of machine learning based web scraping apps. Also, they could let the data lead them to unique stories that are emerging before traditional news reporters find their way to them. They could further also get a quick summary of multiple perspectives on a given topic using custom-built news feed algorithms. Then could they also find further research resources by tweaking their search parameters, even adding quality filters on top to only allow for high-quality citations. This kind of writer has cut down on the time they spent finding and understanding topics - which means more time to actually write compelling pieces and to connect with real sources for further insight. Algorithms can also find and correct language issues in writing now. This means editors can spend more time improving the content quality from a scope perspective. You can quickly start to see how artificial intelligence can complement the work you do and help you grow in your career. Yes, all this sounds lovely in theory, but is it really happening in practice? There are others like you who are successfully exploring machine learning Don’t believe me? Mason Fish, a software Engineer at Docker, Inc was earlier a musician. He had done his bachelor’s and masters from two different music conservatories. After graduating, he worked for five years as a professional musician. But, today he helps build and maintain services for Docker, a tool used by software engineers all over the world! This was just one case of a non-programmer diving into the computer science world. When musicians can learn to code and get core developer jobs in cutting-edge tech companies, it is not far fetched to say they can also learn to build machine learning models. Below are some examples of non-programmers of varied experience levels who are exploring the Machine Learning world. Per Harald Borgen, an economics graduate was able to boost the sales at his workplace Xeneta using machine learning algorithms, an accomplishment that helped accelerate his career. You can read his blog to see how he transformed from a machine learning newbie to a seasoned practitioner. Another example is a 14-year-old Tanmay Bakshi, who started a youtube channel at just 7 years of age where he teaches coding, algorithms, AI and machine learning concepts. Similarly, Sean Le Van created an AI chatbot when he was 14 years old using ML algorithms.   Rosebud Anwuri is another great example as she switched from chemical engineering to Data science. “My first exposure to Data Science was from a book that had nothing to do with Data Science,” writes Anwuri on her blog. She created her first Data Science learning path from an answer on Quora, last year. Fast forward to this year, she has been invited to speak at Stanford’s Women in Data Science Conference in Nigeria and has facilitated a workshop at The Women in Machine Learning and Data Science among others. She also writes on Machine Learning and Data Science on her blog.   Like Anwuri, Sce Pike dreamed of being an artist or singer in college and did her major in fine arts and anthropology. Pike went from art to web design to “human factors design,” which involves human-machine interactions, for the telecommunications giant Qualcomm. In addition to that, Pike started her own company IOTAS, that offers smart-home services to renters and homeowners. “I have had to approach my work with logic, research, and great design. Looking back, I’m amazed where I am now,” says Sce Pike. Read also: Data science for non-techies: How I got started (Part 1) Adapt or perish in the oncoming job automation wave of the fourth industrial revolution Ok, so maybe you’re happy with how you are growing anyway in your career. Be warned though, your job may not look the same even in the next few years. Automation is expected to replace up to 30% of jobs in the next 10 years, so upskilling to machine learning is a wise choice. Last month, Bank of England’s Chief Economist warned that 15 million jobs in Britain could be at stake because of artificial intelligence. Machine learning as a skill could help you stay relevant in the future and prepare for what’s being called, “the third machine age”. You can develop machine learning apps with no to minimal coding experience Thanks to great advancements by big tech companies and open source projects, machine learning today is accessible to people with varying degrees of programming experience - from new developers and even those who have never written a line of code in their life. So, whether you’re a curious web/UX designer, a news reporter, an artist, a school student, a filmmaker or an NGO worker, you will find good use of machine learning in your field. There are tools for machine learning for users with varying levels of experience. In fact, there are certain Machine Learning Applications that you can build even today. Some examples are Image and text classification with Neural Network, Facial recognition, Gaming bots, music generation, object detection, etc. Machine learning skills are highly rewarded Machine learning is a nascent field where demand far outweighs supply. According to research done by Indeed.com, the number one job requirement in AI is that of a Machine Learning Engineer, with data scientist jobs taking the second spot. In fact, AI researchers can earn more than 1 million dollar per year and the AI geniuses at Elon Musk’s OpenAI are a living proof for this. OpenAI paid its top AI researcher, Ilya Sutskever, more than  $1.9 million, back in 2016. Another leading researcher, Ian Goodfellow, in OpenAI was paid more than $800,000. Machine Learning is not hard to learn. It might seem intimidating at first, but once you get the basics right, the rest of the ML journey becomes easier. If you’re convinced that ML is for you, but are confused about how to get started then don’t worry, we’ve got you covered. To help you get started, here is a non-programmer’s guide to learning Machine Learning. So, yes, it doesn’t matter if you’re a non-programmer, musician, a librarian, or a student, the future is AI-driven so don’t be afraid to make that dive into Machine Learning. As Robert Frost said, “Two roads diverged in a wood, and I took the one less traveled by, And that has made all the difference”. 8 Machine learning best practices [Tutorial] Google introduces Machine Learning courses for AI beginners Top languages for Artificial Intelligence development

Generative adversarial networks, or GANs, are a powerful type of neural network used for unsupervised machine learning. Made up of two competing models which run in competition with one another, GANs are able to capture and copy variations within a dataset. They’re great for image manipulation and generation, but they can also be deployed for tasks like understanding risk and recovery in healthcare and pharmacology. GANs are actually pretty new - they were first introduced by Ian Goodfellow in 2014. Goodfellow developed them to tackle some of the issues with similar neural networks, including the Boltzmann machine and autoencoders. Both the Boltzmann machine and autoencoders use the Markov Decision Chain which has a pretty high computational cost. This efficiency gives engineers significant gains - which you need if you’re working at the cutting edge of artificial intelligence. How do Generative Adversarial Networks work? Let's start with a simple analogy. You have a painting - say the Mona Lisa - and we have a master forger who wants to create a duplicate painting. The forger does this by learning how the original painter - Leonardo Da Vinci - produced the painting. Meanwhile, you have an investigator trying to capture the forger and ‘second guess’ the rules the forger is learning. To map this onto the architecture of a GAN, the forger is the generator network, which learns the distribution of classes while the investigator is the discriminator network, which learning the boundaries between those classes - the formal ‘shape’ of the dataset. Applications of GANs Generative adversarial networks are used for a number of different applications. One of the best examples is a Google Brain project back in 2016 - researchers used GANs to develop a method of encryption. This project used 3 neural networks - Alice, Bob, and Eve. Alice’s job was to send an encrypted message to Bob. Bob’s job was to decode that message, while Eve’s job was to intercept it. To begin with Alice’s messages were easily intercepted by Eve. However, thanks to Eve’s adversarial work, Alice began to develop its own encryption strategy - it took 15,000 runs for Alice to successfully encrypt a message that could be deciphered by Bob that Eve couldn’t intercept. Elsewhere, GANs are also being used in fields such as drug research. The neural networks can be trained on the existing drugs and suggest new synthetic chemical structures that improve on drugs that already exist. Generative adversarial networks: the cutting edge of artificial intelligence As we’ve seen, GANs offer some really exciting opportunities in artificial intelligence. There are two key advantages you need to remember: GANs solve the problem of generating data when you don’t have enough to begin with and they require no human supervision. This is crucial when you think about the cutting edge of artificial intelligence, both in terms of the efficiency of running the models, and the real-world data we want to use - which could be poor quality or have privacy and confidentiality issues, as much healthcare data does.

There are over 70 Java-based open source machine learning projects listed on the MLOSS.org website and probably many more unlisted projects live at university servers, GitHub, or Bitbucket. In this article, we will review the major machine learning libraries and platforms in Java, the kind of problems they can solve, the algorithms they support, and the kind of data they can work with. This article is an excerpt taken from Machine learning in Java, written by Bostjan Kaluza and published by Packt Publishing Ltd. Weka Weka, which is short for Waikato Environment for Knowledge Analysis, is a machine learning library developed at the University of Waikato, New Zealand, and is probably the most well-known Java library. It is a general-purpose library that is able to solve a wide variety of machine learning tasks, such as classification, regression, and clustering. It features a rich graphical user interface, command-line interface, and Java API. You can check out Weka at http://www.cs.waikato.ac.nz/ml/weka/. At the time of writing this book, Weka contains 267 algorithms in total: data pre-processing (82), attribute selection (33), classification and regression (133), clustering (12), and association rules mining (7). Graphical interfaces are well-suited for exploring your data, while Java API allows you to develop new machine learning schemes and use the algorithms in your applications. Weka is distributed under GNU General Public License (GNU GPL), which means that you can copy, distribute, and modify it as long as you track changes in source files and keep it under GNU GPL. You can even distribute it commercially, but you must disclose the source code or obtain a commercial license. In addition to several supported file formats, Weka features its own default data format, ARFF, to describe data by attribute-data pairs. It consists of two parts. The first part contains header, which specifies all the attributes (that is, features) and their type; for instance, nominal, numeric, date, and string. The second part contains data, where each line corresponds to an instance. The last attribute in the header is implicitly considered as the target variable, missing data are marked with a question mark. For example, the Bob instance written in an ARFF file format would be as follows: @RELATION person_dataset @ATTRIBUTE `Name`  STRING @ATTRIBUTE `Height`  NUMERIC @ATTRIBUTE `Eye color`{blue, brown, green} @ATTRIBUTE `Hobbies`  STRING @DATA 'Bob', 185.0, blue, 'climbing, sky diving' 'Anna', 163.0, brown, 'reading' 'Jane', 168.0, ?, ? The file consists of three sections. The first section starts with the @RELATION <String> keyword, specifying the dataset name. The next section starts with the @ATTRIBUTE keyword, followed by the attribute name and type. The available types are STRING, NUMERIC, DATE, and a set of categorical values. The last attribute is implicitly assumed to be the target variable that we want to predict. The last section starts with the @DATA keyword, followed by one instance per line. Instance values are separated by comma and must follow the same order as attributes in the second section. Weka's Java API is organized in the following top-level packages: weka.associations: These are data structures and algorithms for association rules learning, including Apriori, predictive apriori, FilteredAssociator, FP-Growth, Generalized Sequential Patterns (GSP), Hotspot, and Tertius. weka.classifiers: These are supervised learning algorithms, evaluators, and data structures. Thepackage is further split into the following components: weka.classifiers.bayes: This implements Bayesian methods, including naive Bayes, Bayes net, Bayesian logistic regression, and so on weka.classifiers.evaluation: These are supervised evaluation algorithms for nominal and numerical prediction, such as evaluation statistics, confusion matrix, ROC curve, and so on weka.classifiers.functions: These are regression algorithms, including linear regression, isotonic regression, Gaussian processes, support vector machine, multilayer perceptron, voted perceptron, and others weka.classifiers.lazy: These are instance-based algorithms such as k-nearest neighbors, K*, and lazy Bayesian rules weka.classifiers.meta: These are supervised learning meta-algorithms, including AdaBoost, bagging, additive regression, random committee, and so on weka.classifiers.mi: These are multiple-instance learning algorithms, such as citation k-nn, diverse density, MI AdaBoost, and others weka.classifiers.rules: These are decision tables and decision rules based on the separate-and-conquer approach, Ripper, Part, Prism, and so on weka.classifiers.trees: These are various decision trees algorithms, including ID3, C4.5, M5, functional tree, logistic tree, random forest, and so on weka.clusterers: These are clustering algorithms, including k-means, Clope, Cobweb, DBSCAN hierarchical clustering, and farthest. weka.core: These are various utility classes, data presentations, configuration files, and so on. weka.datagenerators: These are data generators for classification, regression, and clustering algorithms. weka.estimators: These are various data distribution estimators for discrete/nominal domains, conditional probability estimations, and so on. weka.experiment: These are a set of classes supporting necessary configuration, datasets, model setups, and statistics to run experiments. weka.filters: These are attribute-based and instance-based selection algorithms for both supervised and unsupervised data preprocessing. weka.gui: These are graphical interface implementing explorer, experimenter, and knowledge flowapplications. Explorer allows you to investigate dataset, algorithms, as well as their parameters, and visualize dataset with scatter plots and other visualizations. Experimenter is used to design batches of experiment, but it can only be used for classification and regression problems. Knowledge flows implements a visual drag-and-drop user interface to build data flows, for example, load data, apply filter, build classifier, and evaluate. Java-ML for machine learning Java machine learning library, or Java-ML, is a collection of machine learning algorithms with a common interface for algorithms of the same type. It only features Java API, therefore, it is primarily aimed at software engineers and programmers. Java-ML contains algorithms for data preprocessing, feature selection, classification, and clustering. In addition, it features several Weka bridges to access Weka's algorithms directly through the Java-ML API. It can be downloaded from http://java-ml.sourceforge.net; where, the latest release was in 2012 (at the time of writing this book). Java-ML is also a general-purpose machine learning library. Compared to Weka, it offers more consistent interfaces and implementations of recent algorithms that are not present in other packages, such as an extensive set of state-of-the-art similarity measures and feature-selection techniques, for example, dynamic time warping, random forest attribute evaluation, and so on. Java-ML is also available under the GNU GPL license. Java-ML supports any type of file as long as it contains one data sample per line and the features are separated by a symbol such as comma, semi-colon, and tab. The library is organized around the following top-level packages: net.sf.javaml.classification: These are classification algorithms, including naive Bayes, random forests, bagging, self-organizing maps, k-nearest neighbors, and so on net.sf.javaml.clustering: These are clustering algorithms such as k-means, self-organizing maps, spatial clustering, Cobweb, AQBC, and others net.sf.javaml.core: These are classes representing instances and datasets net.sf.javaml.distance: These are algorithms that measure instance distance and similarity, for example, Chebyshev distance, cosine distance/similarity, Euclidian distance, Jaccard distance/similarity, Mahalanobis distance, Manhattan distance, Minkowski distance, Pearson correlation coefficient, Spearman's footrule distance, dynamic time wrapping (DTW), and so on net.sf.javaml.featureselection: These are algorithms for feature evaluation, scoring, selection, and ranking, for instance, gain ratio, ReliefF, Kullback-Liebler divergence, symmetrical uncertainty, and so on net.sf.javaml.filter: These are methods for manipulating instances by filtering, removing attributes, setting classes or attribute values, and so on net.sf.javaml.matrix: This implements in-memory or file-based array net.sf.javaml.sampling: This implements sampling algorithms to select a subset of dataset net.sf.javaml.tools: These are utility methods on dataset, instance manipulation, serialization, Weka API interface, and so on net.sf.javaml.utils: These are utility methods for algorithms, for example, statistics, math methods, contingency tables, and others Apache Mahout The Apache Mahout project aims to build a scalable machine learning library. It is built atop scalable, distributed architectures, such as Hadoop, using the MapReduce paradigm, which is an approach for processing and generating large datasets with a parallel, distributed algorithm using a cluster of servers. Mahout features console interface and Java API to scalable algorithms for clustering, classification, and collaborative filtering. It is able to solve three business problems: item recommendation, for example, recommending items such as people who liked this movie also liked…; clustering, for example, of text documents into groups of topically-related documents; and classification, for example, learning which topic to assign to an unlabeled document. Mahout is distributed under a commercially-friendly Apache License, which means that you can use it as long as you keep the Apache license included and display it in your program's copyright notice. Mahout features the following libraries: org.apache.mahout.cf.taste: These are collaborative filtering algorithms based on user-based and item-based collaborative filtering and matrix factorization with ALS org.apache.mahout.classifier: These are in-memory and distributed implementations, includinglogistic regression, naive Bayes, random forest, hidden Markov models (HMM), and multilayer perceptron org.apache.mahout.clustering: These are clustering algorithms such as canopy clustering, k-means, fuzzy k-means, streaming k-means, and spectral clustering org.apache.mahout.common: These are utility methods for algorithms, including distances, MapReduce operations, iterators, and so on org.apache.mahout.driver: This implements a general-purpose driver to run main methods of other classes org.apache.mahout.ep: This is the evolutionary optimization using the recorded-step mutation org.apache.mahout.math: These are various math utility methods and implementations in Hadoop org.apache.mahout.vectorizer: These are classes for data presentation, manipulation, andMapReduce jobs Apache Spark Apache Spark, or simply Spark, is a platform for large-scale data processing builds atop Hadoop, but, in contrast to Mahout, it is not tied to the MapReduce paradigm. Instead, it uses in-memory caches to extract a working set of data, process it, and repeat the query. This is reported to be up to ten times as fast as a Mahout implementation that works directly with disk-stored data. It can be grabbed from https://spark.apache.org. There are many modules built atop Spark, for instance, GraphX for graph processing, Spark Streaming for processing real-time data streams, and MLlib for machine learning library featuring classification, regression, collaborative filtering, clustering, dimensionality reduction, and optimization. Spark's MLlib can use a Hadoop-based data source, for example, Hadoop Distributed File System (HDFS) or HBase, as well as local files. The supported data types include the following: Local vector is stored on a single machine. Dense vectors are presented as an array of double-typed values, for example, (2.0, 0.0, 1.0, 0.0); while sparse vector is presented by the size of the vector, an array of indices, and an array of values, for example, [4, (0, 2), (2.0, 1.0)]. Labeled point is used for supervised learning algorithms and consists of a local vector labeled with a double-typed class values. Label can be class index, binary outcome, or a list of multiple class indices (multiclass classification). For example, a labeled dense vector is presented as [1.0, (2.0, 0.0, 1.0, 0.0)]. Local matrix stores a dense matrix on a single machine. It is defined by matrix dimensions and a single double-array arranged in a column-major order. Distributed matrix operates on data stored in Spark's Resilient Distributed Dataset (RDD), which represents a collection of elements that can be operated on in parallel. There are three presentations: row matrix, where each row is a local vector that can be stored on a single machine, row indices are meaningless; and indexed row matrix, which is similar to row matrix, but the row indices are meaningful, that is, rows can be identified and joins can be executed; and coordinate matrix, which is used when a row cannot be stored on a single machine and the matrix is very sparse. Spark's MLlib API library provides interfaces to various learning algorithms and utilities as outlined in the following list: org.apache.spark.mllib.classification: These are binary and multiclass classification algorithms, including linear SVMs, logistic regression, decision trees, and naive Bayes org.apache.spark.mllib.clustering: These are k-means clustering org.apache.spark.mllib.linalg: These are data presentations, including dense vectors, sparse vectors, and matrices org.apache.spark.mllib.optimization: These are the various optimization algorithms used as low-level primitives in MLlib, including gradient descent, stochastic gradient descent, update schemes for distributed SGD, and limited-memory BFGS org.apache.spark.mllib.recommendation: These are model-based collaborative filtering implemented with alternating least squares matrix factorization org.apache.spark.mllib.regression: These are regression learning algorithms, such as linear least squares, decision trees, Lasso, and Ridge regression org.apache.spark.mllib.stat: These are statistical functions for samples in sparse or dense vector format to compute the mean, variance, minimum, maximum, counts, and nonzero counts org.apache.spark.mllib.tree: This implements classification and regression decision tree-learning algorithms org.apache.spark.mllib.util: These are a collection of methods to load, save, preprocess, generate, and validate the data Deeplearning4j Deeplearning4j, or DL4J, is a deep-learning library written in Java. It features a distributed as well as a single-machinedeep-learning framework that includes and supports various neural network structures such as feedforward neural networks, RBM, convolutional neural nets, deep belief networks, autoencoders, and others. DL4J can solve distinct problems, such as identifying faces, voices, spam or e-commerce fraud. Deeplearning4j is also distributed under Apache 2.0 license and can be downloaded from http://deeplearning4j.org. The library is organized as follows: org.deeplearning4j.base: These are loading classes org.deeplearning4j.berkeley: These are math utility methods org.deeplearning4j.clustering: This is the implementation of k-means clustering org.deeplearning4j.datasets: This is dataset manipulation, including import, creation, iterating, and so on org.deeplearning4j.distributions: These are utility methods for distributions org.deeplearning4j.eval: These are evaluation classes, including the confusion matrix org.deeplearning4j.exceptions: This implements exception handlers org.deeplearning4j.models: These are supervised learning algorithms, including deep belief network, stacked autoencoder, stacked denoising autoencoder, and RBM org.deeplearning4j.nn: These are the implementation of components and algorithms based on neural networks, such as neural network, multi-layer network, convolutional multi-layer network, and so on org.deeplearning4j.optimize: These are neural net optimization algorithms, including back propagation, multi-layer optimization, output layer optimization, and so on org.deeplearning4j.plot: These are various methods for rendering data org.deeplearning4j.rng: This is a random data generator org.deeplearning4j.util: These are helper and utility methods MALLET Machine Learning for Language Toolkit (MALLET), is a large library of natural language processing algorithms and utilities. It can be used in a variety of tasks such as document classification, document clustering, information extraction, and topic modeling. It features command-line interface as well as Java API for several algorithms such as naive Bayes, HMM, Latent Dirichlet topic models, logistic regression, and conditional random fields. MALLET is available under Common Public License 1.0, which means that you can even use it in commercial applications. It can be downloaded from http://mallet.cs.umass.edu. MALLET instance is represented by name, label, data, and source. However, there are two methods to import data into the MALLET format, as shown in the following list: Instance per file: Each file, that is, document, corresponds to an instance and MALLET accepts the directory name for the input. Instance per line: Each line corresponds to an instance, where the following format is assumed: the instance_name label token. Data will be a feature vector, consisting of distinct words that appear as tokens and their occurrence count. The library comprises the following packages: cc.mallet.classify: These are algorithms for training and classifying instances, including AdaBoost, bagging, C4.5, as well as other decision tree models, multivariate logistic regression, naive Bayes, and Winnow2. cc.mallet.cluster: These are unsupervised clustering algorithms, including greedy agglomerative, hill climbing, k-best, and k-means clustering. cc.mallet.extract: This implements tokenizers, document extractors, document viewers, cleaners, and so on. cc.mallet.fst: This implements sequence models, including conditional random fields, HMM, maximum entropy Markov models, and corresponding algorithms and evaluators. cc.mallet.grmm: This implements graphical models and factor graphs such as inference algorithms, learning, and testing. For example, loopy belief propagation, Gibbs sampling, and so on. cc.mallet.optimize: These are optimization algorithms for finding the maximum of a function, such as gradient ascent, limited-memory BFGS, stochastic meta ascent, and so on. cc.mallet.pipe: These are methods as pipelines to process data into MALLET instances. cc.mallet.topics: These are topics modeling algorithms, such as Latent Dirichlet allocation, four-level pachinko allocation, hierarchical PAM, DMRT, and so on. cc.mallet.types: This implements fundamental data types such as dataset, feature vector, instance, and label. cc.mallet.util: These are miscellaneous utility functions such as command-line processing, search, math, test, and so on. To design, build, and deploy your own machine learning applications by leveraging key Java machine learning libraries, check out this book Machine learning in Java, published by Packt Publishing. 5 JavaScript machine learning libraries you need to know A non programmer’s guide to learning Machine learning Why use JavaScript for machine learning?  

Although chatbots have been under development for at least a few decades, they did not become mainstream channels for customer engagement until recently. Due to serious efforts by industry giants like Apple, Google, Microsoft, Facebook, IBM, and Amazon, and their subsequent investments in developing toolkits, chatbots and conversational interfaces have become a serious contender to other customer contact channels. In this time, chatbots have been applied in various sectors and various conversational scenarios within sectors like retail, banking and finance, governmental, health, legal, and many more. This tutorial is an excerpt from a book written by Srini Janarthanam titled Hands-On Chatbots and Conversational UI Development. This book is organized as eight chatbot projects that will introduce the ecosystem of tools, techniques, concepts, and even gadgets relating to conversational interfaces. Over the last few years, an ecosystem of tools and services has grown around the idea of conversational interfaces. There are a number of tools that we can plug and play to design, develop, and manage chatbots. Mockup tools Mockups can be used to show clients as to how a chatbot would look and behave. These are tools that you may want to consider using during conversation design, after coming up with sample conversations between the user and the bot. Mockup tools allow you to visualize the conversation between the user and the bot and showcase the dynamics of conversational turn-taking. Some of these tools allow you to export the mockup design and make videos. BotSociety.io and BotMock.com are some of the popular mockup tools. Channels in Chatbots Channels refer to places where users can interact with the chatbot. There are several deployment channels over which your bots can be exposed to users. These include Messaging services such as Facebook Messenger, Skype, Kik, Telegram, WeChat, and Line Office and team chat services such as Slack, Microsoft Teams, and many more Traditional channels such as the web chat, SMS, and voice calls Smart speakers such as Amazon Echo and Google Home. Choose the channel based on your users and the requirements of the project. For instance, if you are building a chatbot targeting consumers, Facebook Messenger can be the best channel because of the growing number of users who use the service already to keep in touch with friends and family. To add your chatbot to their contact list may be easier than getting them to download your app. If the user needs to interact with the bot using voice in a home or office environment, smart speaker channels can be an ideal choice. And finally, there are tools that can connect chatbots to many channels simultaneously (for example, Dialogflow integration, MS Bot Service, and Smooch.io, and so on). Chatbot development tools There are many tools that you can use to build chatbots without having to code even a single line: Chatfuel, ManyChat, Dialogflow, and so on. Chatfuel allows designers to create the conversational flow using visual elements. With ManyChat, you can build the flow using a visual map called the FlowBuilder. Conversational elements such as bot utterances and user response buttons can be configured using drag and drop UI elements. Dialogflow can be used to build chatbots that require advanced natural language understanding to interact with users. On the other hand, there are scripting languages such as Artificial Intelligence Markup Language (AIML), ChatScript, and RiveScript that can be used to build chatbots. These scripts will contain the conversational content and flow that then needs to be fed into an interpreter program or a rules engine to bring the chatbot to life. The interpreter decides how to progress the conversation by matching user utterances to templates in the scripts. While it is straightforward to build conversational chatbots using this approach, it becomes difficult to build transactional chatbots without generating explicit semantic representations of user utterances. PandoraBots is a popular web-based platform for building AIML chatbots. Alternatively, there are SDK libraries that one can use to build chatbots: MS Bot Builder, BotKit, BotFuel, and so on provide SDKs in one or more programming languages to assist developers in building the core conversational management module. The ability to code the conversational manager gives developers the flexibility to mold the conversation and integrate the bot to backend tasks better than no-code and scripting platforms. Once built, the conversation manager can then be plugged into other services such as natural language understanding to understand user utterances. Analytics in Chatbots Like other digital solutions, chatbots can benefit from collecting and analyzing their usage statistics. While you can build a bespoke analytics platform from scratch, you can also use off-the-shelf toolkits that are widely available now. Many off-the-shelf analytics toolkits are available that can be plugged into a chatbot, using which incoming and outgoing messages can be logged and examined. These tools tell chatbot builders and managers the kind of conversations that actually transpire between users and the chatbot. The data will give useful information such as the conversational tasks that are popular, places where conversational experience breaks down, utterances that the bot did not understand, and the requests which the chatbots still need to scale up to. Dashbot.io, BotAnalytics, and Google's Chatbase are a few analytic toolkits that you can use to analyze your chatbot's performance. Natural language understanding Chatbots can be built without having to understand utterances from the user. However, adding the natural language understanding capability is not very difficult. It is one of the hallmark features that sets chatbots apart from their digital counterparts such as websites and apps with visual elements. There are many natural language understanding modules that are available as cloud services. Major IT players like Google, Microsoft, Facebook, and IBM have created tools that you can plug into your chatbot. Google's Dialogflow, Microsoft LUIS, IBM Watson, SoundHound, and Facebook's Wit.ai are some of the NLU tools that you can try. Directory services One of the challenges of building the bot is to get users to discover and use it. Chatbots are not as popular as websites and mobile apps, so a potential user may not know where to look to find the bot. Once your chatbot is deployed, you need to help users find it. There are directories that list bots in various categories. Chatbots.org is one of the oldest directory services that has been listing chatbots and virtual assistants since 2008. Other popular ones are Botlist.co, BotPages, BotFinder, and ChatBottle. These directories categorize bots in terms of purpose, sector, languages supported, countries, and so on. In addition to these, channels such as Facebook and Telegram have their own directories for the bots hosted on their channel. In the case of Facebook, you can help users find your Messenger bot using their Discover service. Monetization Chatbots are built for many purposes: to create awareness, to support customers after sales, to provide paid services, and many more. In addition to all these, chatbots with interesting content can engage users for a long time and can be used to make some money through targeted personalized advertising. Services such as CashBot.ai and AddyBot.com can integrate with your chatbot to send targeted advertisements and recommendations to users, and when users engage, your chatbot makes money. In this article, we saw tools that can help you build a chatbot, collect and analyze its usage statistics, add features like natural language understanding, and many more. The aforementioned is not an exhaustive list of tools and nor are the services listed under each type. These tools are evolving over time as chatbots are finding their niche in the market. This list gives you an idea of how multidimensional the conversational UI ecosystem is and help you explore the space and feed your creative mind. If you found this post useful, do check out the book, Hands-On Chatbots and Conversational UI Development, which will help you explore the world of conversational user interfaces. How to build a chatbot with Microsoft Bot framework Facebook’s Wit.ai: Why we need yet another chatbot development framework? How to build a basic server side chatbot using Go

A formal definition of machine learning proposed by computer scientist Tom M. Mitchell states that a machine learns whenever it is able to utilize an experience such that its performance improves on similar experiences in the future. Although this definition is intuitive, it completely ignores the process of exactly how experience can be translated into future action—and of course, learning is always easier said than done! While human brains are naturally capable of learning from birth, the conditions necessary for computers to learn must be made explicit. For this reason, although it is not strictly necessary to understand the theoretical basis of learning, this foundation helps to understand, distinguish, and implement machine learning algorithms. This article is taken from the book Machine learning with R - Second Edition, written by Brett Lantz. Regardless of whether the learner is a human or machine, the basic learning process is similar. It can be divided into four interrelated components: Data storage utilizes observation, memory, and recall to provide a factual basis for further reasoning. Abstraction involves the translation of stored data into broader representations and concepts. Generalization uses abstracted data to create knowledge and inferences that drive action in new contexts. Evaluation provides a feedback mechanism to measure the utility of learned knowledge and inform potential improvements. The following figure illustrates the steps in the learning process: Keep in mind that although the learning process has been conceptualized as four distinct components, they are merely organized this way for illustrative purposes. In reality, the entire learning process is inextricably linked. In human beings, the process occurs subconsciously. We recollect, deduce, induct, and intuit with the confines of our mind's eye, and because this process is hidden, any differences from person to person are attributed to a vague notion of subjectivity. In contrast, with computers these processes are explicit, and because the entire process is transparent, the learned knowledge can be examined, transferred, and utilized for future action. Data storage for advanced reasoning All learning must begin with data. Humans and computers alike utilize data storage as a foundation for more advanced reasoning. In a human being, this consists of a brain that uses electrochemical signals in a network of biological cells to store and process observations for short- and long-term future recall. Computers have similar capabilities of short- and long-term recall using hard disk drives, flash memory, and random access memory (RAM) in combination with a central processing unit (CPU). It may seem obvious to say so, but the ability to store and retrieve data alone is not sufficient for learning. Without a higher level of understanding, knowledge is limited exclusively to recall, meaning exclusively what is seen before and nothing else. The data is merely ones and zeros on a disk. They are stored memories with no broader meaning. To better understand the nuances of this idea, it may help to think about the last time you studied for a difficult test, perhaps for a university final exam or a career certification. Did you wish for an eidetic (photographic) memory? If so, you may be disappointed to learn that perfect recall is unlikely to be of much assistance. Even if you could memorize material perfectly, your rote learning is of no use, unless you know in advance the exact questions and answers that will appear in the exam. Otherwise, you would be stuck in an attempt to memorize answers to every question that could conceivably be asked. Obviously, this is an unsustainable strategy. Instead, a better approach is to spend time selectively, memorizing a small set of representative ideas while developing strategies on how the ideas relate and how to use the stored information. In this way, large ideas can be understood without needing to memorize them by rote. Abstraction of stored data This work of assigning meaning to stored data occurs during the abstraction process, in which raw data comes to have a more abstract meaning. This type of connection, say between an object and its representation, is exemplified by the famous René Magritte painting The Treachery of Images: Source: http://collections.lacma.org/node/239578 The painting depicts a tobacco pipe with the caption Ceci n'est pas une pipe ("this is not a pipe"). The point Magritte was illustrating is that a representation of a pipe is not truly a pipe. Yet, in spite of the fact that the pipe is not real, anybody viewing the painting easily recognizes it as a pipe. This suggests that the observer's mind is able to connect the picture of a pipe to the idea of a pipe, to a memory of a physical pipe that could be held in the hand. Abstracted connections like these are the basis of knowledge representation, the formation of logical structures that assist in turning raw sensory information into a meaningful insight. During a machine's process of knowledge representation, the computer summarizes stored raw data using a model, an explicit description of the patterns within the data. Just like Magritte's pipe, the model representation takes on a life beyond the raw data. It represents an idea greater than the sum of its parts. There are many different types of models. You may be already familiar with some. Examples include: Mathematical equations Relational diagrams such as trees and graphs Logical if/else rules Groupings of data known as clusters The choice of model is typically not left up to the machine. Instead, the learning task and data on hand inform model selection. The process of fitting a model to a dataset is known as training. When the model has been trained, the data is transformed into an abstract form that summarizes the original information. It is important to note that a learned model does not itself provide new data, yet it does result in new knowledge. How can this be? The answer is that imposing an assumed structure on the underlying data gives insight into the unseen by supposing a concept about how data elements are related. Take for instance the discovery of gravity. By fitting equations to observational data, Sir Isaac Newton inferred the concept of gravity. But the force we now know as gravity was always present. It simply wasn't recognized until Newton recognized it as an abstract concept that relates some data to others—specifically, by becoming the g term in a model that explains observations of falling objects. Most models may not result in the development of theories that shake up scientific thought for centuries. Still, your model might result in the discovery of previously unseen relationships among data. A model trained on genomic data might find several genes that, when combined, are responsible for the onset of diabetes; banks might discover a seemingly innocuous type of transaction that systematically appears prior to fraudulent activity; and psychologists might identify a combination of personality characteristics indicating a new disorder. These underlying patterns were always present, but by simply presenting information in a different format, a new idea is conceptualized. Generalization for future action The learning process is not complete until the learner is able to use its abstracted knowledge for future action. However, among the countless underlying patterns that might be identified during the abstraction process and the myriad ways to model these patterns, some will be more useful than others. Unless the production of abstractions is limited, the learner will be unable to proceed. It would be stuck where it started—with a large pool of information, but no actionable insight. The term generalization describes the process of turning abstracted knowledge into a form that can be utilized for future action, on tasks that are similar, but not identical, to those it has seen before. Generalization is a somewhat vague process that is a bit difficult to describe. Traditionally, it has been imagined as a search through the entire set of models (that is, theories or inferences) that could be abstracted during training. In other words, if you can imagine a hypothetical set containing every possible theory that could be established from the data, generalization involves the reduction of this set into a manageable number of important findings. In generalization, the learner is tasked with limiting the patterns it discovers to only those that will be most relevant to its future tasks. Generally, it is not feasible to reduce the number of patterns by examining them one-by-one and ranking them by future utility. Instead, machine learning algorithms generally employ shortcuts that reduce the search space more quickly. Toward this end, the algorithm will employ heuristics, which are educated guesses about where to find the most useful inferences. Heuristics are routinely used by human beings to quickly generalize experience to new scenarios. If you have ever utilized your gut instinct to make a snap decision prior to fully evaluating your circumstances, you were intuitively using mental heuristics. The incredible human ability to make quick decisions often relies not on computer-like logic, but rather on heuristics guided by emotions. Sometimes, this can result in illogical conclusions. For example, more people express fear of airline travel versus automobile travel, despite automobiles being statistically more dangerous. This can be explained by the availability heuristic, which is the tendency of people to estimate the likelihood of an event by how easily its examples can be recalled. Accidents involving air travel are highly publicized. Being traumatic events, they are likely to be recalled very easily, whereas car accidents barely warrant a mention in the newspaper. The folly of misapplied heuristics is not limited to human beings. The heuristics employed by machine learning algorithms also sometimes result in erroneous conclusions. The algorithm is said to have a bias if the conclusions are systematically erroneous, or wrong in a predictable manner. For example, suppose that a machine learning algorithm learned to identify faces by finding two dark circles representing eyes, positioned above a straight line indicating a mouth. The algorithm might then have trouble with, or be biased against, faces that do not conform to its model. Faces with glasses, turned at an angle, looking sideways, or with various skin tones might not be detected by the algorithm. Similarly, it could be biased toward faces with certain skin tones, face shapes, or other characteristics that do not conform to its understanding of the world. In modern usage, the word bias has come to carry quite negative connotations. Various forms of media frequently claim to be free from bias, and claim to report the facts objectively, untainted by emotion. Still, consider for a moment the possibility that a little bias might be useful. Without a bit of arbitrariness, might it be a bit difficult to decide among several competing choices, each with distinct strengths and weaknesses? Indeed, some recent studies in the field of psychology have suggested that individuals born with damage to portions of the brain responsible for emotion are ineffectual in decision making, and might spend hours debating simple decisions such as what color shirt to wear or where to eat lunch. Paradoxically, bias is what blinds us from some information while also allowing us to utilize other information for action. It is how machine learning algorithms choose among the countless ways to understand a set of data. Evaluate the learner’s success Bias is a necessary evil associated with the abstraction and generalization processes inherent in any learning task. In order to drive action in the face of limitless possibility, each learner must be biased in a particular way. Consequently, each learner has its weaknesses and there is no single learning algorithm to rule them all. Therefore, the final step in the generalization process is to evaluate or measure the learner's success in spite of its biases and use this information to inform additional training if needed. Generally, evaluation occurs after a model has been trained on an initial training dataset. Then, the model is evaluated on a new test dataset in order to judge how well its characterization of the training data generalizes to new, unseen data. It's worth noting that it is exceedingly rare for a model to perfectly generalize to every unforeseen case. In parts, models fail to perfectly generalize due to the problem of noise, a term that describes unexplained or unexplainable variations in data. Noisy data is caused by seemingly random events, such as: Measurement error due to imprecise sensors that sometimes add or subtract a bit from the readings Issues with human subjects, such as survey respondents reporting random answers to survey questions, in order to finish more quickly Data quality problems, including missing, null, truncated, incorrectly coded, or corrupted values Phenomena that are so complex or so little understood that they impact the data in ways that appear to be unsystematic Trying to model noise is the basis of a problem called overfitting. Because most noisy data is unexplainable by definition, attempting to explain the noise will result in erroneous conclusions that do not generalize well to new cases. Efforts to explain the noise will also typically result in more complex models that will miss the true pattern that the learner tries to identify. A model that seems to perform well during training, but does poorly during evaluation, is said to be overfitted to the training dataset, as it does not generalize well to the test dataset. Solutions to the problem of overfitting are specific to particular machine learning approaches. For now, the important point is to be aware of the issue. How well the models are able to handle noisy data is an important source of distinction among them. We saw that machine learning process is similar to how humans learn in their daily lives.To To discover how to build machine learning algorithms, prepare data, and dig deep into data prediction techniques with R, check out this book Machine learning with R - Second edition. A Machine learning roadmap for Web Developers Why TensorFlow always tops machine learning and artificial intelligence tool surveys Intelligent Edge Analytics: 7 ways machine learning is driving edge computing adoption in 2018

There has been a huge shift in the way that businesses build technology in recent years driven by a move towards cloud and microservices. Public cloud services like AWS, Microsoft Azure, and Google Cloud Platform are transforming the way companies of all sizes understand and use software. Not only do public cloud services reduce the resourcing costs associated with on site server resources, they also make it easier to leverage cutting edge technological innovations like machine learning and artificial intelligence. Cloud is giving rise to what’s known as ‘Machine Learning as a Service’ - a trend that could prove to be transformative for organizations of all types and sizes. According to a report published on Research and Markets, Machine Learning as a Service is set to face a compound annual growth rate (CAGR) of 49% between 2017 and 2023. The main drivers of this growth include the increased application of advanced analytics in manufacturing, the high volume of structured and unstructured data, and the integration of machine learning with big data. Of course, with machine learning a relatively new area for many businesses, demand for MLaaS is ultimately self-fulfilling - if it’s there and people can see the benefits it can bring, demand is only going to continue. But it’s important not to get fazed by the hype. Plenty of money will be spent on cloud based machine learning products that won’t help anyone but the tech giants who run the public clouds. With that in mind, let’s dive deeper into Machine Learning as a Service and what the biggest cloud vendors offer. What does Machine Learning as a Service (MLaaS) mean? Machine learning as a Service (MLaaS) is an array of services that provides machine learning tools to users. Businesses and developers can incorporate a machine learning model into their application without having to work on its implementation. These services range from data visualization, facial recognition, natural language processing, chatbots, predictive analytics and deep learning, among others. Typically, for a given machine learning task, a user has to perform various steps. These steps include data preprocessing, feature identification, implementing the machine learning model, and training the model. MLaaS services simplify this process by only exposing a subset of the steps to the user while automatically managing the remaining steps. Some services can also provide 1-click mode, where the users does not have to perform any of the steps mentioned earlier. What type of businesses can benefit from Machine Learning as a Service? Large companies Large companies can afford to hire expert machine learning engineers and data scientists, but they still have to build and manage their own custom machine learning model. This is time-intensive and complicated process. By leveraging MLaaS services these companies can use pre-trained machine learning models via APIs that perform specific tasks and save time. Small and mid-sized businesses Big companies can invest in their own machine learning solutions because they have the resources. For small and mid-sized businesses (SMBs), however, this simply isn’t the case. Fortunately, MLaaS changes all that and makes machine learning accessible to organizations with resource limitations. By using MLaaS, businesses can leverage machine learning without the huge investment in infrastructure or talent. Whether it’s for smarter and more intelligent customer-facing apps, or improved operational intelligence and automation, this could bring huge gains for a reasonable amount of spending. What types of roles will benefit from MLaaS? Machine learning can contribute to any kind of app development provided you have data to train your app. However, adding AI features to your app is not easy. As a developer, you’ve to worry about a lot of other factors besides regular app development checklist, in order to make your app intelligent. Some of them are: Data preprocessing Model training Model evaluation Predictions Expertise in data science The development tools provided by MLaaS can simplify these tasks allowing you to easily embed machine learning in your applications. Developers can build quickly and efficiently with MLaaS offerings, because they have access to pre-built algorithms and models that would take them extensive resources to build otherwise. MLaaS can also support data scientists and analysts. While most data scientists should have the necessary skills to build and train machine learning models from scratch, it can nevertheless still be a time consuming task. MLaaS can, as already mentioned, simplify the machine learning engineering process, which means data scientists can focus on optimizations that require more thought and expertise. Top machine learning as a service (MLaaS) providers Amazon Web Services (AWS), Azure, and Google, all have MLaaS products in their cloud offerings. Let’s take a look at them. Google Cloud AI at a glance Google Cloud AI Google’s Cloud AI provides modern machine learning services. It consists of pre-trained models and a service to generate your own tailored models. The services provided are fast, scalable, and easy to use. The following are the services that Google provides at an unprecedented scale and speed to your applications: Cloud AutoML Beta It is a suite of machine learning products, with the help of which developers with limited machine learning expertise can train high-quality models specific to their business needs. It provides you a simple GUI to train, evaluate, improve, and deploy models based on your own data. Read also: AmoebaNets: Google’s new evolutionary AutoML Google Cloud Machine Learning (ML) Engine Google Cloud Machine Learning Engine is a service that offers training and prediction services to enable developers and data scientists to build superior machine learning models and deploy in production. You don’t have to worry about infrastructure and can instead focus on the model development and deployment. It offers two types of predictions: Online prediction deploys ML models with serverless, fully managed hosting that responds in real time with high availability. Batch predictions is cost-effective and provides unparalleled throughput for asynchronous applications. Read also: Google announces Cloud TPUs on the Cloud Machine Learning Engine (ML Engine) Google BigQuery It is a cloud data warehouse for data analytics. It uses SQL and provides Java Database Connectivity (JDBC) and Open Database Connectivity (ODBC) drivers to make integration fast and easy. It provides benefits like auto scaling and high-performance streaming to load data. You can create amazing reports and dashboards using your favorite BI tool, like Tableau, MicroStrategy, Looker etc. Read also: Getting started with Google Data Studio: An intuitive tool for visualizing BigQuery Data Dialogflow Enterprise Edition Dialogflow is an end-to-end, build-once deploy-everywhere development suite for creating conversational interfaces for websites, mobile applications, popular messaging platforms, and IoT devices. Dialogflow Enterprise Edition users have access to Google Cloud Support and a service level agreement (SLA) for production deployments. Read also: Google launches the Enterprise edition of Dialogflow, its chatbot API Cloud Speech-to-Text Google Cloud Speech-to-Text allows you to convert speech to text by applying neural network models. 120 languages are supported by the API, which will help you extend your user base. It can process both real-time streaming and prerecorded audio. Read also: Google announce the largest overhaul of their Cloud Speech-to-Text Microsoft Azure AI at a glance The Azure platform consists of various AI tools and services that can help you build smart applications. It provides Cognitive Services and Conversational AI with Bot tools, which facilitate building custom models with Azure Machine Learning for any scenario. You can run AI workloads anywhere at scale using its enterprise-grade AI infrastructure The following are services provided by Azure AI to help you achieve maximum productivity and reliability: Pre-built services You need not be an expert in data science to make your systems more intelligent and engaging. The pre-built services come with high-quality RESTful intelligent APIs for the following: Vision: Make your apps identify and analyze content within images and videos. Provides capabilities such as, image classification, optical character recognition in images, face detection, person identification, and emotion identification. Speech: Integrate speech processing capabilities in your app or services such as, text-to-speech, speech-to-text, speaker recognition, and speech translation. Language: Your application or service will understand meaning of the unstructured text or the intent behind a speaker's utterances. It comes with capabilities such as, text sentiment analysis, key phrase extraction, automated and customizable text translation. Knowledge: Create knowledge rich resources that can be integrated into apps and services. It provides features such as, QnA extraction from unstructured text, knowledge base creation from collections of Q&As, and semantic matching for knowledge bases. Search: Using Search API you can find exactly what you are looking for across billions of web pages. It provides features like, ad-free, safe, location-aware web search, Bing visual search, custom search engine creation, and many more. Custom services Azure Machine Learning is a fully managed cloud service which helps you to easily prepare data, build, and train your own models: You can rapidly prototype on your desktop, then scale up on VMs or scale out using Spark clusters. You can manage model performance, identify the best model, and promote it using data-driven insight. Deploy and manage your models everywhere. Using Docker containers, you can deploy the models into production faster in the cloud, on-premises or at the edge. Promote your best performing models into production and retrain them whenever necessary. Read also: Microsoft supercharges its Azure AI platform with new features AWS machine learning services at a glance Machine learning services provided by AWS help developers to easily add intelligence to any application with pre-trained services. For training and inferencing, it offers a broad array of compute options with powerful GPU-based instances, compute and memory optimized instances, and even FPGAs. You will get to choose from a set of services for data analysis including data warehousing, business intelligence, batch processing, stream processing, and data workflow orchestration. The following are the services provided by AWS: AWS machine learning applications Amazon Comprehend: This is a natural language processing (NLP) service that identifies relationships and finds insights in text using machine learning. It recognizes the language of the text and understands how positive or negative it is and extracts key phrases, places, people, brands, or events. It then analyzes text using tokenization and parts of speech, and automatically organizes a collection of text files by topic. Amazon Lex: This service provides the same deep learning technologies used by Amazon Alexa to developers in helping them build sophisticated, natural language, conversational bots easily. It comes with advanced deep learning functionalities like, automatic speech recognition (ASR) and natural language understanding (NLU) to facilitate a more life like conversational interaction with the users. Amazon Polly: This text-to-speech service produces speech that sounds like human voice using advanced deep learning technologies. It provides you dozens of life like voices across a variety of languages. You can simply select the ideal voice and build speech-enabled applications that work in many different countries. Amazon Rekognition: This service can identify the objects, people, text, scenes, and activities, and any inappropriate content in an image or a video. It also provides highly accurate facial analysis and facial recognition on images and video. Read also: AWS makes Amazon Rekognition, its image recognition AI, available for Asia-Pacific developers AWS machine learning platforms Amazon SageMaker: It is a platform that solves the complexities in the machine learning process, from building to deploying a model. It is a fully-managed platform that helps developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. AWS DeepLens: It is a fully programmable video camera, which comes with tutorials, code, and pre-trained models designed to expand deep learning skills. It provides you sample projects giving you practical and hands-on experience in deep learning in less than 10 minutes. Models trained in Amazon SageMaker can be sent to AWS DeepLens with just a few clicks from the AWS Management Console. Amazon ML: This is a service that provides visualization tools and wizards that direct you to create a machine learning model without having to learn complex ML algorithms and technology. Using simple APIs it makes it easy for you to obtain predictions for your application. It is highly scalable and can generate billions of predictions daily, and serve those predictions in real-time and at high throughput Read also: Amazon Sagemaker makes machine learning on the cloud easy. Deep Learning on AWS AWS Deep Learning AMIs: This provides the infrastructure and tools to accelerate deep learning in the cloud, at any scale. To train sophisticated, custom AI models, or to experiment with new algorithms you can quickly launch Amazon EC2 instances which are pre-installed in popular deep learning frameworks such as Apache MXNet and Gluon, TensorFlow, Microsoft Cognitive Toolkit, Caffe, Caffe2, Theano, Torch, PyTorch, Chainer, and Keras. Apache MXNet on AWS: This is a fast and scalable training and inference framework with an easy-to-use, concise API for machine learning. It allows developers of all skill levels to get started with deep learning on the cloud, on edge devices, and mobile apps using Gluon. You can build linear regressions, convolutional networks and recurrent LSTMs for object detection, speech recognition, recommendation, and personalization, in just a few lines of Gluon code. TensorFlow on AWS: You can quickly and easily get started with deep learning in the cloud using TensorFlow. AWS provides you a fully-managed TensorFlow experience with Amazon SageMaker. You can also use the AWS Deep Learning AMIs to build custom environment and workflow with TensorFlow and other popular frameworks such as Apache MXNet and Gluon, Caffe, Caffe2, Chainer, Torch, Keras, and Microsoft Cognitive Toolkit. Conclusion Machine learning and artificial intelligence can be expensive - skills and resources can cost a lot. For that reason, MLaaS is going to be a hugely influential development within cloud. Yes, the range of services on offer are impressive from AWS, Azure and GCP, but it’s really the ease and convenience that is most remarkable. With these services it’s easy to set up and run machine learning algorithms that enhance business processes and operations, customer interactions and overall business strategy. You don’t need a PhD, and you don’t need to code algorithms from scratch. The MLaaS market will likely continue to grow as more companies realise the potential machine learning has on their business - however, whether anyone can deliver a better set of services than the established cloud providers remains to be seen. Predictive Analytics with AWS: A quick look at Amazon ML Microsoft supercharges its Azure AI platform with new features AmoebaNets: Google’s new evolutionary AutoML

Gaming - whether board games or games set in the virtual realm - has been a massively popular form of entertainment since time immemorial. In the pursuit of creating more sophisticated, thrilling, and intelligent games, game developers have delved into ML and AI technologies to fuel innovation in the gaming sphere. The gaming domain is the ideal experimentation bed for evolving technologies because not only do they put up complex and challenging problems for ML and AI to solve, they also pose as a ground for creativity - a meeting ground for machine learning and the art of interaction. Machine Learning and Artificial Intelligence in Gaming The reliance on AI for gaming is not a recent development. In fact, it dates back to 1949, when the famous cryptographer and mathematician Claude Shannon made his musings public about how a supercomputer could be made to master Chess. Then again, in 1952, a graduate student in the UK developed an AI that could play tic-tac-toe with ultimate perfection. Source : Medium However, it isn’t just ML and AI that are progressing through experimentations on games. Game development, too, has benefited a great deal from these pioneering technologies. AI and ML have helped enhance the gaming experience on many grounds such as gaming design, the interactive quotient, as well as the inner functionalities of games. The above mentioned AI use cases focus on two primary things: one is to impart enhanced realism in virtual gaming environment and the second is to create a more naturalistic interface between the gaming environment and the players. As of now, the focus of game developers, data scientists, and ML researchers lies in two specific categories of the gaming domain - games of perfect information and games of imperfect information. In games of perfect information, a player is aware of all the aspects of the game throughout the playing session, whereas, in games of imperfect information, players are oblivious to specific aspects of the game. When it comes to games of perfect information such as Chess and Go, AI has shown various instances of overpowering human intelligence. Back in 1997, IBM’s Deep Blue successfully defeated world Chess champion, Garry Kasparov in a six-game match. In 2016, Google’s AlphaGo emerged as the victor in a Go match scoring 4-1 after defeating South Korean Go champion, Lee Sedol. One of the most advanced chess AIs developed yet, Stockfish, uses a combination of advanced heuristics and brute force to compute numeric values for each and every move in a specific position in Chess. It also effectively eliminates bad moves using the Alpha-beta pruning search algorithm. While the progress and contribution of AI and ML to the field of games of perfect information is laudable, researchers are now intrigued by games of imperfect information. Games of imperfect information offer much more challenging situations that are essentially difficult for machines to learn and master. Thus, the next evolution in the world of gaming will be to create spontaneous gaming environment using AI technology, in which developers will build only the gaming environment and its mechanics instead of creating a game with pre-programmed/scripted plots. In such a scenario, the AI will have to confront and solve spontaneous challenges with personalized scenarios generated on the spot. Games like StarCraft and StarCraft II have stirred up massive interest among game researchers and developers. In these games, the players are only partially aware of the gaming aspects and the game is largely determined not just by the AI moves and the previous state of the game, but also by the moves of other players. Since in these games you will have little knowledge about your rival’s moves, you have to take decisions on the go and your moves have to be spontaneous. The recent win of OpenAI Five over amateur human players in Dota2 is a good case in point. OpenAI Five is a team of five neural networks that leverages an advanced version of Proximal Policy Optimization and uses a separate LSTM to learn identifiable strategies. The progress of OpenAI Five shows that even without human data, reinforcement learning can facilitate long-term planning, thus, allowing us to make further progress in the games of imperfect information. Career in Game Development With ML and AI As ML and AI continue to penetrate the gaming industry, it is creating a huge demand for talented and skilled game developers who are well-versed in these technologies. Today, game development is at a place where it’s no longer necessary to build games using time-consuming manual techniques. ML and AI have made the task of game developers easier as by leveraging these technologies, they can design and build innovative gaming environment, and test them automatically. The integration of AI and ML in the gaming domain is giving birth to new job positions like Gameplay Software Engineer (AI), Gameplay Programmer (AI), and Game Security Data Scientist, to name a few. The salaries of traditional game developers is in stark contrast with that of those having AI/ML skills. While the average salary of game developers is usually around $44,000, it can scale up to and over $1,20,000 if one possesses AI/ML skills. Gameplay Engineer Average salary - $73,000 - $1,16,000 Gameplay engineers are usually part of the core game dev team and are entrusted with the responsibility of enhancing the existing gameplay systems to enrich the player experience. Companies today demand for gameplay engineers who are proficient in C/C++ and well-versed with AI/ML technologies. Gameplay Programmer Average salary - $98,000 - $1,49,000 Gameplay programmers work in close collaboration with the production and design team to develop cutting edge features in the existing and upcoming gameplay systems. Programming skills are a must and knowledge of AI/ML technologies is an added bonus. Game Security Data Scientist Average salary - $73,000 - $1,06,000 The role of a gameplay security data scientist is to combine both security and data science approaches to detect anomalies and fraudulent behavior in games. This calls for a high degree of expertise in AI, ML, and other statistical methods. With impressive salaries and exciting job opportunities cropping up fast in the game development sphere, the industry is attracting some major talent towards it. Game developers and software developers around the world are choosing the field due to the promises of rapid career growth. If you wish to bag better and more challenging roles in the domain of game development, you should definitely try and upskill your talent and knowledge base by mastering the fields of ML and AI. Packt Publishing is the leading UK provider of Technology eBooks, Coding eBooks, Videos and Blogs; helping IT professionals to put software to work. It offers several books and videos on Game development with AI and machine learning. It’s never too late to learn new disciplines and expand your knowledge base. There are numerous online platforms that offer great artificial intelligent courses. The perk of learning from a registered online platform is that you can learn and grow at your own pace and according to your convenience. So, enroll yourself in one and spice up your career in game development! About Author: Abhinav Rai is the Data Analyst at UpGrad, an online education platform providing industry oriented programs in collaboration with world-class institutes, some of which are MICA, IIIT Bangalore, BITS and various industry leaders which include MakeMyTrip, Ola, Flipkart etc.   Best game engines for AI game development Implementing Unity game engine and assets for 2D game development [Tutorial] How to use arrays, lists, and dictionaries in Unity for 3D game development      

Artificial intelligence might seem intimidating, but it isn’t actually as complex as you might think. Many of the tools that have been developed over the last decade or so have all helped to make artificial intelligence and machine learning more accessible to engineers with varying degrees of experience and knowledge. Today, we’ve got to a stage where it’s now accessible even to people who have barely written a line of code in their life! Pretty exciting, right? But if you’re completely new to the field, it can be challenging to know how to get started - fortunately, we’re about to help you overcome that first hurdle. If you are an AI denier, then be sure to first read ‘why learn Machine Learning as a non-techie’ before you move forward. A strong purpose and belief is the first step to learning anything new. Alright, now here’s how you can get started with artificial intelligence and machine learning techniques quickly. 0. Use a free MLaaS or a no code interactive machine learning tool to experience first hand what is possible with learning machine learning: Some popular examples of no code machine learning as a service option are Microsoft Azure, BigML, Orange, and Amazon ML. Read Q2 under the FAQ section below to know more on this topic. 1. Learn Linear Algebra: Linear Algebra is the elementary unit for ML. It helps you effectively comprehend the theory behind the Machine learning algorithms and how they work. It also improves your math skills such as statistics, programming skills, which are all other skills that helps in ML. Learning Resources: Linear Algebra for Beginners: Open Doors to Great Careers Linear algebra Basics 2. Learn just enough Python or any programming: Now, you can get started with any language of your interest, but we suggest Python as  it’s great for people who are new to programming. It’s easy to learn due to its simple syntax. You’ll be able to quickly implement the ML algorithms. Also,  It has a rich development ecosystem that offers a ton of libraries and frameworks in Machine Learning such as Scikit Learn, Lasagne, Numpy, Scipy, Theano, Tensorflow, etc. Learning Resources: Python Machine Learning Learn Python in 7 Days Python for Beginners 2017 [Video] Learn Python with codecademy Python editor for beginner programmers 3. Learn basic Probability Theory and statistics: A lot of fundamental Statistical and Probability Theories form the basis for ML. You’ve probably already learned Probability and statistics in school, it easy to dive into advanced statistics for ML. Machine learning in its currently widely used form is a way to predict odds and see patterns. Knowing statistics and probability is important as it will help you with better understanding of why any machine learning algorithm works. For example, your grounding in this area, will help to ask the right questions, choose the right set of algorithms and know what to expect as answers from your ML model on questions such as: What are the odds of this person also liking this movie given their current movie watching choices ( Collaborative filtering and content-based filtering) How similar is this user to that group of users who brought a bunch of stuff on my site (clustering, collaborative filtering, and classification) Could this person be at risk of cancer given a certain set of traits and health indicator observations (logistic regression) Should you buy that stock (decision tree) Also, check out our interview with James D. Miller to know more about why learning stats is important in this field. Learning resources: Statistics for Data Science [Video] 4. Learn machine learning algorithms: Do not get intimidated!  You don’t have to be an expert to learn ML algorithms. Knowing basic ML algorithms that are majorly used in the real world applications like linear regression, naive Bayes, and decision trees, are enough to get you started. Learn what they do and how they are used in Machine Learning. 5. Learn numpy sci-kit learn,Keras or any other popular machine learning framework: It can be confusing initially to decide which framework to learn. Each one has its own advantages and disadvantages. Numpy is a linear algebra library which is useful for performing mathematical and logical operations. You can easily work with large multidimensional arrays using Numpy. Sci-kit learn helps with quick implementation of popular algorithms on datasets as just one line of code makes different algorithms available for you. Keras is minimalistic and straightforward with high-levels of extensibility, so it is easier to approach. Learning Resources:  Hands-on Machine Learning with TensorFlow [Video]  Hands-on Scikit-learn for Machine Learning [Video] If you have reached till here, it is time to put your learning into practice. Go ahead and create a simple linear regression model using some publicly available dataset in your area of interest. Kaggle, ourworldindata.org, UC Irvine Machine Learning repository, elitedatascience, all have a rich set of clean datasets in varied fields. Now, it is necessary to commit and put in daily efforts to practise these skills. Quora, Reddit, Medium, and stackoverflow will be your best friends when it comes to solving doubts regarding any of these skills. Data Helpers is another great resource that provides newcomers with help on queries regarding entering the ML field and related topics. Additionally, once you start getting hang of these skills, identify your strengths and interests, to realign your career goals. Research on the kind of work you want to put your newly gained Machine Learning skill to use. It needn’t be professional or serious, it just needs to be something that you deeply care about or are passionate about. This will pull you through your learning milestones, should you feel low at some point. Also, don’t forget to collaborate with other people and learn from them. You can work with web developers, software programmers, data analysts, data administrators, game developers etc. Finally, keep yourself updated with all the latest happenings in the ML world. Follow top experts and influencers on social media, top blogs on Machine Learning, and conferences. Once you are done checking off these steps off your list, you’ll be ready to start off with your ML project.                                                  Now, we’ll be looking at the most frequently asked questions by beginners in the field of Machine learning. Frequently asked questions by Beginners in ML As a beginner, it’s natural to have a lot of questions regarding ML. We’ll be addressing the top three frequently asked questions by beginners or non-programmers when it comes to Machine learning: Q.1 I am looking to make a career in Machine learning but I have no prior programming experience. Do I need to know programming for Machine learning? In a nutshell, Yes. If you want a career in Machine learning then having some form of programming knowledge really helps. As mentioned earlier in this article, learning a programming language can really help you with implementing ML algorithms. It also lets you know the internal mechanism behind Machine learning. So, having programming as a prior skill is great. Again, as mentioned before, you can get started with Python which is the easiest and the most common languages for ML. However, programming is just a part of Machine learning. For instance, “machine learning engineers” typically write more code than develop models, while “research scientists” work more on modelling and analyzing different models. Now, ML is based on the principles of statistical inference and for talking statistically to the computer, we need a language, there comes Coding. So, even though the nature of your job in ML might not require you to code as much, there’s still some amount of coding required. Read Also: Why is Python so good for AI and ML? 5 Python Experts Explain Top languages for Artificial Intelligence development Q.2 Are there any tools that can help me with Machine learning without touching a single line of code? Yes. With the rise of MLaaS (Machine learning as a service), there are certain tools that help you get started with machine learning right-away. These are especially useful for business applications of ML, such as predictive modelling and clustering. Read Also: How MLaaS is transforming cloud Some of the most popular ones are: BigML:  This cloud based web-service lets you upload your data, prepare it and run algorithms on it. It’s great for people with not so extensive data science backgrounds. It offers a clean and easy to use interfaces for configuring algorithms (decision trees) and reviewing the results. Being focused “only” on Machine Learning, it comes with a wide set of features, all well integrated within a usable Web UI. Other than that, it also offers an API so that if you like it you can build an application around it. Microsoft Azure: The Microsoft Azure ML studio is a “GUI-based integrated development environment for constructing and operationalizing Machine Learning workflow on Azure”. So, via an integrated development environment called ML Studio, people without data science background or non-programmers can also build data models with the help of drag-and-drop gestures and simple data flow diagrams. This also saves a lot of time through ML Studio's library of sample experiments. Learning resources: Microsoft Azure Machine Learning Machine Learning In The Cloud With Azure ML[Video] Orange: This is an open source machine learning and data visualization studio for novice and experts alike. It provides a toolbox comprising of text mining (topic modelling) and image recognition. It also offers a design tool for visual programming which allows you to connect together data preparation, algorithms, and result evaluation, thereby, creating machine learning “programs”. Apart from that, it provides over 100 widgets for the environment and there’s also a Python API and library available which you can integrate into your application. Amazon ML: Amazon ML is a part of Amazon Web Services ( AWS ) that combines powerful machine learning algorithms with interactive visual tools to guide you towards easily creating, evaluating, and deploying machine learning models. So, whether you are a data scientist or a newbie, it offers ML services and tools tailored to meet your needs and level of expertise. Building ML models using Amazon ML consists of three operations: data analysis, model training, and evaluation. Learning Resources: Effective Amazon Machine Learning Q.3  Do I need to know advanced mathematics ( college graduate level ) to learn Machine learning? It depends. As mentioned earlier, understanding of the following mathematical topics: Probability, Statistics and Linear Algebra can really make your machine learning journey easier and also help simplify your code. These help you understand the “why” behind the working of the machine learning algorithms, which is quite fundamental to understanding ML. However, not knowing advanced mathematics is not an excuse to not learning Machine Learning. There a lot of libraries which makes the task of applying an ML algorithm to solve a task easier. One such example is the widely used Python’s scikit-learn library. With scikit-learn, you just need one line of code and you’ll have the most common algorithms there for you, ready to be used. But, if you want to go deeper into machine learning then knowing advanced mathematics is a prerequisite as it will help you understand the algorithms, the formulas, how the learning is done and many other Machine Learning concepts. Also, with so many courses and tutorials online, you can always learn advanced mathematics on the side while exploring Machine learning. So, we looked at the three most asked questions by beginners in the field of Machine Learning. In the past, machine learning has provided us with self-driving cars, effective web search, speech recognition, etc. Machine learning is extremely pervasive, in fact, many researchers believe that ML is the best way to make progress towards human-level AI. Learning ML is not an easy task but its not next to impossible either. In the end, it all depends on the amount of dedication and efforts that you’re willing to put in to get a grasp of it. We just touched the tip of the iceberg in this article, there’s a lot more to know in Machine Learning which you will get a hang of as you get your feet dirty in it. That being said, all the best for the road ahead! Facebook launches a 6-part ML video series 7 of the best ML conferences for the rest of 2018 Google introduces Machine Learning courses for AI beginners