How-To Tutorials - Artificial Intelligence

The media has gone into a frenzy over Google’s latest facial recognition patent that shows an algorithm can track you across social media and gather your personal details. We thought, we’d dive further into what other patents Google has applied for in facial recognition tehnology in 2018. What we discovered was an eye opener (pun intended). Google is only the 3rd largest applicant with IBM and Samsung leading the patents race in facial recognition. As of 10th Aug, 2018, 1292 patents have been granted in 2018 on Facial recognition. Of those, IBM received 53. Here is the summary comparison of leading companies in facial recognition patents in 2018. Read Also: Top four Amazon patents in 2018 that use machine learning, AR, and robotics IBM has always been at the forefront of innovation. Let’s go back about a quarter of a century, when IBM invented its first general-purpose computer for business. It built complex software programs that helped in launching Apollo missions, putting the first man on the moon. It’s chess playing computer, Deep Blue, back in 1997,  beat Garry Kasparov, in a traditional chess match (the first time a computer beat a world champion). Its researchers are known for winning Nobel Prizes. Coming back to 2018, IBM unveiled the world’s fastest supercomputer with AI capabilities, and beat the Wall Street expectations by making $20 billion in revenue in Q3 2018 last month, with market capitalization worth $132.14 billion as of August 9, 2018. Its patents are a major part of why it continues to be valuable highly. IBM continues to come up with cutting-edge innovations and to protect these proprietary inventions, it applies for patent grants. United States is the largest consumer market in the world, so patenting the technologies that the companies come out with is a standard way to attain competitive advantage. As per the United States Patent and Trademark Office (USPTO), Patent is an exclusive right to invention and “the right to exclude others from making, using, offering for sale, or selling the invention in the United States or “importing” the invention into the United States”. As always, IBM has applied for patents for a wide spectrum of technologies this year from Artificial Intelligence, Cloud, Blockchain, Cybersecurity, to Quantum Computing. Today we focus on IBM’s patents in facial recognition field in 2018. Four IBM facial recognition innovations patented in 2018 Facial recognition is a technology which identifies and verifies a person from a digital image or a video frame from a video source and IBM seems quite invested in it. Controlling privacy in a face recognition application Date of patent: January 2, 2018 Filed: December 15, 2015 Features: IBM has patented for a face-recognition application titled “Controlling privacy in a face recognition application”. Face recognition technologies can be used on mobile phones and wearable devices which may hamper the user privacy. This happens when a "sensor" mobile user identifies a "target" mobile user without his or her consent. The present mobile device manufacturers don’t provide the privacy mechanisms for addressing this issue. This is the major reason why IBM has patented this technology. Editor’s Note: This looks like an answer to the concerns raised over Google’s recent social media profiling facial recognition patent.   How it works? Controlling privacy in a face recognition application It consists of a privacy control system, which is implemented using a cloud computing node. The system uses a camera to find out information about the people, by using a face recognition service deployed in the cloud. As per the patent application “the face recognition service may have access to a face database, privacy database, and a profile database”. Controlling privacy in a face recognition application The facial database consists of one or more facial signatures of one or more users. The privacy database includes privacy preferences of target users. Privacy preferences will be provided by the target user and stored in the privacy database.The profile database contains information about the target user such as name, age, gender, and location. It works by receiving an input which includes a face recognition query and a digital image of a face. The privacy control system then detects a facial signature from the digital image. The target user associated with the facial signature is identified, and profile of the target user is extracted. It then checks the privacy preferences of the user. If there are no privacy preferences set, then it transmits the profile to the sensor user. But, if there are privacy preferences then the censored profile of the user is generated omitting out the private elements in the profile. There are no announcements, as for now, regarding when this technology will hit the market. Evaluating an impact of a user's content utilized in a social network Date of patent: January 30, 2018 Filed: April 11, 2015 Features:  IBM has patented for an application titled “Evaluating an impact of a user's content utilized in a social network”.  With so much data floating around on social network websites, it is quite common for the content of a document (e.g., e-mail message, a post, a word processing document, a presentation) to be reused, without the knowledge of an original author. Evaluating an impact of a user's content utilised in a social network Evaluating an impact of a user's content utilized in a social network Because of this, the original author of the content may not receive any credit, which creates less motivation for the users to post their original content in a social network. This is why IBM has decided to patent for this application. Evaluating an impact of a user's content utilized in a social network As per the patent application, the method/system/product  “comprises detecting content in a document posted on a social network environment being reused by a second user. The method further comprises identifying an author of the content. The method additionally comprises incrementing a first counter keeping track of a number of times the content has been adopted in derivative works”. There’s a processor, which generates an “impact score” which  represents the author's ability to influence other users to adopt the content. This is based on the number of times the content has been adopted in the derivative works. Also, “the method comprises providing social credit to the author of the content using the impact score”. Editor’s Note: This is particularly interesting to us as IBM, unlike other tech giants, doesn’t own a popular social network or media product. (Google has Google+, Microsoft has LinkedIn, Facebook and Twitter are social, even Amazon has stakes in a media entity in the form of Washington Post). No information is present about when or if this system will be used among social network sites. Spoof detection for facial recognition Date of patent: February 20, 2018 Filed: December 10, 2015 Features: IBM patented an application named “Spoof detection for facial recognition”.  It provides a method to determine whether the image is authentic or not. As per the patent “A facial recognition system is a computer application for automatically identifying or verifying a person from a digital image or a video frame from a video source.” Editor’s Note: This seems to have a direct impact on the work around tackling deepFakes, which incidentally is something DARPA is very keen on. Could IBM be vying for a long term contract with the government? How it works? The patent consists of a system that helps detect “if a face in a facial recognition authentication system is a three-dimensional structure based on multiple selected images from the input video”.                                      Spoof detection for facial recognition There are four or more two-dimensional feature points which are located via an image processing device connected to the camera. Here the two-dimensional feature points do not lie on the same two-dimensional plane. The patent reads that “one or more additional images of the user's face can be received with the camera; and, the at least four two-dimensional feature points can be located on each additional image with the image processor. The image processor can identify displacements between the two-dimensional feature points on the additional image and the two-dimensional feature points on the first image for each additional image” Spoof detection for facial recognition There is also a processor connected to the image processing device that helps figure out whether the displacements conform to a three-dimensional surface model. The processor can then determine whether to authenticate the user depending on whether the displacements conform to the three-dimensional surface model. Facial feature location using symmetry line Date of patent: June 5, 2018 Filed: July 20, 2015 Features: IBM patented for an application titled “Facial feature location using symmetry line”. As per the patent, “In many image processing applications, identifying facial features of the subject may be desired. Currently, location of facial features require a search in four dimensions using local templates that match the target features. Such a search tends to be complex and prone to errors because it has to locate both (x, y) coordinates, scale parameter and rotation parameter”. Facial feature location using symmetry line Facial feature location using symmetry line The application consists of a computer-implemented method that obtains an image of the subject’s face. After that it automatically detects a symmetry line of the face in the image, where the symmetry line intersects at least a mouth region of the face. It then automatically locates a facial feature of the face using the symmetry line. There’s also a computerised apparatus with a processor which performs the steps of obtaining an image of a subject’s face and helps locate the facial feature.  Editor’s note: Atleast, this patent makes direct sense to us. IBM is majorly focusing on bring AI to healthcare. A patent like this can find a lot of use in not just diagnostics and patient care, but also in cutting edge areas like robotics enabled surgeries. IBM is continually working on new technologies to provide the world with groundbreaking innovations. Its big investments in facial recognition technology speaks volumes about how IBM is well-versed with its endless possibilities. With the facial recognition technological progress,  come the privacy fears. But, IBM’s facial recognition application patent has got it covered as it lets the users set privacy preferences. This can be a great benchmark for IBM as no many existing applications are currently doing it. The social credit score evaluating app can really help bring the voice back to the users interested in posting content on social media platforms. The spoof detection application will help maintain authenticity by detecting forged images. Lastly, the facial feature detection can act as a great additional feature for image processing applications. IBM has been heavily investing in facial recognition technology. There are no guarantees by IBM as to whether these patents will ever make it to practical applications, but it does say a lot about how IBM thinks about the technology. Four interesting Amazon patents in 2018 that use machine learning, AR, and robotics Facebook patents its news feed filter tool to provide more relevant news to its users Google’s new facial recognition patent uses your social network to identify you!  

Alphabet, Google’s parent company, saw its stock price rise quickly after it announced its Q2 2018 earning results, shocking analysts (in a good way) all over the world. Shares of Alphabet have jumped more than 5% in after-hours trading Monday, hitting a new record high. Source: NASDAQ It would seem that the EU’s fine of €4.34 billion on Google for breaching EU antitrust laws had little effect on its progress in terms of Q2 earnings. According to Ruth Porat, Google's CFO, Alphabet generated revenue of $32.66 billion during Q2 2018, compared to $26.01 billion during the same quarter last year. Excluding the fine, Alphabet still booked a net income of $3.2 billion, which equals earnings of $4.54 per share. Had the EU decision gone the other way, Alphabet would have had $32.6 billion in revenue and a profit of $8.2 billion. “We want Google to be the source you think of when you run into a problem.” - Sundar Pichai, Google CEO, in the Q2 2018 Earnings Call In Monday afternoon’s earnings call, CEO Sundar Pichai focused on three major domains that have helped Alphabet achieve its Q2 earnings. First, he claimed that machine learning and AI was becoming a crucial unifying component across all of Google's products and offerings helping to cement and consolidate its position in the market. Second, Pichai suggested that investments in computing, video, cloud and advertising platforms have helped push Google into new valuable markets. And third, the company's investment in new businesses and emerging markets was proving to be a real growth driver which should secure Google's future success. Let us look at the various facets of Google’s growth strategy that have proven to be successful this quarter. Investing in AI With the world spinning around the axis of AI, Alphabet is empowering all of its product and service offerings with AI and machine learning. At its annual developer conference earlier this year, Google I/O, Google announced new updates to their products that rely on machine learning. For example, the revamped Google news app uses machine learning to provide relevant news stories for users, and improvements to Google assistant also helped the organization strengthen its position in that particular market. (By the end of 2018, it will be available in more than 30 languages in 80 countries.) This is another smart move by Alphabet in its plan to make information accessible to all while generating more revenue-generating options for themselves and expanding their partnerships to new vendors and enterprise clients. Google Translate also saw a huge bump in volume especially during the World Cup, as fans all over the world traveled to Russia to witness the football gala. Another smart decision was adding updates to Google Maps. This has achieved a 50% year-on-year growth in Indonesia, India, and Nigeria, three very big and expanding markets. Defending its Android ecosystem and business model The first Android Phone arrived in 2008. The project was built on the simple idea of a mobile platform that was free and open to everyone. Today, there are more than 24,000 Android-powered devices from over 1400 phone manufacturers. Google’s decision to build a business model that encourages this open ecosystem to thrive has been a clever strategy. It not only generates significant revenue for the company but it also brings a world of developers and businesses into its ecosystem. It's vendor lock-in with a friendly face. Of course, with the EU watching closely, Google has to be careful to follow regulation. Failure to comply could mean the company would face penalty payments of up to 5% of its average daily worldwide turnover of Alphabet. According to Brian Wieser, an analyst at Pivotal Research Group, however, “There do not appear to be any signs that should cause a meaningful slow down anytime soon, as fines from the EU are not likely to hamper Alphabet’s growth rate. Conversely, regulatory changes such as GDPR in Europe (and similar laws implemented elsewhere) could have the effect of reinforcing Alphabet’s growth.” Forming new partnerships Google has always been very keen to form new partnerships and strategic alliances with a wide variety of companies and startups. It has been very smart in systematically looking for partners that will complement their strengths and bring the end product to the market. Partnering also provides flexibility; instead of developing new solutions and tools in-house, Google can instead bring interesting innovations into the Google ecosystem simply thanks to its financial clout. For example, Google has partnered with many electronic companies to expand the number of devices compatible with Google assistant. Furthermore, its investment in computing platforms and AI has also helped the organization to generate considerable momentum in their Made by Google hardware business across Pixel, Home, Nest, and Chromecast. Interestingly, we also saw an acceleration in business adoption of Chromebooks. Chromebooks are the most cost-efficient and secure way for businesses to enable their employees to work in the cloud. The unit sales of managed Chromebooks in Q2 grew by more than 175% year-on-year. “Advertising on Youtube has always been an incredibly strong and growing source of income for its creators. Now Google is also building new ways for creators to source income such as paid channel memberships, merchandise shelves on Youtube channels, and endorsements opportunities through Famebit.”, said Pichai. Famebit is a startup they acquired in 2016 which uses data analytics to build tools to connect brands with the right creators. This acquisition proved to be quite successful as almost half of the creators that used Famebit in 2018 doubled their revenue in the first 3 months. Google has also made significant strides in developing new shopping and commerce partnerships such as with leading global retailers like Carrefour, designed to give people the power to shop wherever and however they want. Such collaborations are great for Google as it brings their shopping, ads, and cloud products under one hood. The success of Google Cloud’s vertical strategy and customer-centric approach was illustrated by key wins including Domino's Pizza, Soundcloud, and PwC moving to GCP this quarter. Target, the chain of department store retailers in the US, is also migrating key areas of it’s business to GCP. AirAsia has also expanded its relationship with Google for using ML and data analytics. This shows that the cloud business is only going to grow further. Further, Google Cloud Platform catering to clients from across very different industries and domains signals a robust way to expand their cloud empire. Supporting future customers Google is not just thinking about its current customer base but also working on specialized products to support the next wave of people which are coming online for the first time, enabled the rise in accessibility of mobile devices. They have established high-speed public WiFi in 400 train stations in India in collaboration with the Indian railways and proposed the system in Indonesia and Mexico as well. They have also announced Google AI research center in Ghana Africa to spur AI innovation with researchers and engineers from Africa. They have also expanded the Google IT support professional certificate program to more than 25 community colleges in the US. This massive uproar by Alphabet even in the midst of EU antitrust case was the most talked about news among Wall Street analysts. Most of them consider it to be buy-in terms of stocks. For the next quarter, Google wants to continue fueling its growing cloud business “We are investing for the long run.” Pichai said. They also don’t plan to dramatically alter their Android strategy and continue to give the OS for free. Pichai said, “I’m confident that we will find a way to make sure Android is available at scale to users everywhere.” A quick look at E.U.’s antitrust case against Google’s Android Is Google planning to replace Android with Project Fuchsia? Google Cloud Launches Blockchain Toolkit to help developers build apps easily

If you are a deep learning practitioner or someone who wants to get into the world of deep learning, you might be well acquainted with neural networks already. Neural networks, inspired by biological neural networks, are pretty useful when it comes to solving complex, multi-layered computational problems. Deep learning has stood out pretty well in several high-profile research fields - including facial and speech recognition, natural language processing, machine translation, and more. In this article, we look at the top 5 popular and widely-used deep learning architectures you should know in order to advance your knowledge or deep learning research. Convolutional Neural Networks Convolutional Neural Networks, or CNNs in short, are the popular choice of neural networks for different Computer Vision tasks such as image recognition. The name ‘convolution’ is derived from a mathematical operation involving the convolution of different functions. There are 4 primary steps or stages in designing a CNN: Convolution: The input signal is received at this stage Subsampling: Inputs received from the convolution layer are smoothened to reduce the sensitivity of the filters to noise or any other variation Activation: This layer controls how the signal flows from one layer to the other, similar to the neurons in our brain Fully connected: In this stage, all the layers of the network are connected with every neuron from a preceding layer to the neurons from the subsequent layer Here is an in-depth look at the CNN Architecture and its working, as explained by the popular AI Researcher Giancarlo Zaccone. A sample CNN in action Advantages of CNN Very good for visual recognition Once a segment within a particular sector of an image is learned, the CNN can recognize that segment present anywhere else in the image Disadvantages of CNN CNN is highly dependent on the size and quality of the training data Highly susceptible to noise Recurrent Neural Networks Recurrent Neural Networks (RNNs) have been very popular in areas where the sequence in which the information is presented is crucial. As a result, they find a lot applications in real-world domains such as natural language processing, speech synthesis and machine translation. RNNs are called ‘recurrent’ mainly because a uniform task is performed for every single element of a sequence, with the output dependant on the previous computations as well. Think of these networks as having a memory, where every calculated information is captured, stored and utilized to calculate the final outcome. Over the years, quite a few varieties of RNNs have been researched and developed: Bidirectional RNN - The output in this type of RNN depends not only on the past but also the future outcomes Deep RNN - In this type of RNN, there are multiple layers present per step, allowing for a greater rate of learning and more accuracy RNNs can be used to build industry-standard chatbots that can be used to interact with customers on websites. Given a sequence of signals from an audio wave, RNNs can also be used to predict a correct sequence of phonetic segments with a given probability. Advantages of RNN Unlike a traditional neural network, an RNN shares the same parameters across all steps. This greatly reduces the number of parameters that we need to learn RNNs can be used along with CNNs to generate accurate descriptions for unlabeled images. Disadvantages of RNN RNNs find it difficult to track long-term dependencies. This is especially true in case of long sentences and paragraphs having too many words in between the noun and the verb. RNNs cannot be stacked into very deep models. This is due to the activation function used in RNN models, making the gradient decay over multiple layers. Autoencoders Autoencoders apply the principle of backpropagation in an unsupervised environment. Autoencoders, interestingly, have a close resemblance to PCA (Principal Component Analysis) except that they are more flexible. Some of the popular applications of Autoencoders is anomaly detection - for example detecting fraud in financial transactions in banks. Basically, the core task of autoencoders is to identify and determine what constitutes regular, normal data and then identify the outliers or anomalies. Autoencoders usually represent data through multiple hidden layers such that the output signal is as close to the input signal. There are 4 major types of autoencoders being used today: Vanilla autoencoder - the simplest form of autoencoders there is, i.e. a neural net with one hidden layer Multilayer autoencoder - when one hidden layer is not enough, an autoencoder can be extended to include more hidden layers Convolutional autoencoder - In this type, convolutions are used in the autoencoders instead of fully-connected layers Regularized autoencoder - this type of autoencoders use a special loss function that enables the model to have properties beyond the basic ability to copy a given input to the output. This article demonstrates training an autoencoder using H20, a popular machine learning and AI platform. A basic representation of Autoencoder Advantages of Autoencoders Autoencoders give a resultant model which is primarily based on the data rather than predefined filters Very less complexity means it’s easier to train them Disadvantages of Autoencoders Training time can be very high sometimes If the training data is not representative of the testing data, then the information that comes out of the model can be obscured and unclear Some autoencoders, especially of the variational type, cause a deterministic bias being introduced in the model Generative Adversarial Networks The basic premise of Generative Adversarial Networks (GANs) is the training of two deep learning models simultaneously. These deep learning networks basically compete with each other - one model that tries to generate new instances or examples is called as the generator. The other model that tries to classify if a particular instance originates from the training data or from the generator is called as the discriminator. GANs, a breakthrough recently in the field of deep learning,  was a concept put forth by the popular deep learning expert Ian Goodfellow in 2014. It finds large and important applications in Computer Vision, especially image generation. Read more about the structure and the functionality of the GAN from the official paper submitted by Ian Goodfellow. General architecture of GAN (Source: deeplearning4j) Advantages of GAN Per Goodfellow, GANs allow for efficient training of classifiers in a semi-supervised manner Because of the improved accuracy of the model, the generated data is almost indistinguishable from the original data GANs do not introduce any deterministic bias unlike variational autoencoders Disadvantages of GAN Generator and discriminator working efficiently is crucial to the success of GAN. The whole system fails even if one of them fails Both the generator and discriminator are separate systems and trained with different loss functions. Hence the time required to train the entire system can get quite high. Interested to know more about GANs? Here’s what you need to know about them. ResNets Ever since they gained popularity in 2015, ResNets or Deep Residual Networks have been widely adopted and used by many data scientists and AI researchers. As you already know, CNNs are highly useful when it comes to solving image classification and visual recognition problems. As these tasks become more complex, training of the neural network starts to get a lot more difficult, as additional deep layers are required to compute and enhance the accuracy of the model. Residual learning is a concept designed to tackle this very problem, and the resultant architecture is popularly known as a ResNet. A ResNet consists of a number of residual modules - where each module represents a layer. Each layer consists of a set of functions to be performed on the input. The depth of a ResNet can vary greatly - the one developed by Microsoft researchers for an image classification problem had 152 layers! A basic building block of ResNet (Source: Quora) Advantages of ResNets ResNets are more accurate and require less weights than LSTMs and RNNs in some cases They are highly modular. Hundreds and thousands of residual layers can be added to create a network and then trained. ResNets can be designed to determine how deep a particular network needs to be. Disadvantages of ResNets If the layers in a ResNet are too deep, errors can be hard to detect and cannot be propagated back quickly and correctly. At the same time, if the layers are too narrow, the learning might not be very efficient. Apart from the ones above, a few more deep learning models are being increasingly adopted and preferred by data scientists. These definitely deserve a honorable mention: LSTM: LSTMs are a special kind of Recurrent Neural Networks that include a special memory cell that can hold information for long periods of time. A set of gates is used to determine when a particular information enters the memory and when it is forgotten. SqueezeNet: One of the newer but very powerful deep learning architectures which is extremely efficient for low bandwidth platforms such as mobile. CapsNet: CapsNet, or Capsule Networks, is a recent breakthrough in the field of Deep Learning and neural network modeling. Mainly used for accurate image recognition tasks, and is an advanced variation of the CNNs. SegNet: A popular deep learning architecture especially used to solve the image segmentation problem. Seq2Seq: An upcoming deep learning architecture being increasingly used for machine translation and building efficient chatbots So there you have it! Thanks to the intense efforts in research in deep learning and AI, we now have a variety of deep learning models at our disposal to solve a variety of problems - both functional and computational. What’s even better is that we have the liberty to choose the most appropriate deep learning architecture based on the problem at hand. [box type="shadow" align="" class="" width=""]Editor’s Tip: It is very important to know the best deep learning frameworks you can use to train your models. Here are the top 10 deep learning frameworks for you.[/box] In contrast to the traditional programming approach where we tell the computer what to do, the deep learning models figure out the problem and devise the most appropriate solution on their own - however complex the problem may be. No wonder these deep learning architectures are being researched on and deployed on a large scale by the major market players such as Google, Facebook, Microsoft and many others. Packt Explains… Deep Learning in 90 seconds Behind the scenes: Deep learning evolution and core concepts Facelifting NLP with Deep Learning  

One thing that every data professional absolutely dreads is the day their skills are no longer relevant in the market. In an ever-changing tech landscape, one must be constantly on the lookout for the most relevant, industrially-accepted tools and frameworks. This is applicable everywhere - from application and web developers to cybersecurity professionals. Not even the data professionals are excluded from this, as new ways and means to extract actionable insights from raw data are being found out almost every day. Gone are the days when data pros stuck to a single language and a framework to work with their data. Frameworks are more flexible now, with multiple dependencies across various tools and languages. Not just that, new domains are being identified where these frameworks can be applied, and how they can be applied varies massively as well. A whole new arena of possibilities has opened up, and with that new set of skills and toolkits to work on these domains have also been unlocked. What’s the next big thing for data professionals? We recently polled thousands of data professionals as part of our Skill-Up program, and got some very interesting insights into what they think the future of data science looks like. We asked them what they were planning to learn in the next 12 months. The following word cloud is the result of their responses, weighted by frequency of the tools they chose: What data professionals are planning on learning in the next 12 months Unsurprisingly, Python comes out on top as the language many data pros want to learn in the coming months. With its general-purpose nature and innumerable applications across various use-cases, Python’s sky-rocketing popularity is the reason everybody wants to learn it. Machine learning and AI are finding significant applications in the web development domain today. They are revolutionizing the customers’ digital experience through conversational UIs or chatbots. Not just that, smart machine learning algorithms are being used to personalize websites and their UX. With all these reasons, who wouldn’t want to learn JavaScript, as an important tool to have in their data science toolkit? Add to that the trending web dev framework Angular, and you have all the tools to build smart, responsive front-end web applications. We also saw data professionals taking active interest in the mobile and cloud domains as well. They aim to learn Kotlin and combine its power with the data science tools for developing smarter and more intelligent Android apps. When it comes to the cloud, Microsoft’s Azure platform has introduced many built-in machine learning capabilities, as well as a workbench for data scientists to develop effective, enterprise-grade models. Data professionals also prefer Docker containers to run their applications seamlessly, and hence its learning need seems to be quite high. [box type="shadow" align="" class="" width=""]Has machine learning with JavaScript caught your interest? Don’t worry, we got you covered - check out Hands-on Machine Learning with JavaScript for a practical, hands-on coverage of the essential machine learning concepts using the leading web development language. [/box] With Crypto’s popularity off the roof (sadly, we can’t say the same about Bitcoin’s price), data pros see Blockchain as a valuable skill. Building secure, decentralized apps is on the agenda for many, perhaps. Cloud, Big Data, Artificial Intelligence are some of the other domains that the data pros find interesting, and feel worth skilling up in. Work-related skills that data pros want to learn We also asked the data professionals what skills the data pros wanted to learn in the near future that could help them with their daily jobs more effectively. The following word cloud of their responses paints a pretty clear picture: Valuable skills data professionals want to learn for their everyday work As Machine learning and AI go mainstream, so do their applications in mainstream domains - often resulting in complex problems. Well, there’s deep learning and specifically neural networks to tackle these problems, and these are exactly the skills data pros want to master in order to excel at their work. [box type="shadow" align="" class="" width=""]Data pros want to learn Machine Learning in Python. Do you? Here’s a useful resource for you to get started - check out Python Machine Learning, Second Edition today![/box] So, there it is! What are the tools, languages or frameworks that you are planning to learn in the coming months? Do you agree with the results of the poll? Do let us know. What are web developers favorite front-end tools? Packt’s Skill Up report reveals all Data cleaning is the worst part of data analysis, say data scientists 15 Useful Python Libraries to make your Data Science tasks Easier

Mapquest, part of the Verizon company, is the second largest provider of mapping services in the world, after Google Maps. It provides advanced cartography services to companies like Snap and PapaJohns pizza. The company is about to release an app that users can install on their smartphone. Their new application will record and transmit video images of what’s happening in front of your vehicle, as you travel. Data can be sent from any phone with a camera – using the most common of tools – a simple mobile data plan, for example. In exchange, you’ll get live traffic updates, among other things. Mapquest will use the video image data they gather to provide more accurate and up to date maps to their partners. The real world is changing all the time – roads get added, cities re-route traffic from time to time. The new AI based technology Mapquest employ could well improve the reliability of driverless cars, which have to engage with this ever changing landscape, in a safe manner. No-one disagrees with safety improvements. Mapquests solution is impressive technology. The fact that they can use AI to interpret the images they see and upload the information they receive to update maps is incredible. And, in this regard, the company is just one of the myriad daily news stories which excite and astound us. These stories do, however, often have another side to them which is rarely acknowledged. In the wrong hands, Mapquest’s solution could create a surveillance database which tracked people in real time. Surveillance technology involves the use of data and information products to capture details about individuals. The act of surveillance is usually undertaken with a view to achieving a goal. The principle is simple. The more ‘they’ know about you, the easier it will be to influence you towards their ends. Surveillance information can be used to find you, apprehend you or potentially, to change your mind, without even realising that you had been watched. Mapquest’s innovation is just a single example of surveillance technology in government hands which has expanded in capability far beyond what most people realise. Read also: What does the US government know about you? The truth beyond the Facebook scandal Facebook’s share price fell 14% in early 2018 as a result of public outcry related to the Cambridge Analytica announcements the company made. The idea that a private company had allowed detailed information about individuals to be provided to a third party without their consent appeared to genuinely shock and appall people. Technology tools like Mapquest’s tracking capabilities and Facebook’s profiling techniques are being taken and used by police forces and corporate entities around the world. The reality of current private and public surveillance capabilities is that facilities exist, and are in use, to collect and analyse data on most people in the developing world. The known limits of these services may surprise even those who are on the cutting edge of technology. There are so many examples from all over the world listed below that will genuinely make you want to consider going off grid! Innovative, Ingenious overlords: US companies have a flare for surveillance The US is the centre for information based technology companies. Much of what they develop is exported as well as used domestically. The police are using human genome matching to track down criminals and can find ‘any family in the country’ There have been 2 recent examples of police arresting a suspect after using human genome databases to investigate crimes. A growing number of private individuals have now used publicly available services such as 23andme to sequence their genome (DNA) either to investigate further their family tree, or to determine the potential of a pre-disposition to the gene based component of a disease. In one example, The Golden State Killer, an ex cop, was arrested 32 years after the last reported rape in a series of 45 (in addition to 12 murders) which occurred between 1976 and 1986. To track him down, police approached sites like 23andme with DNA found at crime scenes, established a family match and then progressed the investigation using conventional means. More than 12 million Americans have now used a genetic sequencing service and it is believed that investigators could find a family match for the DNA of anyone who has committed a crime in America. In simple terms, whether you want it or not, the law enforcement has the DNA of every individual in the country available to them. Domain Awareness Centers (DAC) bring the Truman Show to life The 400,000 Residents of Oakland, California discovered in 2012, that they had been the subject of an undisclosed mass surveillance project, by the local police force, for many years. Feeds from CCTV cameras installed in Oakland’s suburbs were augmented with weather information feeds, social media feeds and extracted email conversations, as well as a variety of other sources. The scheme began at Oakland’s port with Federal funding as part of a national response to the events of 9.11.2001 but was extended to cover the near half million residents of the city. Hundreds of additional video cameras were installed, along with gunshot recognition microphones and some of the other surveillance technologies provided in this article. The police force conducting the surveillance had no policy on what information was recorded or for how long it was kept. Internet connected toys spy on children The FBI has warned Americans that children’s toys connected to the internet ‘could put the privacy and safety of children at risk.' Children’s toy Hello Barbie was specifically admonished for poor privacy controls as part of the FBI’s press release. Internet connected toys could be used to record video of children at any point in the day or, conceivably, to relay a human voice, making it appear to the child that the toy was talking to them. Oracle suggest Google’s Android operating system routinely tracks users’ position even when maps are turned off In Australia, two American companies have been involved in a disagreement about the potential monitoring of Android phones. Oracle accused Google of monitoring users’ location (including altitude), even when mapping software is turned off on the device. The tracking is performed in the background of their phone. In Australia alone, Oracle suggested that Google’s monitoring could involve around 1GB of additional mobile data every month, costing users nearly half a billion dollars a year, collectively. Amazon facial recognition in real time helps US law enforcement services Amazon are providing facial recognition services which take a feed from public video cameras, to a number of US Police Forces. Amazon can match images taken in real time to a database containing ‘millions of faces.’ Are there any state or Federal rules in place to govern police facial recognition? Wired reported that there are ‘more or less none.’ Amazon’s scheme is a trial taking place in Florida. There are at least 2 other companies offering similar schemes in the US to law enforcement services. Big glass microphone can help agencies keep an ear on the ground Project ‘Big Glass Microphone’ uses the vibrations that the movements of cars (among other things) cause in the buried fiber optic telecommunications links. A successful test of the technology has been undertaken on the fiber optic cables which run underground on the Stanford University Campus, to record vehicle movements. Fiber optic links now make up the backbone of much data transport infrastructure - the way your phone and computer connect to the internet. Big glass microphone as it stands is the first step towards ‘invisible’ monitoring of people and their assets. It appears the FBI now have the ability to crack/access any phone Those in the know suggest that Apple’s iPhone is the most secure smart device against government surveillance. In 2016, this was put to the test. The Justice Department came into possession of an iPhone allegedly belonging to one of the San Bernadino shooters and ultimately sued Apple in an attempt to force the company to grant access to it, as part of their investigation. The case was ultimately dropped leading some to speculate that NAND mirroring techniques were used to gain access to the phone without Apple’s assistance, implying that even the most secure phones can now be accessed by authorities. Cornell University’s lie detecting algorithm Groundbreaking work by Cornell University will provide ‘at a distance’ access to information that previously required close personal access to an accused subject. Cornell’s solution interprets feeds from a number of video cameras on subjects and analyses the results to judge their heart rate. They believe the system can be used to determine if someone is lying from behind a screen. University of Southern California can anticipate social unrest with social media feeds Researchers at the University Of Southern California have developed an AI tool to study Social Media posts and determine whether those writing them are likely to cause Social Unrest. The software claims to have identified an association between both the volume of tweets written / the content of those tweets and protests turning physical. They can now offer advice to law enforcement on the likelihood of a protest turning violent so they can be properly prepared based on this information. The UK, an epicenter of AI progress, is not far behind in tracking people The UK has a similarly impressive array of tools at its disposal to watch the people that representatives of the country feels may be required. Given the close levels of cooperation between the UK and US governments, it is likely that many of these UK facilities are shared with the US and other NATO partners. Project stingray – fake cell phone/mobile phone ‘towers’ to intercept communications Stingray is a brand name for an IMSI (the unique identifier on a SIM card) tracker. They ‘spoof’ real towers, presenting themselves as the closest mobile phone tower. This ‘fools’ phones in to connecting to them. The technology has been used to spy on criminals in the UK but it is not just the UK government which use Stingray or its equivalents. The Washington Post reported in June 2018 that a number of domestically compiled intelligence reports suggest that foreign governments acting on US soil, including China and Russia, have been eavesdropping on the Whitehouse, using the same technology. UK developed Spyware is being used by authoritarian regimes Gamma International is a company based in Hampshire UK, which provided the (notably authoritarian) Egyptian government with a facility to install what was effectively spyware delivered with a virus on to computers in their country. Once installed, the software permitted the government to monitor private digital interactions, without the need to engage the phone company or ISP offering those services. Any internet based technology could be tracked, assisting in tracking down individuals who may have negative feelings about the Egyptian government. Individual arrested when his fingerprint was taken from a WhatsApp picture of his hand A Drug Dealer was pictured holding an assortment of pills in the UK two months ago. The image of his hand was used to extract an image of his fingerprint. From that, forensic scientists used by UK police, confirmed that officers had arrested the correct person and associated him with drugs. AI solutions to speed up evidence processing including scanning laptops and phones UK police forces are trying out AI software to speed up processing evidence from digital devices. A dozen departments around the UK are using software, called Cellebrite, which employs AI algorithms to search through data found on devices, including phones and laptops. Cellbrite can recognize images that contain child abuse, accepts feeds from multiple devices to see when multiple owners were in the same physical location at the same time and can read text from screenshots. Officers can even feed it photos of suspects to see if a picture of them show up on someone’s hard drive. China takes the surveillance biscuit and may show us a glimpse of the future There are 600 million mobile phone users in China, each producing a great deal of information about their users. China has a notorious record of human rights abuses and the ruling Communist Party takes a controlling interest (a board seat) in many of their largest technology companies, to ensure the work done is in the interest of the party as well as profitable for the corporate. As a result, China is on the front foot when it comes to both AI and surveillance technology. China’s surveillance tools could be a harbinger of the future in the Western world. Chinese cities will be run by a private company Alibaba, China’s equivalent of Amazon, already has control over the traffic lights in one Chinese city, Hangzhou. Alibaba is far from shy about it’s ambitions. It has 120,000 developers working on the problem and intends to commercialise and sell the data it gathers about citizens. The AI based product they’re using is called CityBrain. In the future, all Chinese cities could well all be run by AI from the Alibaba corporation the idea is to use this trial as a template for every city. The technology is likely to be placed in Kuala Lumpur next. In the areas under CityBrain’s control, traffic speeds have increased by 15% already. However, some of those observing the situation have expressed concerns not just about (the lack of) oversight on CityBrain’s current capabilities but the potential for future abuse. What to make of this incredible list of surveillance capabilities Facilities like Mapquest’s new mapping service are beguiling. They’re clever ideas which create a better works. Similar technology, however, behind the scenes, is being adopted by law enforcement bodies in an ever growing list of countries. Even for someone who understands cutting edge technology, the sum of those facilities may be surprising. Literally any aspect of your behaviour, from the way you walk, to your face, your heatmap and, of course, the contents of your phone and laptops can now be monitored. Law enforcement can access and review information feeds with Artificial Intelligence software, to process and summarise findings quickly. In some cases, this is being done without the need for a warrant. Concerningly, these advances seem to be coming without policy or, in many cases any form of oversight. We must change how we think about AI, urge AI founding fathers  

In Manhattan, nearly 15,000 Taxis make around 30 journeys each, per day. That’s nearly half a million paid trips. The yellow cabs are part of the never ending, slow progression of vehicles which churn through the streets of New York. The good news is, after a century of worsening traffic, congestion is about to be ameliorated, at least to a degree. Researchers at MIT announced this week, that they have developed an algorithm to optimise the way taxis find their customers. Their product is allegedly so efficient, it can reduce the required number of cabs (for now, the ones with human drivers) in Manhattan, by a third. That’s a non trivial improvement. The trick, apparently, is to use the cabs as a hustler might cue the ball in Pool – lining the next pick up to start where the last drop off ended. The technology behind the improvement offered by the MIT research team, is the same one that is behind most of the incredible technology news stories of the last 3 years – Artificial Intelligence. AI is now a part of most of the digital interactions we have. It fuels the recommendation engines in YouTube, Spotify and Netflix. It shows you products you might like in Google’s search results and on Amazon’s homepage. Undoubtedly, AI is the hot topic of the time – as you cannot possibly have failed to notice. How AI was created – and nearly died AI was, until recently, a long forgotten scientific curiosity, employed seriously only in Sci-Fi movies. The technology fell in to a ‘Winter’– a time when AI related projects couldn’t get funding and decision makers had given up on the technology - in the late 1980s. It was at that time that much of the fundamental work which underpins today’s AI, concepts like neural networks and backpropagation were codified. Artificial Intelligence is now enjoying a rebirth. Almost every new idea funded by Venture Capitalists has AI baked in. The potential excites business owners, especially those involved in the technology sphere, and scares governments in equal measure. It offers better profits and the potential for mass unemployment as if they are two sides of the same coin. Is is a one in a generation technology improvement, similar to Air Conditioning, mass produced motor car and the smartphone, in that it can be applied to all aspects of the economy at the same time. Just as the iPhone has propelled telecommunications technology forward, and created billions of dollars of sales for phone companies selling mobile data plans, AI is fueling totally new businesses and making existing operations significantly more efficient. Behind the fanfare associated with AI, however, lies a simple truth. Today’s AI algorithms use what’s called ‘narrow’ or ‘domain specific’ intelligence. In simple terms, each current AI implementation is specific to the job it is given. IBM trained their AI system ‘Watson’, to beat human contestants at ‘Jeopardy!’ When Google want to build an ‘AI product’ that can be used to beat a living counterpart at the Chinese board game ‘Go’, they create a new AI system. And so on. A new task requires a new AI system. Judea Pearl, inventor of Bayesian networks and Turing Awardee On AI systems that can move from predicting what will happen to what will cause something Now, one of the people behind those original concepts from the 1980s, which underpin today’s AI solutions is back with an even bigger idea which might push AI forward. Judea Pearl, Chancellor's professor of computer science and statistics at UCLA, and a distinguished visiting professor at the Technion, Israel Institute of Technology was awarded the Turing Award 30 years ago. This award was given to him for the Bayesian mathematical models, which gave modern AI its strength. Pearl’s fundamental contribution to computer science was in providing the logic and decision making framework for computers to operate under uncertainty. Some say it was he who provided the spark which thawed that AI winter. Today, he laments the current state of AI, concerned that the field has evolved very little in the last 3 decades since his important theory was presented. Pearl likens current AI implementations to simple tools which can tell you what’s likely to come next, based on the recognition of a familiar pattern. For example, a medical AI algorithm might be able to look at X-Rays of a human chest and ‘discern’ that the patient has, or does not have, lung cancer based on patterns it has learnt from its training datasets. The AI in this scenario doesn’t ‘know’ what lung cancer is or what a tumor is. Importantly, it is a very long way from understanding that smoking can cause the affliction. What’s needed in AI next, says Pearl, is a critical difference: AIs which are evolved to the point where they can determine not just what will happen next, but what will cause it. It’s a fundamental improvement, of the same magnitude as his earlier contributions. Causality – what Pearl is proposing - is one of the most basic units of scientific thought and progress. The ability to conduct a repeatable experiment, showing that A caused B, in multiple locations and have independent peers review the results is one of the fundamentals of establishing truth. In his most recent publication, ‘The Book Of Why’,  Pearl outlines how we can get AI, from where it is now, to where it can develop an understanding of these causal relationships. He believes the first step is to cement the building blocks of reality – ‘what is a lung’, ‘what is smoke’ and that we’ll be able to do in the next 10 years. Geoff Hinton, Inventor of backprop and capsule nets On AI which more closely mimics the human brain Geoff Hinton’s was the mind behind backpropagation, another of the fundamental technologies which has brought AI to the point it is at today. To progress AI, however, he says we might have to start all over again. Hinton has developed (and produced two papers for the University of Toronto to articulate) a new way of training AI systems, involving something he calls ‘Capsule Networks’ – a concept he’s been working on for 30 years, in an effort to improve the capabilities of the backpropagation algorithms he developed. Capsule networks operate in a manner similar to the human brain. When we see an image, our brains breaks it down to it’s components and processes them in parallel. Some brain neurons recognise edges through contrast differences. Others look for corners by examining the points at which edges intersect. Capsule Networks are similar, several acting on a picture at one time, identifying, for example, an ear or a nose on an animal, irrespective of the angle from which it is being viewed. This is a big deal as until now, CNNs (convolution neural networks), the set of AI algorithms that are most often used in image and video recognition systems, could recognize images as well as humans do. CNNs, however, find it hard to recognize images if their angle is changed. It’s too early to judge whether capsule networks are the key to the next step in the AI revolution, but in many tasks, Capsule Networks are identifying images faster and more accurately than current capabilities allow. Andrew Ng, Chief Scientist at Baidu On AI that can learn without humans Andrew Ng is the co-inventor of Google Brain, the team and project that Alphabet put together in 2011 to explore Artificial Intelligence. He now works for Baidu, China’s most successful search engine – analogous in size and scope to Google in the rest of the world. At the moment, he heads up Baidu’s Silicon Valley AI research facility. Beyond concerns over potential job displacement caused by AI, an issue so significant he says it is perhaps all we should be thinking about when it comes to Artificial Intelligence, he suggests that, in the future, the most progress will be made when AI systems can team themselves without human involvement. At the moment, training an AI, even on something that, to us is simple, such as what a cat looks like, is a complicated process. The procedure involves ‘supervised learning.’ It’s shown a lot of pictures (when they did this at Google, they used 10 million images), some of which are cats - labelled appropriately by humans. Once a sufficient level of ‘education’ has been undertaken, the AI can then accurately label cats, most of the time. Ng thinks supervision is problematic, he describes it as having an Achilles heel in the form of the quantity of data that is required. To go beyond current capabilities, says Ng, will require a completely new type of technology – one which can learn through ‘unsupervised learning’ -  machines learning from data that has not been classified by humans. Progress on unsupervised learning is slow. At both Baidu and Google, engineers are focussing on constrained versions of unsupervised learning such as training AI systems to learn about a human face and then using them to create a face themselves. The activity requires that the AI develops what we would call an ‘internal representation’ of a face – something which is required in any unsupervised learning. Other avenues to train without supervision include, ingeniously, pitting an AI system against a computer game – an environment in which they receive feedback (through points awarded in the game) for ‘constructive’ activities, but within which they are not taught directly by a human. Next generation AI depends on ‘scrubbing away’ existing assumptions Artificial Intelligence, as it stands will deliver economy wide efficiency improvements, the likes of which we have not seen in decades. It seems incredible to think that the field is still in its infancy when it can deliver such substantial benefits – like reduced traffic congestion, lower carbon emissions and saved time in New York Taxis. But it is. Isaac Azimov who developed his own concepts behind how Artificial Intelligence might be trained with simple rules said “Your assumptions are your windows on the world. Scrub them off every once in a while, or the light won't come in.” The author should rest assured. Between them, Pearl, Hinton and Ng are each taking revolutionary approaches to elevate AI beyond even the incredible heights it has reached, and starting without reference to the concepts which have brought us this far. 5 polarizing Quotes from Professor Stephen Hawking on artificial intelligence Toward Safe AI – Maximizing your control over Artificial Intelligence Decoding the Human Brain for Artificial Intelligence to make smarter decisions

Machine learning has always been dependent on the selection of the right features within a given model; even the selection of the right algorithm. But deep learning changed this. The selection process is now built into the models themselves. Researchers and engineers are now shofting their focus from feature engineering to network engineering. Out of this, AutoML, or meta learning, has become an increasingly important part of deep learning. AutoML is an emerging research topic which aims at auto-selecting the most efficient neural network for a given learning task. In other words, AutoML represents a set of methodologies for learning how to learn efficiently. Consider for instance the tasks of machine translation, image recognition, or game playing. Typically, the models are manually designed by a team of engineers, data scientist, and domain experts. If you consider that a typical 10-layer network can have ~1010 candidate network, you understand how expensive, error prone, and ultimately sub-optimal the process can be. This article is an excerpt from a book written by Antonio Gulli and Amita Kapoor titled TensorFlow 1.x Deep Learning Cookbook. This book is an easy-to-follow guide that lets you explore reinforcement learning, GANs, autoencoders, multilayer perceptrons and more. AutoML with recurrent networks and with reinforcement learning The key idea to tackle this problem is to have a controller network which proposes a child model architecture with probability p, given a particular network given in input. The child is trained and evaluated for the particular task to be solved (say for instance that the child gets accuracy R). This evaluation R is passed back to the controller which, in turn, uses R to improve the next candidate architecture. Given this framework, it is possible to model the feedback from the candidate child to the controller as the task of computing the gradient of p and then scale this gradient by R. The controller can be implemented as a Recurrent Neural Network (see the following figure). In doing so, the controller will tend to privilege iteration after iterations candidate areas of architecture that achieve better R and will tend to assign a lower probability to candidate areas that do not score so well. For instance, a controller recurrent neural network can sample a convolutional network. The controller can predict many hyper-parameters such as filter height, filter width, stride height, stride width, and the number of filters for one layer and then can repeat. Every prediction can be carried out by a softmax classifier and then fed into the next RNN time step as input. This is well expressed by the following images taken from Neural Architecture Search with Reinforcement Learning, Barret Zoph, Quoc V. Le: Predicting hyperparameters is not enough as it would be optimal to define a set of actions to create new layers in the network. This is particularly difficult because the reward function that describes the new layers is most likely not differentiable. This makes it impossible to optimize using standard techniques such as SGD. The solution comes from reinforcement learning. It consists of adopting a policy gradient network. Besides that, parallelism can be used for optimizing the parameters of the controller RNN. Quoc Le & Barret Zoph proposed to adopt a parameter-server scheme where we have a parameter server of S shards, that store the shared parameters for K controller replicas. Each controller replica samples m different child architectures that are trained in parallel as illustrated in the following images, taken from Neural Architecture Search with Reinforcement Learning, Barret Zoph, Quoc V. Le: Quoc and Barret applied AutoML techniques for Neural Architecture Search to the Penn Treebank dataset, a well-known benchmark for language modeling. Their results improve the manually designed networks currently considered the state-of-the-art. In particular, they achieve a test set perplexity of 62.4 on the Penn Treebank, which is 3.6 perplexity better than the previous state-of-the-art model. Similarly, on the CIFAR-10 dataset, starting from scratch, the method can design a novel network architecture that rivals the best human-invented architecture in terms of test set accuracy. The proposed CIFAR-10 model achieves a test error rate of 3.65, which is 0.09 percent better and 1.05x faster than the previous state-of-the-art model that used a similar architectural scheme. Meta-learning blocks In Learning Transferable Architectures for Scalable Image Recognition, Barret Zoph, Vijay Vasudevan, Jonathon Shlens, Quoc V. Le, 2017. propose to learn an architectural building block on a small dataset that can be transferred to a large dataset. The authors propose to search for the best convolutional layer (or cell) on the CIFAR-10 dataset and then apply this learned cell to the ImageNet dataset by stacking together more copies of this cell, each with their own parameters. Precisely, all convolutional networks are made of convolutional layers (or cells) with identical structures but different weights. Searching for the best convolutional architectures is therefore reduced to searching for the best cell structures, which is faster more likely to generalize to other problems. Although the cell is not learned directly on ImageNet, an architecture constructed from the best learned cell achieves, among the published work, state-of-the-art accuracy of 82.7 percent top-1 and 96.2 percent top-5 on ImageNet. The model is 1.2 percent better in top-1 accuracy than the best human-invented architectures while having 9 billion fewer FLOPS—a reduction of 28% from the previous state of the art model. What is also important to notice is that the model learned with RNN+RL (Recurrent Neural Networks + Reinforcement Learning) is beating the baseline represented by Random Search (RS) as shown in the figure taken from the paper. In the mean performance of the top-5 and top-25 models identified in RL versus RS, RL is always winning: AutoML and learning new tasks Meta-learning systems can be trained to achieve a large number of tasks and are then tested for their ability to learn new tasks. A famous example of this kind of meta-learning is transfer learning, where networks can successfully learn new image-based tasks from relatively small datasets. However, there is no analogous pre-training scheme for non-vision domains such as speech, language, and text. Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks, Chelsea Finn, Pieter Abbeel, Sergey Levine, 2017, proposes a model- agnostic approach names MAML, compatible with any model trained with gradient descent and applicable to a variety of different learning problems, including classification, regression, and reinforcement learning. The goal of meta-learning is to train a model on a variety of learning tasks, such that it can solve new learning tasks using only a small number of training samples. The meta-learner aims at finding an initialization that rapidly adapts to various problems quickly (in a small number of steps) and efficiently (using only a few examples). A model represented by a parametrized function fθ with parameters θ.When adapting to a new task Ti, the model's parameters θ become θi  . In MAML, the updated parameter vector θi  is computed using one or more gradient descent updates on task Ti. For example, when using one gradient update, θ ~ = θ − α∇θLTi (fθ) where LTi is the loss function for the task T and α is a meta-learning parameter. The MAML algorithm is reported in this figure: MAML was able to substantially outperform a number of existing approaches on popular few-shot image classification benchmark. Few shot image is a quite challenging problem aiming at learning new concepts from one or a few instances of that concept. As an example, Human-level concept learning through probabilistic program induction, Brenden M. Lake, Ruslan Salakhutdinov, Joshua B. Tenenbaum, 2015, suggested that humans can learn to identify novel two-wheel vehicles from a single picture such as the one contained in the box as follows: If you enjoyed this excerpt, check out the book TensorFlow 1.x Deep Learning Cookbook, to skill up and implement tricky neural networks using Google's TensorFlow 1.x AmoebaNets: Google’s new evolutionary AutoML AutoML : Developments and where is it heading to What is Automated Machine Learning (AutoML)?

Deep Learning is one of the major players for facilitating the analytics and learning in the IoT domain. A really good roundup of the state of deep learning advances for big data and IoT is described in the paper Deep Learning for IoT Big Data and Streaming Analytics: A Survey by Mehdi Mohammadi, Ala Al-Fuqaha, Sameh Sorour, and Mohsen Guizani. In this article, we have attempted to draw inspiration from this research paper to establish the importance of IoT datasets for deep learning applications. The paper also provides a handy list of commonly used datasets suitable for building deep learning applications in IoT, which we have added at the end of the article. IoT and Big Data: The relationship IoT and Big data have a two-way relationship. IoT is the main producer of big data, and as such an important target for big data analytics to improve the processes and services of IoT. However, there is a difference between the two. Large-Scale Streaming data: IoT data is a large-scale streaming data. This is because a large number of IoT devices generate streams of data continuously. Big data, on the other hand, lack real-time processing. Heterogeneity: IoT data is heterogeneous as various IoT data acquisition devices gather different information. Big data devices are generally homogeneous in nature. Time and space correlation: IoT sensor devices are also attached to a specific location, and thus have a location and time-stamp for each of the data items. Big data sensors lack time-stamp resolution. High noise data: IoT data is highly noisy, owing to the tiny pieces of data in IoT applications, which are prone to errors and noise during acquisition and transmission. Big data, in contrast, is generally less noisy. Big data, on the other hand, is classified according to conventional 3V’s, Volume, Velocity, and Variety. As such techniques used for Big data analytics are not sufficient to analyze the kind of data, that is being generated by IoT devices. For instance, autonomous cars need to make fast decisions on driving actions such as lane or speed change. These decisions should be supported by fast analytics with data streaming from multiple sources (e.g., cameras, radars, left/right signals, traffic light etc.). This changes the definition of IoT big data classification to 6V’s. Volume: The quantity of generated data using IoT devices is much more than before and clearly fits this feature. Velocity: Advanced tools and technologies for analytics are needed to efficiently operate the high rate of data production. Variety: Big data may be structured, semi-structured, and unstructured data. The data types produced by IoT include text, audio, video, sensory data and so on. Veracity: Veracity refers to the quality, consistency, and trustworthiness of the data, which in turn leads to accurate analytics. Variability: This property refers to the different rates of data flow. Value: Value is the transformation of big data to useful information and insights that bring competitive advantage to organizations. Despite the recent advancement in DL for big data, there are still significant challenges that need to be addressed to mature this technology. Every 6 characteristics of IoT big data imposes a challenge for DL techniques. One common denominator for all is the lack of availability of IoT big data datasets.   IoT datasets and why are they needed Deep learning methods have been promising with state-of-the-art results in several areas, such as signal processing, natural language processing, and image recognition. The trend is going up in IoT verticals as well. IoT datasets play a major role in improving the IoT analytics. Real-world IoT datasets generate more data which in turn improve the accuracy of DL algorithms. However, the lack of availability of large real-world datasets for IoT applications is a major hurdle for incorporating DL models in IoT. The shortage of these datasets acts as a barrier to deployment and acceptance of IoT analytics based on DL since the empirical validation and evaluation of the system should be shown promising in the natural world. The lack of availability is mainly because: Most IoT datasets are available with large organizations who are unwilling to share it so easily. Access to the copyrighted datasets or privacy considerations. These are more common in domains with human data such as healthcare and education. While there is a lot of ground to be covered in terms of making datasets for IoT available, here is a list of commonly used datasets suitable for building deep learning applications in IoT. Dataset Name Domain Provider Notes Address/Link CGIAR dataset Agriculture, Climate CCAFS High-resolution climate datasets for a variety of fields including agricultural http://www.ccafs-climate.org/ Educational Process Mining Education University of Genova Recordings of 115 subjects’ activities through a logging application while learning with an educational simulator http://archive.ics.uci.edu/ml/datasets/Educational+Process+Mining+%28EPM%29%3A+A+Learning+Analytics+Data+Set Commercial Building Energy Dataset Energy, Smart Building IIITD Energy related data set from a commercial building where data is sampled more than once a minute. http://combed.github.io/ Individual household electric power consumption Energy, Smart home EDF R&D, Clamart, France One-minute sampling rate over a period of almost 4 years http://archive.ics.uci.edu/ml/datasets/Individual+household+electric+power+consumption AMPds dataset Energy, Smart home S. Makonin AMPds contains electricity, water, and natural gas measurements at one minute intervals for 2 years of monitoring http://ampds.org/ UK Domestic Appliance-Level Electricity Energy, Smart Home Kelly and Knottenbelt Power demand from five houses. In each house both the whole-house mains power demand as well as power demand from individual appliances are recorded. http://www.doc.ic.ac.uk/∼dk3810/data/ PhysioBank databases Healthcare PhysioNet Archive of over 80 physiological datasets. https://physionet.org/physiobank/database/ Saarbruecken Voice Database Healthcare Universitat¨ des Saarlandes A collection of voice recordings from more than 2000 persons for pathological voice detection. http://www.stimmdatebank.coli.uni-saarland.de/help_en.php4   T-LESS   Industry CMP at Czech Technical University An RGB-D dataset and evaluation methodology for detection and 6D pose estimation of texture-less objects http://cmp.felk.cvut.cz/t-less/ CityPulse Dataset Collection Smart City CityPulse EU FP7 project Road Traffic Data, Pollution Data, Weather, Parking http://iot.ee.surrey.ac.uk:8080/datasets.html Open Data Institute - node Trento Smart City Telecom Italia Weather, Air quality, Electricity, Telecommunication http://theodi.fbk.eu/openbigdata/ Malaga datasets Smart City City of Malaga A broad range of categories such as energy, ITS, weather, Industry, Sport, etc. http://datosabiertos.malaga.eu/dataset Gas sensors for home activity monitoring Smart home Univ. of California San Diego Recordings of 8 gas sensors under three conditions including background, wine and banana presentations. http://archive.ics.uci.edu/ml/datasets/Gas+sensors+for+home+activity+monitoring CASAS datasets for activities of daily living Smart home Washington State University Several public datasets related to Activities of Daily Living (ADL) performance in a two story home, an apartment, and an office settings. http://ailab.wsu.edu/casas/datasets.html ARAS Human Activity Dataset Smart home Bogazici University Human activity recognition datasets collected from two real houses with multiple residents during two months. https://www.cmpe.boun.edu.tr/aras/ MERLSense Data Smart home, building Mitsubishi Electric Research Labs Motion sensor data of residual traces from a network of over 200 sensors for two years, containing over 50 million records. http://www.merl.com/wmd SportVU   Sport Stats LLC   Video of basketball and soccer games captured from 6 cameras. http://go.stats.com/sportvu RealDisp Sport O. Banos   Includes a wide range of physical activities (warm up, cool down and fitness exercises). http://orestibanos.com/datasets.htm   Taxi Service Trajectory Transportation Prediction Challenge, ECML PKDD 2015 Trajectories performed by all the 442 taxis running in the city of Porto, in Portugal. http://www.geolink.pt/ecmlpkdd2015-challenge/dataset.html GeoLife GPS Trajectories Transportation Microsoft A GPS trajectory by a sequence of time-stamped points https://www.microsoft.com/en-us/download/details.aspx?id=52367 T-Drive trajectory data Transportation Microsoft Contains a one-week trajectories of 10,357 taxis https://www.microsoft.com/en-us/research/publication/t-drive-trajectory-data-sample/ Chicago Bus Traces data Transportation M. Doering   Bus traces from the Chicago Transport Authority for 18 days with a rate between 20 and 40 seconds. http://www.ibr.cs.tu-bs.de/users/mdoering/bustraces/   Uber trip data Transportation FiveThirtyEight About 20 million Uber pickups in New York City during 12 months. https://github.com/fivethirtyeight/uber-tlc-foil-response Traffic Sign Recognition Transportation K. Lim   Three datasets: Korean daytime, Korean nighttime, and German daytime traffic signs based on Vienna traffic rules. https://figshare.com/articles/Traffic_Sign_Recognition_Testsets/4597795 DDD17   Transportation J. Binas End-To-End DAVIS Driving Dataset. http://sensors.ini.uzh.ch/databases.html      

Professor Stephen Hawking died today (March 14, 2018) aged 76 at his home in Cambridge, UK. Best known for his theory of cosmology that unified quantum mechanics with Einstein’s General Theory of Relativity, and for his book a Brief History of Time that brought his concepts to a wider general audience, Professor Hawking is quite possibly one of the most important and well-known voices in the scientific world. Among many things, Professor Hawking had a lot to say about artificial intelligence - its dangers, its opportunities and what we should be thinking about, not just as scientists and technologists, but as humans. Over the years, Hawking has remained cautious and consistent in his views on the topic constantly urging AI researchers and machine learning developers to consider the wider implications of their work on society and the human race itself.  The machine learning community is quite divided on all the issues Hawking has raised and will probably continue to be so as the field grows faster than it can be fathomed. Here are 5 widely debated things Stephen Hawking said about AI arranged in chronological order - and if you’re going to listen to anyone, you’ve surely got to listen to him?   On artificial intelligence ending the human race The development of full artificial intelligence could spell the end of the human race….It would take off on its own, and re-design itself at an ever-increasing rate. Humans, who are limited by slow biological evolution, couldn't compete and would be superseded. From an interview with the BBC, December 2014 On the future of AI research The establishment of shared theoretical frameworks, combined with the availability of data and processing power, has yielded remarkable successes in various component tasks such as speech recognition, image classification, autonomous vehicles, machine translation, legged locomotion, and question-answering systems. As capabilities in these areas and others cross the threshold from laboratory research to economically valuable technologies, a virtuous cycle takes hold whereby even small improvements in performance are worth large sums of money, prompting greater investments in research. There is now a broad consensus that AI research is progressing steadily, and that its impact on society is likely to increase.... Because of the great potential of AI, it is important to research how to reap its benefits while avoiding potential pitfalls. From Research Priorities for Robust and Beneficial Artificial Intelligence, an open letter co-signed by Hawking, January 2015 On AI emulating human intelligence I believe there is no deep difference between what can be achieved by a biological brain and what can be achieved by a computer. It, therefore, follows that computers can, in theory, emulate human intelligence — and exceed it From a speech given by Hawking at the opening of the Leverhulme Centre of the Future of Intelligence, Cambridge, U.K., October 2016 On making artificial intelligence benefit humanity Perhaps we should all stop for a moment and focus not only on making our AI better and more successful but also on the benefit of humanity. Taken from a speech given by Hawking at Web Summit in Lisbon, November 2017 On AI replacing humans The genie is out of the bottle. We need to move forward on artificial intelligence development but we also need to be mindful of its very real dangers. I fear that AI may replace humans altogether. If people design computer viruses, someone will design AI that replicates itself. This will be a new form of life that will outperform humans. From an interview with Wired, November 2017

Stack Overflow recently published their annual developer survey in which over 100,000 developers and professionals participated. The survey shed light on some very interesting insights - from the developers’ preferred language for programming, to the development platform they hate the most. As the survey is quite detailed and comprehensive, we thought why not present the most important takeaways and findings for you to go through very quickly? If you are short of time and want to scan through the results of the survey quickly, read on.. Developer Profile Young developers form the majority: Half the developer population falls in the age group of 25-34 years while almost all respondents (90%) fall within the 18 - 44 year age group. Limited professional coding experience: Majority of the developers have been coding from the last 2 to 10 years. That said, almost half of the respondents have a professional coding experience of less than 5 years. Continuous learning is key to surviving as a developer: Almost 75% of the developers have a bachelor’s degree, or higher. In addition, almost 90% of the respondents say they have learnt a new language, framework or a tool without taking any formal course, but with the help of the official documentation and/or Stack Overflow Back-end developers form the majority: Among the top developer roles, more than half the developers identify themselves as back-end developers, while the percentage of data scientists and analysts is quite low. About 20% of the respondents identify themselves as mobile developers Working full-time: More than 75% of the developers responded that they work a full-time job. Close to 10% are freelancers, or self-employed. Popularly used languages and frameworks The Javascript family continue their reign: For the sixth year running, JavaScript has continued to be the most popular programming language, and is the choice of language for more than 70% of the respondents. In terms of frameworks, Node.js and Angular continue to be the most popular choice of the developers. Desktop development ain’t dead yet: When it comes to the platforms, developers prefer Linux and Windows Desktop or Server for their development work. Cloud platforms have not gained that much adoption, as yet, but there is a slow but steady rise. What about Data Science? Machine Learning and DevOps rule the roost: Machine Learning and DevOps are two trends which are trending highly due to the vast applications and research that is being done on these fronts. Tensorflow rises, Hadoop falls: About 75% of the respondents love the Tensorflow framework, and say they would love to continue using it for their machine learning/deep learning tasks. Hadoop’s popularity seems to be going down, on the other hand, as other Big Data frameworks like Apache Spark gain more traction and popularity. Python - the next big programming language: Popular data science languages like R and Python are on the rise in terms of popularity. Python, which surpassed PHP last year, has surpassed C# this year, indicating its continuing rise in popularity. Python based Frameworks like Tensorflow and pyTorch are gaining a lot of adoption. Learn F# for more moolah: Languages like F#, Clojure and Rust are associated with high global salaries, with median salaries above $70,000. The likes of R and Python are associated with median salaries of up to $57,000. PostgreSQL growing rapidly, Redis most loved database: MySQL and SQL Server are the two most widely used databases as per the survey, while the usage of PostgreSQL has surpassed that of the traditionally popular databases like MongoDB and Redis. In terms of popularity, Redis is the most loved database while the developers dread (read looking to switch from) databases like IBM DB2 and Oracle. Job-hunt for data scientists: Approximately 18% of the 76,000+ respondents who are actively looking for jobs are data scientists or work as academicians and researchers. AI more exciting than worrying: Close to 75% of the 69,000+ respondents are excited about the future possibilities with AI than worried about the dangers posed by AI. Some of the major concerns include AI making important business decisions. The big surprise was that most developers find automation of jobs as the most exciting part of a future enabled by AI. So that’s it then! What do you think about the Stack Overflow Developer survey results? Do you agree with the developers’ responses? We would love to know your thoughts. In the coming days, watch out for more fine grained analysis of the Stack Overflow survey data.

This session of FAT 2018 is about Recommenders, etc. Recommender systems are algorithmic tools for identifying items of interest to users. They are usually deployed to help mitigate information overload. Internet-scale item spaces offer many more choices than humans can process, diminishing the quality of their decision-making abilities. Recommender systems alleviate this problem by allowing users to more quickly focus on items likely to match their particular tastes. They are deployed across the modern Internet, suggesting products in e-commerce sites, movies and music in streaming media platforms, new connections on social networks, and many more types of items. This session explains what Fairness, Accountability, and Transparency means in the context of recommendation. The session also includes a paper that talks about predictive policing, which is defined as ‘Given historical crime incident data for a collection of regions, decide how to allocate patrol officers to areas to detect crime.’ The Conference on Fairness, Accountability, and Transparency (FAT), which would be held on the 23rd and 24th of February, 2018 is a multi-disciplinary conference that brings together researchers and practitioners interested in fairness, accountability, and transparency in socio-technical systems. The FAT 2018 conference will witness 17 research papers, 6 tutorials, and 2 keynote presentations from leading experts in the field. This article covers research papers pertaining to the 5th session that is dedicated to FAT Recommenders, etc. Paper 1: Runaway Feedback Loops in Predictive Policing Predictive policing systems are increasingly being used to determine how to allocate police across a city in order to best prevent crime. To update the model, discovered crime data (e.g., arrest counts) are used. Such systems have been empirically shown to be susceptible to runaway feedback loops, where police are repeatedly sent back to the same neighborhoods regardless of the true crime rate. This paper is in response to this system, where the authors have developed a mathematical model of predictive policing that proves why this feedback loop occurs.The paper also empirically shows how this model exhibits such problems, and demonstrates ways to change the inputs to a predictive policing system (in a black-box manner) so the runaway feedback loop does not occur, allowing the true crime rate to be learned. Key takeaways: The results stated in the paper establish a link between the degree to which runaway feedback causes problems and the disparity in crime rates between areas. The paper also demonstrates ways in which reported incidents of crime (reported by residents) and discovered incidents of crime (directly observed by police officers dispatched as a result of the predictive policing algorithm) interact. In this paper, the authors have used the theory of urns (a common framework in reinforcement learning) to analyze existing methods for predictive policing. There are formal as well as empirical results which shows why these methods will not work. Subsequently, the authors have also provided remedies that can be used directly with these methods in a black-box fashion that improve their behavior, and provide theoretical justification for these remedies. Paper 2: All The Cool Kids, How Do They Fit In? Popularity and Demographic Biases in Recommender Evaluation and Effectiveness There have been many advances in the information retrieval evaluation, which demonstrate the importance of considering the distribution of effectiveness across diverse groups of varying sizes. This paper addresses this question, ‘do users of different ages or genders obtain similar utility from the system, particularly if their group is a relatively small subset of the user base?’ The authors have applied this consideration to recommender systems, using offline evaluation and a utility-based metric of recommendation effectiveness to explore whether different user demographic groups experience similar recommendation accuracy. The paper shows that there are demographic differences in measured recommender effectiveness across two data sets containing different types of feedback in different domains; these differences sometimes, but not always, correlate with the size of the user group in question. Demographic effects also have a complex— and likely detrimental—interaction with popularity bias, a known deficiency of recommender evaluation. Key takeaways: The paper presents an empirical analysis of the effectiveness of collaborative filtering recommendation strategies, stratified by the gender and age of the users in the data set. The authors applied widely-used recommendation techniques across two domains, musical artists and movies, using publicly-available data. The paper explains whether recommender systems produced equal utility for users of different demographic groups. The authors made use of publicly available datasets, they compared the utility, as measured with nDCG, for users grouped by age and gender. Regardless of the recommender strategy considered, they found significant differences for the nDCG among demographic groups. Paper 3: Recommendation Independence In this paper the authors have showcased new methods that can deal with variance of recommendation outcomes without increasing the computational complexity. These methods can more strictly remove the sensitive information, and experimental results demonstrate that the new algorithms can more effectively eliminate the factors that undermine fairness. Additionally, the paper also explores potential applications for independence enhanced recommendation, and discuss its relation to other concepts, such as recommendation diversity. Key takeaways from the paper: The authors have developed new independence-enhanced recommendation models that can deal with the second moment of distributions without sacrificing computational efficiency. The paper also explores applications in which recommendation independence would be useful, and reveal the relation of independence to the other concepts in recommendation research. It also presents the concept of recommendation independence, and discuss how the concept would be useful for solving real-world problems. Paper 4: Balanced Neighborhoods for Multi-sided Fairness in Recommendation In this paper, the authors examine two different cases of fairness-aware recommender systems: consumer-centered and provider-centered. The paper explores the concept of a balanced neighborhood as a mechanism to preserve personalization in recommendation while enhancing the fairness of recommendation outcomes. It shows that a modified version of the Sparse Linear Method (SLIM) can be used to improve the balance of user and item neighborhoods, with the result of achieving greater outcome fairness in real-world datasets with minimal loss in ranking performance. Key takeaways: In this paper, the authors examine applications in which fairness with respect to consumers and to item providers is important. They have shown that variants of the well-known sparse linear method (SLIM) can be used to negotiate the tradeoff between fairness and accuracy. This paper also introduces the concept of multisided fairness, relevant in multisided platforms that serve a matchmaking function. It demonstrates that the concept of balanced neighborhoods in conjunction with the well-known sparse linear method can be used to balance personalization with fairness considerations. If you’ve missed our summaries on the previous sessions, visit the article links to be on track. Session 1: Online Discrimination and Privacy Session 2: Interpretability and Explainability Session 3: Fairness in Computer Vision and NLP Session 4: Fair Classification

As algorithms are increasingly used to make decisions of social consequence, the social values encoded in these decision-making procedures are the subject of increasing study, with fairness being a chief concern. The Conference on Fairness, Accountability, and Transparency (FAT) scheduled on Feb 23 and 24 this year in New York is an annual conference dedicated to bringing theory and practice of fair and interpretable Machine Learning, Information Retrieval, NLP, Computer Vision, Recommender systems, and other technical disciplines. This year's program includes 17 peer-reviewed papers and 6 tutorials from leading experts in the field. The conference will have three sessions. Session 4 of the two-day conference on Saturday, February 24, is in the field of fair classification. In this article, we give our readers a peek into the four papers that have been selected for presentation in Session 4. You can also check out Session 1,  Session 2, and Session 3 summaries in case you’ve missed them. The cost of fairness in binary classification What is the paper about? This paper provides a simple approach to the Fairness-aware problem which involves suitably thresholding class-probability estimates. It has been awarded Best paper in Technical contribution category. The authors have studied the inherent tradeoffs in learning classifiers with a fairness constraint in the form of two questions: What is the best accuracy we can expect for a given level of fairness? What is the nature of these optimal fairness aware classifiers? The authors showed that for cost-sensitive approximate fairness measures, the optimal classifier is an instance-dependent thresholding of the class probability function. They have quantified the degradation in performance by a measure of alignment of the target and sensitive variable. This analysis is then used to derive a simple plugin approach for the fairness problem. Key takeaways For Fairness-aware learning, the authors have designed an algorithm targeting a particular measure of fairness. They have reduced two popular fairness measures (disparate impact and mean difference) to cost-sensitive risks. They show that for cost-sensitive fairness measures, the optimal Fairness-aware classifier is an instance-dependent thresholding of the class-probability function. They quantify the intrinsic, method independent impact of the fairness requirement on accuracy via a notion of alignment between the target and sensitive feature. The ability to theoretically compute the tradeoffs between fairness and utility is perhaps the most interesting aspect of their technical results. They have stressed that the tradeoff is intrinsic to the underlying data. That is, any fairness or unfairness, is a property of the data, not of any particular technique. They have theoretically computed what price one has to pay (in utility) in order to achieve a desired degree of fairness: in other words, they have computed the cost of fairness. Decoupled Classifiers for Group-Fair and Efficient Machine Learning What is the paper about? This paper considers how to use a sensitive attribute such as gender or race to maximize fairness and accuracy, assuming that it is legal and ethical. Simple linear classifiers may use the raw data, upweight/oversample data from minority groups, or employ advanced approaches to fitting linear classifiers that aim to be accurate and fair. However, an inherent tradeoff between accuracy on one group and accuracy on another still prevails. This paper defines and explores decoupled classification systems, in which a separate classifier is trained on each group. The authors present experiments on 47 datasets. The experiments are “semi-synthetic” in the sense that the first binary feature was used as a substitute sensitive feature. The authors found that on many data sets the decoupling algorithm improves performance while less often decreasing performance. Key takeaways The paper describes a simple technical approach for a practitioner using ML to incorporate sensitive attributes. This approach avoids unnecessary accuracy tradeoffs between groups and can accommodate an application-specific objective, generalizing the standard ML notion of loss. For a certain family of “weakly monotonic” fairness objectives, the authors provide a black-box reduction that can use any off-the-shelf classifier to efficiently optimize the objective. This work requires the application designer to pin down a specific loss function that trades off accuracy for fairness. Experiments demonstrate that decoupling can reduce the loss on some datasets for some potentially sensitive features A case study of algorithm-assisted decision making in child maltreatment hotline screening decisions What is the paper about? The work is based on the use of predictive analytics in the area of child welfare. It won the best paper award in the Technical and Interdisciplinary Contribution. The authors have worked on developing, validating, fairness auditing, and deploying a risk prediction model in Allegheny County, PA, USA. The authors have described competing models that are being developed in the Allegheny County as part of an ongoing redesign process in comparison to the previous models. Next, they investigate the predictive bias properties of the current tool and a Random forest model that has emerged as one of the best performing competing models. Their predictive bias assessment is motivated both by considerations of human bias and recent work on fairness criteria. They then discuss some of the challenges in incorporating algorithms into human decision-making processes and reflect on the predictive bias analysis in the context of how the model is actually being used. They also propose an “oracle test” as a tool for clarifying whether particular concerns pertain to the statistical properties of a model or if these concerns are targeted at other potential deficiencies. Key takeaways The goal in Allegheny County is to improve both the accuracy and equity of screening decisions by taking a Fairness-aware approach to incorporating prediction models into the decision-making pipeline. The paper reports on the lessons learned so far by the authors, their approaches to predictive bias assessment, and several outstanding challenges in the child maltreatment hotline context. This report contributes to the ongoing conversation concerning the use of algorithms in supporting critical decisions in government—and the importance of considering fairness and discrimination in data-driven decision making. The paper discussion and general analytic approach are also broadly applicable to other domains where predictive risk modeling may be used. Fairness in Machine Learning: Lessons from Political Philosophy What is the paper about? Plenty of moral and political philosophers have expended significant efforts in formalizing and defending the central concepts of discrimination, egalitarianism, and justice. Thus it is unsurprising to know that the attempts to formalize ‘fairness’ in machine learning contain echoes of these old philosophical debates. This paper draws on existing work in moral and political philosophy in order to elucidate emerging debates about fair machine learning. It answers the following questions: What does it mean for a machine learning model to be ‘fair’, in terms which can be operationalized? Should fairness consist of ensuring everyone has an equal probability of obtaining some benefit, or should we aim instead to minimize the harms to the least advantaged? Can the relevant ideal be determined by reference to some alternative state of affairs in which a particular social pattern of discrimination does not exist? Key takeaways This paper aims to provide an overview of some of the relevant philosophical literature on discrimination, fairness, and egalitarianism in order to clarify and situate the emerging debate within fair machine learning literature. The author addresses the conceptual distinctions drawn between terms frequently used in the fair ML literature–including ‘discrimination’ and ‘fairness’–and the use of related terms in the philosophical literature. He suggests that ‘fairness’ as used in the fair machine learning community is best understood as a placeholder term for a variety of normative egalitarian considerations. He also provides an overview of implications for the incorporation of ‘fairness’ into algorithmic decision-making systems. We hope you like the coverage of Session 4. Don’t miss our coverage on Session 5 on Fat recommenders and more.

Machine learning has emerged with a vast new ecosystem of techniques and infrastructure and we are just beginning to learn their full capabilities. But with the exciting innovations happening, there are also some really concerning problems arising. Forms of bias, stereotyping and unfair determination are being found in computer vision systems, object recognition models, and in natural language processing and word embeddings. The Conference on Fairness, Accountability, and Transparency (FAT) scheduled on Feb 23 and 24 this year in New York is an annual conference dedicating to bringing theory and practice of fair and interpretable Machine Learning, Information Retrieval, NLP, Computer Vision, Recommender systems, and other technical disciplines. This year's program includes 17 peer-reviewed papers and 6 tutorials from leading experts in the field. The conference will have three sessions. Session 3 of the two-day conference on Saturday, February 24, is in the field of fairness in computer vision and NLP. In this article, we give our readers a peek into the three papers that have been selected for presentation in Session 3. You can also check out Session 1 and Session 2, in case you’ve missed them. Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification What is the paper about The paper talks about substantial disparities in the accuracy of classifying darker and lighter females and males in gender classification systems. The authors have evaluated bias present in automated facial analysis algorithms and datasets with respect to phenotypic subgroups. Using the dermatologist approved Fitzpatrick Skin Type classification system, they have characterized the gender and skin type distribution of two facial analysis benchmarks, IJB-A and Adience.  They have also evaluated 3 commercial gender classification systems using this dataset. Key takeaways The paper measures accuracy of 3 commercial gender classification algorithms by Microsoft, IBM, and Face++ on the new Pilot Parliaments Benchmark which is balanced by gender and skin type. On annotating the dataset with the Fitzpatrick skin classification system and testing gender classification performance on 4 subgroups, they found : All classifiers perform better on male faces than on female faces (8.1% − 20.6% difference in error rate) All classifiers perform better on lighter faces than darker faces (11.8% − 19.2% difference in error rate) All classifiers perform worst on darker female faces (20.8% − 34.7% error rate) Microsoft and IBM classifiers perform best on lighter male faces (error rates of 0.0% and 0.3% respectively) Face++ classifiers perform best on darker male faces (0.7% error rate) The maximum difference in error rate between the best and worst classified groups is 34.4% They encourage further work to see if the substantial error rate gaps on the basis of gender, skin type and intersectional subgroup revealed in this study of gender classification persist in other human-based computer vision tasks as well. Analyze, Detect and Remove Gender Stereotyping from Bollywood Movies What is the paper about The paper studies gender stereotypes and cases of bias in the Hindi movie industry (Bollywood) and propose an algorithm to remove these stereotypes from text. The authors have analyzed movie plots and posters for all movies released since 1970. The gender bias is detected by semantic modeling of plots at sentence and intra-sentence level. Different features like occupation, introductions, associated actions and descriptions are captured to show the pervasiveness of gender bias and stereotype in movies. Next, they have developed an algorithm to generate debiased stories. The proposed debiasing algorithm extracts gender biased graphs from unstructured piece of text in stories from movies and de-bias these graphs to generate plausible unbiased stories. Key takeaways The analysis is performed at sentence at multi-sentence level and uses word embeddings by adding context vector and studying the bias in data. Data observation showed that while analyzing occupations for males and females, higher level roles are designated to males while lower level roles are designated to females. A similar trend has been observed for centrality where females were less central in the plot vs their male counterparts. Also, while predicting gender using context word vectors, with very small training data, a very high accuracy was observed in gender prediction for test data reflecting a substantial amount of bias present in the data. The authors have also presented an algorithm to remove such bias present in text. They show that by interchanging the gender of high centrality male character with a high centrality female character in the plot text, leaves no change in the story but de-biases it completely. Mixed Messages? The Limits of Automated Social Media Content Analysis What is the paper about This paper broadcasts that a knowledge gap exists between data scientists studying NLP and policymakers advocating for the wide adoption of automated social media analysis and moderation. It urges policymakers to understand the capabilities and limits of NLP before endorsing or adopting automated content analysis tools, particularly for making decisions that affect fundamental rights or access to government benefits. It draws on existing research to explain the capabilities and limitations of text classifiers for social media posts and other online content. This paper is aimed at helping researchers and technical experts address the gaps in policymakers knowledge about what is possible with automated text analysis. Key takeaways The authors have provided an overview of how NLP classifiers work and identified five key limitations of these tools that must be communicated to policymakers: NLP classifiers require domain-specific training and cannot be applied with the same reliability across different domains. NLP tools can amplify social bias reflected in language and are likely to have lower accuracy for minority groups. Accurate text classification requires clear, consistent definitions of the type of speech to be identified. Policy debates around content moderation and social media mining tend to lack such precise definitions. The accuracy achieved in NLP studies does not warrant widespread application of these tools to social media content analysis and moderation. Text filters remain easy to evade and fall far short of humans ability to parse meaning from text. The paper concludes with recommendations for NLP researchers to bridge the knowledge gap between technical experts and policymakers, including Clearly describe the domain limitations of NLP tools. Increase development of non-English training resources. Provide more detail and context for accuracy measures. Publish more information about definitions and instructions provided to annotators. Don’t miss our coverage on Session 4 and Session 5 on Fair Classification, Fat recommenders, etc.

This session of the FAT* 2018 is about interpretability and explainability in machine learning models. With the advances in Deep learning, machine learning models have become more accurate. However, with accuracy and advancements, it is a tough task to keep the models highly explainable. This means, these models may appear as black boxes to business users, who utilize them without knowing what lies within. Thus, it is equally important to make ML models interpretable and explainable, which can be beneficial and essential for understanding ML models and to have a ‘behind the scenes’ knowledge of what’s happening within them. This understanding can be highly essential for heavily regulated industries like Finance, Medicine, Defence and so on. The Conference on Fairness, Accountability, and Transparency (FAT), which would be held on the 23rd and 24th of February, 2018 is a multi-disciplinary conference that brings together researchers and practitioners interested in fairness, accountability, and transparency in socio-technical systems. The FAT 2018 conference will witness 17 research papers, 6 tutorials, and 2 keynote presentations from leading experts in the field. This article covers research papers pertaining to the 2nd session that is dedicated to Interpretability and Explainability of machine-learned decisions. If you’ve missed our summary of the 1st session on Online Discrimination and Privacy, visit the article link for a catch up. Paper 1: Meaningful Information and the Right to Explanation This paper addresses an active debate in policy, industry, academia, and the media about whether and to what extent Europe’s new General Data Protection Regulation (GDPR) grants individuals a “right to explanation” of automated decisions. The paper explores two major papers, European Union Regulations on Algorithmic Decision Making and a “Right to Explanation” by Goodman and Flaxman (2017) Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation by Wachter et al. (2017) This paper demonstrates that the specified framework is built on incorrect legal and technical assumptions. In addition to responding to the existing scholarly contributions, the article articulates a positive conception of the right to explanation, located in the text and purpose of the GDPR. The authors take a position that the right should be interpreted functionally, flexibly, and should, at a minimum, enable a data subject to exercise his or her rights under the GDPR and human rights law. Key takeaways: The first paper by Goodman and Flaxman states that GDPR creates a "right to explanation" but without any argument. The second paper is in response to the first paper, where Watcher et al. have published an extensive critique, arguing against the existence of such a right. The current paper, on the other hand, is partially concerned with responding to the arguments of Watcher et al. Paper 2: Interpretable Active Learning The paper tries to highlight how due to complex and opaque ML models, the process of active learning has also become opaque. Not much has been known about what specific trends and patterns, the active learning strategy may be exploring. The paper expands on explaining about LIME (Local Interpretable Model-agnostic Explanations framework) to provide explanations for active learning recommendations. The authors, Richard Phillips, Kyu Hyun Chang, and Sorelle A. Friedler, demonstrate uses of LIME in generating locally faithful explanations for an active learning strategy. Further, the paper shows how these explanations can be used to understand how different models and datasets explore a problem space over time. Key takeaways: The paper demonstrates how active learning choices can be made more interpretable to non-experts. It also discusses techniques that make active learning interpretable to expert labelers, so that queries and query batches can be explained and the uncertainty bias can be tracked via interpretable clusters. It showcases per-query explanations of uncertainty to develop a system that allows experts to choose whether to label a query. This will allow them to incorporate domain knowledge and their own interests into the labeling process. It introduces a quantified notion of uncertainty bias, the idea that an algorithm may be less certain about its decisions on some data clusters than others. Paper 3: Interventions over Predictions: Reframing the Ethical Debate for Actuarial Risk Assessment Actuarial risk assessments might be unduly perceived as a neutral way to counteract implicit bias and increase the fairness of decisions made within the criminal justice system, from pretrial release to sentencing, parole, and probation. However, recently, these assessments have come under increased scrutiny, as critics claim that the statistical techniques underlying them might reproduce existing patterns of discrimination and historical biases that are reflected in the data. The paper proposes that machine learning should not be used for prediction, but rather to surface covariates that are fed into a causal model for understanding the social, structural and psychological drivers of crime. The authors, Chelsea Barabas, Madars Virza, Karthik Dinakar, Joichi Ito (MIT), Jonathan Zittrain (Harvard),  propose an alternative application of machine learning and causal inference away from predicting risk scores to risk mitigation. Key takeaways: The paper gives a brief overview of how risk assessments have evolved from a tool used solely for prediction to one that is diagnostic at its core. The paper places a debate around risk assessment in a broader context. One can get a fuller understanding of the way these actuarial tools have evolved to achieve a varied set of social and institutional agendas. It argues for a shift away from predictive technologies, towards diagnostic methods that will help in understanding the criminogenic effects of the criminal justice system itself, as well as evaluate the effectiveness of interventions designed to interrupt cycles of crime. It proposes that risk assessments, when viewed as a diagnostic tool, can be used to understand the underlying social, economic and psychological drivers of crime. The authors also posit that causal inference offers the best framework for pursuing the goals to achieve a fair and ethical risk assessment tool.

We have already talked about a simple learning roadmap for you to develop your data science skills in the first resolution. We also talked about the importance of staying relevant in an increasingly automated job market, in our second resolution. Now it’s time to think about the kind of person you want to be and the legacy you will leave behind. 3rd Resolution: Choose projects wisely and be mindful of their impact. Your work has real consequences. And your projects will often be larger than what you know or can do. As such, the first step toward creating impact with intention is to define the project scope, purpose, outcomes and assets clearly. The next most important factor is choosing the project team. 1. Seek out, learn from and work with a diverse group of people To become a successful data scientist you must learn how to collaborate. Not only does it make projects fun and efficient, but it also brings in diverse points of view and expertise from other disciplines. This is a great advantage for machine learning projects that attempt to solve complex real-world problems. You could benefit from working with other technical professionals like web developers, software programmers, data analysts, data administrators, game developers etc. Collaborating with such people will enhance your own domain knowledge and skills and also let you see your work from a broader technical perspective. Apart from the people involved in the core data and software domain, there are others who also have a primary stake in your project’s success. These include UX designers, people with humanities background if you are building a product intended to participate in society (which most products often are), business development folks, who actually sell your product and bring revenue, marketing people, who are responsible for bringing your product to a much wider audience to name a few. Working with people of diverse skill sets will help market your product right and make it useful and interpretable to the target audience. In addition to working with a melange of people with diverse skill sets and educational background it is also important to work with people who think differently from you, and who have experiences that are different from yours to get a more holistic idea of the problems your project is trying to tackle and to arrive at a richer and unique set of solutions to solve those problems. 2. Educate yourself on ethics for data science As an aspiring data scientist, you should always keep in mind the ethical aspects surrounding privacy, data sharing, and algorithmic decision-making.  Here are some ways to develop a mind inclined to designing ethically-sound data science projects and models. Listen to seminars and talks by experts and researchers in fairness, accountability, and transparency in machine learning systems. Our favorites include Kate Crawford’s talk on The trouble with bias, Tricia Wang on The human insights missing from big data and Ethics & Data Science by Jeff Hammerbacher. Follow top influencers on social media and catch up with their blogs and about their work regularly. Some of these researchers include Kate Crawford, Margaret Mitchell, Rich Caruana, Jake Metcalf, Michael Veale, and Kristian Lum among others. Take up courses which will guide you on how to eliminate unintended bias while designing data-driven algorithms. We recommend Data Science Ethics by the University of Michigan, available on edX. You can also take up a course on basic Philosophy from your choice of University.   Start at the beginning. Read books on ethics and philosophy when you get long weekends this year. You can begin with Aristotle's Nicomachean Ethics to understand the real meaning of ethics, a term Aristotle helped develop. We recommend browsing through The Stanford Encyclopedia of Philosophy, which is an online archive of peer-reviewed publication of original papers in philosophy, freely accessible to Internet users. You can also try Practical Ethics, a book by Peter Singer and The Elements of Moral Philosophy by James Rachels. Attend or follow upcoming conferences in the field of bringing transparency in socio-technical systems. For starters, FAT* (Conference on Fairness, Accountability, and Transparency) is scheduled on February 23 and 24th, 2018 at New York University, NYC. We also have the 5th annual conference of FAT/ML, later in the year.  3. Question/Reassess your hypotheses before, during and after actual implementation Finally, for any data science project, always reassess your hypotheses before, during, and after the actual implementation. Always ask yourself these questions after each of the above steps and compare them with the previous answers. What question are you asking? What is your project about? Whose needs is it addressing? Who could it adversely impact? What data are you using? Is the data-type suitable for your type of model? Is the data relevant and fresh? What are its inherent biases and limitations? How robust are your workarounds for them? What techniques are you going to try? What algorithms are you going to implement? What would be its complexity? Is it interpretable and transparent? How will you evaluate your methods and results? What do you expect the results to be? Are the results biased? Are they reproducible? These pointers will help you evaluate your project goals from a customer and business point of view. Additionally, it will also help you in building efficient models which can benefit the society and your organization at large. With this, we come to the end of our new year resolutions for an aspiring data scientist. However, the beauty of the ideas behind these resolutions is that they are easily transferable to anyone in any job. All you gotta do is get your foundations right, stay relevant, and be mindful of your impact. We hope this gives a great kick start to your career in 2018. “Motivation is what gets you started. Habit is what keeps you going.” ― Jim Ryun Happy New Year! May the odds and the God(s) be in your favor this year to help you build your resolutions into your daily routines and habits!