Tech Guides - Artificial Intelligence

If you've been looking into the world of Machine Learning lately you might have heard about a mysterious thing called “Deep Learning”. But just what is Deep Learning, and what does it mean for the world of Machine Learning as a whole? Take less than two minutes out of your day to find out and fully realize the awesome potential Deep Learning has with this video today. Plus, if you’re already in love with Deep Learning, or want to finally start your Deep Learning journey then be sure to pick up one of our recommendations below and get started right now.

The world of data has really boomed in the last few years. When I first joined Packt Hadoop was The Next Big Thing on the horizon and what people are now doing with all the data we have available to us was unthinkable. Even in the first few weeks of 2016 we’re already seeing machine learning being used in ways we probably wouldn’t have thought about even a few years ago – we’re using machine learning for everything from discovering a supernova that was 570 billion times brighter than the sun to attempting to predict this year’s Super Bowl winners based on past results, but So what else can we expect in the next year for machine learning and how will it affect us? Based on what we’ve seen over the last three years here are a few predictions about what we can expect to happen in 2016 (With maybe a little wishful thinking mixed in too!) Machine Learning becomes the new Cloud Not too long ago every business started noticing the cloud, and with it came a shift in how companies were structured. Infrastructure was radically adapted to take full advantage that the benefits that the cloud offers and it doesn’t look to be slowing down with Microsoft recently promising to spend over $1 billion in providing free cloud resources for non-profits. Starting this year it’s plausible that we’ll see a new drive to also bake machine learning into the infrastructure. Why? Because every company will want to jump on that machine learning bandwagon! The benefits and boons to every company are pretty enticing – ML offers everything from grandiose artificial intelligence to much more mundane such as improvements to recommendation engines and targeted ads; so don’t be surprised if this year everyone attempts to work out what ML can do for them and starts investing in it. The growth of MLaaS Last year we saw Machine Learning as a Service appear on the market in bigger numbers. Amazon, Google, IBM, and Microsoft all have their own algorithms available to customers. It’s a pretty logical move and why that’s not all surprising. Why? Well, for one thing, data scientists are still as rare as unicorns. Sure, universities are creating new course and training has become more common, but the fact remains we won’t be seeing the benefits of these initiatives for a few years. Second, setting up everything for your own business is going to be expensive. Lots of smaller companies simply don’t have the money to invest in their own personal machine learning systems right now, or have the time needed to fine tune it. This is where sellers are going to be putting their investments this year – the smaller companies who can’t afford a full ML experience without outside help. Smarter Security with better protection The next logical step in security is tech that can sense when there are holes in its own defenses and adapt to them before trouble strikes. ML has been used in one form or another for several years in fraud prevention, but in the IT sector we’ve been relying on static rules to detect attack patterns. Imagine if systems could detect irregular behavior accurately or set up risk scores dynamically in order to ensure users had the best protection they could at any time? We’re a long way from this being fool-proof unfortunately, but as the year progresses we can expect to see the foundations of this start being seen. After all, we’re already starting to talk about it. Machine Learning and Internet of Things combine We’re already nearly there, but with the rise in interest in the IoT we can expect that these two powerhouses to finally combine. The perfect dream for IoT hobbyists has always been like something out of the Jetsons or Wallace and Gromit –when you pass that sensor by the frame of your door in the morning your kettle suddenly springs to life so you’re able to have that morning coffee without waiting like the rest of us primals; but in truth the Internet of Things has the potential to be so much more than just making the lives of hobbyists much easier. By 2020 it is expected that over 25 billion ‘Things’ will be connected to the internet, and each one will be collating reams and reams of data. For a business with the capacity to process this data they can collect the insight they could collect is a huge boon for everything from new products to marketing strategy. For IoT to really live up to the dreams we have for it we need a system that can recognize and collate relevant data, which is where a ML system is sure to take center stage. Big things are happening in the world of machine learning, and I wouldn’t be surprised if something incredibly left field happens in the data world that takes us all by surprise, but what do you think is next for ML? If you’re looking to either start getting into the art of machine learning or boosting your skills to the next level then be sure to give our Machine Learning tech page a look; it’s filled our latest and greatest ML books and videos out right now along with the titles we’re realizing soon, available to preorder in your format of choice.

In 2012, a year which feels a lot like the very early years of the era of data, Wired published this article on Narrative Science, an organization based in Chicago that uses Machine Learning algorithms to write news articles. Its founder and CEO, Kris Hammond, is a man whose enthusiasm for algorithmic possibilities is unparalleled. When asked whether an algorithm would win a Pulitzer in the next 20 years he goes further, claiming that it could happen in the next 5 years. Hammond’s excitement at what his organization is doing is not unwarranted. But his optimism certainly is. Unless 2017 is a particularly poor year for journalism and literary nonfiction, a Pulitzer for one of Narrative Science’s algorithms looks unlikely to say the least. But there are a couple of problems with Hammond’s enthusiasm. He fails to recognise the limitations of algorithms, the fact that the job of even the most intricate and complex Deep Learning algorithm is very specific is quite literally determined by the people who create it. “We are humanising the machine” he’s quoted as saying in a Guardian interview from June 2015. “Based on general ideas of what is important and a close understanding of who the audience is, we are giving it the tools to know how to tell us stories”. It’s important to notice here how he talks - it’s all about what ‘we’re’ doing. The algorithms that are central to Narrative Science’s mission are things that are created by people, by data scientists. It’s easy to read what’s going on as a simple case of the machines taking over. True, perhaps there is cause for concern among writers when he suggests that in 25 years 90% of news stories will be created by algorithms, but in actual fact there’s just a simple shift in where labour is focused. It's time to rethink algorithms We need to rethink how we view and talk about data science, Machine Learning and algorithms. We see, for example, algorithms as impersonal, blandly futuristic things. Although they might be crucial to our personalized online experiences, they are regarded as the hypermodern equivalent of the inauthentic handshake of a door to door salesman. Similarly, at the other end, the process of creating them are viewed as a feat of engineering, maths and statistics nerds tackling the complex interplay of statistics and machinery. Instead, we should think of algorithms as something creative, things that organize and present the world in a specific way, like a well-designed building. If an algorithm did indeed win a Pulitzer, wouldn’t it really be the team behind it that deserves it? When Hammond talks, for example, about “general ideas of what is important and a close understanding who the audience is”, he is referring very much to a creative process. Sure, it’s the algorithm that learns this, but it nevertheless requires the insight of a scientist, an analyst to consider these factors, and to consider how their algorithm will interact with the irritating complexity and unpredictability of reality. Machine Learning projects, then, are as much about designing algorithms as they are programming them. There’s a certain architecture, a politics that informs them. It’s all about prioritization and organization, and those two things aren’t just obvious; they’re certainly not things which can be identified and quantified. They are instead things that inform the way we quantify, the way we label. The very real fingerprints of human imagination, and indeed fallibility are in algorithms we experience every single day. Algorithms are made by people Perhaps we’ve all fallen for Hammond’s enthusiasm. It’s easy to see the algorithms as the key to the future, and forget that really they’re just things that are made by people. Indeed, it might well be that they’re so successful that we forget they’ve been made by anyone - it’s usually only when algorithms don’t work that the human aspect emerges. The data-team have done their job when no one realises they are there. An obvious example: You can see it when Spotify recommends some bizarre songs that you would never even consider listening to. The problem here isn’t simply a technical one, it’s about how different tracks or artists are tagged and grouped, how they are made to fit within a particular dataset that is the problem. It’s an issue of context - to build a great Machine Learning system you need to be alive to the stories and ideas that permeate within the world in which your algorithm operates - if you, as the data scientist lack this awareness, so will your Machine Learning project. But there have been more problematic and disturbing incidents such as when Flickr auto tags people of color in pictures as apes, due to the way a visual recognition algorithm has been trained. In this case, the issue is with a lack of sensitivity about the way in which an algorithm may work - the things it might run up against when it’s faced with the messiness of the real-world, with its conflicts, its identities, ideas and stories. The story of Solid Gold Bomb too, is a reminder of the unintended consequences of algorithms. It’s a reminder of the fact that we can be lazy with algorithms; instead of being designed with thought and care they become a surrogate for it - what’s more is that they always give us a get out clause; we can blame the machine if something goes wrong. If this all sounds like I’m simply down on algorithms, that I’m a technological pessimist, you’re wrong. What I’m trying to say is that it’s humans that are really in control. If an algorithm won a Pulitzer, what would that imply – it would mean the machines have won. It would mean we’re no longer the ones doing the thinking, solving problems, finding new ones. Data scientists are designers As the economy becomes reliant on technological innovation, it’s easy to remove ourselves, to underplay the creative thinking that drives what we do. That’s what Hammond’s doing, in his frenzied excitement about his company - he’s forgetting that it’s him and his team that are finding their way through today’s stories. It might be easier to see creativity at work when we cast our eyes towards game development and web design, but data scientists are designers and creators too. We’re often so keen to stress the technical aspects of these sort of roles that we forget this important aspect of the data scientist skillset.

Artificial Intelligence is just around the corner. Of course, it's been just around the corner for decades, but in part that's our own tendency to move the goalposts about what 'intelligence' is. Once, playing chess was one of the smartest things you could do. Now that a computer can easily beat a Grand Master, we've reclassified it as just standard computation, not requiring proper thinking skills. With the rise of deep learning and the proliferation of machine learning analytics, we edge ever closer to the moment where a computer system will be able to accomplish anything and everything better than a human can. So should we start worrying about SkyNet? Yes and no. Rule of the Human Overlords Early use of artificial intelligence will probably look a lot like how we used machine learning today. We'll see 'AI empowered humans' being the Human Overlords to their robot servants. These AI are smart enough to come up with the 'best options' to address human problems, but haven't been given the capability to execute them. Think about Google Maps - there, an extremely 'intelligent' artificial program comes up with the quickest route for you to take to get from point A to point B. But it doesn't force you to take it - you get to decide from the options offered which one will best suit your needs. This is likely what working alongside the first AI will look like. Rise of the Driverless Car The problem is that we are almost certainly going to see the power of AI increase exponentially - and any human greenlighting will become an increasingly inefficient part of the system. In much the same way that we'll let the Google Maps AI start to make decisions for us when we let it drive our driverless cars, we'll likely start turning more and more of our decisions over for AI to take responsibility for. Super smart AI will also likely be able to comprehend things that humans just can't understand. The mass of data that it's analysed will be beyond any one human to be able to judge effectively. Even today, financial algorithms are making instantaneous choices about the stock market - with humans just clicking 'yes' because the computer knows best. We've already seen electronic trading glitches leading to economic crises - six years ago! Just how much responsibility might we start turning over to smart machines? The Need to Solve Ethics If we've given power to an AI to make decisions for us, we'll want to ensure it has our best interests at heart, right? It's vital to program some sort of ethical system into our AI - the problem is, humans aren't very good at deciding what is and isn't ethical! Think about a simple and seemingly universal rule like 'Don't kill people'. Now think about all the ways we disagree about when it's okay to break that rule - in self-defence, in executing dangerous criminals, to end suffering, in combat. Imagine trying to code all of that into an AI, for every different moral variation. Arguably, it might be beyond human capacity. And as for right and wrong, well, we've had thousands of years of debate about that and we still can't agree exactly what is and isn't ethical. So how can we hope to program a morality system we'd be happy to give to an increasingly powerful AI? Avoiding SkyNet It may seem a little ridiculous to start worrying about the existential threat of AI when your machine learning algorithms keep bugging out on your constantly. And certainly, the possibilities offered by AI are amazing - more intelligence means faster, cheaper, and more effective solutions to humanity's problems. So despite the risk of us being outpaced by alien machine minds that have no concept of our human value system, we must always balance that risk against the amazing potential rewards. Perhaps what's most important is just not to be blase about what super-intelligent means for AI. And frankly, I can't remember how I lived before Google Maps.

The new phenomenon to hit the world of ‘Big Data’ seems to be ‘Deep Learning’. I’ve read many articles and papers where people question whether there’s a future for it, or if it’s just a buzzword that will die out like many a term before it. Likewise I have seen people who are genuinely excited and truly believe it is the future of Artificial intelligence; the one solution that can greatly improve the accuracy of our data and development of systems. Deep learning is currently a very active research area, by no means is it established as an industry standard, but rather one which is picking up pace and brings a strong promise of being a game changer when dealing with raw, unstructured data. So what is Deep Learning? Deep learning is a concept conceived from machine learning. In very simple terms, we think of machine learning as a method of teaching machines (using complex algorithms to form neural networks) to make improved predictions of outcomes based on patterns and behaviour from initial data sets.   The concept goes a step further however. The idea is based around a set of techniques used to train machines (Neural Networks) in processing information that can generate levels of accuracy nearly equivalent to that of a human eye. Deep learning is currently one of the best providers of solutions regarding problems in image recognition, speech recognition, object recognition and natural language processing. There are a growing number of libraries that are available, in a wide range of different languages (Python, R, Java) and frameworks such as: Caffe,Theanodarch, H20, Deeplearning4j, DeepDist etc.   How does Deep Learning work? The central idea is around ‘Deep Neural Networks’. Deep Neural Networks take traditional neural networks (or artificial neural networks) and build them on top of one another to form layers that are represented in a hierarchy. Deep learning allows each layer in the hierarchy to learn more about the qualities of the initial data. To put this in perspective; the output of data in level one is then the input of data in level 2. The same process of filtering is used a number of times until the level of accuracy allows the machine to identify its goal as accurately as possible. It’s essentially a repeat process that keeps refining the initial dataset. Here is a simple example of Deep learning. Imagine a face, we as humans are very good at making sense of what our eyes show us, all the while doing it without even realising. We can easily make out ones: face shape, eyes, ears, nose, mouth etc. We take this for granted and don’t fully appreciate how difficult (and complex) it can get whilst writing programs for machines to do what comes naturally to us. The difficulty for machines in this case is pattern recognition - identifying edges, shapes, objects etc. The aim is to develop these ‘deep neural networks’ by increasing and improving the number of layers - training each network to learn more about the data to the point where (in our example) it’s equal to human accuracy. What is the future of Deep Learning? Deep learning seems to have a bright future for sure, not that it is a new concept, I would actually argue it’s now practical rather than theoretical. We can expect to see the development of new tools, libraries and platforms, even improvements on current technologies such as Hadoop to accommodate the growth of Deep Learning. However it may not be all smooth sailing. It is still by far very difficult and time consuming task to understand, especially when trying to optimise networks as datasets grow larger and larger, surely they will be prone to errors? Additionally, the hierarchy of networks formed would surely have to be scaled for larger complex and data intensive AI problems.     Nonetheless, the popularity around Deep learning has seen large organisations invest heavily, such as: Yahoo, Facebook, Googles acquisition of Deepmind for $400 million and Twitter’s purchase of Madbits. They are just few of the high profile investments amongst many. 2015 really does seem like the year Deep learning will show its true potential. Prepare for the advent of deep learning by ensuring you know all there is to know about machine learning with our article. Read 'How to do Machine Learning with Python' now. Discover more Machine Learning tutorials and content on our dedicated page. Find it here.