D'oh! You'd never believe it, but in a new research paper, computer scientists at Google DeepMind have admitted that its artificial intelligence technology still struggles to identify many common human behaviours that Homer Simpson exhibits – whether it's eating doughnuts or crisps, falling on his face, yawning or drinking beer. To try to get the DeepMind AI neural network to understand and recognise human behaviour, researchers created a huge dataset of over 300,000 YouTube video clips showing human actions. While none of the clips actually featured Homer Simpson, many of the foods, actions and behaviours the system was unable to recognise bore a striking pattern to the animated character's ways, which the researchers had a little fun with. They also enlisted online workers from Amazon's Mechanical Turk service to look at common types of human behaviour and break it down into a labelled guide containing 400 human action classes, ranging from everything from "brushing hair" to "riding a unicycle" to "playing a violin", with each class corresponding to at least 400 video clips. When the Kinetics dataset was complete, the researchers then began training the neural network to recognise human actions – a machine learning technique known as "supervised learning".

Honest question, and I'm really not trying to be adversarial, but what has Francois actually done that would merit him writing a book on DL? Keras is good for the community because it's accessible, even people who don't like it for research generally agree on that, and he has a high kaggle score, and a paper or two that look like a promising start to a research career (though Xception is IMO incremental it's still a decent paper). To me, this honestly seems like him riding the popularity of Keras for a moneygrab, practically on par with that PyImageSearch dude. Counterpoint: the Goodfellow DL book is a regular ole textbook; Maybe the point of this is that it abstracts most of the details and gives a higher level overview that's targeted at laymen? Briefly browsing the table of contents, it looks like a list of "topics that have recently been popular and that you might like to play with or build a neat applet with." Counter counterpoint: To me that doesn't merit wasting ink and paper, especially when there are so many solid resources and tutorials people have put out on the internet.

The world is a confusing place, especially for an AI. But a neural network developed by UK artificial intelligence firm DeepMind that gives computers the ability to understand how different objects are related to each other could help bring it into focus. Humans use this type of inference – called relational reasoning – all the time, whether we are choosing the best bunch of bananas at the supermarket or piecing together evidence from a crime scene. The ability to transfer abstract relations – such as whether something is to the left of another or bigger than it – from one domain to another gives us a powerful mental toolset with which to understand the world. It is a fundamental part of our intelligence says Sam Gershman, a computational neuroscientist at Harvard University.

Data scientists are expected to be familiar with the differences between supervised machine learning and unsupervised machine learning -- as well as ensemble modeling, which uses a combination of techniques, and semi-supervised learning, which combines supervised and unsupervised approaches. While it's not necessarily new, deep learning has recently seen a surge in popularity as a way to accelerate the solution of certain types of difficult computer problems, most notably in the computer vision and natural language processing (NLP) fields. By extracting high-level, complex abstractions as data representations through a hierarchical learning process, deep learning models yield results more quickly than standard machine learning approaches. Machine learning, deep learning, and artificial intelligence all have relatively specific meanings, but are often broadly used to refer to any sort of modern, big-data related processing approach.

When you're an A.I. researcher at Google, even your days off are filled with neural nets. Mike Tyka is a Google scientist who recently helped create the company's DeepDream venture, but this week he posted details of a personal project that could someday make DeepDream seem primitive. That famous program works by basically blending together elements of other pictures, and then modifying that collage, but Tyka's new approach takes the much more difficult and potentially rewarding path: teaching an A.I. to create all-new portraits from scratch. "I don't mind if the results are not necessarily realistic but fine texture is important no matter what even if it's surreal but [high-resolution] texture," Tyka commented Tuesday on his blog. The approach uses "generative adversarial networks" (GANs) to refine the A.I.'s abilities over time.

Any sufficiently advanced technology is indistinguishable from magic – Arthur C. Clarke As a marketer, it can sometimes seem like the work my engineering colleagues are doing is magic -- pure computer science sorcery. I mean, they're building a chatbot that can communicate using human language and learn from the conversations it's having. AI can be a tough topic to wrap your head around. And with all of the various branches -- machine learning, deep learning, natural language processing -- it's not a topic that you can hope to master by reading a single blog post. So the purpose of this post isn't to provide you with an exhaustive, engineering-degree-level understanding of AI.

Those fretfully debating artificial intelligence (AI) might best start by appraising the half dozen general pathways under exploration in laboratories around the world. While these general approaches overlap, they offer distinct implications for what characteristics emerging, synthetic minds might display, including (for example) whether it will be easy or hard to instill human-style ethical values. Most problematic may be those efforts taking place in secret. The "Moore's Law crossing" argument is appraised, in light of discoveries that brain computation may involve much more than just synapses. Will efforts to develop Sympathetic Robotics tweak compassion from humans long before automatons are truly self-aware? It is argued that most foreseeable problems might be dealt with the same way that human versions of oppression and error are best addressed -- via reciprocal accountability. For this to happen, there should be diversity of types, designs and minds, interacting under fair competition in a generally open environment. As varied concepts from science fiction are reified by rapidly advancing technology, some trends are viewed worriedly by our smartest peers. Portions of the intelligencia -- typified by Google's Ray Kurzweil [1] -- foresee AI, or Artificial General Intelligence (AGI) as likely to bring good news, perhaps even transcendence for members of the Olde Race of bio-organic humanity 1.0. Others, such as Stephen Hawking and Francis Fukuyama, warn that the arrival of sapient, or supersapient machinery may bring an end to our species -- or at least its relevance on the cosmic stage -- a potentiality evoked in many a lurid Hollywood film. Swedish philosopher Nicholas Bostrom, in Superintelligence [2], suggests that even advanced AIs who obey their initial, human defined goals will likely generate "instrumental subgoals" such as self-preservation, cognitive enhancement, and resource acquisition. In one nightmare scenario, Bostrom posits an AI that -- ordered to "make paperclips" -- proceeds to overcome all obstacles and transform the solar system into paper clips. A variant on this theme makes up the grand arc in the famed "three laws" robotic series by science fiction author Isaac Asimov [3]. Taking middle ground, SpaceX/Tesla entrepreneur Elon Musk has joined with YCombinator founder Sam Altman to establish OpenAI [4], an endeavor that aims to keep artificial intelligence research -- and its products -- accountable by maximizing transparency and accountability. As one who has promoted those two key words for a quarter of a century, I wholly approve [5].

We present a recurrent encoder-decoder deep neural network architecture that directly translates speech in one language into text in another. The model does not explicitly transcribe the speech into text in the source language, nor does it require supervision from the ground truth source language transcription during training. We apply a slightly modified sequence-to-sequence with attention architecture that has previously been used for speech recognition and show that it can be repurposed for this more complex task, illustrating the power of attention-based models. A single model trained end-to-end obtains state-of-the-art performance on the Fisher Callhome Spanish-English speech translation task, outperforming a cascade of independently trained sequence-to-sequence speech recognition and machine translation models by 1.8 BLEU points on the Fisher test set. In addition, we find that making use of the training data in both languages by multi-task training sequence-to-sequence speech translation and recognition models with a shared encoder network can improve performance by a further 1.4 BLEU points.

We learn recurrent neural network optimizers trained on simple synthetic functions by gradient descent. We show that these learned optimizers exhibit a remarkable degree of transfer in that they can be used to efficiently optimize a broad range of derivative-free black-box functions, including Gaussian process bandits, simple control objectives, global optimization benchmarks and hyper-parameter tuning tasks. Up to the training horizon, the learned optimizers learn to trade-off exploration and exploitation, and compare favourably with heavily engineered Bayesian optimization packages for hyper-parameter tuning.

While the optimization problem behind deep neural networks is highly non-convex, it is frequently observed in practice that training deep networks seems possible without getting stuck in suboptimal points. It has been argued that this is the case as all local minima are close to being globally optimal. We show that this is (almost) true, in fact almost all local minima are globally optimal, for a fully connected network with squared loss and analytic activation function given that the number of hidden units of one layer of the network is larger than the number of training points and the network structure from this layer on is pyramidal.