Children learn to speak, as well as recognize objects, people, and places, long before they learn to read or write. They can learn from hearing, seeing, and interacting without being given any instructions. So why shouldn't artificial intelligence systems be able to work the same way? That's the key insight driving a research project under way at MIT that takes a novel approach to speech and image recognition: Teaching a computer to successfully associate specific elements of images with corresponding sound files in order to identify imagery (say, a lighthouse in a photographic landscape) when someone in an audio clip says the word "lighthouse." Though in the very early stages of what could be a years-long process of research and development, the implications of the MIT project, led by PhD student David Harwath and senior research scientist Jim Glass, are substantial. Along with being able to automatically surface images based on corresponding audio clips and vice versa, the research opens a path to creating language-to-language translation without needing to go through the laborious steps of training AI systems on the correlation between two languages' words.

Artificial intelligence is learning to take on an increasing number of sophisticated tasks. Google Deepmind's AI is now able to imitate human speech, and just this past August IBM's Watson successfully diagnosed a rare case of leukemia. Rather than viewing these advances as threats to job security, we can look at them as opportunities for AI to fill in critical gaps in existing service providers, such as mental healthcare professionals. In the US alone, nearly eight percent of the population suffers from depression (that's about one in every 13 American adults), and yet about 45 percent of this population does not seek professional care due to the costs. There are many barriers to getting quality mental healthcare, from searching for a provider who's within your insurance network to screening multiple potential therapists in order to find someone you feel comfortable speaking with.

Artificial intelligence is coming to banking -- scratch that, it's already here, but customers may not have noticed. AI is already playing a role in consumers' lives, whether they know it or not. Talking to Siri, looking at recommendations from Amazon or Netflix, or chatting with Google Home about the temperature -- AI is all around us, and we're growing more comfortable with it all the time. That's good, says Arif Ahmed, senior vice president of payments innovation for U.S. Bank, because AI is set to help customers in important ways, and in the not-too-distant future. "Emerging artificial intelligence will improve the customer experience without compromising privacy," Ahmed told Bank Innovation.

If you could measure all the information you consume online, what would you learn about yourself? Created by developers Hang Do Thi Duc and Regina Flores Mir, the application gives users a peek into what kind of digital footprint they might be leaving behind as they browse Facebook--and makes the hidden mechanisms of Facebook's data collection more transparent. How It Works Data Selfie collects data about what you click on (through likes and links), what you type, and what you look at, and for how long. Based on this information, the app compiles a personality profile using personality insights from the supercomputer IBM Watson and the machine learning algorithm Apply Magic Sauce and presents this "data selfie" for you to peruse. In the name of transparency and privacy, all of Data Selfie's code is on Github, and all of the data it tracks is stored on your personal computer.

Android Things is the evolution of Brillo, which we've talked about before. Google needs an internet-of-things (IoT) platform, and Android Things seems to be the software platform they're banking on for this niche of the market. While waiting for the first release, developers get to test out the second Developer Preview that just rolled out. Android Things Developer Preview 2 has some new features to flaunt coming from the initial version. The new DP now has USB audio support for the Intel Edison and Raspberry Pi 3 devices.

Deep learning is a recent trend in machine learning that models highly non-linear representations of data. In the past years, deep learning has gained a tremendous momentum and prevalence for a variety of applications (Wikipedia 2016a). Among these are image and speech recognition, driverless cars, natural language processing and many more. Interestingly, the majority of mathematical concepts for deep learning have been known for decades. However, it is only through several recent developments that the full potential of deep learning has been unleashed (Nair and Hinton 2010; Srivastava et al. 2014). Previously, it was hard to train artificial neural networks due to vanishing gradients and overfitting problems.

Perhaps the most positive technical theme of 2016 was the long-delayed triumph of artificial intelligence, machine learning, and in particular deep learning. In this article we'll discuss what that means and how you might make use of deep learning yourself. Perhaps you noticed in the fall of 2016 that Google Translate suddenly went from producing, on the average, word salad with a vague connection to the original language to emitting polished, coherent sentences more often than not -- at least for supported language pairs, such as English-French, English-Chinese, and English-Japanese. That dramatic improvement was the result of a nine-month concerted effort by the Google Brain and Google Translate teams to revamp Translate from using its old phrase-based statistical machine translation algorithms to working with a neural network trained with deep learning and word embeddings employing Google's TensorFlow framework. The researchers working on the conversion had access to a huge corpus of translations from which to train their networks, but they soon discovered that they needed thousands of GPUs for training and would have to create a new kind of chip, a Tensor Processing Unit (TPU), to run Translate on their trained neural networks at scale.

When our robot overlords arrive, will they decide to kill us or cooperate with us? New research from DeepMind, Alphabet Inc.'s London-based artificial intelligence unit, could ultimately shed light on this fundamental question. They have been investigating the conditions in which reward-optimizing beings, whether human or robot, would choose to cooperate, rather than compete. The answer could have implications for how computer intelligence may eventually be deployed to manage complex systems such as an economy, city traffic flows, or environmental policy. Joel Leibo, the lead author of a paper DeepMind published online Thursday, said in an e-mail that his team's research indicates that whether agents learn to cooperate or compete depends strongly on the environment in which they operate.

Did you know that the self-driving car market could reach $87 billion by 2030¹? How many of us are ready to sit in the passenger seat of a speeding, driverless taxi? It would require complete trust in the artificial intelligence manning the wheel and controlling the brakes. We are at an inflection point as AI proliferates across virtually every industry. Yet, according to PwC's Global Data and Analytics Survey 2016: Big Decisions, only 39% of companies are highly data-driven.

I believe that there exist classic deep learning papers which are worth reading regardless of their application areas. Rather than providing overwhelming amount of papers, I would like to provide a curated list of the classic deep learning papers which can be considered as must-reads in some research areas. Please read the contributing guide before you make a pull request. Distinguished deep learning researchers who have published 3 ( 6) papers on the awesome list (The papers in Hardware / Software, Papers Worth Reading, Classic Papers sections are excluded in counting.) Thank you for all your contributions.