"I think the deep learning system performs better because it's had a dispassionate look at quite a lot of audio material," says Monty Barlow, director of Machine Learning at Cambridge Consultants. Cambridge Consultants says its algorithm could lead to more sophisticated methods of organizing and searching music databases. It could potentially be used to quickly evaluate a patient's health using sensor waveforms (although Cambridge Consultants hasn't exactly said how this will work). In the future, all of our doctors are going to be AI music snobs.

When did you last visit your primary care physician? During your appointment, the doctor placed a stethoscope over your chest, listening for whispers of abnormality in your heart beat. But most heart arrhythmias occur sporadically. Picture a world where your heart can be monitored continuously using a device you could purchase at a Best Buy or Target. Algorithms transform the raw data coming from your watch into diagnoses, and your doctor will be notified when a problem is detected.

Malware has proven increasingly difficult to detect via signature or heuristic-based methods, which means most Antivirus (AV) programs are woefully ineffective against mutating malware, and especially ineffective against APT attacks (Advanced Persistent Threats). Typical malware consists of about 10,000 lines of code. Five to six years ago marked the beginning of the use of machine learning to solve non-linear problems such as facial recognition or understanding malware, and what features one needs to extract to uniquely identify such programs. Other techniques, such as sandboxing and machine-based techniques, are not as fast nor as accurate as Deep Learning. Deep Instinct, founded by Guy Caspi and Eli David, Israeli Defense Force Cybersecurity veterans, applies artificial intelligence Deep Learning algorithms to detect structures and program functions that are indicative of malware.

Language translation has typically been done by recurrent neural networks (RNN), which process language one word at a time in a linear order, either right-to-left or left-to-right, depending on the language. This CNN-based architecture pays attention to words farther along in a sentence to help understand the meaning from context farther along the string of words, much like humans do. Facebook hopes to use the new methodology to scale its translation efforts to cover "more of the world's 6,500 languages." Now that the popular social network has chosen CNN translation processing architecture, it will be interesting to see what comes next.

Nvidia announced a brand new accelerator based on the company's latest Volta GPU architecture, called the Tesla V100. The chip's newest breakout feature is what Nvidia calls a "Tensor Core." According to Nvidia, Tensor Cores can make the Tesla V100 up to 12x faster for deep learning applications compared to the company's previous Tesla P100 accelerator. A tensor is a mathematical object represented by an array of components that are functions of the coordinates of a space. Google created its own machine learning framework that uses tensors because tensors allow for highly scalable neural networks. Google surprised industry analysts when it open sourced its Tensorflow machine learning software library, but this may have been a stroke of genius because Tensorflow quickly became one of the most popular machine learning frameworks used by developers.

It has long been assumed that high dimensional continuous control problems cannot be solved effectively by discretizing individual dimensions of the action space due to the exponentially large number of bins over which policies would have to be learned. In this paper, we draw inspiration from the recent success of sequence-to-sequence models for structured prediction problems to develop policies over discretized spaces. Central to this method is the realization that complex functions over high dimensional spaces can be modeled by neural networks that use next step prediction. Specifically, we show how Q-values and policies over continuous spaces can be modeled using a next step prediction model over discretized dimensions. With this parameterization, it is possible to both leverage the compositional structure of action spaces during learning, as well as compute maxima over action spaces (approximately). On a simple example task we demonstrate empirically that our method can perform global search, which effectively gets around the local optimization issues that plague DDPG and NAF. We apply the technique to off-policy (Q-learning) methods and show that our method can achieve the state-of-the-art for off-policy methods on several continuous control tasks.

I've been wondering some things about various neural network architectures and I have a question. Can all neural network architectures (recurrent, convolutional, GAN etc.) be described simply as a computational graph with fully connected layers where a subset of the trainable weights are clamped together (ie. Is there something missing in this description? Lots of different deep learning papers go on to great lengths to describe some sort of new neural network architecture and at a first glance, the differences can seem really huge. Some of the architectures seem to be only applicable to some domains and inherently, different than others.

Researchers have overcome one of the major stumbling blocks in artificial intelligence with a program that can learn one task after another using skills it acquires on the way. Developed by Google's AI company, DeepMind, the program has taken on a range of different tasks and performed almost as well as a human. Crucially, and uniquely, the AI does not forget how it solved past problems, and uses the knowledge to tackle new ones. The AI is not capable of the general intelligence that humans draw on when they are faced with new challenges; its use of past lessons is more limited. But the work shows a way around a problem that had to be solved if researchers are ever to build so-called artificial general intelligence (AGI) machines that match human intelligence.

At this year's GPU Technology Conference, Nvidia's premier conference for technical computing with graphic processors, the company reserved the top keynote for its CEO Jensen Huang. Over the years, the GTC conference went from a segment in a larger, mostly gaming-oriented and somewhat scattershot conference called "nVision" to become one of the key conferences that mixes academic and commercial high-performance computing. Jensen's message was that GPU-accelerated machine learning is growing to touch every aspect of computing. While it's becoming easier to use neural nets, the technology still has a way to go to reach a broader audience. It's a hard problem, but Nvidia likes to tackle hard problems.

Microsoft is making some bold promises with Story Remix, its recently announced app for the Windows 10 Fall Creators update. Together with the company's deep learning technology, it can automatically craft your photos and videos into short films. Story Remix resembles Apple Clips and Google's Photo Assistant, but it goes a bit farther with the ability to analyze everything on a pixel level-basis to detect people, objects and the overall setting. If it works as advertised, it could be a transformational app for consumers fed up with their ever-growing libraries of digital media. It's the latest attempt by Microsoft to make your life easier by predicting what you want.