Deep learning AI could one day work as an early warning system to allow earlier medical intervention to patients. This AI will tell people when they're likely to die -- and that's a good thing. That's because scientists from the University of Adelaide in Australia have used deep learning technology to analyze the computerized tomography (CT) scans of patient organs, in what could one day serve as an early warning system to catch heart disease, cancer, and other diseases early so that intervention can take place. Using a dataset of historical CT scans, and excluding other predictive factors like age, the system developed by the team was able to predict whether patients would die within five years around 70 percent of the time. The work was described in an article published in the journal Scientific Reports.

Artificial intelligence is not a contact sport. Currently, algorithms mostly just compete to win old Atari games, or accomplish historic board gaming feats like pwning five human Go champions at once. These are just practice rounds, though, for the way more complicated (and practical) goal of teaching robots how to navigate human environments. Vicarious, an AI company, has developed a new AI that is absolutely slammin' at Breakout, the paddle vs. brick arcade classic. Its AI, called Schema Networks, even succeeds at tweaked versions of the game--for instance, when the paddle is moved closer to the bricks.

Researchers from OpenAI and DeepMind are hoping to make artificial intelligence safer using a new algorithm that learns from human feedback. Both companies are experts in reinforcement learning – an area of machine learning that rewards agents if they take the right actions to complete a task under a given environment. The goal is specified through an algorithm, and the agent is programmed to chase the reward, like winning points in a game. Reinforcement learning has been successful in teaching machines how to play games like Doom or Pong or drive autonomous cars via simulation. It's a powerful method to explore an agent's behavior, but it can be dangerous if the hard-coded algorithm is wrong or produces undesirable effects.

Specifically, you can tell the robot what flavor you like, such as "chewy candy," "sweet chocolate" or "hard mint." The robot then processes your instructions via voice recognition and natural language processing, recommends a particular kind of candy and uses image recognition to recognize and select that recommendation. The entire demo is powered by deep-learning technology running on Cloud Machine Learning Engine (the fully-managed TensorFlow runtime from Google Cloud) and Cloud machine learning APIs. This demo is intended to serve as a microcosm of a real-world machine learning (ML) solution. For example, Kewpie, a major food manufacturer in Japan, used the same Google Cloud technology to build a successful Proof of Concept (PoC) for doing anomaly detection for diced potato in a factory.

The effectiveness of machine learning models may vary between the test phase and their use "in the wild" on actual consumer data. Many research papers claim high rates of malware detection and false positives with machine learning, and often deep learning, models. However, nearly all of these rates are within the context of a single source of data, which authors use to train and test their models. Machine learning has become more advanced but isn't used enough yet in security, says Hillary Sanders, data scientist for Sophos' data science research group. She anticipates usage will increase in coming years to address the rise of different forms of malware.

A new photonic technology has enabled a computer system to mimic the way human brains learn from accumulating experience. Researchers from the Massachusetts Institute of Technology has developed a new approach for the deep learning computation using light, instead of electricity. A deep learning computer is a way computer system accumulate experiences and data and recognized the pattern in the accumulative data. Unfortunately, even the most powerful computer is limited with its transistor capacity to perform such function. In order to improve the deep learning computer system, researchers from the Massachusetts Institute of Technology discovered that light is a much better answer to perform such function, instead of electricity.

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But the computations these systems must carry out are highly complex and demanding, even for the most powerful computers. Now, a team of researchers at MIT and elsewhere has developed a new approach to such computations, using light instead of electricity, which they say could vastly improve the speed and efficiency of certain deep learning computations. Their results appear today in the journal Nature Photonics in a paper by MIT postdoc Yichen Shen, graduate student Nicholas Harris, professors Marin Soljacic and Dirk Englund, and eight others. Soljacic says that many researchers over the years have made claims about optics-based computers, but that "people dramatically over-promised, and it backfired." While many proposed uses of such photonic computers turned out not to be practical, a light-based neural-network system developed by this team "may be applicable for deep-learning for some applications," he says.

Summary: Quantum computing is already being used in deep learning and promises dramatic reductions in processing time and resource utilization to train even the most complex models. Here are a few things you need to know. So far in this series of articles on Quantum computing we showed that Quantum is in fact commercially available today and being used operationally. We talked about what's available in the market now and whether it's a good idea to get started now or wait a year, but not too long because it's coming fast. We also talked about some of the pragmatic issues such as how do you actually program these devices and how faster they really are.

Everyday, we depend on many systems and machines. We use a car to travel, a lift go up and down, and a plane to fly. Electricity comes through turbines and in a hospital machine keeps us alive. Some failures are an just an inconvenience, while others could mean life or death. When stakes are high, we perform regular maintenance on our systems. For example, cars are serviced once every few months and aircrafts are serviced daily.