Some scientists devote themselves to curing diseases. Others are researching an end to famine or global climate change. And some spend their time making nightmare machines, deep learning algorithms that utilize Artificial Intelligence to tap into humans' deepest and darkest fears. Like Google's Deep Dream, only with way more dangling, bloodied flesh. MIT teamed up with Australia's Commonwealth Scientific and Industrial Research Organisation (CSIRO) to create the "nightmare machine" in an attempt to study what terrifies us as a species, utilizing a pair of deep learning algorithms for maximum terrorizing impact and applying them to otherwise benign images like the Taj Mahal, an Ikea catalog and, naturally, Kermit the Frog.

Google's DeepMind has been making use of Blizzard's StarCraft II Game as a testing platform for AI as well as important machine learning research. It's not just Google that can benefit from this platform either; it's open to anyone who wants to make use of it and is available worldwide. The company commented, "We've worked closely with the StarCraft II team to develop an API that supports something similar to previous bots written with a'scripted' interface, allowing programmatic control of individual units and access to the full game state (with some new options as well)." The two companies are still working together now, this time creating "curriculum scenarios." These are tasks that get more difficult as they are completed and are designed to allow AI researchers to get the system up and running, benchmark algorithms, and advance.

Reusability: One of the reasons why AI progress is accelerating is the community is creating more and more reusable components that can be plugged into different domains, frequently attaining performance equivalent to or better than hand-designed algorithms. The 2015 ImageNet challenge was won by a Microsoft system built out of Residual Networks, then in 2016 Microsoft made a speech processing breakthrough via a system that also relied on Residual Networks. Similarly, DeepMind's WaveNet system has been slightly tweaked and re-applied to the domain of neural machine translation (PDF). This kind of re-use is a good thing as it suggests we are beginning to create the right sorts of low-level primitives that general intelligences can be built out of.

Initiatives such as language translation and image, facial, activity and emotion recognition - are based on predictive analytics that get more accurate as the data behind them gets richer. In particular, the emergence of GPU-based computing can greatly accelerate neural network processing capabilities - and if more processing power is needed there are the vast cloud computing resources of Amazon, Microsoft, Google. "Taken together, deep learning software and parallel processing hardware now provide a powerful [machine intelligence] platform," the report said. Cloud business models: The emergence of machine learning business models based on the use of the cloud is the single biggest reason that the field is so energized today, the report said: "We are essentially seeing the merger of machine intelligence with cloud economics."

Thanks to artificial intelligence, we have autonomous cars, chat bots, and speech recognition. Microsoft's CNTK (Cognitive Toolkit) is one among many platforms that trains computers to learn, and it's getting an upgrade. CNTK drives the Microsoft services Cortana and Skype language translation, and it boasts more than 90 percent accuracy in speech recognition tasks. Microsoft will soon release an upgraded CNTK toolkit, and one hardware maker wants to ensure the toolkit works best on its hardware. Nvidia is partnering with Microsoft to optimize its GPU development tools for CNTK.

Advances in machine learning are making security systems easier to train and more flexible in dealing with changing conditions, but not all use cases are benefitting at the same rate. Machine learning, and artificial intelligence, has been getting a lot of attention lately and there's a lot of justified excitement about the technology. One of the side effects is that pretty much everything is now being relabeled as "machine learning," making the term extremely difficult to pin down. Just as the word "cloud" has come to mean pretty much anything that happens online, so "artificial intelligence" is rapidly moving to the point where almost anything involving a computer is getting that label slapped on it. "There is also a lot of hype," said Anand Rao, innovation lead for US analytics at PricewaterhouseCoopers LLC.

On Friday during the BlizzCon 2016 opening keynote, Blizzard revealed that it teamed up with Google to provide an application programming interface (API) for DeepMind to be used in StarCraft II. This will enable artificial intelligence (AI) and Machine Learning researchers from around the world to create intelligent "bots" to play the game. In return, the knowledge gained while playing will be used in real-world applications. "An agent that can play StarCraft will need to demonstrate effective use of memory, an ability to plan over a long time, and the capacity to adapt plans based on new information," said research scientist Oriol Vinyals of the DeepMind team. "Computers are capable of extremely fast control, but that doesn't necessarily demonstrate intelligence, so agents must interact with the game within limits of human dexterity in terms of'Actions Per Minute.'"

The next frontier for artificial intelligence may involve teaching computers to distinguish albacore tuna from its yellowfin cousin. The Nature Conservancy, an environmental non-profit, is working with several Pacific Island nations and a big tuna fishing company to more easily count and identify fish caught at sea using cutting edge technology. The goal is to use trendy artificial intelligence techniques like deep learning to help fishermen reduce the number of protected animals like sharks and turtles that are accidentally caught along with the tuna. The Nature Conservancy hopes that the program could prevent overfishing and help threatened and endangered sea life recover without putting fishermen out of work. "We have real optimism that data science community can help us differentiate a turtle from a tuna, and flag when a shark comes on board," said Mark Zimring, a project director for The Nature Conservancy.

Combining deep neural networks with structured logic rules is desirable to harness flexibility and reduce uninterpretability of the neural models. We propose a general framework capable of enhancing various types of neural networks (e.g., CNNs and RNNs) with declarative first-order logic rules. Specifically, we develop an iterative distillation method that transfers the structured information of logic rules into the weights of neural networks. We deploy the framework on a CNN for sentiment analysis, and an RNN for named entity recognition. With a few highly intuitive rules, we obtain substantial improvements and achieve state-of-the-art or comparable results to previous best-performing systems.

We now experience life through an algorithmic lens. Whether we realize it or not, machine learning algorithms shape how we behave, engage, interact, and transact with each other and with the world around us. Deep learning is the next advance in machine learning. While machine learning has traditionally been applied to textual data, deep learning goes beyond that to find meaningful patterns within streaming media and other complex content types, including video, voice, music, images, and sensor data. Deep learning enables your smartphone's voice-activated virtual assistant to understand spoken intentions.