That's a 3D printed shell around a sea-slug mouth muscle. The cyborg's leg is a sea slug's mouth. The robot, built from printed parts and organic material, moves on land like a sea turtle. There is no feeling to the partially organic machine. An external electrical field provides stimulation, and the robot moves.

Some of the most enthusiastic proponents of autonomous vehicles and features are worried that autopilot -- not the technology itself, but the very name, which some find misleading -- might slow down the evolution of the driverless car. They say that would be a shame, as autonomous technologies are designed to make driving safer by preventing minor fender-benders as well as reducing the number of traffic fatalities. A May 7 crash involving a Model S being driven in autopilot mode is all the buzz at the Automated Vehicle Symposium here this week, a major annual event for engineers, auto executives, software developers, policymakers and others involved in the transition of automobiles into robots. Most years, technology dominates the conference. This year, it's safety and consumer readiness for robots that take over the driving.

It's fair to say that Google-owned AI company DeepMind's big push into the health space via data-access collaborations with the UK's National Health Service -- announced with much fanfare in February this year -- has not been running entirely smoothly so far. But there are more regulatory bumps in the road ahead for DeepMind Health. TechCrunch has learned the company won't continue using one of the apps it co-designed with the NHS until the software has been registered as a medical device with the relevant regulatory body, the MHRA. That's especially interesting given that this app, called Streams, has already been used for patient care in multiple NHS hospitals. The Royal Free NHS Trust previously told TechCrunch the app had been used by up to six of its clinicians in three "user tests" in its London hospitals. Which, put another way, means a profit-driven commercial entity has been involved in a real-world test of an unregistered medical device on actual hospital patients.

Google is putting DeepMind's machine learning to work on managing their sprawling data centers' energy usage, and it's is performing like a boss -- the company reports a 15% drop in consumption since the AI took over. Google is undeniably a huge part of western civilization. The company's data servers pretty much handle all of my mail at this point, along with YouTube, social media platforms and much more. But even so, it's easy to forget that the Google we know and interact with every day is just the tip of the iceberg; it relies on huge data servers to process, transfer and store information -- and all this hardware needs a lot of power. So much power, in fact, that the company decided to do something about it.

Google is using a powerful new machine-learning approach to save huge amounts of energy (and hundreds of millions of dollars) each year at its vast data centers. It might not be long before the technique, which involves a machine-learning algorithm gradually learning to perfect a task with positive reinforcement, catches on in a range of other areas. Demis Hassabis, the CEO of Google DeepMind, a subsidiary based in the U.K. that is focused on artificial intelligence, said at a conference recently that Google was using techniques developed by his company to improve the energy efficiency of its data centers. Because Google spends so much on electricity for the buildings that house its massive server farms, a savings of just a few percent equals hundreds of millions of dollars per year. DeepMind, which Google acquired in 2014 for around 600 million, has shown how large artificial neural networks combined with reinforcement learning can train computers to perform complex tasks incredibly well.

Researchers used a combination of three imaging techniques and a machine learning system to create this new map of the brain, which includes 180 distinct regions in each hemisphere of the cerebral cortex (the brain's outermost region). The different colors relate to how connected an area is to a specific sensory input: Red is hearing, green is touch, and blue is vision. Mixed colors represent areas where two senses overlap. A group of researchers have developed a new map of the cerebral cortex of the human brain, revealing 100 new distinct regions in each hemisphere. Representing the most detailed map of the brain yet, it's an achievement of a longstanding goal, and researchers say it will provide a crucial tool to understand how differences in even extremely small brain regions relate to behavior and disease.

The blockish and slightly dorky computer game Minecraft may turn out to be a great place for humans and AI to learn how to work together. An experimental new version of the game, released by Microsoft researchers this month, can be used to train an AI to perform all sorts of tasks, from crossing bridges to building complex objects. The new platform, called Project Malmo, makes it possible for a learning algorithm to control a Minecraft character that's normally operated by a human player. But it also provides ways for human players and AI agents to work together, and a chat window through which a person can talk with a nascent AI. "In the long run I want to work toward AI that can be taught by any user to help them achieve their goals," says Katja Hoffman, a researcher at Microsoft Cambridge in the U.K. who leads the project. Hoffman, who gave a demo of the software to AI researchers at an academic conference in New York last week, says that human-AI collaboration is a key goal for the project: "We've built in all the capabilities that a researcher would need in order to work toward collaborative AI." Malmo is geared toward testing reinforcement-learning algorithms, a way of training a computer to perform a task by providing simulated rewards.

Tesla CEO Elon Musk speaks at the Model X launch event in Fremont, Calif. SAN FRANCISCO – Watch out, fellow automakers: Elon Musk announced Wednesday evening that Tesla Motors, known for pricey electric sedans, hopes to expand into small SUVs, a pickup and even heavy-duty semis and buses in a bid to revolutionize transportation as we know it. That bombshell was just part of Musk's long-touted Tesla master plan, a follow-up to one issued when the company was just starting out, outlining a strategy that pivots the company from a builder of niche automobiles to a producer of a broad range of passenger and commercial vehicles that eschew fossil fuel. Also noted in the plan, which Musk published on the company's website, is the company's more well-known pursuit of the consumer solar business through its planned acquisition of SolarCity. Tesla will "create stunning solar roofs with seamlessly integrated battery storage, expand the electric vehicle product line to address all major segments, develop a self-driving capability that is 10 (times) safer than manual via massive fleet learning, (and) enable your car to make money for you when you aren't using it," reads Musk's "Master Plan, Part Deux."

Machine learning is quickly becoming an indispensable tool for many large companies. Everyone has, for sure, heard about Google's AI algorithm beating the World Champion in Go, as well as technologies like RankBrain, but machine learning does not have to be a mystical subject relegated to the domain of math researchers. There are many approachable libraries and technologies that show promise of being very useful to any industry that has data to play with. Machine learning also has the ability to turn traditional website marketing and SEO on its head. Late last year, my colleagues and I (rather naively) began an experiment in which we threw several popular machine learning algorithms at the task of predicting ranking in Google. We ended up with an assembly that achieved 41 percent true positive and 41 percent true negative on our data set. In the following paragraphs, I will take you through our experiment, and I will also discuss a few important libraries and technologies that are important for SEOs to begin understanding.

Google has finally revealed a commercial use for DeepMind -- a British artificial intelligence company it acquired for over 600 million in 2014. DeepMind made headlines for beating the best human in the world at the notoriously complex board game Go and it's recently started working with hospitals in the UK on a number of healthcare projects but the startup is yet to make any money for Google, until now. Google announced on Wednesday that it has been using a DeepMind-built AI system to control certain parts of its power-hungry data centers over the last few months as it looks to make its vast server farms more environmentally friendly. Last year, a Greenpeace report predicted that the electricity consumption of data centers is set to account for 12% of global electricity consumption by 2017 and companies like Google, Amazon, Facebook and Apple have some of the biggest data centers in the world. Google said it has been able to reduce the energy consumption of its data center cooling units -- used to stop Google's self-built servers from overheating -- by as much as 40% with the help of a DeepMind AI system.