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.

Tinder Social came about after a test in Australia that proved a glut of the app's users have been looking for a feature to aid in planning out their night, their hangouts, and ways to meet new people for concerts and other friendly excursions aside from romantic encounters. When using the feature, Tinder users can "unlock" Tinder Social to create a group of 1-3 friends with a sole focus, like attending a movie or hitting up a bar. The group access can be turned on or off at any time. When users swipe right on a group they like, the groups will combine and offer up a group chat for planning the event. The feature is available in the US, UK and select other areas around the world.

Artificial intelligence platform startup that seeks to automate the notification and messaging process to personalize consumer experience is seeking a Machine Learning Engineer. The Machine Learning Engineer will develop the recommendation models and systems behind this AI platform. Benefits include competitive compensation package, equity, bonus, full benefits, paid time-off, and a great team work environment with company outings and collaborative work culture. This startup has received close to 20M in funding, and this is a ground floor opportunity to apply industry data science experience and develop something from scratch. You will be able to see your impact on the core product!

Ever wanted a way to easily extract and analyze meaningful data from text? The new Cloud Natural Language API has a feature that lets you extract entities from text -- like people, places and events -- with a single API call. Let's take the following sentence from a recent news article: LONDON -- J. K. Rowling always said that the seventh Harry Potter book, "Harry Potter and the Deathly Hallows," would be the last in the series, and so far she has kept to her word. I could write my own algorithm to find the people and locations mentioned in this sentence, but that would be difficult. And it would be even more difficult if I wanted to gather more data on each of the mentioned entities, or analyze entities across thousands of sentences, while accounting for mentions of the same entity that are phrased differently (e.g., "Rowling" and "J.K. Rowling"). With Cloud Natural Language API, I can analyze the above sentence with the API's analyzeEntities method.

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.

While the idea of Tesla car-sharing seems new, it is not the first time the company's cars, coupled with autonomous technology, have come up in discussions about the sharing economy. Back in 2015, Steve Jurvetson, a partner at Uber investor DFJ, told an audience that Uber's Travis Kalanick had indicated he would buy every Tesla car produced if they could be made autonomous by 2020. These were the days when it appeared a Uber and Tesla partnership could be on the horizon. Musk himself paused long enough on an earnings call to raise suspicions when discussing the possibility of such a partnership. These days, it seems that Uber has gone its own way in developing an autonomous car solution.

Next month Las Vegas will host the Final Event of the DARPA Cyber grand Challenge as an all-computer cyber-defence Capture the Flag tournament. From an initial field of over 100 applicant seven teams will compete for the 3.5 million prize pool. As we reported at the time, DARPA announced this contest in October 2013. "to vastly improve the speed and effectiveness of IT security against escalating cyber threats." These AI systems would be designed to compete in CTF (Capture the Flag) contests, speed-driven bug hunting tournaments where experts reverse-engineer software, probe its weaknesses, search for deeply hidden flaws and create securely patched replacements.

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.