Lifeboat Foundation

Four MIT graduates have just opened a restaurant where a robotic kitchen prepares the meals.

When competing, it makes sense to compete on/with that worth competing (for). AI or not, it doesn’t matter.

The South Korean Go champion Lee Se-dol has retired from professional play, telling Yonhap news agency that his decision was motivated by the ascendancy of AI.

“With the debut of AI in Go games, I’ve realized that I’m not at the top even if I become the number one through frantic efforts,” Lee told Yonhap. “Even if I become the number one, there is an entity that cannot be defeated.”

Continue reading “Former Go champion beaten by DeepMind retires after declaring AI invincible” »

By analysing people’s brainwaves, artificial intelligence could work out what videos people were watching and roughly recreate them on a screen.

The US Navy has awarded Boeing contracts worth a total of $274.4m to produce five Orca Extra Large Unmanned Undersea Vehicles (XLUUVs). Based on Boeing’s Echo Voyager prototype UUV, the 15.5m-long submersible could be used for mine countermeasures, anti-submarine warfare, anti-surface warfare, electronic warfare and strike missions. Berenice Baker finds out more.

“We want a new space race—space races are exciting,” declared SpaceX founder Elon Musk after the successful inaugural flight last year of the Falcon Heavy, the most powerful rocket since the Space Shuttle.

Hawks and headline writers think space races are exciting too, especially the “new space race” between China and the United States. That’s why they keep referring to it—even though it doesn’t exist.

Historic changes are indeed afoot in the space sector. Private crewed spaceflight is about to come of age. Mobile robotic spacecraft are being built to rendezvous with satellites to service them. Vast swarms of broadband satellites are set to make the Internet truly global for the first time, and increase the number of spacecraft in orbit tenfold. Back on Earth, satellite imagery fed through artificial intelligence algorithms promises powerful insights into all manner of human activity. Dozens of countries are active in space and the number is growing all the time. The tired trope of the superpower space race does little to make sense of all this.

The next time you get sick, your care may involve a form of the technology people use to navigate road trips or pick the right vacuum cleaner online.

Artificial intelligence is spreading into health care, often as software or a computer program capable of learning from large amounts of data and making predictions to guide care or help patients.

It already detects an eye disease tied to diabetes and does other behind-the-scenes work like helping doctors interpret MRI scans and other imaging tests for some forms of cancer.

AI-powered wearable tech can help make swimming more interesting while tracking your laps for you.

Uncovering trolls and malicious or spammy accounts on social media is increasingly difficult as the miscreants find more and more ways to camouflage themselves as seemingly legitimate. Writing in the International Journal of Intelligent Engineering Informatics, researchers in India have developed an algorithm based on ant-colony optimization that can effectively detect accounts that represent a threat to normal users.

Asha Kumari and Balkishan Department of Computer Science and Applications at Maharshi Dayanand University, in Rohtak, India, explain that the connections between twitter users are analogous to the pheromone chemical communication between ants and this can be modeled in an algorithm based on how ant colonies behave to reveal the strongest connections in the twitter network and so uncover the accounts that one might deem as threatening to legitimate users.

The team’s tests on their system were successful in terms of precision, recall, f-measure, true-positive rate, and false-positive rate based on 26 features examined by the system played against almost 41,500 user accounts attracted to honeypots. Moreover, they report that the approach is superior to existing techniques. The team adds that they hope to be able to improve the system still further by adding so-called machine learning into the algorithm so that it can be trained to better identify threatening accounts based on data from known threats and legitimate accounts.

Over the last few years, rapid progress in AI has enabled our smartphones, social networks, and search engines to understand our voice, recognize our faces, and identify objects in our photos with very good accuracy. These dramatic improvements are due in large part to the emergence of a new class of machine learning methods known as Deep Learning.

Animals and humans can learn to see, perceive, act, and communicate with an efficiency that no Machine Learning method can approach. The brains of humans and animals are “deep”, in the sense that each action is the result of a long chain of synaptic communications (many layers of processing). We are currently researching efficient learning algorithms for such “deep architectures”. We are currently concentrating on unsupervised learning algorithms that can be used to produce deep hierarchies of features for visual recognition. We surmise that understanding deep learning will not only enable us to build more intelligent machines but will also help us understand human intelligence and the mechanisms of human learning. http://www.cs.nyu.edu/~yann/research/deep/