Lifeboat Foundation

In May 2017, researchers at Google Brain announced the creation of AutoML, an artificial intelligence (AI) that’s capable of generating its own AIs.

More recently, they decided to present AutoML with its biggest challenge to date, and the AI that can build AI created a ‘child’ that outperformed all of its human-made counterparts.

The Google researchers automated the design of machine learning models using an approach called reinforcement learning. AutoML acts as a controller neural network that develops a child AI network for a specific task.

Continue reading “Google’s AI Built Its Own AI That Outperforms Any Made” »

Three new reports combine to suggest these answers: It can probably do less right now than you think. But it will eventually do more than you probably think, in more places than you probably think, and will probably evolve faster than powerful technologies have in the past.

Three new reports suggest that artificial intelligence can probably do less right now than you think. But by one estimation, up to a third of American workers will have to switch jobs by 2030 largely because of it.

Last week, Chancellor Philip Hammond announced in the Autumn Budget a £500m package of investment into tech initiatives, including the development of artificial intelligence.

Which must have had the Channel 4 executives ordering trebles all round, because with perfect timing they’ve designated this week the “Rise of the Robots season”, with a schedule that includes documentaries on the take-off of artificial intelligences (AIs) as consulting doctors, a David Tennant -narrated piece on the challenge of making robots as human as possible, and the one that’s had the tabloids hot under the collar, today’s The Sex Robots Are Coming – which needs little further explanation.

Doctor Who and the Invasion of the Sex-Bots aside, though, is it actually possible that the dream of science fiction writers going back a century or more is on the verge of reality? Are we really about to live in the long-promised future of robots and AIs?

Continue reading “The robots are coming – but will they really take all our jobs?” »

And it shreds.

https://youtube.com/watch?v=jNRJkNjSljA

Google’s AutoML project, designed to make AI build other AIs, has now developed a computer vision system that vastly outperforms state-of-the-art-models. The project could improve how autonomous vehicles and next-generation AI robots “see.”

In May 2017, researchers at Google Brain announced the creation of AutoML, an artificial intelligence (AI) that’s capable of generating its own AIs. More recently, they decided to present AutoML with its biggest challenge to date, and the AI that can build AI created a “child” that outperformed all of its human-made counterparts.

By Lindsay Brownell

(CAMBRIDGE, Mass.) — Soft robotics has made leaps and bounds over the last decade as researchers around the world have experimented with different materials and designs to allow once rigid, jerky machines to bend and flex in ways that mimic and can interact more naturally with living organisms. However, increased flexibility and dexterity has a trade-off of reduced strength, as softer materials are generally not as strong or resilient as inflexible ones, which limits their use.

In a Silicon Valley office park, a startup is developing a system that could automate greenhouse farming and help feed the world.

This robot dog has deep learning capabilities which allow it to recognize faces, understand commands, and each unit can even develop its own “personality.”

Artificial intelligence could be the powerful tool we need to solve some of the biggest problems facing our world, argues Raia Hadsell. In this talk, she offers an insight into how she and her colleagues are developing robots with the capacity to learn. Their superhuman ability to play video games is just the start.

Raia Hadsell is a research scientist on the Deep Learning team at DeepMind. She moved to London to join DeepMind in early 2014, feeling that her fundamental research interests in robotics, neural networks, and real world learning systems were well-aligned with the agenda of Demis, Shane, Koray, and other members of the original team. Raia’s research at DeepMind focuses on a number of fundamental challenges in AGI, including continual and transfer learning, deep reinforcement learning, and neural models of navigation. Raia came to AI research obliquely. After an undergraduate degree in religion and philosophy from Reed College, she veered off-course (on-course?) and became a computer scientist. Raia’s PhD with Yann LeCun, at NYU, focused on machine learning using Siamese neural nets (often called a ‘triplet loss’ today) and on deep learning for mobile robots in the wild. Her thesis, ‘Learning Long-range vision for offroad robots’, was awarded the Outstanding Dissertation award in 2009.

Continue reading “Artificial Intelligence, Video Games and The Mysteries of the Mind” »