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Category: robotics/AI

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.

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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.

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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?

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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.

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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.

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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.

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The international authorities overseeing the creation of a unified standard for 5G mobile technologies are expected to release the initial specifications next year and the final phase in 2019, paving the way for the commercial deployment of 5G services by mobile network operators from 2020.

China is one step closer to achieving the reality of seamless, super high speed communications that will enable driverless cars, virtual reality education and nationwide health care services after a partnership comprising China Mobile, Qualcomm and ZTE accelerated efforts to finalise technical standards for the next generation of smartphones.

With the world’s largest population and most number of internet users, China is making a huge bet that the wide roll-out of 5G mobile infrastructure by the country’s three main telecommunications network operators would support the country’s rapid digital transformation – enabling a raft of advanced applications and services that were the stuff of science fiction just decades ago.

Shenzhen-based ZTE Corp and larger rival Huawei Technologies, two of the world’s biggest suppliers of telecommunications equipment used by mobile network operators, are currently leading the development and commercialisation of 5G-related technologies on the mainland.

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