Lifeboat Foundation

A vegetable-picking robot that uses machine learning to identify and harvest a commonplace, but challenging, agricultural crop has been developed by engineers.

The ‘Vegebot’, developed by a team at the University of Cambridge, was initially trained to recognise and harvest iceberg lettuce in a lab setting. It has now been successfully tested in a variety of field conditions in cooperation with G’s Growers, a local fruit and vegetable co-operative.

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By Donna Lu

It’s the smartest piece of glass in the world. Zongfu Yu at the University of Wisconsin–Madison and his colleagues have created a glass artificial intelligence that uses light to recognise and distinguish between images. What’s more, the glass AI doesn’t need to be powered to operate.

The proof-of-principle glass AI that Yu’s team created can distinguish between handwritten single digit numbers. It can also recognise, in real time, when a number it is presented with changes – for instance, when a 3 is altered to an 8.

At Annual Meeting of the New Champions 2019 in Dalian, 1–3 July “Next 10 Years: What To Expect In China’s Artificial Intelligence Future” was discussed.

Analysis: humans make about 35,000 decisions every day so is it possible for AI to deal with a similar volume of high decision uncertainty?

Artificial intelligence (AI) that can think for itself may still seem like something from a science-fiction film. In the recent TV series Westworld, Robert Ford, played by Anthony Hopkins, gave a thought-provoking speech: “we can’t define consciousness because consciousness does not exist. Humans fancy that there’s something special about the way we perceive the world and yet, we live in loops as tight and as closed as the [robots] do, seldom questioning our choices – content, for the most part, to be told what to do next.”

Mimicking realistic human-like cognition in AI has recently become more plausible. This is especially the case in Computational Neuroscience, a rapidly expanding research area that involves the computational modelling of the brain to provide quantitative, computational theories.

First published in 2016, predictors of chronological and biological age developed using deep learning (DL) are rapidly gaining popularity in the aging research community.

These deep aging clocks can be used in a broad range of applications in the pharmaceutical industry, spanning target identification, drug discovery, data economics, and synthetic patient data generation. We provide here a brief overview of recent advances in this important subset, or perhaps superset, of aging clocks that have been developed using artificial intelligence (AI).

These robotic tiles can track your every step to create an immersive VR experience via 筑波大学｜University of Tsukuba.

Man’s best friend isn’t a dog—it’s a doglike robot, designed to perform tricks and tug at your heartstrings.

O.o.

How does the universe work? It appears that a new AI could help find the answer as it was able to work well beyond the parameters set by its developers.

DARPA’s Ground X-Vehicle Technologies (GXV-T) program aims to improve mobility, survivability, safety, and effectiveness of future combat vehicles without piling on armor. The demonstrations featured here show progress on technologies for traveling quickly over varied terrain and improving situational awareness and ease of operation.

These demonstrations feature technologies developed for DARPA by:

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