A modular multicellular system has been created by mixing and matching discrete engineered bacterial cells in an artificial neural network-type architecture. The system is capable of solving multiple computational decision problems like identifying a number as prime and a letter as a vowel.
Robotic exoskeletons are an increasingly popular method for assisting human labor in the workplace. Those that specifically support the back, however, can result in bad lifting form by the wearer. To combat this, researchers at the University of Michigan have built a pair of robot knee exoskeletons, using commercially available drone motors and knee braces.
“Rather than directly bracing the back and giving up on proper lifting form,” U-M professor Robert Gregg notes, “we strengthen the legs to maintain it.”
Test subjects were required to move a 30-pound kettlebell up and down a flight of stairs. Researchers note that the tech helped them maintain good lifting form, while lifting more quickly.
The study, published Monday in the Canadian Medical Association Journal, found a 26 per cent reduction in non-palliative deaths among patients in St. Michael’s Hospital’s general internal medicine unit when the AI tool was used.
“We’ve seen that there is a lot of hype and excitement around artificial intelligence in medicine. We’ve also seen not as much actual deployment of these tools in real clinical environments,” said lead author Dr. Amol Verma, a general internal medicine specialist and scientist at the hospital in Toronto.
First thoughts and preliminary insights into OpenAI’s GPT o1 Strawberry in the medical domain, with some expected and unexpected findings. We have a \.
Our final estimate of the achievable inter data center bandwidth by 2030 is 4 to 20 Pbps, which would allow for training runs of 3e29 to 2e31 FLOP. In light of this, bandwidth is unlikely to be a major constraint for a distributed training run compared to achieving the necessary power supply in the first place.
Expanding bandwidth capacity for distributed training networks presents a relatively straightforward engineering challenge, achievable through the deployment of additional fiber pairs between data centers. In the context of AI training runs potentially costing hundreds of billions of dollars, the financial investment required for such bandwidth expansion appears comparatively modest.44
We conclude that training runs in 2030 supported by a local power supply could likely involve 1 to 5 GW and reach 1e28 to 3e29 FLOP by 2030. Meanwhile, geographically distributed training runs could amass a supply of 2 to 45 GW and achieve 4 to 20 Pbps connections between data center pairs, allowing for training runs of 2e28 to 2e30 FLOP.45 All in all, it seems likely that training runs between 2e28 to 2e30 FLOP will be possible by 2030.46 The assumptions behind these estimates can be found in Figure 3 below.
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Beijing opted out of a pact to keep humans in control of decision-making on nuclear weapons, a move observers say underlines its dilemma.
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