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The first playgrounds were for adults, not kids

Popular Science

Early playgrounds were more about fitness than fun--and children didn't enter the equation for decades. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. Playgrounds have never been just fun and games. Breakthroughs, discoveries, and DIY tips sent six days a week. You can learn a lot about a society from the way they raise children.


'Your craft is obsolete': WiseTech staff in limbo as AI touted as better than humans

The Guardian

WiseTech's headquarters in Sydney, where staff fear many jobs will be lost to AI. WiseTech's headquarters in Sydney, where staff fear many jobs will be lost to AI. 'Your craft is obsolete': WiseTech staff in limbo as AI touted as better than humans Staff at WiseTech have been waiting almost three months to be told if they are among the 2,000 people the logistics software company is to cut due to advances in AI, with workers criticising the wait as stressful and "ridiculous". The comments come as its founder on Tuesday told investors an AI agent could learn a human's job in just 15 minutes, according to the Australian Financial Review. The Australian Stock Exchange-listed company announced in late February that it would lay off almost 30% of its workforce across 40 countries, with 2,000 of the 7,000 jobs set to go over the next 18 months. Sign up for the Breaking News Australia email Some areas would be hit harder than others, with product and development and customer service teams expected to be reduced by up to 50%, the chief executive, Zubin Appoo, told an investor briefing in February. "The era of manually writing code as the core act of engineering is over," Appoo said.


This startup's new mechanistic interpretability tool lets you debug LLMs

MIT Technology Review

This startup's new mechanistic interpretability tool lets you debug LLMs Goodfire wants to make training AI models more like good old-fashioned software engineering. The San Francisco-based startup Goodfire just released a new tool, called Silico, that lets researchers and engineers peer inside an AI model and adjust its parameters--the settings that determine a model's behavior --during training. This could give model makers more fine-grained control over how this technology is built than was once thought possible. Goodfire claims Silico is the first off-the-shelf tool of its kind that can help developers debug all stages of the development process, from building a data set to training a model. LLMs contain a LOT of parameters. The company says its mission is to make building AI models less like alchemy and more like a science.


Tim Cook's Legacy Is Turning Apple Into a Subscription

WIRED

Tim Cook's Legacy Is Turning Apple Into a Subscription The soon-to-exit Apple CEO went all in on services. Now, the incoming CEO, John Ternus, will need to embrace the AI era. Tim Cook's tenure as CEO at Apple, which is coming to a close September 1, will likely be defined by operational efficiency and financial growth, ushering Apple into its trillion-dollar era. But his most significant achievement might be in doubling down on Apple's services business, which includes iCloud, the App Store, Apple Music, Apple TV+, News+, and more. It's the subscription layer on top of iOS, and almost all of the service apps are tightly integrated with Messages, the glue that keeps people stuck to their iPhones .


One-Shot Imitation Learning

Neural Information Processing Systems

Imitation learning has been commonly applied to solve different tasks in isolation. This usually requires either careful feature engineering, or a significant number of samples. This is far from what we desire: ideally, robots should be able to learn from very few demonstrations of any given task, and instantly generalize to new situations of the same task, without requiring task-specific engineering. In this paper, we propose a meta-learning framework for achieving such capability, which we call one-shot imitation learning. Specifically, we consider the setting where there is a very large (maybe infinite) set of tasks, and each task has many instantiations.