Goto

Collaborating Authors

 Energy


Thomas Edison's failed rechargeable battery may get a second life

Popular Science

Technology Engineering Thomas Edison's failed rechargeable battery may get a second life The famed inventor's nickel-iron idea isn't suited for EVs, but it could help solar farms and data centers. Breakthroughs, discoveries, and DIY tips sent six days a week. A rechargeable battery based on technology pioneered by Thomas Edison may finally get its due. But while the famous inventor envisioned nickel-iron batteries powering the automobile industry over a century ago, researchers now believe the underlying concepts are more suited for renewable energy centers. According to a study published in the journal, a team including engineers from the University of California, Los Angeles have developed a prototype battery that recharges in seconds and withstands over 12,000 cycles of use--an equivalent to over 30 years of daily activity.







The Download: inside the QuitGPT movement, and EVs in Africa

MIT Technology Review

Plus: social media firms have agreed to be assessed on how effectively they protect teens' mental health A "QuitGPT" campaign is urging people to cancel their ChatGPT subscriptions In September, Alfred Stephen, a freelance software developer in Singapore, purchased a ChatGPT Plus subscription, which costs $20 a month and offers more access to advanced models, to speed up his work. But he grew frustrated with the chatbot's coding abilities and its gushing, meandering replies. Then he came across a post on Reddit about a campaign called QuitGPT. QuitGPT is one of the latest salvos in a growing movement by activists and disaffected users to cancel their subscriptions. In just the past few weeks, users have flooded Reddit with stories about quitting the chatbot. And while it's unclear how many users have joined the boycott, there's no denying QuitGPT is getting attention.



LIPS-Learning IndustrialPhysicalSimulation benchmarksuite-Appendix

Neural Information Processing Systems

For each benchmark, we generate three different training datasets. If the dataset is a sample, then what is the larger set? Is the samplerepresentativeofthe larger set(e.g., geographic coverage)? The provided datasets are self-contained and will remain constant. However, more datasets could be generated using the proposed benchmarking platform.