Large language models combined with confidence scores help them recognize uncertainty. That could be key to making robots safe and trustworthy.
Category: robotics/AI – Page 578
Recent advances allow imaging of neurons inside freely moving animals. However, to decode circuit activity, these imaged neurons must be computationally identified and tracked. This becomes particularly challenging when the brain itself moves and deforms inside an organism’s flexible body, e.g. in a worm. Until now, the scientific community has lacked the tools to address the problem.
Now, a team of scientists from EPFL and Harvard have developed a pioneering AI method to track neurons inside moving and deforming animals. The study, now published in Nature Methods, was led by Sahand Jamal Rahi at EPFL’s School of Basic Sciences.
The new method is based on a convolutional neural network (CNN), which is a type of AI that has been trained to recognize and understand patterns in images. This involves a process called “convolution”, which looks at small parts of the picture – like edges, colors, or shapes – at a time and then combines all that information together to make sense of it and to identify objects or patterns.
South Korean conglomerate LG’s AI research division launched an AI-run ETF with Qraft Technologies in November.
In just one year, artificial intelligence has gone from being the stuff of science fiction movies to being used as a tool to help us polish our resumes and plan European getaways.
Although generative AI models may be capable of writing emails and reviewing code, these tech experts don’t see them replacing humans any time soon. Here’s why.
The chatbot’s summarization feature relies on preprocessed video data or existing subtitles and transcripts.
One feature added to Microsoft’s AI Copilot in the Edge browser this week is the ability to generate text summaries of videos.
It leans on text from subtitles and transcriptions.
NotebookLM is also getting a feature that transforms your notes into another type of document.
NotebookLM got an upgrade.
Most space heaters run electricity through high resistance wires to toast your toes. The Heatbit mini, however, trains large language models, builds AI for large corporations, or mines crypto while it warms up your home. In so doing, it pays its owners up to $28/month while in use.
“What we really do is zero-energy computing,” says CEO Alex Busarov, who I met at Web Summit in Lisbon recently.
Bitcoin mining and AI training—which has increased one million times over the last seven years—together consume more energy than the entire United Kingdom, Busarov says. All of that goes to waste as heat, which then itself often needs to be cooled in a data or compute center, costing yet more energy.
The recent drama at OpenAI, where CEO Sam Altman was briefly dismissed by the board of directors only to be rapidly reinstated, has sparked discussion about the company prioritizing profits over its original nonprofit mission.
In fact, no company is purely profit-driven, nor should they be. While profits are paramount to survival in the competitive marketplace, they should always be pursued with a degree of social responsibility. What that means will of course vary from person to person and context to context. Still, profits act like a guardrail that prevents managers of organizations from over-indulging their passions. OpenAI’s strict reversal in recent weeks is a clear-cut example of this.
The embrace of capitalism and the pursuit of profits is not just a pragmatic choice to satisfy investors. It will make OpenAI better at achieving its mission of safe and responsible AI, by curtailing the worst excesses of those with power within the company. In a world where artificial intelligence may hold the key to our future, profit isn’t just good, it’s essential for ensuring progress stays on track and remains ethical.