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The latest AI News. Learn about LLMs, Gen AI and get ready for the rollout of AGI. Wes Roth covers the latest happenings in the world of OpenAI, Google, Anthropic, NVIDIA and Open Source AI.
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Two new reports confirm what we all know deep down: Lots of people are using Sam Altmanâs ChatGPT to âhelpâ with their homework.
Sonovaâs new hearing aids use AI to enhance speech in noisy environments.
The hearing aid features the DEEPSONIC chip, offering 53 times more processing power for superior speech clarity amidst background noise.
Weâre living in an aging society with cognitive loss placing stress on caregivers to monitor older adults struggling with memory decline.
MemPal is a wearable voice-based memory assistant that helps older adults live more independently and safely at home while also reducing caregiver burden. MemPal uses AI to automatically log the userâs actions in real-time based on visual context from a wearable camera without storing any image data, thereby preserving user privacy. With this activity log, MemPal helps older adults recall locations of misplaced objects and completion of past actions using simple voice-based queries such as âHey Pal, where is my phone?â Additionally, MemPal provides context-based proactive safety reminders (e.g., âyou may have forgotten to turn off the stoveâ orâ you already took your medicine an hour agoâ) and automatically tracks the completion on the MemPal app, allowing for remote monitoring by caregivers. Lastly MemPal can generate an automatic, summarized diary of activities for caregivers that may also prove useful for physicians to better understand patient behavior within their home.
MemPal was tested within the homes of 15 older adults (ages 65+). Our study demonstrated improved performance of object finding with audio-based assistance compared to no aid and positive overall user perceptions on the designed system. We discuss future design guidelines to adapt these types of wearable systems to various older adultsâ needs.
Researchers develop energy-efficient supercomputing with neural networks and charge density waves.
Researchers are creating efficient systems using neural networks and charge density waves to reduce supercomputingâs massive energy use.
Artificial neural networksâalgorithms inspired by biological brainsâare at the center of modern artificial intelligence, behind both chatbots and image generators. But with their many neurons, they can be black boxes, their inner workings uninterpretable to users.
Researchers have now created a fundamentally new way to make neural networks that in some ways surpasses traditional systems. These new networks are more interpretable and also more accurate, proponents say, even when theyâre smaller. Their developers say the way they learn to represent physics data concisely could help scientists uncover new laws of nature.
Now, scientists in China have developed robots that give human-like realistic expressions.
The humanoid robot with highly expressive facial features is developed by Liu Xiaofeng, a professor at Hohai University in east Chinaâs Jiangsu Province, and his research team.
For the development of this robot, the research team developed a new algorithm for generating facial expressions on humanoid robots.
He writes that AI is now exceeding the human brain at several cognitive tasks and that it will eventually do all things far better than even the most expert humans.
These new machines can learn, reason, plan and act with intention, and they are becoming far smarter far faster than most people, save Kurzweil, could have predicted.
Soon, he forecasts, they will be indistinguishable from human brains, before accelerating past them in nearly every way.
Electronic waste, or e-waste, is a rapidly growing global problem, and itâs expected to worsen with the production of new kinds of flexible electronics for robotics, wearable devices, health monitors, and other new applications, including single-use devices.
A new kind of flexible substrate material developed at MIT, the University of Utah, and Meta has the potential to enable not only the recycling of materials and components at the end of a deviceâs useful life, but also the scalable manufacture of more complex multilayered circuits than existing substrates provide.
The development of this new material is described in the journal RSC Applied Polymers (âPhotopatternable, Degradable, and Performant Polyimide Network Substrates for E-Waste Mitigationâ), in a paper by MIT Professor Thomas J. Wallin, University of Utah Professor Chen Wang, and seven others.
Figure 2 can pick up objects as heavy as 55 pounds and put parts in place accurately, down to the millimeter. It was created by Figure AI, a startup backed by OpenAI, Jeff Bezos, Nvidia, and Microsoft.
