For the first time, a physical neural network has successfully been shown to learn and remember “on the fly,” in a way inspired by and similar to how the brain’s neurons work.
The result opens a pathway for developing efficient and low-energy machine intelligence for more complex, real-world learning and memory tasks.
Published today in Nature Communications, the research is a collaboration between scientists at the University of Sydney and University of California at Los Angeles.
Tesla is ramping up hiring for its humanoid robot program, Optimus, including some reinforcement learning engineers.
It was hard to take Tesla Bot seriously when Elon Musk announced it by having someone dressed as a robot dance on stage.
To this day, many people are taking the project seriously, but Tesla certainly is.
President Joe Biden’s executive order plans for standardized digital watermarking rules.
President Joe Biden’s executive order on artificial intelligence is a first-of-its-kind action from the government to tackle some of the technology’s greatest challenges — like how to identify if an image is real or fake.
Among a myriad of other demands, the order, signed Monday, calls for a new set of government-led standards on watermarking AI-generated content. Like watermarks on photographs or paper money, digital watermarks help users distinguish between a real object and a fake one and determine who owns it.
Digital watermarks are easily broken and abused.
Nearly five years ago, DeepMind, one of Google’s more prolific AI-centered research labs, debuted AlphaFold, an AI system that can accurately predict the structures of many proteins inside the human body. Since then, DeepMind has improved on the system, releasing an updated and more capable version of AlphaFold — AlphaFold 2 — in 2020.
And the lab’s work continues.
Today, DeepMind revealed that the newest release of AlphaFold, the successor to AlphaFold 2, can generate predictions for nearly all molecules in the Protein Data Bank, the world’s largest open access database of biological molecules.
It’s strange times we’re living in when a blockchain billionaire, not the usual Big Tech suspects, is the one supplying the compute capacity needed to develop generative AI.
It was revealed yesterday that Jed McCaleb, the co-founder of blockchain startups Stellar, Ripple and Mt Gox and aerospace company Vast, launched a 501©(3) nonprofit that purchased 24,000 Nvidia H100 GPUs to build data centers that’ll lease capacity to AI projects.
Already, the cluster of GPUs — worth an estimated half a billion dollars and among the largest in the world — is being used by startups Imbue and Character.ai for AI model experimentation, claims Eric Park, the CEO of a newly formed organization, Voltage Park, that’s charged with running and managing the data centers.
Siemens and Microsoft team up to introduce the Siemens Industrial Copilot and enhance human-machine collaboration.
Microsoft and Siemens have unveiled a groundbreaking partnership aimed at leveraging generative AI to revolutionize industries on a global scale, Microsoft announced.
This collaboration introduces the Siemens Industrial Copilot, an AI-powered assistant designed to enhance human-machine cooperation, particularly in the manufacturing sector significantly. The joint initiative represents a substantial leap forward in AI technology, intending to accelerate innovation and streamline operations across diverse industries.
NVIDIA looking to ramp up chip production in the face of supply shortage.
NVIDIA, the most profitable chip-making company in the world, has unveiled a custom large language model, the technology on which artificial intelligence tools like ChatGPT are based, which the company has developed for their internal use.
Trained on NVIDIA’s proprietary data, “ChipNeMo” will generate and optimize software and provide assistance to human designers in building semiconductors. Developed by NVIDIA researchers, ChipNeMo would be highly beneficial in the context of the company’s work in graphics processing, artificial intelligence, and other technologies.
The robot guide dog possesses the ability to respond to tugs on a leash.
Researchers have created a robot guide dog to make life easier for the visually impaired with its ability to respond to tugs on a leash. The team of engineers at Binghamton University’s Computer Science Department in New York State has been developing a robotic seeing-eye dog to improve accessibility for those who are visually impaired. Last year, they performed a trick-or-treating exercise with its quadruped robotic dog.
Now, they have demonstrated a robot dog leading a person down a lab hallway, confidently and carefully reacting to directive instructions. Engineers were surprised that throughout the visually impaired… More.
Stephen Folkerts ‘24.
The team of engineers at Binghamton University’s Computer Science Department in New York State has been developing a robotic seeing-eye dog to improve accessibility for those who are visually impaired. Last year, they performed a trick-or-treating exercise with its quadruped robotic dog.
Human decision-making has been the focus of countless neuroscience studies, which try to identify the neural circuits and brain regions that support different types of decisions. Some of these research efforts focus on the choices humans make while gambling and taking risks, yet the neural underpinnings of these choices have not yet been fully elucidated.
Researchers at University of Louisville carried out a study aimed at better understanding the patterns in neural network communication associated with ‘bad’ decisions made while gambling. Their paper, published in Frontiers in Neuroscience, shows that different types of ‘bad’ decisions made while gambling, namely avoidant and approach decisions, are associated with distinct neural communication patterns.
“Our recent work follows a line of research that examines how humans approach rewarding and punishing situations in the environment,” Brendan Depue and Siraj Lyons, the researchers who carried out the study, told Medical Xpress.
