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Category: robotics/AI – Page 529

Brain aging research: A need for balance

“Testing people early, so early interventions are possible, is key to longevity,” she says. “Technology, data and AI, and what we’re starting to be able to do with it, are propelling us to a time of greater understanding which will foster earlier, more effective treatment. Or perhaps even therapies that can delay neurodegenerative diseases, pushing back dementia to beyond our lifespan.”

Mind Over Matter goes out on 29th and 30th June on BBC News and 30th June on BBC One.

Lara Lewington will be hosting a Fireside Chat on AI drug discovery with Alex Zhavoronkov at next week’s Founders Longevity Forum. Register your interest to discover how AI is accelerating drug discovery, commercialization and licensing models HERE.

Introducing Generative Physical AI

@NVIDIAOmniverse is a development platform for virtual world simulation, combining real-time physically based rendering, physics simulation, and generative AI technologies.

In Omniverse, robots can learn to be robots – minimizing the sim-to-real gap, and maximizing the transfer of learned behavior.

Building robots with generative physical AI requires three computers:

- NVIDIA AI supercomputers to train the models.
- NVIDIA Jetson Orin, and next generation Jetson Thor robotics supercomputer, to run the models.
- And NVIDIA Omniverse, where robots can learn and refine their skills in simulated worlds.

Read the press release.

#OpenUSD #Robotics #COMPUTEX2024 #IsaacSim

Continue reading “Introducing Generative Physical AI” | >

Transforming AI | NVIDIA GTC 2024 Panel Hosted by Jensen Huang

The Transforming AI panel from GTC 2024 features the authors of “Attention Is All You Need,” the groundbreaking paper that introduced the transformer neural network architecture. Transformers have since dominated all areas of AI, revolutionizing the industry. The discussion covers the following topics: ▫️ The development and impact of the Transformer model. ▫️ The evolution of computing and its democratization. ▫️ The future of AI technology and its potential applications. ▫️ The role of accelerated computing. ▫️ The potential for AI to revolutionize various industries. ▫️ The need for new data and learning techniques in AI development. Feature panel host: ▫️ Jensen Huang, Founder and Chief Executive Officer, NVIDIA Panelists: ▫️ Ashish Vaswani, Co-Founder and CEO, Essential AI ▫️ Noam Shazeer, Chief Executive Officer and Co-Founder, Character. AI ▫️ Jakob Uszkoreit, Co-Founder and Chief Executive Officer, Inceptive ▫️ Llion Jones, Co-Founder and Chief Technology Officer, Sakana AI ▫️ Aidan Gomez, Co-Founder and Chief Executive Officer, Cohere ▫️ Lukasz Kaiser, Member of Technical Staff, OpenAI ▫️ Illia Polosukhin, Co-Founder, NEAR Protocol Explore more GTC 2024 sessions like this on NVIDIA On-Demand: https://nvda.ws/3U33qo Read and subscribe to the NVIDIA Technical Blog: https://nvda.ws/3XHae9F 00:00 Introduction 11:00 Panelist Discussion #GTC24 #NVIDIA #GTC #AI #GenAI #Generative AI #Transformers #FutureOfAI

Scientists spot 60 stars appearing to show signs of alien power plants

I don’t know if this true but it definitely could be as most civilizations are probably more advanced than the earth.

A survey of five million distant solar systems, aided by ‘neural network’ algorithms, has discovered 60 stars that appear to be surrounded by giant alien power plants.

Seven of the stars — so-called M-dwarf stars that range between 60 percent and 8 percent the size of our sun — were recorded giving off unexpectedly high infrared ‘heat signatures,’ according to the astronomers.

Natural, and better understood, outer space ‘phenomena,’ as they report in their new study, ‘cannot easily account for the observed infrared excess emission.’

21 New Laser Materials Uncovered in Groundbreaking Global Study

Utilizing distributed self-driving lab technology, the University of Toronto’s Acceleration Consortium rapidly identified 21 high-performing organic solid-state lasers (OSL) materials, marking a significant advance in molecular optoelectronics and paving the way for future materials discovery. (Artist’s concept). Credit: SciTechDaily.com.

Organic solid-state lasers (OSLs) offer significant potential for various applications thanks to their flexibility, color adjustability, and high efficiency. Nevertheless, they are challenging to produce. With the need for potentially over 150,000 experiments to identify viable new materials, fully exploring this space could take many lifetimes. In fact, in the previous few decades, only 10–20 new OSL materials have been tested.

Researchers with the Acceleration Consortium based at the University of Toronto, took up this challenge and used self-driving lab (SDL) technology that, once set up, enabled them to synthesize and test over 1,000 potential OSL materials and discover at least 21 top performing OSL gain candidates in a matter of months.

Combining proteomics and AI to enable ‘a new era in healthcare’

Understanding aging and age-related diseases requires analyzing a vast number of factors, including an individual’s genetics, immune system, epigenetics, environment and beyond. While AI has long been touted for its potential to shed light on these complexities of human biology and enable the next generation of healthcare, we’ve yet to see the emergence of tools that truly deliver on this promise.

Leveraging advanced plasma proteomics, US startup Alden Scientific has developed AI models capable of making the connections needed to accurately assess an individual’s state of health and risk of disease. The company’s tool measures more than 200 different conditions, including leading causes of morbidity and mortality such as Alzheimer’s, heart disease, diabetes and stroke. Significantly, its models also enable an individual to understand how an intervention impacts these risks.

With a host of top Silicon Valley investors among its early adopters, Alden is now using its platform to conduct an IRB-approved health study designed to provide a “longitudinal understanding of the interplay between environmental, biological, and medical data.”

MIT: New AI power teaches robots how to hammer, screw nuts, cook toast

MIT researchers use AI to empower robots for versatile tool use in simulations and real-world settings.

MIT researchers have utilized artificial intelligence (AI) models to combine data from multiple sources to help robots learn better.

The technique employs diffusion models, a type of generative AI, to integrate multiple data sources across various domains, modalities, and tasks.

The training strategy allowed a robot to execute various tool-use activities and adapt to new tasks it was not exposed to during training, both in simulations and real-world tests.

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