Annika Hauptvogel, head of technology and innovation management at Siemens, describes the industrial metaverse as “immersive, making users feel as if they’re in a real environment; collaborative in real time; open enough for different applications to seamlessly interact; and trusted by the individuals and businesses that participate”—far more than simply a digital world.
The industrial metaverse will revolutionize the way work is done, but it will also unlock significant new value for business and societies. By allowing businesses to model, prototype, and test dozens, hundreds, or millions of design iterations in real time and in an immersive, physics-based environment before committing physical and human resources to a project, industrial metaverse tools will usher in a new era of solving real-world problems digitally.
“The real world is very messy, noisy, and sometimes hard to really understand,” says Danny Lange, senior vice president of artificial intelligence at Unity Technologies, a leading platform for creating and growing real-time 3D content. “The idea of the industrial metaverse is to create a cleaner connection between the real world and the virtual world, because the virtual world is so much easier and cheaper to work with.”
Researchers from the University of Jyväskylä were able to simplify the most popular technique of artificial intelligence, deep learning, using 18th-century mathematics. They also found that classical training algorithms that date back 50 years work better than the more recently popular techniques. Their simpler approach advances green IT and is easier to use and understand.
The recent success of artificial intelligence is significantly based on the use of one core technique: deep learning. Deep learning refers to artificial intelligence techniques where networks with a large number of data processing layers are trained using massive datasets and a substantial amount of computational resources.
Deep learning enables computers to perform complex tasks such as analyzing and generating images and music, playing digitized games and, most recently in connection with ChatGPT and other generative AI techniques, acting as a natural language conversational agent that provides high-quality summaries of existing knowledge.
Scientists worldwide are already using artificial intelligence to sort through huge amounts of data, suggesting that the future of astronomy belongs to AI.
That looks promising. 90% accuracy isn’t bad. Now the trick is getting there though we have options on our own solar system possibly. You never know until you try. I doubt we’ll find high level life remnants but perhaps something much less like at most insect level but more likely microbial. I’m just guessing of course.
A team of scientists supported in part by NASA have outlined a simple and reliable method to search for signs of past or present life on other worlds that employs machine learning techniques. The results show that the method can distinguish both modern and ancient biosignatures with an accuracy of 90 percent.
The method is able to detect whether or not a sample contains materials that were tied to biological activity. What the research team refers to as a “routine analytical method” could be performed with instruments on missions including spacecraft, landers, and rovers, even before samples are returned to Earth. In addition, the method could be used to shed light on the history of ancient rocks on our own planet.
The team used molecular analyses of 134 samples containing carbon from abiotic and biotic sources to train their software to predict a new sample’s origin. Using pyrolysis gas chromatography, the method can detect subtle differences in a sample’s molecular patterns and determine whether or not a sample is biotic in origin. When testing the method, samples originating from a wide variety of biotic sources were identified, including things like shells, human hair, and cells preserved in fine-grained rock. The method was even able to identify remnants of life that have been altered by geological processes, such as coal and amber.
“Wish I had this to cite,” lamented Jacob Andreas, a professor at MIT, who had just published a paper exploring the extent to which language models mirror the internal motivations of human communicators.
Jan Leike, the head of alignment at OpenAI, who is chiefly responsible for guiding new models like GPT-4 to help, rather than harm, human progress, responded to the paper by offering Burns a job, which Burns initially declined, before a personal appeal from Sam Altman, the cofounder and CEO of OpenAI, changed his mind.
“Collin’s work on ‘Discovering Latent Knowledge in Language Models Without Supervision’ is a novel approach to determining what language models truly believe about the world,” Leike says. “What’s exciting about his work is that it can work in situations where humans don’t actually know what’s true themselves, so it could apply to systems that are smarter than humans.”
SAN FRANCISCO/WASHINGTON, Oct 5 (Reuters) — OpenAI, the company behind ChatGPT, is exploring making its own artificial intelligence chips and has gone as far as evaluating a potential acquisition target, according to people familiar with the company’s plans.
The company has not yet decided to move ahead, according to recent internal discussions described to Reuters. However, since at least last year it discussed various options to solve the shortage of expensive AI chips that OpenAI relies on, according to people familiar with the matter.
These options have included building its own AI chip, working more closely with other chipmakers including Nvidia and also diversifying its suppliers beyond Nvidia (NVDA.O).
LOS ANGELES — Northrop Grumman will drop plans to develop its own commercial space station and instead assist a competing effort led by Voyager Space, the companies announced Oct. 4.
Under the new partnership, the companies will cooperate on the development of fully autonomous docking systems for Northrop’s Cygnus cargo spacecraft, allowing it to dock with Voyager’s Starlab space station. The companies also said they will “further explore opportunities to strengthen the development of Starlab” that could include Northrop providing engineering design services for that station. Ars Technica first reported about a potential partnership between the companies.
“This collaboration is a major step forward for the Starlab program,” said Dylan Taylor, chairman and chief executive of Voyager Space, in a statement. “Northrop Grumman’s technical capability and proven success in cargo resupply services will play a pivotal role as we accelerate Starlab’s development.”
Tina Woods, serving as Healthy Longevity Champion for the National Innovation Center for Aging, sets forth her vision for a blueprint for healthy longevity for all. Her emphasis is on reaping the “longevity dividend” and achieving five additional years of healthy life expectancy while reducing health and wellbeing inequality. Woods elaborates on the role of emerging technologies like AI, machine learning, and advanced data analysis in comprehending and influencing biological systems related to aging. She also underscores the crucial role of lifestyle changes and the consideration of socio-economic factors in increasing lifespan. The talk also explores the burgeoning field of emotion AI and its application in developing environments for better health outcomes, with a mention of “Longevity Cities,” starting with a trial in Newcastle. In closing, Woods mentions the development of a framework for incentivizing businesses through measurement of their contribution to health in three areas: workforce health, consumer health through products and services, and community health. Woods envisions a future where businesses impacting health negatively are disincentivized, and concludes with the hope that the UK’s healthy longevity innovation mission can harness longevity science and data innovation to improve life expectancy.
00:00:00 — Introduction, National Innovation Center for Aging. 00:00:56 — Discussion on stagnating life expectancy and UK’s life sciences vision. 00:03:50 — Technological breakthroughs (including AI) in analyzing biological systems. 00:06:22 — Understanding what maintains health & wellbeing. 00:08:30 — Hype, hope, important of purpose. 00:10:00 — Psychological aging and “brain capital.“ 00:13:15 — Ageism — a barrier to progress in the field of aging. 00:15:46 — Health data, AI and wearables. 00:18:44 — Prevention is key, Health is an asset to invest in. 00:19:13 — Longevity Cities. 00:21:19 — Business for Health and industry incentives. 00:23:13 — Closing.
About the Speaker: Tina Woods is a social entrepreneur and system architect with a focus on health innovation at the intersection of science, technology, policy, and investment. She is the Founder and CEO of Collider Health and Business for Health, driving systemic change for better health through these platforms. She contributes to key UK health strategies and initiatives, like UKRI’s Healthy Ageing Industrial Strategy, and served as the Healthy Longevity Champion for the National Innovation Centre for Ageing. Woods has made significant contributions to AI in health and care, co-leading the Quantum Healthy Longevity Innovation Mission and authoring the book, “Live Longer with AI.” Previously, she served as the director of the All Party Parliamentary Group for Longevity secretariat. Woods is also the CEO & Founder of Collider Science, a social enterprise that encourages young people’s interest in science and technology. She holds a degree in genetics from Cornell University and an MBA from Bayes Business School in London.
