Lifeboat Foundation: Safeguarding Humanity
Toggle light / dark theme

Blog

Category: robotics/AI – Page 339

‘AI-at-scale’ method accelerates atomistic simulations for scientists

Quantum calculations of molecular systems often require extraordinary amounts of computing power; these calculations are typically performed on the world’s largest supercomputers to better understand real-world products such as batteries and semiconductors.

Now, UC Berkeley and Lawrence Berkeley National Laboratory (Berkeley Lab) researchers have developed a new machine learning method that significantly speeds up by improving model scalability. This approach reduces the computing memory required for simulations by more than fivefold compared to existing models and delivers results over ten times faster.

Their research has been accepted at Neural Information Processing Systems (NeurIPS) 2024, a conference and publication venue in artificial intelligence and machine learning. They will present their work at the conference on December 13, and a version of their paper is available on the arXiv preprint server.

FunMap reveals a functional network of genes and proteins in human cancer

Large-scale protein and gene profiling have massively expanded the landscape of cancer-associated proteins and gene mutations, but it has been difficult to discern whether they play an active role in the disease or are innocent bystanders. In a study published in Nature Cancer, researchers at Baylor College of Medicine revealed a powerful and unbiased machine learning-based approach called FunMap for assessing the role of cancer-associated mutations and understudied proteins, with broad implications for advancing cancer biology and informing therapeutic strategies.

“Gaining functional information on the genes and proteins associated with cancer is an important step toward better understanding the disease and identifying potential therapeutic targets,” said corresponding author Dr. Bing Zhang, professor of molecular and human genetics and part of the Lester and Sue Smith Breast Center at Baylor.

“Our approach to gain functional insights into these genes and proteins involved using machine learning to develop a network mapping their functional relationships,” said Zhang, member of Baylor’s Dan L Duncan Comprehensive Cancer Center and a McNair Scholar. “It’s like, I may not know anything about you, but if I know your LinkedIn connections, I can infer what you do.”

Building A Data Strategy For Successful AI Implementation

Artificial intelligence is no longer just a buzzword; it’s a transformative force reshaping industries, from healthcare to finance to retail. However, behind every successful AI system lies an often-overlooked truth: AI is only as good as the data that powers it.

Organizations eager to adopt AI frequently focus on algorithms and technologies while neglecting the critical foundation—data. Even the most advanced AI initiatives are doomed to fail without a robust data strategy. I’ll explore why a solid data strategy is the cornerstone of successful AI implementation and provide actionable steps to craft one.

Imagine building a skyscraper without solid ground beneath it. Data plays a similar foundational role for AI. It feeds machine learning models, drives predictions and shapes insights. However, as faulty materials weaken a structure, poor-quality data can derail an AI project.

What Is Agentic AI, and How Will It Change Work?

From the early days of mechanical automatons to more recent conversational bots, scientists and engineers have dreamed of a future where AI systems can work and act intelligently and independently. Recent advances in agentic AI bring that autonomous future a step closer to reality. With their supercharged reasoning and execution capabilities, agentic AI systems promise to transform many aspects of human-machine collaboration. The agentic AI prize could be great, with the promise of greater productivity, innovation and insights for the human workforce. But so, too, are the risks: the potential for bias, mistakes, and inappropriate use. Early action by business and government leaders now will help set the right course for agentic AI development, so that its benefits can be achieved safely and fairly.

Page-utils class= article-utils—vertical hide-for-print data-js-target= page-utils data-id= tag: blogs.harvardbusiness.org, 2007/03/31:999.397606 data-title= What Is Agentic AI, and How Will It Change Work? data-url=/2024/12/what-is-agentic-ai-and-how-will-it-change-work data-topic= Generative AI data-authors= Mark Purdy data-content-type= Digital Article data-content-image=/resources/images/article_assets/2024/12/Dec24_12_1450615814-383x215.jpg data-summary=

The next era of human-machine collaboration will present new opportunities and challenges.

Beyond batteries: Researchers bring body-heat powered wearable devices closer to reality

Noting that recent advances in artificial intelligence and the existence of large-scale experimental data about human biology have reached a critical mass, a team of researchers from Stanford University, Genentech, and the Chan-Zuckerberg Initiative says that science has an “unprecedented opportunity” to use artificial intelligence (AI) to create the world’s first virtual human cell. Such a cell would be able to represent and simulate the precise behavior of human biomolecules, cells, and, eventually, tissues and organs.

“Modeling human cells can be considered the holy grail of biology,” said Emma Lundberg, associate professor of bioengineering and of pathology in the schools of Engineering and Medicine at Stanford and a senior author of a new article in the journal Cell proposing a concerted, global effort to create the world’s first AI virtual cell. “AI offers the ability to learn directly from data and to move beyond assumptions and hunches to discover the emergent properties of complex biological systems.”

Lundberg’s fellow senior authors include two Stanford colleagues, Stephen Quake, a professor of bioengineering and science director at the Chan-Zuckerberg Initiative, and Jure Leskovec, a professor of computer science in the School of Engineering, as well as Theofanis Karaletsos, head of artificial intelligence for science at the Chan Zuckerberg Initiative, and Aviv Regev executive vice president of research at Genentech.

Max Tegmark: Will AI Surpass Human Intelligence?

Expand your scientific horizon with Brilliant! 🧠 Use my link https://brilliant.org/DrBrianKeating/ to get 20% off the annual premium subscription.

Will AI ever surpass human intelligence, discover new laws of physics, and solve the greatest mysteries of our universe?

This week on Into the Impossible, I explore the potential and dangers of artificial intelligence with none other than Max Tegmark!

Max Tegmark is a renowned physicist and machine learning expert who dedicated his career to uncovering the mathematical fabric of reality, proposing that our universe itself might be a vast mathematical structure and that we could be living in a multiverse of endless possibilities. His work goes beyond physics to tackle the transformative power and ethical challenges of artificial intelligence, an area where he believes humanity must tread carefully.

In the second part of our fascinating interview, we discuss the development of AI, the impact it will have on science, and our role in all of this.

Tune in to discover if AI will ever surpass human intelligence!

Continue reading “Max Tegmark: Will AI Surpass Human Intelligence?” | >

TidyBot++: An Open-Source Holonomic Mobile Manipulator for Robot Learning

Exploiting the promise of recent advances in imitation learning for mobile manipulation will require the collection of large numbers of human-guided demonstrations. This paper proposes an open-source design for an inexpensive, robust, and flexible mobile manipulator that can support arbitrary arms, enabling a wide range of real-world household mobile manipulation tasks. Crucially, our design uses powered casters to enable the mobile base to be fully holonomic, able to control all planar degrees of freedom independently and simultaneously. This feature makes the base more maneuverable and simplifies many mobile manipulation tasks, eliminating the kinematic constraints that create complex and time-consuming motions in nonholonomic bases. We equip our robot with an intuitive mobile phone teleoperation interface to enable easy data acquisition for imitation learning.

/* */