A new, more sustainable AI model recognizes visual scenes by mirroring brain processes, opening doors for applications in medical diagnostics, drug discovery and beyond.
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.
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.
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!
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.
The instrument uses light to move atoms to measure incredibly small forces.
A self-correcting atom interferometer amplifies signals, aiding detection of ultra-weak forces from dark matter, dark energy, and waves.