Anusorn nakdee/iStock.
The results were published last week in bioRxiv.org.
Category: robotics/AI – Page 1,358
Optimizing Human-System Performance — Dr. Greg Lieberman, Ph.D., Neuroscientist / Lead, U.S. Army Combat Capabilities Development Command Army Research Laboratory, U.S. Army Futures Command
Dr. Greg Lieberman, Ph.D. (https://www.arl.army.mil/arl25/meet-arl.php?gregory_lieberman) is a Neuroscientist, and Lead, Optimizing Human-System Performance, at the U.S. Army Combat Capabilities Development Command, Army Research Laboratory (DEVCOM ARL).
DEVCOM ARL, as an integral part of the Army Futures Command, is the Army’s foundational research laboratory focused on operationalizing science to ensure overmatch in any future conflict. DEVCOM ARL shapes future concepts with scientific research and knowledge and delivers technology for modernization solutions to win in the future operating environment.
With a Ph.D. from the University of Vermont in Neuroscience, a Postdoctoral Fellowship in Cognitive Neuroscience from University of New Mexico, and a BA from University of Massachusetts Amherst in Psychology, Dr. Lieberman’s research and research leadership experience ranges from genetics to learning theory, animal behavior to artificial intelligence, and human variability to team dynamics; with additional expertise in S&T strategy and the opportunities afforded by the Future of Work.
Specific areas of Dr. Lieberman’s technical expertise include maximizing human potential, human-autonomy teaming; neuroanatomical organization and connectivity; brain structure-function coupling; learning-driven neuroplasticity; non-invasive neurostimulation and cognitive enhancement; neuroimaging; mind-body medicine and mindfulness meditation; and the mechanisms of neurodegenerative disease, neuropathology, and brain injury.
In June, South Korean regulators authorized the first-ever medicine, a COVID vaccine, to be made from a novel protein designed by humans. The vaccine is based on a spherical protein ‘nanoparticle’ that was created by researchers nearly a decade ago, through a labour-intensive trial-and error-process1.
Now, thanks to gargantuan advances in artificial intelligence (AI), a team led by David Baker, a biochemist at the University of Washington (UW) in Seattle, reports in Science2,3 that it can design such molecules in seconds instead of months.
Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! Watch here.
Artificial intelligence (AI) pioneer Geoffrey Hinton, one of the trailblazers of the deep learning “revolution” that began a decade ago, says that the rapid progress in AI will continue to accelerate.
In an interview before the 10-year anniversary of key neural network research that led to a major AI breakthrough in 2012, Hinton and other leading AI luminaries fired back at some critics who say deep learning has “hit a wall.”
Futuristic China | Business Documentary from 2018.
Hear from the leaders of Baidu, China’s equivalent to Google. The smart home is being advanced at Iflytech, robots for business use are developed at UBTECH, while Tiandi demonstrates their latest advances in surveillance technology.
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Artificial General Intelligence or short AGI was commonly referred as Strong AI. The continues advancements in robotics are also spurring the development of AGI. Currently we only have narrow AI or weak AI. But robots are paving the way for strong AI. In the future, robots might possibly become smarter than us or at least, reach human level intelligence. The field of robotics has seen many improvements over the years, as artificial intelligence systems continue to get better. Machine intelligence is a trendy topic among computer scientists and other relevant researchers on the field. As robots continue to get better, concerns for the rise of a superintelligence or an artificial general intelligence that could have different goals from ours, is increasingly getting the attention of computer scientists and lay people alike. We have often seen works of science fiction where robots and AGI have malicious intent. However, things could go really bad fur us even if initially these intelligent machines are programmed to obey human orders and follow our values. As a machine continues to improve itself by modifying it’s own source code, it could lead to an intelligence explosion. A point of time often referred as a technological singularity. Where it becomes hard if not impossible to predict future trajectories of the AI in question. As of the year 2017, there are over 40 organizations focused on the development of AGI. As we’ve said many times before, today’s AI is narrow. However the field of robotics is accelerating the rise of AGI and we will possibly witness a truly general AI in our lifetimes.
#AGI #AI #Artificialintelligence.
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Sources: Boston Dynamics’Big Dog: https://www.youtube.com/watch?v=cNZPRsrwumQ&t=126s.
Aldebaran — Softbank Robotics’ Nao: https://www.softbankrobotics.com/emea/en/node/72
When does a machine become a robot and a robot become a human? — J.D. Fencer.
Yoshua Bengio (MILA), Irina Higgins (DeepMind), Nick Bostrom (FHI), Yi Zeng (Chinese Academy of Sciences), and moderator Joshua Tenenbaum (MIT) discuss possible paths to artificial general intelligence.
The Beneficial AGI 2019 Conference: https://futureoflife.org/beneficial-agi-2019/
After our Puerto Rico AI conference in 2015 and our Asilomar Beneficial AI conference in 2017, we returned to Puerto Rico at the start of 2019 to talk about Beneficial AGI. We couldn’t be more excited to see all of the groups, organizations, conferences and workshops that have cropped up in the last few years to ensure that AI today and in the near future will be safe and beneficial. And so we now wanted to look further ahead to artificial general intelligence (AGI), the classic goal of AI research, which promises tremendous transformation in society. Beyond mitigating risks, we want to explore how we can design AGI to help us create the best future for humanity.
We again brought together an amazing group of AI researchers from academia and industry, as well as thought leaders in economics, law, policy, ethics, and philosophy for five days dedicated to beneficial AI. We hosted a two-day technical workshop to look more deeply at how we can create beneficial AGI, and we followed that with a 2.5-day conference, in which people from a broader AI background considered the opportunities and challenges related to the future of AGI and steps we can take today to move toward an even better future.
IRVINE, Calif., Sept. 13, 2022 /PRNewswire/ — AIVITA Biomedical, Inc., a biotech company specializing in innovative cell applications, today announced that chairman and CEO Hans Keirstead, Ph.D., will deliver a keynote address at AI for Good, a program dedicated to achieving the United Nations Sustainable Development Goals through practical AI applications. Details for the keynote are as follows:
Keynote title: AI in healthcare is an infant. Intelligence augmentation is an athlete. When: Wednesday, September 14, 2022, 15:00 CEST (9:00 EDT) Where: Switzerland — Virtual Presentation
The AI for Good meeting is organized by the International Telecommunication Union (ITU), the United Nations specialized agency for information and communication technologies, in partnership with 40 United Nations sister agencies.
According to 130,000 years’ worth of data on what mammals have been eating, we’re in the midst of a mass biodiversity crisis. Not great!
This revelation was borne of a new study, conducted by an international team of researchers and published in the journal Science, that used machine learning to paint a detailed past — and harrowing future — of what happens to food webs when land mammals go extinct. Spoiler alert: it’s pretty grim stuff.
“While about 6 percent of land mammals have gone extinct in that time, we estimate that more than 50 percent of mammal food web links have disappeared,” Evan Fricke, ecologist and lead author of the study, said in a press release. “And the mammals most likely to decline, both in the past and now, are key for mammal food web complexity.”
A team at Los Alamos National Laboratory has developed a novel approach for comparing neural networks that looks within the “black box” of artificial intelligence to help researchers understand neural network behavior. Neural networks recognize patterns in datasets; they are used everywhere in society, in applications such as virtual assistants, facial recognition systems and self-driving cars.
“The artificial intelligence research community doesn’t necessarily have a complete understanding of what neural networks are doing; they give us good results, but we don’t know how or why,” said Haydn Jones, a researcher in the Advanced Research in Cyber Systems group at Los Alamos. “Our new method does a better job of comparing neural networks, which is a crucial step toward better understanding the mathematics behind AI.”
Jones is the lead author of the paper “If You’ve Trained One You’ve Trained Them All: Inter-Architecture Similarity Increases With Robustness,” which was presented recently at the Conference on Uncertainty in Artificial Intelligence. In addition to studying network similarity, the paper is a crucial step toward characterizing the behavior of robust neural networks.