DeepMind’s AlphaFold has uncovered the structures of more than 200 million proteins, essentially all of those known to science.
Category: robotics/AI – Page 1,576
DeepMind’s AI has now catalogued every protein known to science
In late 2020, Alphabet’s DeepMind division unveiled its novel protein fold prediction algorithm, AlphaFold, and helped solve a scientific quandary that had stumped researchers for half a century. In the year since its beta release, half a million scientists from around the world have accessed the AI system’s results and cited them in their own studies more than 4,000 times. On Thursday, DeepMind announced that it is increasing that access even further by radically expanding its publicly-available AlphaFold Protein Structure Database (AlphaFoldDB) — from 1 million entries to 200 million entries.
Alphabet partnered with EMBL’s European Bioinformatics Institute (EMBL-EBI) for this undertaking, which covers proteins from across the kingdoms of life — animal, plant, fungi, bacteria and others. The results can be viewed on the UniProt, Ensembl, and OpenTargets websites or downloaded individually via GitHub, “for the human proteome and for the proteomes of 47 other key organisms important in research and global health,” per the AlphaFold website.
“AlphaFold is the singular and momentous advance in life science that demonstrates the power of AI,” Eric Topol, Founder and Director of the Scripps Research Translational Institute, siad in a press statement Thursday. “Determining the 3D structure of a protein used to take many months or years, it now takes seconds. AlphaFold has already accelerated and enabled massive discoveries, including cracking the structure of the nuclear pore complex. And with this new addition of structures illuminating nearly the entire protein universe, we can expect more biological mysteries to be solved each day.”
This Video Has Everything You Need to Know to Understand How a Power Grid Works
State company tested how artificial intelligence could minimise electricity disruptions and now looks to expand the technology.
A “Nano-Robot” Built Entirely from DNA to Explore Cell Processes
Constructing a tiny robot from DNA and using it to study cell processes invisible to the naked eye… You would be forgiven for thinking it is science fiction, but it is in fact the subject of serious research by scientists from Inserm, CNRS and Université de Montpellier at the Structural Biology Center in Montpellier[1]. This highly innovative “nano-robot” should enable closer study of the mechanical forces applied at microscopic levels, which are crucial for many biological and pathological processes. It is described in a new study published in Nature Communications.
Our cells are subject to mechanical forces exerted on a microscopic scale, triggering biological signals essential to many cell processes involved in the normal functioning of our body or in the development of diseases.
For example, the feeling of touch is partly conditional on the application of mechanical forces on specific cell receptors (the discovery of which was this year rewarded by the Nobel Prize in Physiology or Medicine).
Combining Neuroscience, Psychology, and AI Yields a Foundational Model of Human Thought
From Human to Artificial General Intelligence
Humans have an almost unbounded set of skills and knowledge, and quickly learn new information without needing to be re-engineered to do so. It is conceivable that an AGI can be built using an approach that is fundamentally different from human intelligence. However, as three longtime researchers in AI and cognitive science, our approach is to draw inspiration and insights from the structure of the human mind. We are working toward AGI by trying to better understand the human mind, and better understand the human mind by working toward AGI.
From research in neuroscience, cognitive science, and psychology, we know that the human brain is neither a huge homogeneous set of neurons nor a massive set of task-specific programs that each solves a single problem. Instead, it is a set of regions with different properties that support the basic cognitive capabilities that together form the human mind.
Roboticists Developed an AI Program That May Have Discovered an ‘Alternative Physics’
Artificial Intelligence has ushered the advancement of several disciplines throughout the years. But could it ever discover a new form of physics?
A group of roboticists from Columbia University wanted to exploit the vast potential of AI and find out if it can ever find an “alternative physics.”
Hence, they created an AI tool that could recognize physical occurrences and identify pertinent variables, essential building blocks for every physics theory.
Human-like behavioral variability blurs the distinction between a human and a machine in a nonverbal Turing test
Human-like temporal variability in movements is a powerful hint that humans use to ascribe humanness to robots.
A team of researchers at the University of Geneva has found that ketamine is unlikely to be addictive to people who use it for extended periods of time. In their paper published in the journal Nature, the group describes their study of the impact of the synthetic compound on the brains of mice and what they learned about its impact on different brain regions. Rianne Campbell and Mary Kay Lobo, with the University of Maryland School of Medicine have published a News and Views piece in the same journal issue outlining the work done by the team in Switzerland.
Do autonomous driving features really make roads safer?
In recent years, more vehicles include partially autonomous driving features, such as blind spot detectors, automatic braking and lane sensing, which are said to increase safety. However, a recent study by researchers from The University of Texas at Austin finds that some of that safety benefit may be offset by people driving more, thereby clogging up roads and exposing themselves to more potential crashes.
The study, published recently in Transportation Research Part A—Policy and Practice, found that drivers with one or more of these autonomous features reported higher miles traveled than those of similar profiles who didn’t have them. This is important, because miles traveled is one of the most—if not the most—significant predictor of crashes. The more you drive, the more likely you are to crash.
“What we showed, without any ambiguity in our results, is that after embracing autonomous features, people tend to drive more,” said Chandra Bhat, one of the authors on the project and professor in the Cockrell School of Engineering’s Department of Civil, Architectural and Environmental Engineering. “There are certainly engineering benefits to these safety features, but they are offset to a good extent because people are driving more and exposed more.”