All the sessions from Transform 2021 are available on-demand now. DeepMind and the European Bioinformatics Institute (EMBL), a life sciences lab based in Hinxton, England, today announced the launch of what they claim is the most complete and accurate database of structures for proteins expressed by the human genome. In a joint press conference hosted by the journal Nature, the two organizations said that the database, the AlphaFold Protein Structure Database, which was created using DeepMind's AlphaFold 2 system, will be made available to the scientific community in the coming weeks. The recipe for proteins -- large molecules consisting of amino acids that are the fundamental building blocks of tissues, muscles, hair, enzymes, antibodies, and other essential parts of living organisms -- are encoded in DNA. It's these genetic definitions that circumscribe their three-dimensional structures, which in turn determine their capabilities.
Ophthalmology, with its heavy reliance on imaging, is an innovator in the field of artificial intelligence (AI) in medicine. Although the opportunities for patients and health care professionals are great, hurdles to fully integrating AI remain, including economic, ethical, and data-privacy issues. Deep learning According to Konstantinos Balaskas, MD, FEBO, MRCOphth, a retinal expert at Moorfields Eye Hospital, London, United Kingdom, and director of the Moorfields Ophthalmic Reading Centre and AI Analytics Hub, AI is a broad term. "The type of AI that has generated a lot of excitement in recent years is called'deep learning,' " he said. "This is a process by which software programs learn to perform certain tasks by processing large quantities of data." Deep learning is what has made ophthalmology a pioneer in the field of implementing AI in medicine, because we are increasingly reliant on imaging tests to monitor our patients.
DeepMind, a Google-owned artificial intelligence (AI) company based in the United Kingdom, made scientific history when it announced last November that it had a solution to a 50-year-old grand challenge in biology--protein folding. This AI machine learning breakthrough may help accelerate the discovery of new medications and novel treatments for diseases. On July 15, 2021 DeepMind revealed details on how its AI works in a new peer-reviewed paper published in Nature, and made its revolutionary AlphaFold version 2.0 model available as open-source on GitHub. The three-dimensional (3D) shape and function of proteins are determined by the sequence of its amino acids. AlphaFold predicts three-dimensional (3D) models of protein structures.
A man unable to speak after a stroke has produced sentences through a system that reads electrical signals from speech production areas of his brain, researchers report this week. The approach has previously been used in nondisabled volunteers to reconstruct spoken or imagined sentences. But this first demonstration in a person who is paralyzed “tackles really the main issue that was left to be tackled—bringing this to the patients that really need it,” says Christian Herff, a computer scientist at Maastricht University who was not involved in the new work. The participant had a stroke more than a decade ago that left him with anarthria—an inability to control the muscles involved in speech. Because his limbs are also paralyzed, he communicates by selecting letters on a screen using small movements of his head, producing roughly five words per minute. To enable faster, more natural communication, neurosurgeon Edward Chang of the University of California, San Francisco, tested an approach that uses a computational model known as a deep-learning algorithm to interpret patterns of brain activity in the sensorimotor cortex, a brain region involved in producing speech ( Science , 4 January 2019, p. ). The approach has so far been tested in volunteers who have electrodes surgically implanted for nonresearch reasons such as to monitor epileptic seizures. In the new study, Chang's team temporarily removed a portion of the participant's skull and laid a thin sheet of electrodes smaller than a credit card directly over his sensorimotor cortex. To “train” a computer algorithm to associate brain activity patterns with the onset of speech and with particular words, the team needed reliable information about what the man intended to say and when. So the researchers repeatedly presented one of 50 words on a screen and asked the man to attempt to say it on cue. Once the algorithm was trained with data from the individual word task, the man tried to read sentences built from the same set of 50 words, such as “Bring my glasses, please.” To improve the algorithm's guesses, the researchers added a processing component called a natural language model, which uses common word sequences to predict the likely next word in a sentence. With that approach, the system only got about 25% of the words in a sentence wrong, they report this week in The New England Journal of Medicine . That's “pretty impressive,” says Stephanie Riès-Cornou, a neuroscientist at San Diego State University. (The error rate for chance performance would be 92%.) Because the brain reorganizes over time, it wasn't clear that speech production areas would give interpretable signals after more than 10 years of anarthria, notes Anne-Lise Giraud, a neuroscientist at the University of Geneva. The signals' preservation “is surprising,” she says. And Herff says the team made a “gigantic” step by generating sentences as the man was attempting to speak rather than from previously recorded brain data, as most studies have done. With the new approach, the man could produce sentences at a rate of up to 18 words per minute, Chang says. That's roughly comparable to the speed achieved with another brain-computer interface, described in Nature in May. That system decoded individual letters from activity in a brain area responsible for planning hand movements while a person who was paralyzed imagined handwriting. These speeds are still far from the 120 to 180 words per minute typical of conversational English, Riès-Cornou notes, but they far exceed what the participant can achieve with his head-controlled device. The system isn't ready for use in everyday life, Chang notes. Future improvements will include expanding its repertoire of words and making it wireless, so the user isn't tethered to a computer roughly the size of a minifridge. : http://www.sciencemag.org/content/363/6422/14
Geometric Deep Learning is an attempt for geometric unification of a broad class of machine learning problems from the perspectives of symmetry and invariance. These principles not only underlie the breakthrough performance of convolutional neural networks and the recent success of graph neural networks but also provide a principled way…
Advances in deep learning and neural networks have delivered huge breakthroughs in natural language processing and computer vision, and they have the potential to solve big problems in manufacturing, retail, supply chain, agriculture, and countless other business domains. Naturally, technology startups are behind some of the most important innovations. In recent articles, we looked at startups revolutionizing natural language processing and startups leading the way in MLops. Here we'll take a look at "applied AI" startups. These are companies that are applying different techniques--whether it be processing images, text, audio, video, categorical or tabular data, or combinations of the above--to address various industry challenges, from fulfilling the promise of self-driving cars to pushing the boundaries of agricultural production.
Arguably one of the premiere events that has brought AI to popular attention in recent years was the invention of the Transformer by Ashish Vaswani and colleagues at Google in 2017. The Transformer led to lots of language programs such as Google's BERT and OpenAI's GPT-3 that have been able to produce surprisingly human-seeming sentences, giving the impression machines can write like a person. Now, scientists at DeepMind in the U.K., which is owned by Google, want to take the benefits of the Transformer beyond text, to let it revolutionize other material including images, sounds and video, and spatial data of the kind a car records with LiDAR. The Perceiver, unveiled this week by DeepMind in a paper posted on arXiv, adapts the transformer with some tweaks to let it consume all those types of input, and to perform on the various tasks, such as image recognition, for which separate kinds of neural networks are usually developed. The DeepMind work appears to be a way station on the way to an envisioned super-model of deep learning, a neural network that could perform a plethora of tasks, and would learn faster and with less data, what Google's head of AI, Jeff Dean, has described as a "grand challenge" for the discipline.
It is not surprising that Europe, despite having a strong industrial base and leading AI research and talent, is dragging behind the US and China. European countries are lagging behind in artificial intelligence due to the fragmentation of the EU's research space and digital market, difficulties in attracting human capital and external investment, lack of commercial competitiveness and geopolitical inequalities. Reading the ESPAS Ideas Paper Series, the Future of AI and Big Data, one could enjoy its deep insights, see the Supplement, as well as the honesty of the report as to the EU AI state of affairs. It specifically reads: "The EU will lag behind in AI for some more time, because it has a more complicated task than others. On the other hand, with a resilient and free economy, a balanced regulatory system, an interested public, intact societies and world class research it will be well-placed in the medium term... Some experts believe that the advances in machine learning are plateauing and that AI will only develop slowly and incrementally from now on. Others see much more change coming, even revolutionary jumps like super intelligent AIs that are able to be employed in many fields at the same time... While many policy makers see the question of AGI as science fiction, huge investments are made into researching it. For example, DeepMind – developers of the Go-champion AI AlphaGo and bought by Google for 500 million USD – spends up to 200 million USD each year to come closer to that goal.OpenAI, funded with an Endowment of 1 billion USD, has the same goal. Since this research is not required to be transparent, it is likely that states such as the US, Chinese and probably others are also already working on such programmes. The biggest project by the European Union is the Human Brain Project, an effort to construct a virtual human brain, although this is not exactly the same as building an AGI... Imagine, in 20 years, there will be a super intelligent, friendly, conscious AI which is a source of pride to the world and fulfils all our wishes. Would this be a paternalistic world? The difficult question goes to the core of the human condition: What are we to do, if we are not needed anymore? What then is the purpose of humanity?"
IMAGE: Deep Learning detects virus infected cells and predicts acute, severe infections. In most cases, this does not lead to the production of new virus particles, as the viruses are suppressed by the immune system. However, adenoviruses and herpes viruses can cause persistent infections that the immune system is unable to completely suppress and that produce viral particles for years. These same viruses can also cause sudden, violent infections where affected cells release large amounts of viruses, such that the infection spreads rapidly. This can lead to serious acute diseases of the lungs or nervous system. The research group of Urs Greber, Professor at the Department of Molecular Life Sciences at the University of Zurich (UZH), has now shown for the first time that a machine-learning algorithm can recognize the cells infected with herpes or adenoviruses based solely on the fluorescence of the cell nucleus.
OHAAI Collaboration, null, Brannstrom, Andreas, Castagna, Federico, Duchatelle, Theo, Foulis, Matt, Kampik, Timotheus, Kuhlmann, Isabelle, Malmqvist, Lars, Morveli-Espinoza, Mariela, Mumford, Jack, Pandzic, Stipe, Schaefer, Robin, Thorburn, Luke, Xydis, Andreas, Yuste-Ginel, Antonio, Zheng, Heng
This volume contains revised versions of the papers selected for the second volume of the Online Handbook of Argumentation for AI (OHAAI). Previously, formal theories of argument and argument interaction have been proposed and studied, and this has led to the more recent study of computational models of argument. Argumentation, as a field within artificial intelligence (AI), is highly relevant for researchers interested in symbolic representations of knowledge and defeasible reasoning. The purpose of this handbook is to provide an open access and curated anthology for the argumentation research community. OHAAI is designed to serve as a research hub to keep track of the latest and upcoming PhD-driven research on the theory and application of argumentation in all areas related to AI.