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An AI-Controlled Drone Racer Has Beaten Human Pilots For The First Time

#artificialintelligence

Drone racing is an increasingly popular sport with big money prizes for skilled professionals. New control algorithms developed at the University of Zurich (UZH) have beaten experienced human pilots for the first time – but they still have significant limitations. In the past, attempts to develop automated algorithms to beat humans have run into problems with accurately simulating the limitations of the quadcopter and the flight path it takes. Traditional flight paths around a complex drone racing course are calculated using polynomial methods which produce a series of smooth curves, and these are not necessarily as fast as the sharper and more jagged paths flown by human pilots. A team from the Robotics and Perception Group at UZH has developed a trajectory planning algorithm to calculates the optimal route at every point in the flight, rather than doing it section by section.


IBM and CERN want to use quantum computing to unlock the mysteries of the universe

ZDNet

It is likely that future quantum computers will significantly boost the understanding of CERN's gigantic particle collider. The potential of quantum computers is currently being discussed in settings ranging from banks to merchant ships, and now the technology has been taken even further afield – or rather, lower down. One hundred meters below the Franco-Swiss border sits the world's largest machine, the Large Hadron Collider (LHC) operated by the European laboratory for particle physics, CERN. And to better understand the mountains of data produced by such a colossal system, CERN's scientists have been asking IBM's quantum team for some assistance. The partnership has been successful: in a new paper, which is yet to be peer-reviewed, IBM's researchers have established that quantum algorithms can help make sense of the LHC's data, meaning that it is likely that future quantum computers will significantly boost scientific discoveries at CERN. With CERN's mission statement being to understand why anything in the universe happens at all, this could have big implications for anyone interested in all things matter, antimatter, dark matter and so on.


New algorithm flies drones faster than human racing pilots

Robohub

To be useful, drones need to be quick. Because of their limited battery life they must complete whatever task they have – searching for survivors on a disaster site, inspecting a building, delivering cargo – in the shortest possible time. And they may have to do it by going through a series of waypoints like windows, rooms, or specific locations to inspect, adopting the best trajectory and the right acceleration or deceleration at each segment. The best human drone pilots are very good at doing this and have so far always outperformed autonomous systems in drone racing. Now, a research group at the University of Zurich (UZH) has created an algorithm that can find the quickest trajectory to guide a quadrotor – a drone with four propellers – through a series of waypoints on a circuit.


New Algorithm Flies Drones Faster than Human Racing Pilots - ELE Times

#artificialintelligence

To be useful, drones need to be quick. Because of their limited battery life, they must complete whatever task they have--searching for survivors on a disaster site, inspecting a building, delivering cargo--in the shortest possible time. And they may have to do it by going through a series of waypoints like windows, rooms, or specific locations to inspect, adopting the best trajectory and the right acceleration or deceleration at each segment. The best human drone pilots are very good at doing this and have so far always outperformed autonomous systems in drone racing. Now, a research group at the University of Zurich (UZH) has created an algorithm that can find the quickest trajectory to guide a quadrotor--a drone with four propellers--through a series of waypoints on a circuit.


Senior, Computer Vision R&D Engineer, SLAM/VIO

#artificialintelligence

Magic Leap's mission is to deliver enterprise a powerful tool for transformation-- an augmented reality platform of great utility and simplicity. Our ultimate vision is to amplify human potential. Our office in Zurich, Switzerland is a center of excellence for Computer Vision and Deep Learning. We are looking for exceptional engineers, passionate about shaping the future of computing. As a Computer Vision R&D Engineer, you'll be responsible for delivering high-performance production software with state-of-the-art computer vision capabilities in the field of SLAM and sensor fusion.


Protein structure prediction now easier, faster

Science

Proteins are the minions of life, working alone or together to build, manage, fuel, protect, and eventually destroy cells. To function, these long chains of amino acids twist and fold and intertwine into complex shapes that can be slow, even impossible, to decipher. Scientists have dreamed of simply predicting a protein's shape from its amino acid sequence—an ability that would open a world of insights into the workings of life. “This problem has been around for 50 years; lots of people have broken their head on it,” says John Moult, a structural biologist at the University of Maryland, Shady Grove. But a practical solution is in their grasp. Several months ago, in a result hailed as a turning point, computational biologists showed that artificial intelligence (AI) could accurately predict protein shapes. Now, David Baker and Minkyung Baek at the University of Washington, Seattle, and their colleagues have made AI-based structure prediction more powerful and accessible. Their method, described online in Science this week, works on not just simple proteins, but also complexes of proteins, and its creators have made their computer code freely available. Since the method was posted online last month, the team has used it to model more than 4500 protein sequences submitted by other researchers. Savvas Savvides, a structural biologist at Ghent University, had tried six times to model a problematic protein. He says Baker's and Baek's program, called RoseTTAFold, “paved the way to a structure solution.” In fall of 2020, DeepMind, a U.K.-based AI company owned by Google, wowed the field with its structure predictions in a biennial competition ( Science , 4 December 2020, p. [1144][1]). Called Critical Assessment of Protein Structure Prediction (CASP), the competition uses structures newly determined using laborious lab techniques such as x-ray crystallography as benchmarks. DeepMind's program, AlphaFold2, did “really extraordinary things [predicting] protein structures with atomic accuracy,” says Moult, who organizes CASP. But for many structural biologists, AlphaFold2 was a tease: “Incredibly exciting but also very frustrating,” says David Agard, a structural biophysicist at the University of California, San Francisco. DeepMind has yet to publish its method and computer code for others to take advantage of. In mid-June, 3 days after the Baker lab posted its RoseTTAFold preprint, Demis Hassabis, DeepMind's CEO, tweeted that AlphaFold2's details were under review at a publication and the company would provide “broad free access to AlphaFold for the scientific community.” DeepMind's 30-minute presentation at CASP was enough to inspire Baek to develop her own approach. Like AlphaFold2, it uses AI's ability to discern patterns in vast databases of examples, generating ever more informed and accurate iterations as it learns. When given a new protein to model, RoseTTAFold proceeds along multiple “tracks.” One compares the protein's amino acid sequence with all similar sequences in protein databases. Another predicts pairwise interactions between amino acids within the protein, and a third compiles the putative 3D structure. The program bounces among the tracks to refine the model, using the output of each one to update the others. DeepMind's approach, although still under wraps, involves just two tracks, Baek and others believe. Gira Bhabha, a cell and structural biologist at New York University School of Medicine, says both methods work well. “Both the DeepMind and Baker lab advances are phenomenal and will change how we can use protein structure predictions to advance biology,” she says. A DeepMind spokesperson wrote in an email, “It's great to see examples such as this where the protein folding community is building on AlphaFold to work towards our shared goal of increasing our understanding of structural biology.” But AlphaFold2 solved the structures of only single proteins, whereas RoseTTAFold has also predicted complexes, such as the structure of the immune molecule interleukin-12 latched onto its receptor. Many biological functions depend on protein-protein interactions, says Torsten Schwede, a computational structural biologist at the University of Basel. “The ability to handle protein-protein complexes directly from sequence information makes it extremely attractive for many questions in biomedical research.” Baker concedes that, in general, AlphaFold2's structures are more accurate. But Savvides says the Baker lab's approach better captures “the essence and particularities of protein structure,” such as identifying strings of atoms sticking out of the sides of the protein—features key to interactions between proteins. Agard adds that Baker's and Baek's approach is faster and requires less computing power than DeepMind's, which relied on Google's massive servers. However, the DeepMind spokesperson wrote that its latest algorithm is more than 16 times as fast as the one it used at CASP in 2020. As a result, she wrote, “It's not clear to us that the system being described is an advance in speed.” Beginning on 1 June, Baker and Baek began to challenge their method by asking researchers to send in their most baffling protein sequences. Fifty-six head scratchers arrived in the first month, all of which have now predicted structures. Agard's group sent in an amino acid sequence with no known similar proteins. Within hours, his group got a protein model back “that probably saved us a year of work,” Agard says. Now, he and his team know where to mutate the protein to test ideas about how it functions. Because Baek's and Baker's group has released its computer code on the web, others can improve on it; the code has been downloaded 250 times since 1 July. “Many researchers will build their own structure prediction methods upon Baker's work,” says Jinbo Xu, a computational structural biologist at the Toyota Technological Institute at Chicago. Moult agrees: “When there's a breakthrough like this, 2 years later, everyone is doing it as well if not better than before.” [1]: http://www.sciencemag.org/content/370/6521/1144


Technion Ranked No. 1 In Europe In Field Of Artificial Intelligence - AI Summary

#artificialintelligence

The center has four main goals: (1) establishing the Technion as a top-five university in the field of AI worldwide; (2) pooling resources, and recruiting researchers and students from Technion departments to advance and conduct joint research in the field; (3) connecting Technion researchers with relevant parties in the industry, especially tech companies and other organizations that generate Big Data; (4) establishing close research collaboration with other prominent research institutes in the AI field in Israel and the world. In May 2021, the Technion entered a long-term collaboration with American software giant PTC, under which the company will transfer its Haifa research campus to the Technion, to advance joint research in AI and manufacturing technology. PTC joins several other organizations that collaborate with the Technion in these fields, including the technological universities of Lausanne (Switzerland), Eindhoven (Netherlands), Munich (Germany), and the Paris Polytechnique (France) in Europe, as well as Cornell Tech, home of the Jacobs Technion-Cornell Institute, Waterloo University, and Carnegie Mellon University, which operates the largest center for AI and robotics in the United States. Currently, 46 Technion researchers are engaged in core AI research areas with more than 100 researchers in AI-related fields: health and medicine, autonomous vehicles, smart cities, industrial robotics, cybersecurity, natural language processing, FinTech, human-machine interaction, and others. The center has four main goals: (1) establishing the Technion as a top-five university in the field of AI worldwide; (2) pooling resources, and recruiting researchers and students from Technion departments to advance and conduct joint research in the field; (3) connecting Technion researchers with relevant parties in the industry, especially tech companies and other organizations that generate Big Data; (4) establishing close research collaboration with other prominent research institutes in the AI field in Israel and the world.


Harnessing AI to Discover New Drugs: Rewriting the Rulebook for Pharmaceutical Research

#artificialintelligence

Artificial intelligence (AI) is able to recognize the biological activity of natural products in a targeted manner, as researchers at ETH Zurich have demonstrated. Moreover, AI helps to find molecules that have the same effect as a natural substance but are easier to manufacture. This opens up huge possibilities for drug discovery, which also has potential to rewrite the rulebook for pharmaceutical research. Nature has a vast store of medicinal substances. "Over 50 percent of all drugs today are inspired by nature," says Gisbert Schneider, Professor of Computer- Assisted Drug Design at ETH Zurich.


Quantifying host-microbiota interactions

Science

The human microbiota is a complex microbial community living on and in our bodies. Its impact on a host's health is immense, affecting digestion ([ 1 ][1]), the immune system ([ 2 ][2]), behavior ([ 3 ][3]), metabolic diseases ([ 4 ][4]), and responses to drugs ([ 5 ][5]–[ 7 ][6]). Rapid advances in experimental and computational methods have moved the human microbiome field from identifying associations between microbiota composition and host health to unraveling the underlying molecular mechanisms ([ 8 ][7]–[ 10 ][8]). However, exactly how much the microbiota contributes to host health is a very difficult question to answer. By focusing on mechanistic and quantitative questions about the microbiome's contributions to host metabolism, I leverage my background in applied mathematics and systems biology to develop computational models describing host-microbiota interactions. Good models require good data from controlled experiments—a challenging proposition in complex host-microbiota systems. As a postdoc, I joined Andy Goodman's lab at Yale University and found myself in a perfect position to collect such data. By combining bacterial genetics with gnotobiotic mouse models, I learned how to modify the microbiome of germ-free, sterile mice. In the Goodman lab, we used these mice to study the contribution of microbiota to host metabolism of a number of pharmaceutical drugs. We found that this was also a good system to quantify host-microbiome interactions in vivo, because the compounds we used can be introduced into the system in a controlled way. We first focused on brivudine, an antiviral compound that can be converted into a potentially toxic metabolite, bromovinyluracil (BVU), by either a host or its microbiome ([ 11 ][9]). To identify bacteria capable of converting brivudine to BVU, we incubated individual bacterial species with the drug in vitro. One of the most potent brivudine metabolizers was Bacteroides thetaiotaomicron , a common gut bacterium with a genetic deletion library readily available. By incubating this library with the drug, we identified one bacterial mutant that had lost the capacity to convert brivudine to BVU. We then colonized germ-free mice with either the wild-type or mutant B. thetaiotaomicron , which provided us with a controllable host-microbiome system and two mouse groups that were identical, save for a single bacterial gene. When we administered brivudine to these two groups, the observed outcome was somewhat puzzling. Although drug levels in the intestine were much higher in mice colonized with the mutant bacterium, serum levels were comparable between the two mouse groups. The metabolite levels showed the opposite pattern: no difference (and very low levels) in the intestine but much higher metabolite levels in the sera of mice colonized with the wild-type bacterium (see the figure). These data could potentially be explained by bacterial conversion of the drug in the intestine and the rapid metabolite absorption into the serum. To test this explanation, we started with a simple kinetic model with two equations describing host drug metabolism in the liver and bacterial drug metabolism in the intestine. Once solved, this equation system showed that the difference between the amounts of metabolite absorbed into the sera of each of the two mouse groups was determined by the amount of BVU produced by microbes in the gut. This controlled experimental setup allowed us to quantify that the bacterial contribution to the toxic drug metabolite in vivo was about 70% ([ 12 ][10]) (see the figure). We expanded the model to describe drug metabolism processes in eight different tissues and in enterohepatic circulation (when the drug metabolized in the liver is secreted back into the small intestine via bile). We then demonstrated that our approach can be generalized to estimate the bacterial contribution to drug metabolism even if the metabolizing species remain unknown by using data from germ-free mice and mice harboring a complex microbial community. We also showed that microbial contribution to the drug metabolite far exceeds the host for sorivudine, an antiviral drug with different host and microbiome metabolism rates, and for clonazepam, an anxiolytic and anticonvulsant drug converted to multiple metabolites ([ 12 ][10]). ![Figure][11] Experimental and computational approaches that quantify host and microbial contributions to drug metabolism Oral drugs are administered to gnotobiotic mice that differ in a single microbial drug-metabolizing enzyme (GNMUT, mutant; GNWT, wild type); drug and drug metabolite kinetics are then quantified across tissues. A microbiome-host pharmacokinetic model developed from these measurements accurately predicts serum metabolite exposure and untangles host and microbiome contributions to drug metabolism. GRAPHIC: ADAPTED FROM M. ZIMMERMANN-KOGADEEVA BY N. CARY/ SCIENCE Quantifying the metabolic host-microbiome interactions is not the only purpose of our model. Having a robust model of host-microbiome interaction allows us to study, explain, and predict the system's behavior in different conditions. By analyzing how drug and metabolite profiles change when model parameters are varied, we found that the similarity of drug serum profiles between germ-free and colonized mice can be explained by the fast and microbiota-independent drug absorption from the small intestine. Our model further suggests that even for rapidly absorbed drugs, microbiome contributions to a host's metabolism can be substantial under certain conditions (e.g., a high microbiome to host ratio of drug metabolism or extensive enterohepatic circulation of the drug and its metabolites) ([ 13 ][12]). Such computational models enable us to investigate host-microbiota interactions in silico, guide experimental design, and help reduce the number of experiments needed to confirm model predictions. To systematically investigate microbial capacity to metabolize drugs, we next conducted a high-throughput in vitro screen. We found that microbiota contribution to drug metabolism might even be more widespread than we anticipated—two-thirds (176 out of 271) of the human-targeted drugs we examined were metabolized by at least one of the 76 tested bacteria ([ 14 ][13]). Although follow-up studies are required to test these microbiota-drug interactions in vivo, our findings emphasize that the microbiota should be considered when developing new drugs, stratifying patients, and choosing the most efficient treatment strategies. In the future, I believe that computational models combined with quantitative experimental data will allow us to measure host-microbiome interactions beyond drug metabolism and to better understand, predict, and control the effect of the microbiome on our health in everyday life. FINALIST Maria Zimmermann-Kogadeeva Maria Zimmermann-Kogadeeva received undergraduate degrees from Lomonosov Moscow State University in Russia and a PhD from ETH Zürich, Switzerland. After completing her postdoctoral fellowships at Yale University in the Goodman group and at European Molecular Biology Laboratory (EMBL) Heidelberg in the Bork group, Maria will start her laboratory in the Genome Biology Unit at EMBL Heidelberg in 2021. Her research combines computational modeling and multiomics data integration to investigate how microbes adapt to their surroundings and how metabolic adaptations of individual bacteria shape the functional outcome of microbial communities and their interactions with the host and the environment. [ www.sciencemag.org/content/373/6551/173.2 ][14] 1. [↵][15]1. H. J. Flint , Nutr. Rev. 70, S10 (2012). [OpenUrl][16][CrossRef][17][PubMed][18] 2. [↵][19]1. A. L. Kau, 2. P. P. Ahern, 3. N. W. Griffin, 4. A. L. Goodman, 5. J. I. Gordon , Nature 474, 327 (2011). [OpenUrl][20][CrossRef][21][PubMed][22][Web of Science][23] 3. [↵][24]1. T. R. Sampson, 2. S. K. Mazmanian , Cell Host Microbe 17, 565 (2015). [OpenUrl][25][CrossRef][26][PubMed][27] 4. [↵][28]1. J. Durack, 2. S. V. Lynch , J. Exp. Med. 216, 20 (2019). [OpenUrl][29][Abstract/FREE Full Text][30] 5. [↵][31]1. P. Spanogiannopoulos, 2. E. N. Bess, 3. R. N. Carmody, 4. P. J. Turnbaugh , Nat. Rev. Microbiol. 14, 273 (2016). [OpenUrl][32][CrossRef][33][PubMed][34] 6. 1. N. Koppel, 2. V. Maini Rekdal, 3. E. P. Balskus , Science 356, eaag2770 (2017). [OpenUrl][35][Abstract/FREE Full Text][36] 7. [↵][37]1. I. D. Wilson, 2. J. K. Nicholson , Transl. Res. 179, 204 (2017). [OpenUrl][38][CrossRef][39][PubMed][40] 8. [↵][41]1. T. S. B. Schmidt, 2. J. Raes, 3. P. Bork , Cell 172, 1198 (2018). [OpenUrl][42][PubMed][43] 9. 1. M. Alexander, 2. P. J. Turnbaugh , Immunity 53, 264 (2020). [OpenUrl][44] 10. [↵][45]1. C. Tropini, 2. K. A. Earle, 3. K. C. Huang, 4. J. L. Sonnenburg , Cell Host Microbe 21, 433 (2017). [OpenUrl][46][CrossRef][47][PubMed][48] 11. [↵][49]1. H. Machida et al. , Biochem. Pharmacol. 49, 763 (1995). [OpenUrl][50][CrossRef][51][PubMed][52] 12. [↵][53]1. M. Zimmermann, 2. M. Zimmermann-Kogadeeva, 3. R. Wegmann, 4. A. L. Goodman , Science 363, eaat9931 (2019). [OpenUrl][54][Abstract/FREE Full Text][55] 13. [↵][56]1. M. Zimmermann-Kogadeeva, 2. M. Zimmermann, 3. A. L. 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Seeking Out the Future of Search

#artificialintelligence

The future of search is the rise of intelligent data and documents. Way back in 1991, Tim Berners-Lee, then a young English software developer working at CERN in Geneva, Switzerland, came up with an intriguing way of combining a communication protocol for retrieving content (HTTP) with a descriptive language for embedding such links into documents (HTML). Shortly thereafter, as more and more people began to create content on these new HTTP servers, it became necessary to be able to provide some kind of mechanism to find this content. Simple lists of content links worked fine when you were dealing with a few hundred documents over a few dozen nodes, but the need to create a specialized index as the web grew led to the first automation of catalogs, and by extension led to the switch from statically retrieved content to dynamically generated content. In many respects, search was the first true application built on top of the nascent World Wide Web, and it is still one of the most fundamental.