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Science  has named nine scientific advances as runners-up for the 2020 Breakthrough of the Year. For 5 decades, scientists have struggled to solve one of biology's biggest challenges: predicting the precise 3D shape a string of amino acids will fold into as it becomes a working protein. This year, they achieved that goal, developing an artificial intelligence (AI) program that predicts most protein structures as accurately as laboratory experiments can map them. Because a protein's precise shape determines its biochemical functions, the new program could help researchers uncover mechanisms of disease, develop new drugs, and even create drought-tolerant plants and cheaper biofuels. Researchers traditionally decipher structures using laborious techniques such as x-ray crystallography and cryo–electron microscopy. But detailed molecular maps only exist for about 170,000 of the 200 million known proteins. Computational biologists have dreamed of simply predicting a protein's structure by modeling the amino acid interactions that govern its 3D shape. But because amino acids can interact in so many ways, the number of possible structures for single protein is astronomical. In 1994, structural biologists launched a biennial competition called the Critical Assessment of Protein Structure Prediction (CASP). Entrants are given amino acid sequences for about 100 proteins with as-yet-unknown structures. Some groups try to predict their structures, while others map the same structures in the lab; afterward, their results are compared. Even in CASP's early years, the predictions for small, simple proteins were on par with experimental observations. But predictions for larger, more challenging proteins lagged far behind. Not anymore. This year, an AI program created by researchers at U.K.-based DeepMind tallied a median score of 92.4 on a 100-point scale, where anything above 90 is considered as accurate as an experimentally derived structure. On the most challenging proteins, the AlphaFold program averaged 87, 25 points ahead of its closest competitor. And because contest rules require competitors to reveal enough of their methods for others to make use of them, organizers say it's only a matter of months before other groups match AlphaFold's success. — Robert F. Service Since the revolutionary genome-snipping tool known as CRISPR burst on the scene in 2012, it has given researchers new power to engineer crops and animals, stirred ethical debates, and earned a Nobel Prize—not to mention Science 's Breakthrough of the Year in 2015. Now, CRISPR is again making waves, scoring its first success in the clinic by treating two inherited blood diseases. People with beta-thalassemia have low levels of the oxygen-carrying hemoglobin protein, leading to weakness and exhaustion; those with sickle cell disease make a defective form of the protein, resulting in sickle-shaped red blood cells that block blood vessels and often cause severe pain, organ damage, and strokes. To treat three sickle cell patients, researchers harvested immature blood cells, known as blood stem cells, from each. They then used CRISPR to disable an “off” switch that—in adults—stops production of the fetal form of hemoglobin, which can counter the effects of the sickling mutation. After the patients received chemotherapy to wipe out their diseased blood stem cells, the CRISPR-treated cells were infused back into their bodies. The patients, treated up to 17 months ago, are now making plentiful fetal hemoglobin, and have not experienced the painful attacks that used to strike every few months, the companies CRISPR Therapeutics and Vertex Pharmaceuticals reported in December. One patient, a young mother of three, says the treatment changed her life. The companies also gave the treatment to seven patients who normally receive blood transfusions for beta-thalassemia. They haven't needed transfusions since, the companies reported in the same paper and meeting presentation. With more testing, the new treatment could rival the success of gene therapies that treat the two diseases by adding hemoglobin DNA to stem cells. But like gene therapy, the CRISPR approach requires high-tech medical care and could cost $1 million or more per patient—putting it out of reach for much of Africa, where most people with sickle cell live. — Jocelyn Kaiser More than 40 years ago, the world's leading climate scientists gathered in Woods Hole, Massachusetts, to answer a simple question: How hot would Earth get if humans kept emitting greenhouse gases? Their answer, informed by rudimentary climate models, was broad: If atmospheric carbon dioxide (CO2) doubled from preindustrial levels, the planet would eventually warm between 1.5°C and 4.5°C, a climate sensitivity range encompassing the merely troubling and the catastrophic. Now, they've finally ruled out the mildest scenarios—and the most dire. Narrowing those bounds has taken decades of scientific advancement. Understanding how clouds trap or reflect heat has been a particular challenge. Depending on their thickness, location, and composition, clouds can amplify warming—or suppress it. Now, high-resolution cloud models, supported by satellite evidence, have shown that global warming thins low, light-blocking clouds: Hotter air dries them out and subdues the turbulence that drives their formation. Longer and better temperature records have also helped narrow the range. Studies of Earth's ancient climate, which estimate paleotemperatures and CO2 levels using ice and ocean sediment cores, suggest how greenhouse gases may have driven previous episodes of warming. And modern global warming has now gone on long enough that surface temperatures, 1.1°C hotter than in preindustrial times, can be used to more confidently project trends into the future. This year, these advances enabled 25 scientists affiliated with the World Climate Research Programme to narrow climate sensitivity to a range between 2.6°C and 3.9°C. The study rules out some of the worst-case scenarios—but it all but guarantees warming that will flood coastal cities, escalate extreme heat waves, and displace millions of people. If we're lucky, such clarity might galvanize action. Atmospheric CO2 is already at 420 parts per million—halfway to the doubling point of 560 ppm. Barring more aggressive action on climate change, humanity could reach that threshold by 2060—and lock in the foreseen warming. — Paul Voosen Everyone loves a good mystery. Take fast radio bursts (FRBs)—short, powerful flashes of radio waves from distant galaxies. For 13 years, they tantalized astronomers keen to understand their origins. One running joke said there were more theories explaining what causes FRBs than there were FRBs. (Currently, astronomers know of more than 100.) Now, cosmic sleuths have fingered a likely culprit: magnetars, neutron stars that fizzle and pop with powerful magnetic fields. Because FRBs are so fast, they must come from a small but intense energy source like a magnetar, which are formed when burned-out stars collapse to the size of a city. But although a handful of FRBs had been traced to particular galaxies, no telescope had sharp enough vision to connect them to an individual magnetar at such great distances. Then, in April, an FRB went off in the Milky Way—close enough that astronomers could examine the scene. The Canadian Hydrogen Intensity Mapping Experiment, a pioneering survey telescope in British Columbia responsible for the discovery of many FRBs, narrowed the source to a small area of sky, which was soon confirmed by the U.S. radio array STARE2. Orbiting observatories sensitive to higher frequencies quickly found that a known magnetar in that part of the sky, called SGR 1935+2154, was acting up at the same time, spewing out bursts of x-rays and gamma rays. Although astronomers studying FRBs believe they have finally found their perpetrator, they still don't know exactly how magnetars produce the radio bursts. They could come from close to the magnetar's surface, as magnetic field lines break and reconnect—similar to the Sun's flaring behavior. Or they could come from farther out, as shock waves slam into clouds of charged particles and generate laserlike radio pulses. Stay tuned for a sequel: Crack theorists are on the case. — Daniel Clery More than 40,000 years ago on the Indonesian island of Sulawesi, a prehistoric Pablo Picasso ventured into the depths of a cave and sketched a series of fantastic animal-headed hunters cornering wild hogs and buffaloes. The age of the paintings, pinned down just 1 year ago, makes them the earliest known figurative art made by modern humans. In 2017, when an Indonesian researcher chanced across the scene, the figures alone told him he had found something special. The animals appear to be Sulawesi warty pigs and dwarf buffaloes, both of which still live on the island. But it was the animallike features of the eight hunters, armed with spears or ropes, that captivated archaeologists. Several of the hunters seem to have long muzzles or snouts. One sports a tail. Another's mouth resembles a bird beak. It's possible the artist was depicting the hunters wearing masks or camouflage, the researchers say, but they may also represent mythical animal-human hybrids. Such hybrids appear in other ancient works of art, including a 35,000-year-old ivory figurine of a lion-man found in the German Alps. Parts of the paintings were covered in white, bumpy mineral deposits known as cave popcorn. Uranium in this popcorn decays at a fixed rate, which allowed researchers to date minerals on top of the pigment to about 44,000 years ago. The cave scene must be at least that old—about 4000 years older than any other known figurative rock art, they reported in late December 2019. It decisively unseats Europe as the first place where modern humans are known to have created figurative art. If the figures do depict mythical human-animal hunters, their creators may have already passed an important cognitive milestone: the ability to imagine beings that do not exist. That, the researchers say, forms the roots of most modern—and ancient—religions. — Michael Price Within days of a racially charged confrontation between a white dog owner and a Black birdwatcher in New York City's Central Park in late May, scientists flocked to Twitter to celebrate—and support—Black nature enthusiasts. The #BlackBirdersWeek hashtag was soon followed by others, in disciplines from neuroscience to physics, all aiming to create community among Black scientists on Twitter, Zoom, and other platforms. “We're few and far between, so having us come together as a conglomerate in one virtual space—it really helped,” says Ti'Air Riggins, a biomedical engineering Ph.D. student at Michigan State University who helped organize #BlackInNeuro week. The social media events took place against the backdrop of the anguished response to police killings in the United States, the Black Lives Matter movement, and discussions within science about the need to create a more equitable, welcoming environment for people of color. Through those discussions, many scientists hoped to reach colleagues who had paid little attention to these issues in the past. “People of color across the board are struggling,” says Tanisha Williams, a botanist at Bucknell University who spearheaded #BlackBotanistsWeek. “It's a systemic problem.” Although it's too early to tell whether the events of this year will spur lasting change, many are hopeful. “This year feels different,” says Shirley Malcom, a senior adviser at AAAS (publisher of Science ) who has worked on diversity, equity, and inclusion issues since the 1970s. “All of a sudden, after George Floyd and everything else that came out after that time, you could at least get people's attention,” she says—adding that many scientists now seem more open to the idea that systemic racism is a problem in their community. “I definitely feel like our voices are being heard, and in a different way [than before],” Williams says. “But it's not going to be a quick fix … we have a long road.” — Katie Langin HIV, like all retroviruses, has a nasty feature that allows it to dodge attack: It integrates its genetic material into human chromosomes, creating “reservoirs” where it can hide, undetected by the immune system and invulnerable to antiretroviral drugs. But where it hides may make all the difference. This year, a study of 64 HIV-infected people who have been healthy for years without antiretroviral drugs reveals a link between their unusual success and where the virus has hunkered down in their genomes. Although the new understanding of these “elite controllers” won't lead directly to a cure, it opens up a novel strategy that may routinely allow other infected people to live for decades without treatment. Many studies have examined elite controllers, who make up about 0.5% of the 38 million people living with HIV. But this new work stood apart in size and scope, comparing integrated HIV in the 64 elite controllers with that in 41 HIV-infected people on treatment. HIV does best when it slots itself within genes. When the cell transcribes the genes, the integrated HIV, or “provirus,” can produce new viruses that infect other cells. If it parks in “gene deserts,” portions of chromosomes that rarely transcribe DNA, the provirus sits around like a fully functioning car stuck in a place that doesn't sell gas. The study found that in the elite controllers, 45% of functioning proviruses resided in gene deserts, compared with just 17.8% for the people on treatment. Presumably, immune responses in the elite controllers somehow cleared proviruses from the more dangerous parking spots. Now, the challenge is to figure out interventions that will train the immune systems of the vast majority of people living with HIV to behave similarly. That new insight suggests long-standing, frustrating attempts to cure people by eliminating HIV reservoirs may be too ambitious an approach. Instead, success may depend on shrinking—and then making peace with—these reservoirs, and minding the old real estate dictum of location, location, location. — Jon Cohen Scientists have spent decades searching for materials that conduct electricity without resistance at room temperature. This year they found the first one, a hydrogen- and carbon-containing compound squeezed to a pressure approaching that at the center of Earth. The discovery is setting off a hunt for room temperature superconductors that work at typical surface pressures; such materials could transform technologies and save the vast amounts of energy wasted when electricity moves through wires. Superconductivity got its start in 1911 when physicist Heike Kamerlingh Onnes found that a mercury wire chilled to 4.2°C above absolute zero, or 4.2 K, conducted electrons without the usual heat-producing friction. In 1986, researchers found the same was true of a family of copper oxide ceramics. Because these superconductors worked above 77 K—the temperature of liquid nitrogen—they spawned a new generation of MRI machines and particle accelerator magnets. There were hints that copper oxides might superconduct at room temperature, but they were never verified. Confirmation now comes from high-pressure physics, in which scientists smash flecks of materials between the flattened points of two diamonds at pressures millions of times higher than those at Earth's surface. With such a diamond anvil, researchers in Germany in 2019 compressed a mix of lanthanum and hydrogen to 170 gigapascals (GPa), yielding superconductivity at temperatures up to 250 K, just under the freezing point of water. This year, researchers in the United States topped that result with a hydrogen, carbon, and sulfur compound compressed to 267 GPa. It conducted without resistance to 287 K, the temperature of a chilly room. So far, the new superconductors fall apart when the pressure is released. But the same isn't true of all high-pressure materials: Diamonds born in the crushing depths of Earth, for example, survive after rising to the surface. Now, researchers hope to find a similarly long-lasting gem for their own field. — Robert F. Service Their eyes are beady and their brains are no bigger than a walnut. But two studies published this year suggest birds have startling mental powers. One reveals that part of the avian brain resembles the human neocortex, the source of human intelligence. The other shows that carrion crows are even more aware than researchers had thought—and may be capable of some conscious thought. In humans, the neocortex consists of horizontal layers laced with interconnected columns of nerve cells, which allow for complex thinking. Bird brains, in contrast, were thought to be arranged in simple clusters of nerve cells. By using a technique called 3D polarized light imaging, neuroanatomists took a closer look at the forebrain of homing pigeons and owls and found that nerves there connect both horizontally—like the layers in the neocortex—and vertically, echoing the columns seen in human brains. Another team of scientists probed this part of the brains of carrion crows—well-known for their intelligence—for clues that they are aware of what they see and do. The researchers first trained lab-raised crows to turn their heads when they saw certain sequences of lights flashing on a computer monitor. Electrodes in the crows' brains detected nerve activity between the moment the birds saw the signal and when they moved their heads. The activity developed even when the lights were barely detectable, suggesting it was not simply a response to sensory input, and it was present regardless of whether the birds reacted. The scientists think the neural chatter represents a kind of awareness—a mental representation of what the birds saw. Such “sensory consciousness” is a rudimentary form of the self-awareness that humans experience. Its presence in both birds and mammals suggests to the researchers that some form of consciousness may date back 320 million years, to our last common ancestor. — Elizabeth Pennisi

Researchers aim to use artificial intelligence to save endangered whales in B.C.


Researchers are aiming to "teach" a computer to recognize the sounds of resident killer whales in order to develop a warning system for preventing ships from fatally striking endangered orcas off British Columbia's coast. Steven Bergner, a computing science research associate at Simon Fraser University's Big Data Hub, said he is collecting and managing a database of sounds picked up 24 hours a day by a network of hydrophones in the Salish Sea. Marine biologists will identify the sounds of different species of whales, including humpbacks and transients, and differentiate the acoustics from other noise such as waves and boats, he said. Machine learning or artificial intelligence would help detect the presence of orcas through patterns in the data. "That (information) goes through another system that then decides whether there should be a warning that ultimately reaches the vessel pilots," Bergner said.

Researchers aim to use artificial intelligence to save endangered whales in B.C. - 660 NEWS


Researchers are aiming to "teach" a computer to recognize the sounds of resident killer whales in order to develop a warning system for preventing ships from fatally striking endangered orcas off British Columbia's coast. Steven Bergner, a computing science research associate at Simon Fraser University's Big Data Hub, said he is collecting and managing a database of sounds picked up 24 hours a day by a network of hydrophones in the Salish Sea. Marine biologists will identify the sounds of different species of whales, including humpbacks and transients, and differentiate the acoustics from other noise such as waves and boats, he said. Machine learning or artificial intelligence would help detect the presence of orcas through patterns in the data. "That (information) goes through another system that then decides whether there should be a warning that ultimately reaches the vessel pilots," Bergner said.

A robot that can track specific people and follow them around


Telling humans apart and following them as they move in their surrounding environment could be two highly valuable skills for service robots. In fact, when combined, these two capabilities would allow robots to follow specific people as they are interacting with them or offering their assistance. Researchers at Monash University, JDQ Systems and University of British Columbia recently developed a service robot designed to assist residents at elderly care homes or patients at other healthcare facilities. In a paper pre-published on arXiv, they presented a computational technique that allows their robot to identify and track people in its vicinity, following specific users as they are assisting them. "Our team has been developing a socially assistive robot platform, Aether, for providing daily routine assistance to staff and residents at elderly care and assisted living facilities," Wesley P. Chan, one of the researchers who carried out the study, told TechXplore.

NexTech AR Launches New Artificial Intelligence Division


* NexTech is expanding its technology stack by launching a new AI division * The Company is establishing a transformative internship program with AI students at Northeastern University and others * The global AI market revenues are expected to surpass $300 billion in 2024, creating a new substantial market opportunity for NexTech VANCOUVER, British Columbia, Dec. 02, 2020 (GLOBE NEWSWIRE) -- NexTech AR Solutions (NexTech) (OTCQB: NEXCF) (CSE: NTAR) (FSE: N29), a leading provider of virtual and augmented reality (AR) experience technologies and services for virtual and hybrid eCommerce, education, conferences and events today announced the creation of its new Artificial Intelligence (AI) division. Through a dedicated initial team of three AI experts focused on enhancing NexTech’s AI capabilities, the company aims to gain a competitive edge and create new portfolio offerings to complement its AR; streamlining operations for clients while tapping into a market that is expected to surpass $300 billion in revenues by 2024.NexTech’s Mirjana Prpa, AR Product Manager, and the new head of the AI division, will drive efforts to identify, develop and deploy AI capabilities within the Company’s existing AR technology and virtual experience portfolio. She will lead a growing team that will include new AI experts and a substantial number of interns. The NexTech AI program plans to create automation within the AR content creation space in order to build a self-service AR platform that easily works for everyone, turning everyone into AR creators. The AI program will give back to students while also tapping into their untapped potential and insight by developing an internship program, starting with computer science students at the nearby Northeastern University in Vancouver.The internships offered by NexTech’s AI division will provide students with a unique hands-on learning experience that equips them with the tools they need to establish their careers in AI and AR. NexTech aims to create a mutually beneficial experience where students will be able to grow with the company by working on various AI-focused projects. In addition, NexTech is committed to creating long-term opportunities that will allow students to work closely with the NexTech team in real-world AI applications.NexTech is building this new AI division with the goal of becoming a leader in AR which it believes can only happen with the merging of these two potent technologies. The team will utilize AI within the content creation of the company’s AR solutions that allow users to not just experience AR but also to create their own experiences, through the support of AI for AR. The ultimate goal is to harness the power of AI to offer a self-serve AR solution for everyone.“I’m excited to lead NexTech’s AI division and work towards the goal of making AR accessible to everyone. By creating a program that connects academia with industry experts, we hope to create long term relationships with students by recognizing their talents and offering them opportunities to aid in their professional development,” says Mirjana Prpa, Product Manager for AR and Head of the AI Division. “The potential of AI in AR applications is limitless; we’re only just starting to scratch the surface with this newfound program. Through the work of professionals and students who are learning new applications and approaches for AI integration, coupled with our growing team at NexTech, we hope to create experiences unlike any other currently available that combine the mesmerizing potential of AR with the streamlined efficiency of AI.”Evan Gappelberg, CEO of NexTech says, “As one of the leaders in the Augmented Reality market, we are excited to continue our leadership and development with the introduction of our AI division as well as our internship program. NexTech’s growth and competitiveness within the market remains an important aspect of the company’s identity, especially as we work to extend our capabilities and services into the rapidly growing AR/AI markets.”To learn more about NexTech AR, please visit NexTech ARNexTech is one of the leaders in the rapidly growing Augmented Reality market estimated to grow from USD $10.7B in 2019 and projected to reach USD $72.7B by 2024 according to Markets & Markets Research; it is expected to grow at a CAGR of 46.6% from 2019 to 2024.The company is pursuing four verticals:InfernoAR: An advanced Augmented Reality and Video Learning Experience Platform for Events, is a SaaS video platform that integrates Interactive Video, Artificial Intelligence and Augmented Reality in one secure platform to allow enterprises the ability to create the world’s most engaging virtual event management and learning experiences. Automated closed captions and translations to over 64 languages. According to Grandview Research the global virtual events market in 2020 is $90B and expected to reach more than $400B by 2027, growing at a 23% CAGR. With NexTech’s InfernoAR platform having augmented reality, AI, end-to-end encryption and built in language translation for 64 languages, the company is well positioned to rapidly take market share as the growth accelerates globally.ARitize™ For eCommerce: The company launched its SaaS platform for webAR in eCommerce early in 2019. NexTech has a ​‘full funnel’ end-to-end eCommerce solution for the AR industry including its Aritize360 app for 3D product capture, 3D/AR ads, its ARitize white label app it’s ‘Try it On’ technology for online apparel, 3D and 360-degree product views, and ‘one click buy’.ARitize™ 3D/AR Advertising Platform: Launched in Q1 2020 the ad platform will be the industry's first end-to-end solution whereby the company will leverage its 3D asset creation into 3D/AR ads. In 2019, according to IDC, global advertising spend will be about $725 billion.ARitize™ Hollywood Studios: The studio is in development producing immersive content using 360 video, and augmented reality as the primary display platform.To learn more, please follow us on Twitter, YouTube, Instagram, LinkedIn, and Facebook, or visit our website: behalf of the Board of NexTech AR Solutions Corp. “Evan Gappelberg” CEO and DirectorFor further information regarding the internship program, please contact:Mirjana Prpa Head of AI Division mirjana.prpa@nextechar.comThe CSE has not reviewed and does not accept responsibility for the adequacy or accuracy of this release.Certain information contained herein may constitute “forward-looking information” under Canadian securities legislation. Generally, forward-looking information can be identified by the use of forward-looking terminology such as, “will be”, “looking forward” or variations of such words and phrases or statements that certain actions, events or results “will” occur. Forward-looking statements regarding the Company increasing investors awareness are based on the Company’s estimates and are subject to known and unknown risks, uncertainties and other factors that may cause the actual results, levels of activity, performance or achievements of NexTech to be materially different from those expressed or implied by such forward-looking statements or forward-looking information, including capital expenditures and other costs. There can be no assurance that such statements will prove to be accurate, as actual results and future events could differ materially from those anticipated in such statements. Accordingly, readers should not place undue reliance on forward-looking statements and forward-looking information. NexTech will not update any forward-looking statements or forward-looking information that are incorporated by reference herein, except as required by applicable securities laws.

'BearID': B.C. researchers use artificial intelligence to identify and track bears


Researchers say the new technology, termed BearID, created a'non-invasive' technique to study the animals. Despite a decade of behavioural research on grizzly bears in B.C.'s Knight Inlet, Melanie Clapham still has trouble telling some individual bears apart. Brown bears, which include grizzly bears, can change dramatically in their appearance during their younger years and, unlike other wildlife that has spots or stripes, they lack distinguishing markings on their bodies. Ms. Clapham, a conservation biologist and postdoctoral research fellow at the University of Victoria, dreamed of technology that could help her individually identify these furry mammals. While she was looking for a tech team to make that idea possible, south of the border, Ed Miller and Mary Nguyen, two Silicon Valley engineers who are also outdoor and wildlife enthusiasts, had started a project to develop machine-learning models that could be adapted to grizzly bears.

How Can India Trump China In Higher Education Reforms For AI


India has many milestones to achieve if it is to catch up with China, concluded a study published by the Observer Research Foundation, that offered a comparative analysis between the two in terms of higher education reforms for the development of talent in artificial intelligence (AI) and its research. The study, which compared various parameters including AI development plans and strategies of the two countries, their automation readiness index, talent retention, and research output, found China and India to have a very evident difference in their approach to bring reforms in higher education for AI. "Countries' strength in AI will potentially impact their position in the power structure in international politics in the long term," said Dr Romi Jain, author of the study and a Postdoctoral Research Fellow at the University of British Columbia. "From the geopolitical perspective, acquisition of robust AI capabilities could even shape the contours and outcomes of Sino-Indian conflict, competition and rivalry." As both countries introduce AI degree programmes in higher education, the results are too premature to be judged.

Byzantine Resilient Distributed Multi-Task Learning Machine Learning

Distributed multi-task learning provides significant advantages in multi-agent networks with heterogeneous data sources where agents aim to learn distinct but correlated models simultaneously. However, distributed algorithms for learning relatedness among tasks are not resilient in the presence of Byzantine agents. In this paper, we present an approach for Byzantine resilient distributed multi-task learning. We propose an efficient online weight assignment rule by measuring the accumulated loss using an agent's data and its neighbors' models. A small accumulated loss indicates a large similarity between the two tasks. In order to ensure the Byzantine resilience of the aggregation at a normal agent, we introduce a step for filtering out larger losses. We analyze the approach for convex models and show that normal agents converge resiliently towards their true targets. Further, an agent's learning performance using the proposed weight assignment rule is guaranteed to be at least as good as in the non-cooperative case as measured by the expected regret. Finally, we demonstrate the approach using three case studies, including regression and classification problems, and show that our method exhibits good empirical performance for non-convex models, such as convolutional neural networks.

Learning Optimal Representations with the Decodable Information Bottleneck Machine Learning

We address the question of characterizing and finding optimal representations for supervised learning. Traditionally, this question has been tackled using the Information Bottleneck, which compresses the inputs while retaining information about the targets, in a decoder-agnostic fashion. In machine learning, however, our goal is not compression but rather generalization, which is intimately linked to the predictive family or decoder of interest (e.g. linear classifier). We propose the Decodable Information Bottleneck (DIB) that considers information retention and compression from the perspective of the desired predictive family. As a result, DIB gives rise to representations that are optimal in terms of expected test performance and can be estimated with guarantees. Empirically, we show that the framework can be used to enforce a small generalization gap on downstream classifiers and to predict the generalization ability of neural networks.

AI's killer (whale) app


The Salish Sea, which extends from British Columbia to Washington State in the U.S., was once home to hundreds of killer whales, also known as orcas. Now, the population of Southern Resident Killer Whales, a subgroup of orcas, is struggling to survive--there are only 73 of them left. Building on our work using AI for Social Good, we're partnering with Fisheries and Oceans Canada (DFO) to apply machine learning to protect killer whales in the Salish Sea. According to DFO, which monitors and protects this endangered population of orcas, the greatest threats to the animals are scarcity of prey (particularly Chinook salmon, their favorite meal), contaminants, and disturbance caused by human activity and passing vessels. Teaming up with DFO and Rainforest Connection, we used deep neural networks to track, monitor and observe the orcas' behavior in the Salish Sea, and send alerts to Canadian authorities.