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Fun AI Apps Are Everywhere Right Now. But a Safety 'Reckoning' Is Coming

TIME - Tech

If you've spent any time on Twitter lately, you may have seen a viral black-and-white image depicting Jar Jar Binks at the Nuremberg Trials, or a courtroom sketch of Snoop Dogg being sued by Snoopy. These surreal creations are the products of Dall-E Mini, a popular web app that creates images on demand. Type in a prompt, and it will rapidly produce a handful of cartoon images depicting whatever you've asked for. More than 200,000 people are now using Dall-E Mini every day, its creator says--a number that is only growing. A Twitter account called "Weird Dall-E Generations," created in February, has more than 890,000 followers at the time of publication.


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The SIOP Foundation has awarded the 2022 Visionary Grant to an international and interdisciplinary team that plans to advance the current understanding of human–artificial intelligence (AI) teamwork and the role of trust in that collaboration. The winning proposal, "We Are In This Together: When an AI Agent Becomes Your Teammate," was submitted by the team of Eleni Georganta, Technical University of Munich; Anna-Sophie Ulfert, Eindhoven University of Technology; Myrthe Tielman, Delft University of Technology; and Tal Oron-Gilad and Shanee Honig, both of Ben-Gurion University. The team will use the $100,000 prize to explore humans and AI working as teammates. "To unlock the potential of these new teams, trust will be the key," Ulfert said. "But what is the meaning of trust in teams consisting of human and AI teammates?"


HPE and Cerebras build new AI supercomputer at LRZ in Munich

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HPE and Cerebras Systems have built a new AI supercomputer in Munich, Germany, pairing a HPE Superdome Flex with the AI accelerator technology from Cerebras for use by the scientific and engineering community. The new system, created for the Leibniz Supercomputing Center (LRZ) in Munich, is being deployed to meet the current and expected future compute needs of researchers, including larger deep learning neural network models and the emergence of multi-modal problems that involve multiple data types such as images and speech, according to Laura Schulz, LRZ's head of Strategic Developments and Partnerships. "We're seeing an increase in large data volumes coming at us that need more and more processing, and models that are taking months to train, we want to be able to speed that up," Schulz said. "And then we're also seeing multi-modal problems, such as integration of natural language processing (NLP) and medical imaging or documents, so we have this complexity, we have this the need for faster, we have this need for bigger that's coming from our user side, from our facility side, and we need to make sure that we're constantly evaluating to have these different novel architectures, to have different usage models to be able to understand all that." The LRZ team decided that the Cerebras technology, with its large shared memory and scalability, was a good match for the "pain points" they were trying to resolve, she said.


Using machine learning to derive different causes from the same symptoms

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Machine learning is playing an ever-increasing role in biomedical research. Scientists at the Technical University of Munich (TUM) have now developed a new method of using molecular data to extract subtypes of illnesses. In the future, this method can help to support the study of larger patient groups. Nowadays doctors define and diagnose most diseases on the basis of symptoms. However, that does not necessarily mean that the illnesses of patients with similar symptoms will have identical causes or demonstrate the same molecular changes.


Machine learning helps distinguishing diseases - Innovation Origins

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Nowadays doctors define and diagnose most diseases on the basis of symptoms. However, that does not necessarily mean that the illnesses of patients with similar symptoms will have identical causes or demonstrate the same molecular changes. In biomedicine, one often speaks of the molecular mechanisms of a disease. This refers to changes in the regulation of genes, proteins or metabolic pathways at the onset of illness. The goal of stratified medicine is to classify patients into various subtypes at the molecular level in order to provide more targeted treatments, wrties the Technical University of Munich in a press release.


Insurance Companies Using AI to Build Safety Systems, Optimize Rates - AI Trends

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INSHUR is aimed at helping rideshare drivers using Uber or Lyft, and limousine drivers, to find competitive rates for auto insurance. Founded in 2016, the company is based in New York City, is backed by Munich Re Digital Partners, and launched in the UK in 2018. INSHUR has signed up over 40,000 drivers. The company supports liability and physical damage policies with minimum limits of insurance as required by the NYC Taxi & Limousine Commission (TLC) for limousines, which is also compatible with requirements for ride sharing services.


Deep Science: AI cuts, flows, and goes green – TechCrunch

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Research in the field of machine learning and AI, now a key technology in practically every industry and company, is far too voluminous for anyone to read it all. This column aims to collect some of the most relevant recent discoveries and papers -- particularly in, but not limited to, artificial intelligence -- and explain why they matter. This week AI applications have been found in several unexpected niches due to its ability to sort through large amounts of data, or alternatively make sensible predictions based on limited evidence. We've seen machine learning models taking on big data sets in biotech and finance, but researchers at ETH Zurich and LMU Munich are applying similar techniques to the data generated by international development aid projects such as disaster relief and housing. The team trained its model on millions of projects (amounting to $2.8 trillion in funding) from the last 20 years, an enormous dataset that is too complex to be manually analyzed in detail.


Researchers claim biometric deepfake detection method improves state-of-the-art

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Biometrics can effectively be used to detect deepfakes, according to a paper from a team of Italian and German researchers reported by Unite.AI, and could be a less "unwieldy" method of doing so than detecting synthetic artefacts and other methods. The framework for the method specifies the use of at least ten genuine videos of the subject to train the biometric model, the researchers from the University of Federico II in Naples and the Technical University of Munich write. The research into'Audio-Visual Person-of-Interest DeepFake Detection' has been posted to Arxive, and describes what the authors say is a new state-of-the-art in deepfake detection. In testing against well-known datasets, the researchers improved area under curve (AUC) scores by 3 and 10 for accuracy identifying genuine high and low-quality videos, respectively, and 7 percent for deepfake videos. Interestingly, on high-quality videos, the worst-performing system delivered deepfake detection accuracy of above 69 percent.


Senior Machine Learning Research Engineer

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Alation continues to hire for roles at various locations with all interviewing and on-boarding done virtually due to COVID-19 crisis. At Alation, we help people find, understand, and trust data, so they not only excel in their work -- they drive value for their enterprise, team and role. In the words of one customer, "Alation makes me look like a rockstar." We help companies like Pfizer, PepsiCo, and Munich Re empower their people with the best data every day. As a platform for innovation, Alation helps customers create game-changing solutions and products (like a program for early-stage disease detection with Pfizer, or a wind farm offering a guaranteed ROI with Munich Re).


Reflections On a Decade Of AI

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Recently, I was asked to be the General Co-Chair for the IEEE International Conference on Connected Vehicles (ICCVE). Founded a decade ago with academic roots, the 2022 version extended beyond the academic model with a significant industry and regulatory emphasis, and concurrent physical locations in Shanghai, Pune, and Munich. Interestingly, the decade-long historical backdrop of the conference and the assembly of the worldwide activity provided an opportunity to be reflective about progress beyond the noise from the tactical day-to-day headlines. Where do we really stand with AI technology? How did we get here?