In public, executives wring their hands over automation’s negative consequences for workers. In private, they talk about how they are racing to automate. More details
Your weekly selection of awesome robot videos Video Friday is your weekly selection of awesome robotics videos, collected by your Automaton bloggers. We’ll also be posting a weekly calendar of upcoming robotics events for the next few months; here’s what we have so far (send us your events!): HRI 2019—11–14 March 2019–Daegu, Korea Nîmes Robotics Festival–17–19 May 2019–Nîmes, France ICRA 2019–20–24 May 2019–Montreal Let us know if you have suggestions for next week, and enjoy today’s videos.
AlphaStar, la déclinaison de l’IA DeepMind entraînée sur Starcraft II, a remporté 10 victoires de suite contre deux des meilleurs joueurs professionnels … Plus de details
Il a également jugé « nécessaire d’explorer l’utilisation de l’intelligence artificielle dans la collecte, la production, la distribution, la réception et le … Plus de details
Mais c’est sans aucun doute le plus riche, le plus fort et le plus développé en matière d’intelligence artificielle.» Cette «délicatesse» a été prononcée … Plus de details
MIT researchers have developed a model that can assimilate multiple types of a patient’s health data to help doctors make decisions with incomplete information. The field of “predictive analytics” holds promise for many health care applications. Machine learning models can be trained to look for patterns in patient data to predict a patient’s risk for disease or dying in the ICU, to aid in sepsis care, or to design safer chemotherapy regimens. The process involves predicting variables of interest, such as disease risk, from known variables, such as symptoms, biometric data, lab tests, and body scans. However, that patient data can come from several different sources and is often incomplete. For example, it might include partial information from health surveys about physical and mental well-being, mixed with highly complex data comprising measurements of heart or brain function. Using machine learning to analyze all available data could help doctors better Continue reading Filling the gaps in a patient’s medical data
Alors que Montréal devient un véritable centre névralgique de l’intelligence artificielle, Edelman a pour mandat de soutenir les communications … Plus de details
Computer algorithms help prosthetics wearers walk within minutes rather than requiring hours of training A movie montage for modern artificial intelligence might show a computer playing millions of games of chess or Go against itself to learn how to win. Now, researchers are exploring how the reinforcement learning technique that helped DeepMind’s AlphaZero conquer chess and Go could tackle an even more complex task—training a robotic knee to help amputees walk smoothly.
Google DeepMind explique de quelle façon son intelligence artificielle, AlphaStar, a été entraînée à jouer sur StarCraft 2. Jeudi 24 janvier, un … Plus de details
The timeline to market a new drug or medical device, from the point of discovery to U.S. Food and Drug Administration approval, can stretch to a decade. By pooling its industry experience and technology, a new health research supergroup led by the Julia Lab within the MIT Computer Science and Artificial Intelligence Laboratory aims to significantly shorten the approval process for pharmaceutical and health care groups. The team aims to leverage real-world evidence, observational data that are generated during routine clinical practice, and patient health care databases to augment label claims and/or support new drug applications with leading-edge software and algorithms and a depth of regulatory and clinical experience. Calling themselves the Health Analytics Collective, the team includes MMS Holdings of Canton, Michigan, a data-focused contract research organization (CRO) to the pharmaceutical, biotechnology, and medical device industries; and the Center for Translational Medicine (CTM) at the University of Maryland School of Pharmacy, where the team will be based. To cut down on the number of required Continue reading Julia Lab joins team to speed up drug approval process