Uniper was recognized as a Machine Learning Master, an organization that uses machine learning and artificial intelligence to reveal data-driven …

If the measure of progress in technology is that devices should become ever smaller and more capable, then OrCam Technologies is on a roll. The Israeli firm's OrCam MyEye, which fits on the arm of a pair of glasses, is far more powerful and much smaller than its predecessor. With new AI-based Smart Reading software released in July, the device not only "reads" text and labels but also identifies people by name and describes other important aspects of the visual world. It also interacts with the user, principally people who are blind or visually impaired, by means of an AI-based smart voice assistant. At the upcoming Sight Tech Global virtual event, we're pleased to announce that OrCam's co-founder and co-CEO, Professor Amnon Shashua, will be a featured speaker.

"Artificial Gods," a collection of stories about artificial intelligence, has been published by New Star Press. It includes 14 tales by 12 Chinese science fiction writers, including some Galaxy Award winners such as "Where the Wind Starts" by Zhang Ran, "Gate of the Machines" by Jiang Bo and "Previous Dusk" by Luo Longxiang. All the stories are about the relationship between humans and AI and worries about a future with AI. In the stories, AI can be a powerful destroyer or an innocent creature. Artificial intelligence is a popular theme for science fiction.

"I did not have lines in the resume that showed my ML expertise. I did not have a Data Science industry experience or relevant papers. For this week's ML practitioner's series, Analytics India Magazine got in touch with Vladimir Iglovikov, an ex-Spetsnaz, theoretical physicist and also a Kaggle GrandMaster. In this exclusive interview, he shares valuable information from his journey in the world of data science. After a brief stint in Russian special forces, Iglovikov enrolled for the Master's programme in theoretical Physics at the St.Petersburg State University whose distinguished alumni include President Vladimir Putin. In September 2010, Iglovikov moved to California to pursue a PhD in Physics from UC Davis and on completion of the degree, he moved to Silicon Valley in the summer of 2015. Currently, Iglovikov works as Sr. Software Engineer at Lyft, a ride-sharing company that operates in the United States and Canada. His work is centered around building robust machine learning models for autonomous vehicles at Lyft, Level5. Post PhD, Iglovikov had two options in hand. One was to pursue postdoc, and the other was to get into the industry as a software engineer. His career took a new turn when one of his friends introduced him to the world of data science. "I attended a lecture where the presenter talked about Data Science as the 4th paradigm of scientific discovery.

Register Debate Welcome to the inaugural Register Debate in which we pitch our writers against each other on contentious topics in IT and enterprise tech, and you – the reader – decide the winning side. The format is simple: a motion is proposed, for and against arguments are published today, then another round of arguments on Wednesday, and we publish a concluding piece on Friday summarizing the brouhaha and the best reader comments. During the week you can cast your vote using the embedded poll below, choosing whether you're in favor or against the motion. The final score will be announced on Friday, revealing whether the for or against argument was most popular. It's up to our writers to convince you to vote for their side.

SUNNY is an Algorithm Selection (AS) technique originally tailored for Constraint Programming (CP). SUNNY enables to schedule, from a portfolio of solvers, a subset of solvers to be run on a given CP problem. This approach has proved to be effective for CP problems, and its parallel version won many gold medals in the Open category of the MiniZinc Challenge -- the yearly international competition for CP solvers. In 2015, the ASlib benchmarks were released for comparing AS systems coming from disparate fields (e.g., ASP, QBF, and SAT) and SUNNY was extended to deal with generic AS problems. This led to the development of sunny-as2, an algorithm selector based on SUNNY for ASlib scenarios. A preliminary version of sunny-as2 was submitted to the Open Algorithm Selection Challenge (OASC) in 2017, where it turned out to be the best approach for the runtime minimization of decision problems. In this work, we present the technical advancements of sunny-as2, including: (i) wrapper-based feature selection; (ii) a training approach combining feature selection and neighbourhood size configuration; (iii) the application of nested cross-validation. We show how sunny-as2 performance varies depending on the considered AS scenarios, and we discuss its strengths and weaknesses. Finally, we also show how sunny-as2 improves on its preliminary version submitted to OASC.

The role of deep learning in science is at a turning point, with weather, climate, and Earth systems modeling emerging as an exciting application area for physics-informed deep learning that can more effectively identify nonlinear relationships in large datasets, extract patterns, emulate complex physical processes, and build predictive models. "Deep learning has had unprecedented success in some very challenging problems, but scientists want to understand exactly how these models work and why they do the things they do," said Karthik Kashinath, a computer scientist and engineer in the Data & Analytics Services Group (DAS) at the National Energy Research Scientific Computing Center (NERSC) who has been deeply involved in NERSC's research and education efforts in this area. "A key goal of deep learning for science is how do you design and train a neural network so that it can capture accurately the complexity of the processes it seeks to model, emulate, or predict, and we're developing ways to infuse physics and domain knowledge into these neural networks so that they obey the laws of nature and their results are explainable, robust, and trustworthy." We caught up with Kashinath following the Artificial Intelligence for Earth System Science (AI4ESS) Summer School, a week-long virtual event hosted in June by the National Center for Atmospheric Research (NCAR) and the University Corporation for Atmospheric Research (UCAR) that was attended by more than 2,400 researchers from around the world. Kashinath was involved in organizing and presenting at the event, along with David John Gagne and Rich Loft of NCAR.

Building an open-domain socialbot that talks to real people is challenging - such a system must meet multiple user expectations such as broad world knowledge, conversational style, and emotional connection. Our socialbot engages users on their terms - prioritizing their interests, feelings and autonomy. As a result, our socialbot provides a responsive, personalized user experience, capable of talking knowledgeably about a wide variety of topics, as well as chatting empathetically about ordinary life. Neural generation plays a key role in achieving these goals, providing the backbone for our conversational and emotional tone. At the end of the competition, Chirpy Cardinal progressed to the finals with an average rating of 3.6/5.0,

Art is subjective and everyone has their own opinion about it. When I saw the expressionist painting Blue Poles, by Jackson Pollock, I was reminded of the famous quote by Rudyard Kipling, "It's clever, but is it Art?" Pollock's piece looks like paint messily spilled onto a drop sheet protecting the floor. The debate of what constitutes art has a long history that will probably never be settled, there is no definitive definition of art. Similarly, there is no broadly accepted objective definition for the quality of a piece of art, with the closest definition being from Orson Welles, "I don't know anything about art but I know what I like." Similarly, people recognize unfair bias when they see it, but it is quite difficult to create a single objective definition.

First, let me tell you how smart I am. My fifth-grade teacher said I was gifted in mathematics and, looking back, I have to admit that she was right. I've properly grasped the character of metaphysics as trope nominalism, and I can tell you that time exists, but that it can't be integrated into a fundamental equation. Most of the things that other people say are only partially true. A paper published in Nature Genetics in 2017 reported that, after analysing tens of thousands of genomes, scientists had tied 52 genes to human intelligence, though no single variant contributed more than a tiny fraction of a single percentage point to intelligence.