Renzo Zagni is the Co-Founder and Head of Product Development at Intelenz, a Silicon Valley Founder Institute portfolio company. Intelenz leverages the power of AI and machine learning to automate workflows and day to day processes for large enterprise organizations. Process automation enables enterprises to design workflows that reduce manual work, minimize risk, and accelerate process execution times while increasing overall business productivity. In short, process automation allows business to do more, with less, while also eliminating the risk of employee burnout, human error and extended product delivery outcomes. Intelenz's platform includes a patented No-Code'Virtual Process Manager' software, which uses AI and machine learning models through an intuitive user interface.
Recently, I was at a party in San Francisco when a man approached me and introduced himself as the founder of a small artificial intelligence (AI) start-up. As soon as the founder figured out that I was a technology writer for The New York Times, he launched into a pitch for his company, which he said was trying to revolutionise the manufacturing sector using a new AI technique called "deep reinforcement learning". The founder explained that his company's AI could run millions of virtual simulations for any given factory, eventually arriving at the exact sequence of processes that would allow it to produce goods most efficiently. This AI, he said, would allow factories to replace entire teams of human production planners, along with most of the outdated software those people relied on. "We call it the Boomer Remover," he said.
Chris Fregly is a Developer Advocate for AI and Machine Learning at Amazon Web Services (AWS) based in San Francisco, California. He is also the founder of the Advanced Spark, TensorFlow, and KubeFlow Meetup Series based in San Francisco. Chris regularly speaks at AI and Machine Learning conferences across the world including the O'Reilly AI, Strata, and Velocity Conferences. Previously, Chris was Founder at PipelineAI where he worked with many AI-first startups and enterprises to continuously deploy ML/AI Pipelines using Apache Spark ML, Kubernetes, TensorFlow, Kubeflow, Amazon EKS, and Amazon SageMaker. He is also the author of the O'Reilly Online Training Series "High Performance TensorFlow in Production with GPUs" Antje Barth is a Developer Advocate for AI and Machine Learning at Amazon Web Services (AWS) based in Düsseldorf, Germany.
Marisa Johnson's six-year-old daughter was just learning to read independently when her Alameda, California, school shut down last year. Without solid literacy skills and lots of time stuck at home, the tot is spending much more time playing video games and watching shows than reading books. "She's definitely reading less," Johnson says. "The only way we can be alone among ourselves is with screens." As many parents know, screen time has ballooned during the pandemic.
GNY's Machine Learning engine went head-to-head with the U.S. Energy Information Administration and outperformed it in predicting energy demand for California. The GNY team are building a larger vision for how GNY can support the work of scientists and advocates fighting for a sustainable and green planet long-term.
Washington – The Mars rover Perseverance has successfully conducted its first test drive on the red planet, the U.S. space agency NASA said Friday. The six-wheeled rover traveled about 6.5 meters (21.3 feet) in 33 minutes on Thursday, NASA said. It drove 4 meters forward, turned in place 150 degrees to the left, and then backed up 2.5 meters, leaving tire tracks in the Martian dust. "This was our first chance to'kick the tires' and take Perseverance out for a spin," said Anais Zarifian, Perseverance mobility test bed engineer at NASA's Jet Propulsion Laboratory in Pasadena, California. Zarifian said the test drive went "incredibly well" and represented a "huge milestone for the mission and the mobility team."
Three years ago, Customs and Border Protection placed an order for self-flying aircraft that could launch on their own, rendezvous, locate and monitor multiple targets on the ground without any human intervention. In its reasoning for the order, CBP said the level of monitoring required to secure America's long land borders from the sky was too cumbersome for people alone. To research and build the drones, CBP handed $500,000 to Mitre Corp., a trusted nonprofit Skunk Works that was already furnishing border police with prototype rapid DNA testing and smartwatch hacking technology. They were "tested but not fielded operationally" as "the gap from simulation to reality turned out to be much larger than the research team originally envisioned," a CBP spokesperson says. This year, America's border police will test automated drones from Skydio, the Redwood City, Calif.-based startup that on Monday announced it had raised an additional $170 million in venture funding at a valuation of $1 billion. That brings the total raised for Skydio to $340 million.
San Francisco: In an advance to building machines with common sense, Facebook researchers have developed a new Artificial Intelligence (AI) model that can learn from any random group of images on the Internet without the need for careful curation and labelling that goes into most computer vision training today. Called SEER (Self-supERvised), the "self-supervised" computer vision model was fed on a billion random, unlabelled and uncurated public Instagram images, Facebook said on Thursday. The future of AI is in creating systems that can learn directly from whatever information they are given -- whether it is text, images, or another type of data -- without relying on carefully curated and labelled data sets to teach them how to recognise objects in a photo, interpret a block of text, or perform any of the countless other tasks that we ask it to. This approach is known as self-supervised learning. According to Facebook AI's Chief Scientist Yann LeCun, the self-supervised learning approach is one of the most promising ways to build machines that have the background knowledge, or "common sense," to tackle tasks that are far beyond today's AI.
Tiger Woods has told authorities he doesn't remember the rollover crash that landed him in a hospital with metal rods and pins in his leg. But the SUV he was driving does. Like other modern cars and trucks, the Genesis GV80 that Woods was driving when he crashed was equipped with an electronic data recorder and other computer hardware meant to serve as a digital witness of sorts -- filled with information investigators can use to piece together the seconds before and during the accident. The devices are part of a broader array of safety technology built into many newer vehicles. Vehicles in the Genesis line -- Hyundai's luxury brand -- for example, also feature artificial intelligence software that keeps a watchful eye, sending alerts if it detects the driver is distracted or closes his or her eyes while driving.