Robots in the work place can perform hazardous or even 'impossible' tasks; e.g., toxic waste clean-up, desert and space exploration, and more. AI researchers are also interested in the intelligent processing involved in moving about and manipulating objects in the real world.
While the most sophisticated driverless cars on public roads can handle haboobs and rainstorms like champs, certain types of precipitation remain a challenge for them -- like snow. That's because snow covers cameras critical to those cars' self-awareness and tricks sensors into perceiving obstacles that aren't there, and because snow obscures road signs and other structures that normally serve as navigational landmarks. In an effort to spur on the development of cars capable of driving in wintry weather, startup Scale AI this week open-sourced Canadian Adverse Driving Conditions (CADC), a data set containing over 56,000 images in conditions including snow created with the University of Waterloo and the University of Toronto. While several corpora with snowy sensor samples have been released to date, including Linköping University's Automotive Multi-Sensor Dataset (AMUSE) and the Mapillary Vistas data set, Scale AI claims that CADC is the first to focus specifically on "real-world" driving in snowy weather. "Snow is hard to drive in -- as many drivers are well aware. But wintry conditions are especially hard for self-driving cars because of the way snow affects the critical hardware and AI algorithms that power them," wrote Scale AI CEO Alexandr Wang in a blog post.
In a test kitchen in a corner building in downtown Pasadena, Flippy the robot grabbed a fryer basket full of chicken fingers, plunged it into hot oil -- its sensors told it exactly how hot -- then lifted, drained and dumped maximally tender tenders into a waiting hopper. A few feet away, another Flippy eyed a beef patty sizzling on a griddle. With its camera eyes feeding pixels to a machine vision brain, it waited until the beef hit the right shade of brown, then smoothly slipped its spatula hand under the burger and plopped it on a tray. The product of decades of research in robotics and machine learning, Flippy represents a synthesis of motors, sensors, chips and processing power that wasn't possible until recently. Now, Flippy's success -- and the success of the company that built it, Miso Robotics -- depends on simple math and a controversial hypothesis of how robots can transform the service economy.
Self-driving cars are one of the high-risk artificial intelligence applications the European Union wants to regulate. The European Commission today unveiled its plan to strictly regulate artificial intelligence (AI), distinguishing itself from more freewheeling approaches to the technology in the United States and China. The commission will draft new laws--including a ban on "black box" AI systems that humans can't interpret--to govern high-risk uses of the technology, such as in medical devices and self-driving cars. Although the regulations would be broader and stricter than any previous EU rules, European Commission President Ursula von der Leyen said at a press conference today announcing the plan that the goal is to promote "trust, not fear." The plan also includes measures to update the European Union's 2018 AI strategy and pump billions into R&D over the next decade.
These days, machine learning and computer vision are all the craze. We've all seen the news about self-driving cars and facial recognition and probably imagined how cool it'd be to build our own computer vision models. However, it's not always easy to break into the field, especially without a strong math background. Libraries like PyTorch and TensorFlow can be tedious to learn if all you want to do is experiment with something small. In this tutorial, I present a simple way for anyone to build fully-functional object detection models with just a few lines of code.
Recent surveys, studies, forecasts and other quantitative assessments of the progress of AI highlight the increasing presence of AI in the healthcare industry, the assistance AI may provide in the future to workers' cognitive tasks, and the continuing acceleration in data production and dissemination. Of the $27 billion raised, U.S. startups accounted for $17 billion, up from $13.3 billion the previous year. Chinese AI startups, on the other hand, raised only $2.9 billion in 2019, down from $4.7 billion in 2018. Big companies wouldn't be investing billions [in AI] if it wasn't producing for them"--Geoffrey Hinton "Artificial intelligence enables us to process the vast quantity of data across our businesses to generate new insights which can keep us ahead of the competition"--Yuri Sebregts, chief technology officer, Shell
"Although I was not directly involved in speeding up the video footage recognition I realised that I was still part of the kill chain; that this would ultimately lead to more people being targeted and killed by the US military in places like Afghanistan." The former Google engineer predicts autonomous weapons currently in development pose a far greater risk to humanity than remote-controlled drones. She outlined how external forces ranging from changing weather systems to machines being unable to work out complex human behaviour might throw killer robots off course, with potentially fatal consequences. She told The Guardian: "You could have a scenario where autonomous weapons that have been sent out to do a job confront unexpected radar signals in an area they are searching; there could be weather that was not factored into its software or they come across a group of armed men who appear to be insurgent enemies but in fact are out with guns hunting for food. "The machine doesn't have the discernment or common sense that the human touch has.
Self-driving cars, home automation, virtual assistants…it's clear we've already seen some outstanding technological advances and are on the brink of more significant breakthroughs. Alain Fiocco, CTO for OVHcloud, calls 2020 "a new era" for technology. But with all new advances, which will pull ahead in 2020? Here is a breakdown of the top five telecom trends to watch for in the year ahead. Right now, the world runs on 4G, also known as LTE.
Maintenance workers in the bowels of the Tokyo Metro system are being assisted by emote-controlled 8.5-inch wide drones. The remote-controlled 2.5 pound (1.15kg) drone is encased in a plastic sphere to protect it from any unfortunate bumps and knocks while navigating the labyrinth. Cameras on the custom-built drone will allow operators to scan hard to reach parts of the tunnel network for signs of damage. Current methods involve humans using a torch and looking up to see signs of damage and then having to use vehicles and platforms to reach them. Maintenance workers in the bowels of the Japanese metro system are being assisted by 8.5-inch wide drones.
Inventions becoming inventors… It might sound like sci-fi, but it's not really such a far-fetched idea. In fact, it's a prospect that's already becoming reality, given the recent news that an AI-created medicine for the treatment of OCD will be tested on humans for the first time. While the world's first patent applications for machine-designed inventions were rejected in January by the EPO, this development brings fresh attention to the AI-inventor debate. What may seem to many like simply an interesting experiment might well have far-reaching implications. Could this be the tipping point when technology goes from being a facilitator and an enhancer of human endeavour, to a developer of innovation in its own right?
Applying AI to the real world is much more difficult than applying it in digital ecosystems; this is what makes robotics use-cases in business so much more difficult than applications such as AI-enabled fraud detection. To elaborate on these differences, Emerj spoke with Dileep George, co-founder of AI company Vicarious, which has raised over $100 million in venture funding, for Kisaco Research's Brain Inspired Computing Congress 2020, which takes place April 21 – 22 in Milpitas, California. We spoke with Dileep about the unique requirements and considerations for adopting AI in robotics use-cases, as well as where AI-enabled robotics will play a role in business in the new decade. Brief Recognition: Prior to co-founding Vicarious, Dileep was co-founder and CTO of Numenta, a machine learning company, from 2005 – 2010. He holds a PhD in Electrical Engineering from Stanford.