Self-driving trains could be greener, carry more stuff, and help unclog America’s congested supply chains. And making them a reality will likely be far easier than perfecting autonomous vehicles.
Category: robotics/AI – Page 1,593
Circa 2020
Artificial intelligence (AI) is evolving—literally. Researchers have created software that borrows concepts from Darwinian evolution, including “survival of the fittest,” to build AI programs that improve generation after generation without human input. The program replicated decades of AI research in a matter of days, and its designers think that one day, it could discover new approaches to AI.
“While most people were taking baby steps, they took a giant leap into the unknown,” says Risto Miikkulainen, a computer scientist at the University of Texas, Austin, who was not involved with the work. “This is one of those papers that could launch a lot of future research.”
Building an AI algorithm takes time. Take neural networks, a common type of machine learning used for translating languages and driving cars. These networks loosely mimic the structure of the brain and learn from training data by altering the strength of connections between artificial neurons. Smaller subcircuits of neurons carry out specific tasks—for instance spotting road signs—and researchers can spend months working out how to connect them so they work together seamlessly.
As well as high-tech greenhouses, vertical farms, where food is grown indoors in vertically stacked beds without soil or natural light, are growing in popularity. NextOn operates a vertical farm in an abandoned tunnel beneath a mountain in South Korea. US company AeroFarms plans to build a 90,000-square-foot indoor vertical farm in Abu Dhabi, and Berlin-based Infarm has brought modular vertical farms directly to grocery stores, growing fresh produce in Tokyo stores.
AppHarvest says its greenhouse in Morehead, Kentucky, uses robotics and artificial intelligence to grow millions of tons of tomatoes, using 90% less water than in open fields.
CNBC got a rare look at Boston Dynamics’ office in Massachusetts to see two of the robots the company is working to commercialize: Spot and Stretch.
Liquid Neural Networks
Posted in information science, robotics/AI
Oct 8 2021
“Abstract: In this talk, we will discuss the nuts and bolts of the novel continuous-time neural network models: Liquid Time-Constant (LTC) Networks. Instead of declaring a learning system’s dynamics by implicit nonlinearities, LTCs construct networks of linear first-order dynamical systems modulated via nonlinear interlinked gates. LTCs represent dynamical systems with varying (i.e., liquid) time-constants, with outputs being computed by numerical differential equation solvers. These neural networks exhibit stable and bounded behavior, yield superior expressivity within the family of neural ordinary differential equations, and give rise to improved performance on time-series prediction tasks compared to advance recurrent network models.”
Ramin Hasani, MIT — intro by Daniela Rus, MIT
Abstract: In this talk, we will discuss the nuts and bolts of the novel continuous-time neural network models: Liquid Time-Constant (LTC) Networks. Instead of declaring a learning system’s dynamics by implicit nonlinearities, LTCs construct networks of linear first-order dynamical systems modulated via nonlinear interlinked gates. LTCs represent dynamical systems with varying (i.e., liquid) time-constants, with outputs being computed by numerical differential equation solvers. These neural networks exhibit stable and bounded behavior, yield superior expressivity within the family of neural ordinary differential equations, and give rise to improved performance on time-series prediction tasks compared to advance recurrent network models.
Speaker Biographies:
Dr. Daniela Rus is the Andrew (1956) and Erna Viterbi Professor of Electrical Engineering and Computer Science and Director of the Computer Science and Artificial Intelligence Laboratory (CSAIL) at MIT. Rus’s research interests are in robotics, mobile computing, and data science. Rus is a Class of 2002 MacArthur Fellow, a fellow of ACM, AAAI and IEEE, and a member of the National Academy of Engineers, and the American Academy of Arts and Sciences. She earned her PhD in Computer Science from Cornell University. Prior to joining MIT, Rus was a professor in the Computer Science Department at Dartmouth College.
Researchers at the California Institute of Technology (Caltech) have built a bipedal robot that combines walking with flying to create a new type of locomotion, making it exceptionally nimble and capable of complex movements.
Part walking robot, part flying drone, the newly developed LEONARDO (short for LEgs ONboARD drOne, or LEO for short) can walk a slackline, hop, and even ride a skateboard. Developed by a team at Caltech’s Center for Autonomous Systems and Technologies (CAST), LEO is the first robot that uses multi-joint legs and propeller-based thrusters to achieve a fine degree of control over its balance.
“We drew inspiration from nature. Think about the way birds are able to flap and hop to navigate telephone lines,” explained Soon-Jo Chung, Professor of Aerospace and Control and Dynamical Systems. “A complex yet intriguing behaviour happens as birds move between walking and flying. We wanted to understand and learn from that.”
Sophia’s Artificial Intelligence technology gives you the ability to increase your knowledge and language through sensors and cameras. This ‘sensitivity’ system captures all the information it receives from the outside and replicates human behaviors in the most natural way possible, even gestures. Therefore, her ‘desire’ to have a baby and start a family would only be a programming of her system to imitate social behaviors.
This is not the first time that Sophia has starred in a controversy. In 2,017 when she was named a citizen of Saudi Arabia 0 many people protested that, even though she is a robot, she has more rights than human women in that country.
Later, in a conversation with David Hanson 0 its creator, he said that it would destroy humans.
This week, The European Parliament, the body responsible for adopting European Union (EU) legislation, passed a non-binding resolution calling for a ban on law enforcement use of facial recognition technology in public places. The resolution, which also proposes a moratorium on the deployment of predictive policing software, would restrict the use of remote biometric identification unless it’s to fight “serious” crime, such as kidnapping and terrorism.
The approach stands in contrast to that of U.S. agencies, which continue to embrace facial recognition even in light of studies showing the potential for ethnic, racial, and gender bias. A recent report from the U.S. Government Accountability Office found that 10 branches including the Departments of Agriculture, Commerce, Defense, and Homeland Security plan to expand their use of facial recognition between 2020 and 2023 as they implement as many as 17 different facial recognition systems.
Commercial face-analyzing systems have been critiqued by scholars and activists alike throughout the past decade, if not longer. The technology and techniques — everything from sepia-tinged film to low-contrast digital cameras — often favor lighter skin, encoding racial bias in algorithms. Indeed, independent benchmarks of vendors’ systems by the Gender Shades project and others have revealed that facial recognition technologies are susceptible to a range of prejudices exacerbated by misuse in the field. For example, a report from Georgetown Law’s Center on Privacy and Technology details how police feed facial recognition software flawed data, including composite sketches and pictures of celebrities who share physical features with suspects.