The Sustainable Development Goals (SDGs) include a call for action to halve the annual rate of road deaths globally and ensure access to safe, affordable, and sustainable transport for everyone by 2030.
According to the newly launched initiative, faster progress on AI is vital to make this happen, especially in low and middle-income countries, where the most lives are lost on the roads each year.
According to the World Health Organization (WHO), approximately 1.3 million people die annually as a result of road traffic crashes. Between 20 and 50 million more suffer non-fatal injuries, with many incurring a disability.
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A woman rushes across a busy road in Brazil., by PAHO
AI can help in different ways, including better collection and analysis of crash data, enhancing road infrastructure, increasing the efficiency of post-crash response, and inspiring innovation in the regulatory frameworks.
Google AI Introduces FLAN: An Instruction-Tuned Generalizable Language (NLP) Model To Perform Zero-Shot Tasks
To generate meaningful text, a machine learning model needs a lot of knowledge about the world and should have the ability to abstract them. While language models that have been trained to accomplish this are becoming increasingly capable of acquiring this knowledge automatically as they grow, it is unclear how to unlock this knowledge and apply it to specific real-world activities.
Fine-tuning is one well-established method for doing so. It involves training a pretrained model like BERT or T5 on a labeled dataset to adjust it to a downstream job. However, it has a large number of training instances and stored model weights for each downstream job, which is not always feasible, especially for large models.
A recent Google study looks into a simple technique known as instruction fine-tuning, sometimes known as instruction tuning. This entails fine-tuning a model to make it more receptive to performing NLP (Natural language processing) tasks in general rather than a specific task.
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.
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.
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 Intelligencetechnology 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.
