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.
Tiernan Ray has been covering technology and business for 27 years. He was most recently technology editor for Barron's where he wrote daily market coverage for the Tech Trader blog and wrote the weekly print column of that name. DeepMind's "Gato" neural network excels at numerous tasks including controlling robotic arms that stack blocks, playing Atari 2600 games, and captioning images. The world is used to seeing headlines about the latest breakthrough by deep learning forms of artificial intelligence. The latest achievement of the DeepMind division of Google, however, might be summarized as, "One AI program that does a so-so job at a lot of things."
It will soon become easy for self-driving cars to hide in plain sight. The rooftop lidar sensors that currently mark many of them out are likely to become smaller. Mercedes vehicles with the new, partially automated Drive Pilot system, which carries its lidar sensors behind the car's front grille, are already indistinguishable to the naked eye from ordinary human-operated vehicles. Is this a good thing? As part of our Driverless Futures project at University College London, my colleagues and I recently concluded the largest and most comprehensive survey of citizens' attitudes to self-driving vehicles and the rules of the road.
Inspired by Her Family's Story, Founder Hopes to Boost Healthcare Equity Through Tech The World's First Solar-Powered Car Gets up to 450 Miles of Range on a Single Charge Choose a web site to get translated content where available and see local events and offers. Based on your location, we recommend that you select: . Other MathWorks country sites are not optimized for visits from your location.
The ultimate achievement to some in the AI industry is creating a system with artificial general intelligence (AGI), or the ability to understand and learn any task that a human can. Long relegated to the domain of science fiction, it's been suggested that AGI would bring about systems with the ability to reason, plan, learn, represent knowledge, and communicate in natural language. Not every expert is convinced that AGI is a realistic goal -- or even possible. Gato is what DeepMind describes as a "general-purpose" system, a system that can be taught to perform many different types of tasks. Researchers at DeepMind trained Gato to complete 604, to be exact, including captioning images, engaging in dialogue, stacking blocks with a real robot arm, and playing Atari games. Jack Hessel, a research scientist at the Allen Institute for AI, points out that a single AI system that can solve many tasks isn't new.
Researchers have created a machine-learning system that efficiently predicts the future trajectories of multiple road users, like drivers, cyclists, and pedestrians, which could enable an autonomous vehicle to more safely navigate city streets. If a robot is going to navigate a vehicle safely through downtown Boston, it must be able to predict what nearby drivers, cyclists, and pedestrians are going to do next. A new machine-learning system may someday help driverless cars predict the next moves of nearby drivers, pedestrians, and cyclists in real-time. Humans may be one of the biggest roadblocks to fully autonomous vehicles operating on city streets. If a robot is going to navigate a vehicle safely through downtown Boston, it must be able to predict what nearby drivers, pedestrians, and cyclists are going to do next.
Self-driving cars are one of the most hotly debated topics when it comes to vehicle safety. Plenty of companies are using autonomous vehicles for various purposes. However, most Americans don't think they're all that safe. Additionally, with videos showing the faults of some of these systems, it's easy to see the hesitation. So, it will undoubtedly get better over time.
Unmanned aerial vehicles (UAVs), or simply drones, are used in a plethora of civil applications due to their ease of deployment, low maintenance cost, high mobility, and ability to hover. A main advantage of drones is that, in contrast to other vehicles, they are not restricted to traveling over a road network and thus, can swiftly move over disperse locations. Such vehicles are utilized for many applications such as the real-time monitoring of road traffic, civil infrastructure inspection, wireless coverage, delivery of goods, security and surveillance, precision agriculture, and healthcare. Regarding the latter, drones can be utilized in natural disaster relief, as search and rescue units, as transfer units, and to support telemedicine. For drones to be efficient in such applications, their scheduled and coordinated flying is crucial. Moreover, given that drones typically use an electric motor and store the required energy in batteries, their scheduled charging is crucial to maximizing their availability.Controlling drones demands efficient algorithms that can solve problems that involve a large number of heterogeneous entities (e.g., drones’ owners), each one having its own goals, needs, and incentives (e.g., amount of goods to transport), while they operate in highly dynamic environments (e.g., variable number of drones) and having to deal with a number of uncertainties (e.g., future requests, emergency situations). In this context, artificial intelligence (AI) techniq...
What images come to mind when you think of AI? Brains made from glowing circuit boards? These are some of the clichéd images commonly used to illustrate AI in print and digital media. A Google image search for'AI' or'artificial intelligence' quickly reveals the striking prevalence of images with blue, brain, robot, circuit or code tropes. It is surprisingly difficult to find images that show the real-life applications and places where we find AI, or the people who develop and use these technologies. Why is this a problem?
Martin Burch had been working for the Wall Street Journal and its parent company Dow Jones for a few years and was looking for new opportunities. One Sunday in May 2021, he applied for a data analyst position at Bloomberg in London that looked like the perfect fit. He received an immediate response, asking him to take a digital assessment. The assessment showed him different shapes and asked him to figure out the pattern. "Shouldn't we be testing my abilities on the job?" he asked himself.
Inspired by progress in large-scale language modelling, we apply a similar approach towards building a single generalist agent beyond the realm of text outputs. The agent, which we refer to as Gato, works as a multi-modal, multi-task, multi-embodiment generalist policy. The same network with the same weights can play Atari, caption images, chat, stack blocks with a real robot arm and much more, deciding based on its context whether to output text, joint torques, button presses, or other tokens. During the training phase of Gato, data from different tasks and modalities are serialised into a flat sequence of tokens, batched, and processed by a transformer neural network similar to a large language model. The loss is masked so that Gato only predicts action and text targets.