Lifeboat Foundation

https://youtube.com/watch?v=E1AxVXt2Gv4

Marcus Hutter is a senior research scientist at DeepMind and professor at Australian National University. Throughout his career of research, including with Jürgen Schmidhuber and Shane Legg, he has proposed a lot of interesting ideas in and around the field of artificial general intelligence, including the development of the AIXI model which is a mathematical approach to AGI that incorporates ideas of Kolmogorov complexity, Solomonoff induction, and reinforcement learning.

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10 ROBOT Predictions For 2022 | Boston Dynamics.

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LEAKED: latest artificial intelligence news | weekly.

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From the home to the workplace, and to the sky above, robots in 2022 are transforming the world we know —and helping us understand the worlds we don’t. Let’s highlight a few of these remarkable machines and how they might transform not only how we interact with ourselves, but also the universe around us. We will take about the following robots: Mars Perseverance & Ingenuity, Moley the Robotic Chef, Toyota’s Robotic Butler, Spot by Boston Dynamics, Digit by Agility Robotics, Atlas by Boston Dynamics, Guardian XO Exoskeleton, Vicarious Medical Robot, British loyal wingman and Xenobots!

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The firm partnered with Israeli startup Corsight AI to develop the facial recognition cameras to be worn by police officers, Tirza told AFP.

By matching faces to old photographs on its database, the camera developed by Yozmot will be able to instantly identify people in a crowd, even if they are wearing camouflage, make-up or masks.

Corsight refused to confirm the collaboration with Yozmot, according to AFP, but has said that it was working with some 230 “integrators” worldwide who incorporated facial recognition software into cameras.

There are few environments as unforgiving as the ocean. Its unpredictable weather patterns and limitations in terms of communications have left large swaths of the ocean unexplored and shrouded in mystery.

“The ocean is a fascinating environment with a number of current challenges like microplastics, algae blooms, coral bleaching, and rising temperatures,” says Wim van Rees, the ABS Career Development Professor at MIT. “At the same time, the ocean holds countless opportunities — from aquaculture to energy harvesting and exploring the many ocean creatures we haven’t discovered yet.”

Ocean engineers and mechanical engineers, like van Rees, are using advances in scientific computing to address the ocean’s many challenges, and seize its opportunities. These researchers are developing technologies to better understand our oceans, and how both organisms and human-made vehicles can move within them, from the micro scale to the macro scale.

After receiving a lot of love here at Bored Panda and all over the internet with the first post, I decided to continue this project of bringing historical figures to modernity through artificial intelligence.

The Korea Advanced Institute of Science and Technology (KAIST) announced on Jan. 5 that professor Lee Sang-wan and his research team at its Department of Bio and Brain Engineering found a way to solve overfitting and underfitting in machine learning.

Artificial intelligence models present optimal solutions to various problems. However, their situational flexibility is still at a very low level. On the other hand, people focus on given problems while responding flexibly to changing conditions and situations.

The more a technology or concept permeates and gets normalized in our day-to-day lives, the more we grow to expect from it. About two decades ago, a sub-56kpbs dial-up internet connection seemed miraculous. Today, with internet speeds as high as 2000Mbps becoming normal, the 56Kbps connection would be considered a failure of sorts—in the developed world, at least. This shift in expectation also applies to AI. Having seen numerous practical AI applications aid human convenience and progress, both the general population and the AI research community now expects every new breakthrough in the field to be more earth-shattering than the previous one. Similarly, what qualifies as AI failure has also seen a massive shift in recent years, especially from a problem owner’s perspective. failure, in most cases, is attributed to technology-centric factors like the quality of data or the capabilities of algorithms and hardware used, ignoring the most crucial aspect of AI success—the end user.