Lifeboat Foundation

Meta’s new tactile-sensing skin could allow robots to “feel” in a new way, which will somehow make the metaverse more life-like.

Thanks to this new category of algorithms that has proved its power of mimicking human skills just by learning through examples. Deep learning is a technology representing the next era of machine learning. Algorithms used in machine learning are created by programmers and they hold the responsibility for learning through data. Decisions are made based on such data.

Some of the AI experts say, t here will a shift in AI trends. For instance, the late 1990s and early 2000s saw the rise of machine learning. Neural networks gained its popularity in the early 2010s, and growth in reinforcement came into light recently.

Well, these are just a couple of caveats we’re experienced throughout the past years.

Australian researchers have developed disruptive technology allowing autonomous vehicles to track running pedestrians hidden behind buildings, and cyclists obscured by larger cars, trucks, and buses.

The autonomous vehicle uses game changing technology that allows it to “see” the world around it, including using X-ray style vision that penetrates through to pedestrians in blind spots and to detect cyclists obscured by fast-moving vehicles.

The iMOVE Cooperative Research Centre-funded project collaborating with the University of Sydney’s Australian Centre for Field Robotics and Australian connected vehicle solutions company Cohda Wireless has just released its new findings in a final report following three years of research and development.

The big question that may be on many viewers’ minds is whether the robots are truly navigating the course on their own—making real-time decisions about how high to jump or how far to extend a foot—or if they’re pre-programmed to execute each motion according to a detailed map of the course.

As engineers explain in a second new video and accompanying blog post, it’s a combination of both.

Continue reading “Watch Boston Dynamics’ Atlas Robot Crush a New Parkour Course” »

DeepMind, part of Google, announces General AI breakthrough with a true learning AI. First, they built a dynamic environment (like a game) that can change it’s own layout — XLand. Then, they use Deep Learning and Reinforcement Learning combined — Deep Reinforcement Learning — to create an AI the can learn without training at all or data about what it’s doing. The AI played 700,000 games in 4,000 unique worlds! The AI performed 200 BILLION training steps while performing 3.4 million UNIQUE (non-taught/programmed) tasks.

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Continue reading “General Artificial Intelligence BREAKTHROUGH? You decide! [EP 149]” »

The technology aims to deliver cost and power consumption improvements for deep learning use cases of inference, the companies said. This development follows NeuReality’s emergence from stealth earlier in February with an $8 million seed round to accelerate AI workloads at scale.

AI inference is a growing area of focus for enterprises, because it’s the part of AI where neural networks actually are applied in real application and yield results. IBM and NeuReality claim their partnership will allow the deployment of computer vision, recommendation systems, natural language processing, and other AI use cases in critical sectors like finance, insurance, healthcare, manufacturing, and smart cities. They also claim the agreement will accelerate deployments in today’s ever-growing AI use cases, which are already deployed in public and private cloud datacenters.

NeuReality has competition in Cast AI, a technology company offering a platform that “allows developers to deploy, manage, and cost-optimize applications in multiple clouds simultaneously.” Some other competitors include Comet.ml, Upright Project, OctoML, Deci, and DeepCube. However, this partnership with IBM will see NeuReality become the first start-up semiconductor product member of the IBM Research AI Hardware Center and a licensee of the Center’s low-precision high performance Digital AI Cores.

If you had told college-aged Anne, “22 years from now, you’re going to be leading a research group focused on AI,” I would have said you’re insane. It would not have been possible to make this shift into machine learning without having made friends with machine learning experts — particularly Jones.

After he and I finished our training at MIT, we started a lab together at the Broad Institute in 2,007 and we brainstormed a lot about how machine learning could help biologists. What allowed these ideas to percolate and develop was both of us hopping over the fence and getting familiar with the terminology and power of both sides, biology and computer science. It’s really a productive partnership.

And it’s not just Jones anymore. My group is about 50–50 in terms of people coming from the biology side versus the computational side.

Facebook plans to shut down its decade-old facial recognition system this month, deleting the face scan data of more than one billion users and effectively eliminating a feature that has fueled privacy concerns, government investigations, a class-action lawsuit and regulatory woes.

Jerome Pesenti, vice president of artificial intelligence at Meta, Facebook’s newly named parent company, said in a blog post on Tuesday that the social network was making the change because of “many concerns about the place of facial recognition technology in society.” He added that the company still saw the software as a powerful tool, but “every new technology brings with it potential for both benefit and concern, and we want to find the right balance.”

The decision shutters a feature that was introduced in December 2010 so that Facebook users could save time. The facial-recognition software automatically identified people who appeared in users’ digital photo albums and suggested users “tag” them all with a click, linking their accounts to the images. Facebook now has built one of the largest repositories of digital photos in the world, partly thanks to this software.

The continuation of Processors and GPU’s improving exponentially through Moore’s Law has become very uncertain over the last few years, but recently the top hardware makers: Intel, AMD and Nvidia are now getting ready to deliver the biggest increases in performance we have ever seen. The fastest GPU’s and CPU’s with secret new features are about to release in the next few years to bring Moore’s Law back from the dead. How they manage to create these new powerful chips, I’ll explain in this video. One thing is for sure, Nvidia Lovelace 4,000 series, Zen 4 and Intel Meteor Lake are the future of Hardware and will accelerate the performance gain of next generation hardware.
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Every day is a day closer to the Technological Singularity. Experience Robots learning to walk & think, humans flying to Mars and us finally merging with technology itself. And as all of that happens, we at AI News cover the absolute cutting edge best technology inventions of Humanity.

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TIMESTAMPS:
00:00 The Return of Moore’s Law.
01:19 What is Moore’s Law?
03:33 Intel & Nvidia: New Players in the field?
05:42 How Moore’s Law and AI are connected.
08:02 TSMC to continue Moore’s Law?
10:07 Last Words.
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#nvidia #intel #mooreslaw