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Category: robotics/AI – Page 343

Tesla Optimus Robot Catching a Ball

Tesla Optimus has taken a step closer to human-like dexterity, showcasing its upgraded hands with impressive capabilities. A recent video highlights the robot catching a tennis ball using its new hands, which now feature 22 degrees of freedom. By comparison, human hands have 27 degrees of freedom, making Optimus’ latest enhancements a significant stride in robotic engineering. In May 2024, Elon Musk hinted at these upgrades, and the results are now visible.

This development aligns closely with Neuralink’s recent milestone—the United States Food and Drug Administration has granted approval for the CONVOY Study. This feasibility trial aims to test the Brain-to-Computer-interface N1 Implant alongside assistive robotic arms, hinting at the possibility of collaboration between Tesla Optimus and Neuralink technologies. During a Neuralink update in July, Elon Musk mentioned the potential for Optimus’ limbs to work in sync with the N1 Implant, emphasizing a vision where human minds control robotic systems seamlessly.

Optimus itself is a technical marvel, standing five feet eight inches tall and weighing 125 pounds. Designed for versatility, it is constructed with lightweight yet durable materials and powered by a 2.3 kilowatt-hour battery. This proprietary energy management system ensures efficient operation for tasks ranging from light to intensive. With 40 electromechanical actuators, Optimus offers precise movements and a human-like range of motion. Capable of walking at speeds up to five miles per hour and carrying up to 45 pounds, this robot is designed for real-world utility, blending innovation with practicality.

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A Modern Approach To The Fundamental Problem of Causal Inference

Originally published on Towards AI.

ABSTRACT: The fundamental problem of causal inference defines the impossibility of associating a causal link to a correlation, in other words: correlation does not prove causality. This problem can be understood from two points of view: experimental and statistical. The experimental approach tells us that this problem arises from the impossibility of simultaneously observing an event both in the presence and absence of a hypothesis. The statistical approach, on the other hand, suggests that this problem stems from the error of treating tested hypotheses as independent of each other. Modern statistics tends to place greater emphasis on the statistical approach because, compared to the experimental point of view, it also shows us a way to solve the problem. Indeed, when testing many hypotheses, a composite hypothesis is constructed that tends to cover the entire solution space. Consequently, the composite hypothesis can be fitted to any data set by generating a random correlation. Furthermore, the probability that the correlation is random is equal to the probability of obtaining the same result by generating an equivalent number of random hypotheses.

Researchers use AI to convert sound recordings into street images

Using generative artificial intelligence, a team of researchers at The University of Texas at Austin has converted sounds from audio recordings into street-view images. The visual accuracy of these generated images demonstrates that machines can replicate human connection between audio and visual perception of environments. The research team describes training a soundscape-to-image AI model using audio and visual data gathered from a variety of urban and rural streetscapes and then using that model to generate images from audio recordings.

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Researchers May Have Solved a Decades-Old Brain Paradox With AI

Cold Spring Harbor Laboratory scientists developed an AI algorithm inspired by the genome’s efficiency, achieving remarkable data compression and task performance.

In a sense, each of us begins life ready for action. Many animals perform amazing feats soon after they’re born. Spiders spin webs. Whales swim. But where do these innate abilities come from? Obviously, the brain plays a key role as it contains the trillions of neural connections needed to control complex behaviors.

However, the genome has space for only a small fraction of that information. This paradox has stumped scientists for decades. Now, Cold Spring Harbor Laboratory (CSHL) Professors Anthony Zador and Alexei Koulakov have devised a potential solution using artificial intelligence.

Can Models of Human Consciousness Enhance AI Capabilities?

Some researchers propose that advancing AI to the next level will require an internal architecture that more closely mirrors the human mind. Rufin VanRullen joins Brian Greene to discuss early results from one such approach, based on the Global Workspace Theory of consciousness.

This program is part of the Big Ideas series, supported by the John Templeton Foundation.

Participant: Rufin VanRullen.
Moderator: Brian Greene.

00:00 — Introduction.
02:06 — Participant Introduction.
03:12 — VanRullin’s journey from neuroscience to artificial neural networks.
05:25 — Algorithmic approach to neural networks.
08:02 — Simulation of information processing.
09:25 — Global Workspace Theory.
21:33 — Global Workspace providing insight on consciousness.
23:14 — Role of language in consciousness and replicating intelligence.
25:30 — Developing consciousness in AI systems.
31:38 — How to recognize if AI has developed consciousness.
32:32 — Time scale of Global Workspace Theory and emergence of consciousness in AI
34:45 — Credits.

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Researchers use laser beams to pioneer new quantum computing breakthrough

Physicists from the University of the Witwatersrand (Wits) have developed an innovative computing system using laser beams and everyday display technology, marking a significant leap forward in the quest for more powerful quantum computing solutions.

The breakthrough, achieved by researchers at the university’s Structured Light Lab, offers a simpler and more cost-effective approach to advanced quantum computing by harnessing the unique properties of light. This development could potentially speed up complex calculations in fields such as logistics, finance and artificial intelligence. The research was published in the journal APL Photonics as the editor’s pick.

“Traditional computers work like switchboards, processing information as simple yes or no decisions. Our approach uses to process multiple possibilities simultaneously, dramatically increasing computing power,” says Dr. Isaac Nape, the Optica Emerging Leader Chair in Optics at Wits.

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