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Category: robotics/AI – Page 463

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.

How Journey Foods is leveraging AI to streamline the CPG industry

As a simple illustration, let’s say someone wanted to create a tomato sauce recipe, optimizing vitamin C and using sustainable tomatoes within a certain cost range. Journey Foods then taps into its database to generate an optimal recipe, and will continually push recommendations of top suppliers.

“Essentially, when people go to ChatGPT or something, and they’re asking them, ‘write this paper for me, or give me a social media post, speak to this audience,’ or whatever, right? It’s the same thing with our generative recipe recommendations,” Lynn said.

Except Lynn doesn’t use ChatGPT. Systems such as ChaptGPT gather data from the open internet, but Journey Foods gets its data from research institutions, academic journals, suppliers and manufacturers. Lynn said her business uses a lot of private, hard data that’s unstructured, with her company then giving it structure and doing so globally.

Mayo Clinic researchers develop new AI tools to reveal seizure hotspots, improve patient care

Mayo Clinic researchers have developed new artificial intelligence (AI)-based tools to pinpoint specific regions of the brain with seizure hotspots more quickly and accurately in patients with drug-resistant epilepsy. Their study, published in Nature Communications Medicine, highlights the potential of AI to revolutionize epilepsy treatment by interpreting brain waves during electrode implantation surgery. This transformative approach could significantly reduce the time patients spend in the hospital, accelerating the identification and removal of seizure-generating brain regions.

“This innovative approach could enable more rapid and accurate identification of seizure-generating areas during stereo-electroencephalography (EEG) implantation surgery, potentially reducing the cost and risks of prolonged monitoring,” says Nuri Ince, Ph.D., senior author of the study and a consultant in the Mayo Clinic Department of Neurologic Surgery.

Drug-resistant epilepsy often requires surgical removal of the seizure-causing brain tissue. A first step in that treatment is typically a surgery that involves implanting electrodes in the brain and monitoring neural activity for several days or weeks to identify the location of the seizures.

Algorithms based on deep learning can improve medical image analysis

Artificial intelligence has the potential to improve the analysis of medical image data. For example, algorithms based on deep learning can determine the location and size of tumors. This is the result of AutoPET, an international competition in medical image analysis, where researchers of Karlsruhe Institute of Technology (KIT) were ranked fifth.

The seven best autoPET teams report in the journal Nature Machine Intelligence on how algorithms can detect lesions in (PET) and computed tomography (CT).

Imaging techniques play a key role in the diagnosis of cancer. Precisely determining the location, size, and type of tumor is essential for choosing the right therapy. The most important imaging techniques include positron emission tomography (PET) and computer tomography (CT).

An AI Chemist Made A Catalyst to Make Oxygen On Mars Using Local Materials

Breaking oxygen out of a water molecule is a relatively simple process, at least chemically. Even so, it does require components, one of the most important of which is a catalyst. Catalysts enable reactions and are linearly scalable, so if you want more reactions quickly, you need a bigger catalyst. In space exploration, bigger means heavier, which translates into more expensive. So, when humanity is looking for a catalyst to split water into oxygen and hydrogen on Mars, creating one from local Martian materials would be worthwhile. That is precisely what a team from Hefei, China, did by using what they called an “AI Chemist.”

Unfortunately, the name “AIChemist” didn’t stick, though that joke might vary depending on the font you read it in. Whatever its name, the team’s work was some serious science. It specifically applied machine learning algorithms that have become all the rage lately to selecting an effective catalyst for an “oxygen evolution reaction” by utilizing materials native to Mars.

To say it only chose the catalyst isn’t giving the system the full credit it’s due, though. It accomplished a series of steps, including developing a catalyst formula, pretreating the ore to create the catalyst, synthesizing it, and testing it once it was complete. The authors estimate that the automated process saved over 2,000 years of human labor by completing all of these tasks and point to the exceptional results of the testing to prove it.

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