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Category: robotics/AI – Page 37

Increasingly, AI systems are interconnected, which is generating new complexities and risks. Managing these ecosystems effectively requires comprehensive training, designing technological infrastructures and processes so they foster collaboration, and robust governance frameworks. Examples from healthcare, financial services, and legal profession illustrate the challenges and ways to overcome them.

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The risks and complexities of these ecosystems require specific training, infrastructure, and governance.

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The once shiny, exciting use cases for quantum technology may turn out to be pretty mundane if a small, but courageous band of researchers proves their theories correct. After all, using quantum computers to find new drug treatments, navigate the world without global positioning systems, and optimize complex portfolios may seem downright boring compared to using them to explore the myriad of questions that surround the hard problems of consciousness. Questions like: what the heck even is consciousness — and, does it have a connection to quantum mechanics? And, can quantum computing help make robots conscious — and should we make them conscious?

Tough questions, for sure, but here we’ll introduce a few researchers and entrepreneurs who are heading in that direction right now and leaning into what might turn out to be the ultimate quantum computing use case of all time: consciousness.

Hartmut Neven, a physicist and computational neuroscientist leading Google’s Quantum Artificial Intelligence Lab, believes quantum computing could help explore consciousness. Speaking to New Scientist, Neven outlined experiments and theories suggesting consciousness might emerge from quantum phenomena, such as entanglement and superposition, within the human brain. He proposes leveraging quantum computers to test these ideas, potentially expanding our understanding of how the mind interacts with the physical world.

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Quantum computers may soon dramatically enhance our ability to solve problems modeled by nonreversible Markov chains, according to a study published on the pre-print server arXiv.

The researchers from Qubit Pharmaceuticals and Sorbonne University, demonstrated that quantum algorithms could achieve exponential speedups in sampling from such chains, with the potential to surpass the capabilities of classical methods. These advances — if fully realized — have a range of implications for fields like drug discovery, machine learning and financial modeling.

Markov chains are mathematical frameworks used to model systems that transition between various states, such as stock prices or molecules in motion. Each transition is governed by a set of probabilities, which defines how likely the system is to move from one state to another. Reversible Markov chains — where the probability of moving from, let’s call them, state A to state B equals the probability of moving from B to A — have traditionally been the focus of computational techniques. However, many real-world systems are nonreversible, meaning their transitions are biased in one direction, as seen in certain biological and chemical processes.

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A study by Michael Gerlich at SBS Swiss Business School has found that increased reliance on artificial intelligence (AI) tools is linked to diminished critical thinking abilities. It points to cognitive offloading as a primary driver of the decline.

AI’s influence is growing fast. A quick search of AI-related science stories reveals how fundamental a tool it has become. Thousands of AI-assisted, AI-supported and AI-driven analyses and decision-making tools help scientists improve their research.

AI has also become more integrated into , from virtual assistants to complex information and decision support. Increased usage is beginning to influence how people think, especially impactful among , who are avid users of the technology in their personal lives.

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AI applications like ChatGPT are based on artificial neural networks that, in many respects, imitate the nerve cells in our brains. They are trained with vast quantities of data on high-performance computers, gobbling up massive amounts of energy in the process.

Spiking , which are much less energy-intensive, could be one solution to this problem. In the past, however, the normal techniques used to train them only worked with significant limitations.

A recent study by the University of Bonn has now presented a possible new answer to this dilemma, potentially paving the way for new AI methods that are much more energy-efficient. The findings have been published in Physical Review Letters.

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In 1956, a small group of scientists gathered for the Dartmouth Summer Research Project on Artificial Intelligence, which was the birth of this field of research.

To celebrate the anniversary, more than 100 researchers and scholars again met at Dartmouth for AI@50, a conference that not only honored the past and assessed present accomplishments, but also helped seed ideas for future artificial intelligence research.

The initial meeting was organized by John McCarthy, then a mathematics professor at the College. In his proposal, he stated that the conference was “to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it.”

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Western researchers have developed a novel technique using math to understand exactly how neural networks make decisions—a widely recognized but poorly understood process in the field of machine learning.

Many of today’s technologies, from digital assistants like Siri and ChatGPT to and self-driving cars, are powered by machine learning. However, the —computer models inspired by the —behind these machine learning systems have been difficult to understand, sometimes earning them the nickname “” among researchers.

“We create neural networks that can perform , while also allowing us to solve the equations that govern the networks’ activity,” said Lyle Muller, mathematics professor and director of Western’s Fields Lab for Network Science, part of the newly created Fields-Western Collaboration Centre. “This mathematical solution lets us ‘open the black box’ to understand precisely how the network does what it does.”

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Second, Synchron will explore the development of a groundbreaking foundation model for brain inference. By processing Synchron’s neural data on an unprecedented scale, this initiative will create scalable, interpretable brain-language models with the potential to transform neuroprosthetics, cognitive expression, and seamless interaction with digital devices.

“Synchron’s vision is to scale neurotechnology to empower humans to connect to the world, and the NVIDIA Holoscan platform provides the ideal foundation,” said Tom Oxley, M.D., Ph.D., CEO & Founder, Synchron. “Through this work, we’re setting a new benchmark for what BCIs can achieve.”

NEW YORK—()— Synchron, a category-defining brain-computer interface (BCI) company, announced today a step forward in implantable BCI technology to drive the future of neurotechnology. Synchron’s BCI technology, in combination with the NVIDIA Holoscan platform, is poised to redefine the possibilities of real-time neural interaction and intelligent edge processing.

Synchron will leverage NVIDIA Holoscan to advance a next-generation implantable BCI in two key domains. First, Synchron will enhance real-time edge AI capabilities for on-device neural processing, improving signal processing and multi-AI inference technology. This will reduce system latency, bolster privacy, and provide users with a more responsive and intuitive BCI experience. NVIDIA Holoscan provides Synchron with: (i) a unified framework supporting diverse AI models and data modalities; (ii) an optimized application framework, from seamless sensor I/O integration, GPU-direct data ingestion, to accelerated computing and real-time AI.

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Jeff Bezos, the billionaire founder of Amazon, has always been a visionary investor, known for his early stakes in companies like Airbnb and Uber. In 2024, Bezos has turned his attention to a new frontier: AI-powered robotics. This bold move signifies a major shift as Bezos bets on the next wave of technological innovation, aiming to revolutionize industries and everyday life.

In April of last year, Marko Bjelonic, co-founder and CEO of Swiss-Mile, a Zurich-based robotics company, reached out to Bezos with a detailed proposal—an Amazon-style “6-Pager”—to pitch his company’s vision. Bjelonic recalls, “I was pleasantly surprised by Jeff’s patience and relaxed demeanor.” What was initially a planned 30-minute call extended to an hour, feeling more like a conversation than a formal interview.

This meeting led Bezos to co-lead a $22 million funding round for Swiss-Mile in August. Swiss-Mile is developing AI-driven robots that resemble headless dogs with wheels instead of feet, designed to deliver packages autonomously. These robots are currently undergoing trials on Zurich’s streets, marking a significant step towards commercial deployment. According to Bjelonic, “Our goal is to see these robots reliably deliver packages from point A to point B, enhancing efficiency and reducing human labor.”

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00:00 — Self-Improving Models.
00:23 — AllStar Math Overview.
01:34 — Monte-Carlo Tree.
02:59 — Framework Steps Explained.
04:46 — Iterative Model Training.
06:11 — Surpassing GPT-4
07:18 — Small Models Dominate.
08:01 — Training Feedback Loop.
10:09 — Math Benchmark Results.
13:19 — Emergent Capabilities Found.
16:09 — Recursive AI Concerns.
20:04 — Towards Superintelligence.
23:34 — Math as Foundation.
27:08 — Superintelligence Predictions.

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Links From Todays Video:
https://arxiv.org/pdf/2501.

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