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The future of AI is here—and it’s running on human brain cells! In a groundbreaking development, scientists have created the first AI system powered by biological neurons, blurring the line between technology and biology. But what does this mean for the future of artificial intelligence, and how does it work?

This revolutionary AI, known as “Brainoware,” uses lab-grown human brain cells to perform complex tasks like speech recognition and decision-making. By combining the adaptability of biological neurons with the precision of AI algorithms, researchers have unlocked a new frontier in computing. But with this innovation comes ethical questions and concerns about the implications of merging human biology with machines.

In this video, we’ll explore how Brainoware works, its potential applications, and the challenges it faces. Could this be the key to creating truly intelligent machines? Or does it raise red flags about the ethical boundaries of AI research?

What is Brainoware, and how does it work? What are the benefits and risks of AI powered by human brain cells? How will this technology shape the future of AI? This video answers all these questions and more. Don’t miss the full story—watch until the end!

#ai.
#artificialintelligence.
#ainews.

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Special thanks to Chuankun Zhang, Tian Ooi, Jacob S. Higgins, and Jack F. Doyle from Prof. Jun Ye’s lab at JILA/NIST/University of Colorado, as well as Prof. Victor Flambaum from UNSW’s Department of Theoretical Physics, for their valuable assistance and consultation on this video.

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Global optimization-based approaches such as basin hopping28,29,30,31, evolutionary algorithms32 and random structure search33 offer principled approaches to comprehensively navigating the ambiguity of active phase. However, these methods usually rely on skillful parameter adjustments and predefined conditions, and face challenges in exploring the entire configuration space and dealing with amorphous structures. The graph theory-based algorithms34,35,36,37, which can enumerate configurations for a specific adsorbate coverage on the surface with graph isomorphism algorithms, even on an asymmetric one. Nevertheless, these methods can only study the adsorbate coverage effect on the surface because the graph representation is insensitive to three-dimensional information, making it unable to consider subsurface and bulk structure sampling. Other geometric-based methods38,39 also have been developed for determining surface adsorption sites but still face difficulties when dealing with non-uniform materials or embedding sites in subsurface.

Topology, independent of metrics or coordinates, presents a novel approach that could potentially offer a comprehensive traversal of structural complexity. Persistent homology, an emerging technique in the field of topological data analysis, bridges the topology and real geometry by capturing geometric structures over various spatial scales through filtration and persistence40. Through embedding geometric information into topological invariants, which are the properties of topological spaces that remain unchanged under specific continuous deformations, it allows the monitoring of the “birth,” “death,” and “persistence” of isolated components, loops, and cavities across all geometric scales using topological measurements. Topological persistence is usually represented by persistent barcodes, where different horizontal line segments or bars denote homology generators41. Persistent homology has been successfully employed to the feature representation for machine learning42,43, molecular science44,45, materials science46,47,48,49,50,51,52,53,54,55, and computational biology56,57. The successful application motivates us to explore its potential as a sampling algorithm due to its capability of characterizing material structures multidimensionally.

In this work, we introduce a topology-based automatic active phase exploration framework, enabling the thorough configuration sampling and efficient computation via MLFF. The core of this framework is a sampling algorithm (PH-SA) in which the persistent homology analysis is leveraged to detect the possible adsorption/embedding sites in space via a bottom-up approach. The PH-SA enables the exploration of interactions between surface, subsurface and even bulk phases with active species, without being limited by morphology and thus can be applied to periodical and amorphous structures. MLFF are then trained through transfer learning to enable rapid structural optimization of sampled configurations. Based on the energetic information, Pourbaix diagram is constructed to describe the response of active phase to external environmental conditions. We validated the effectiveness of the framework with two examples: the formation of Pd hydrides with slab models and the oxidation of Pt clusters in electrochemical conditions. The structure evolution process of these two systems was elucidated by screening 50,000 and 100,000 possible configurations, respectively. The predicted phase diagrams with varying external potentials and their intricate roles in shaping the mechanisms of CO2 electroreduction and oxygen reduction reaction were discussed, demonstrating close alignment with experimental observations. Our algorithm can be easily applied to other heterogeneous catalytic structures of interest and pave the way for the realization of automatic active phase analysis under realistic conditions.

The electrically readable complex dynamics of robust and scalable magnetic tunnel junctions (MTJs) offer promising opportunities for advancing neuromorphic computing. In this work, we present an MTJ design with a free layer and two polarizers capable of computing the sigmoidal activation function and its gradient at the device level. This design enables both feedforward and backpropagation computations within a single device, extending neuromorphic computing frameworks previously explored in the literature by introducing the ability to perform backpropagation directly in hardware. Our algorithm implementation reveals two key findings: (i) the small discrepancies between the MTJ-generated curves and the exact software-generated curves have a negligible impact on the performance of the backpropagation algorithm, (ii) the device implementation is highly robust to inter-device variation and noise, and (iii) the proposed method effectively supports transfer learning and knowledge distillation. To demonstrate this, we evaluated the performance of an edge computing network using weights from a software-trained model implemented with our MTJ design. The results show a minimal loss of accuracy of only 0.4% for the Fashion MNIST dataset and 1.7% for the CIFAR-100 dataset compared to the original software implementation. These results highlight the potential of our MTJ design for compact, hardware-based neural networks in edge computing applications, particularly for transfer learning.

Quantum systems hold the promise of tackling some complex problems faster and more efficiently than classical computers. Despite their potential, so far only a limited number of studies have conclusively demonstrated that quantum computers can outperform classical computers on specific tasks. Most of these studies focused on tasks that involve advanced computations, simulations or optimization, which can be difficult for non-experts to grasp.

Researchers at the University of Oxford and the University of Sevilla recently demonstrated a over a classical scenario on a cooperation task called the odd-cycle game. Their paper, published in Physical Review Letters, shows that a team with can win this game more often than a team without.

“There is a lot of talk about quantum advantage and how will revolutionize entire industries, but if you look closely, in many cases, there is no mathematical proof that classical methods definitely cannot find solutions as efficiently as quantum algorithms,” Peter Drmota, first author of the paper, told Phys.org.

In today’s AI news, believe it or not AI is alive and well, and it’s clearly going to change a lot of things forever. My personal epiphany happened just the other day, while I was “vibe coding” a personal software project. Those of us who have never written a line of code in our lives, but create software programs and applications using AI tools like Bolt or Lovable are called vibe coders.

S how these tools improve automation, multi-agent collaboration, and workflow orchestration for developers. Before we dig into what Then, Anthropic’s CEO Dario Amodei is worried that spies, likely from China, are getting their hands on costly “algorithmic secrets” from the U.S.’s top AI companies — and he wants the U.S. government to step in. Speaking at a Council on Foreign Relations event on Monday, Amodei said that China is known for its “large-scale industrial espionage” and that AI companies like Anthropic are almost certainly being targeted.

Meanwhile, despite all the hype, very few people have had a chance to use Manus. Currently, under 1% of the users on the wait list have received an invite code. It’s unclear how many people are on this list, but for a sense of how much interest there is, Manus’s Discord channel has more than 186,000 members. MIT Technology Review was able to obtain access to Manus, and they gave it a test-drive.

In videos, join Palantir CEO Alexander Karp with New York Times DealBook creator Andrew Ross Sorkin on the promises and peril of Silicon Valley, tech’s changing relationship with Washington, and what it means for our future — and his new book, The Technological Republic. Named “Best CEO of 2024” by The Economist, Alexander Karp is a vital player in Silicon Valley as the CEO of Palantir.

Then, Piers Linney, Co-founder of Implement AI, discusses how artificial intelligence and automation can be maximized across businesses on CNBC International Live. Linney says AI poses a threat to the highest income knowledge workers around the world.

Meanwhile, Nate B. Jones is back with some commentary on how OpenAI has launched a new API aimed at helping developers build AI agents, but its strategic impact remains unclear. While enterprises with strong LLM expertise are already using tools like LangChain effectively, smaller teams struggle with agent complexity. Nate says, despite being a high-quality API, it lacks a distinct differentiator beyond OpenAI’s own ecosystem.

We close out with, Celestial AI CEO Dave Lazovsky outlines how their “Photonic Fabric” technology helps to scale AI as the company raises $250 million in their latest funding round, valuing the company at $2.5 billion. Thats all for today, but AI is moving fast — subscribe.

The article presents an equation of state (EoS) for fluid and solid phases using artificial neural networks. This EoS accurately models thermophysical properties and predicts phaseions, including the critical and triple points. This approach offers a unified way to understand different states of matter.

A team from Princeton University has successfully used artificial intelligence (AI) to solve equations that control the quantum behavior of individual atoms and molecules to replicate the early stages of ice formation. The simulation shows how water molecules transition into solid ice with quantum accuracy.

Roberto Car, Princeton’s Ralph W. *31 Dornte Professor in Chemistry, who co-pioneered the approach of simulating molecular behaviors based on the underlying quantum laws more than 35 years ago, said, “In a sense, this is like a dream come true. Our hope then was that eventually, we would be able to study systems like this one. Still, it was impossible without further conceptual development, and that development came via a completely different field, that of artificial intelligence and data science.”

Modeling the early stages of freezing water, the ice nucleation process could increase the precision of climate and weather modeling and other processes like flash-freezing food. The new approach could help track the activity of hundreds of thousands of atoms over thousands of times longer periods, albeit still just fractions of a second, than in early studies.

A new study has been published in Nature Communications, presenting the first comprehensive atlas of allele-specific DNA methylation across 39 primary human cell types. The study was led by Ph.D. student Jonathan Rosenski under the guidance of Prof. Tommy Kaplan from the School of Computer Science and Engineering and Prof. Yuval Dor from the Faculty of Medicine at the Hebrew University of Jerusalem and Hadassah Medical Center.

Using machine learning algorithms and deep whole-genome bisulfite sequencing on freshly isolated and purified cell populations, the study unveils a detailed landscape of genetic and epigenetic regulation that could reshape our understanding of gene expression and disease.

A key focus of the research is the success in identifying differences between the two alleles and, in some cases, demonstrating that these differences result from —meaning that it is not the sequence (genetics) that matters, but rather whether the allele is inherited from the mother or the father. These findings could reshape our understanding of gene expression and disease.

However, as with much of quantum physics, this “language”—the interaction between spins—is extraordinarily complex. While it can be described mathematically, solving the equations exactly is nearly impossible, even for relatively simple chains of just a few spins. Not exactly ideal conditions for turning theory into reality…

A model becomes reality

Researchers at Empa’s nanotech@surfaces laboratory have now developed a method that allows many spins to “talk” to each other in a controlled manner – and that also enables the researchers to “listen” to them, i.e. to understand their interactions. Together with scientists from the International Iberian Nanotechnology Laboratory and the Technical University of Dresden, they were able to precisely create an archetypal chain of electron spins and measure its properties in detail. Their results have now been published in the renowned journal Nature Nanotechnology.