In a new paper in the journal Nature Machine Intelligence, leading computer scientists from around the world review recent machine learning advances converging towards creating a collective machine-learned intelligence.
An emerging research area in AI is developing multi-agent capabilities with collections of interacting AI systems. Andrea Soltoggio and colleagues develop a vision for combining such approaches with current edge computing technology and lifelong learning advances. The envisioned network of AI agents could quickly learn new tasks in open-ended applications, with individual AI agents independently learning and contributing to and benefiting from collective knowledge.
A recent study published in Nature reveals a potential link between a type of bacteria associated with dental plaque and treatment-resistant colorectal cancer. The Gram-negative, anaerobic bacterium, Fusobacterium nucleatum, was found in 50% of tumors tested, suggesting it may protect tumor cells from cancer-fighting drugs. This discovery opens avenues for new treatments and screening methods. Colorectal cancer, a leading cause of cancer deaths in the United States, is increasingly affecting younger demographics, with cases doubling among those younger than age 55 between 1995 and 2019. While the study doesn’t directly tie the bacterium to this trend, its implications raise questions about its role in rising cases among younger individuals. F. nucleatum has been suspected in colorectal cancer growth. It possesses two subspecies, one of which is capable of evading immune response and promoting tumor formation. These findings suggest a potential mechanism for its journey from the oral cavity to the colon, defying stomach acid’s toxic effects. Future research may explore developing antibiotics targeting specific bacterial subtypes or using genetically modified bacteria for targeted drug delivery into tumors. Understanding the microbiome’s role in cancer risk represents a crucial frontier in cancer research. Click here to read more.
Year 2010 😗😁
The world has waited with bated breath for three decades, and now finally a group of academics, engineers, and math geeks has discovered the number that explains life, the universe, and everything. That number is 20, and it’s the maximum number of moves it takes to solve a Rubik’s Cube.
Known as God’s Number, the magic number required about 35 CPU-years and a good deal of man-hours to solve. Why? Because there’s-1 possible positions of the cube, and the computer algorithm that finally cracked God’s Algorithm had to solve them all. (The terms God’s Number/Algorithm are derived from the fact that if God was solving a Cube, he/she/it would do it in the most efficient way possible. The Creator did not endorse this study, and could not be reached for comment.)
OpenAI has apparently been demonstrating GPT-5, the next generation of its notorious large language model (LLM), to prospective buyers — and they’re very impressed with the merchandise.
“It’s really good, like materially better,” one CEO told Business Insider of the LLM. That same CEO added that in the demo he previewed, OpenAI tailored use cases and data modeling unique to his firm — and teased previously unseen capabilities as well.
According to BI, OpenAI is looking at a summer launch — though its sources say it’s still being trained and in need of “red-teaming,” the tech industry term for hiring hackers to try to exploit one’s wares.
Research often unfolds as a multistage process. The solution to one question can spark several more, inspiring scientists to reach further and look at the larger problem from several different perspectives. Such projects can often be the catalyst for collaborations that leverage the expertise and capabilities of different teams and institutions as they grow.
For half a century, scientists have delved into the mysteries of 1T phase tantalum disulfide (1T-TaS2), an inorganic layered material with some intriguing quantum properties, like superconductivity and charge density waves (CDW). To unlock the complex structure and behavior of this material, researchers from the Jozef Stefan Institute in Slovenia and Université Paris-Saclay in France reached out to experts utilizing the Pair Distribution Function (PDF) beamline at the National Synchrotron Light Source II (NSLS-II), a U.S. Department of Energy (DOE) Office of Science User Facility located at DOE’s Brookhaven National Laboratory, to learn more about the material’s structure. While the team in Slovenia had been studying these kinds of materials for decades, they were lacking the specific structural characterization that PDF could provide.
The results of this collaboration, recently published in Nature Communications, revealed a hidden electronic state that could only be seen by a local structure probe like the pair distribution function technique. With a more complete understanding of 1T-TaS2’s electronic states, this material may one day play a role in data storage, quantum computing, and superconductivity.
Bayesian neural networks (BNNs) combine the generalizability of deep neural networks (DNNs) with a rigorous quantification of predictive uncertainty, which mitigates overfitting and makes them valuable for high-reliability or safety-critical applications. However, the probabilistic nature of BNNs makes them more computationally intensive on digital hardware and so far, less directly amenable to acceleration by analog in-memory computing as compared to DNNs. This work exploits a novel spintronic bit cell that efficiently and compactly implements Gaussian-distributed BNN values. Specifically, the bit cell combines a tunable stochastic magnetic tunnel junction (MTJ) encoding the trained standard deviation and a multi-bit domain-wall MTJ device independently encoding the trained mean. The two devices can be integrated within the same array, enabling highly efficient, fully analog, probabilistic matrix-vector multiplications. We use micromagnetics simulations as the basis of a system-level model of the spintronic BNN accelerator, demonstrating that our design yields accurate, well-calibrated uncertainty estimates for both classification and regression problems and matches software BNN performance. This result paves the way to spintronic in-memory computing systems implementing trusted neural networks at a modest energy budget.
The powerful ability of deep neural networks (DNNs) to generalize has driven their wide proliferation in the last decade to many applications. However, particularly in applications where the cost of a wrong prediction is high, there is a strong desire for algorithms that can reliably quantify the confidence in their predictions (Jiang et al., 2018). Bayesian neural networks (BNNs) can provide the generalizability of DNNs, while also enabling rigorous uncertainty estimates by encoding their parameters as probability distributions learned through Bayes’ theorem such that predictions sample trained distributions (MacKay, 1992). Probabilistic weights can also be viewed as an efficient form of model ensembling, reducing overfitting (Jospin et al., 2022). In spite of this, the probabilistic nature of BNNs makes them slower and more power-intensive to deploy in conventional hardware, due to the large number of random number generation operations required (Cai et al., 2018a).
Nice figures in this newly published survey on Scaled Optimal Transport with 200+ references.
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Optimal Transport (OT) is a mathematical framework that first emerged in the eighteenth century and has led to a plethora of methods for answering many theoretical and applied questions. The last decade has been a witness to the remarkable contributions of this classical optimization problem to machine learning. This paper is about where and how optimal transport is used in machine learning with a focus on the question of scalable optimal transport. We provide a comprehensive survey of optimal transport while ensuring an accessible presentation as permitted by the nature of the topic and the context. First, we explain the optimal transport background and introduce different flavors (i.e. mathematical formulations), properties, and notable applications.
In a new study, scientists propose the concept of “virtual quantum broadcasting,” which provides a workaround to the longstanding no-cloning theorem, thereby offering new possibilities for the transmission of quantum information.
Chipmaker Nvidia has shown off a clone of our entire planet that could help meteorologists simulate and visualize global weather patterns at an “unprecedented scale,” according to a press release.
The “Earth climate digital twin,” dubbed Earth-2, was designed to help recoup some of the economic losses caused by climate change-driven extreme weather.
Customers can access the digital twin through an API, allowing “virtually any user to create AI-powered emulations to speed delivery of interactive, high-resolution simulations ranging from the global atmosphere and local cloud cover to typhoons and turbulence.”
