Tests aim to see if AI models can determine when you’re speaking to your phone without needing a trigger phrase.
Category: robotics/AI – Page 385
AI technology developed by Johns Hopkins University to detect COVID-19 in lung ultrasound images, revolutionizing medical diagnostics.
Researchers develop skyrmion-based microelectronic device for sustainable, high-performance AI computing with energy-efficient technology.
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Recent advancements in artificial intelligence make it increasingly harder to detect deepfake voices, and the solution might actually come from AI itself.
Scientists at Klick Labs were inspired by their clinical studies using vocal biomarkers to help enhance health outcomes and created an audio deepfake detection method that taps into signs of life like breathing patterns and micropauses in speech.
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.
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.
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.
The brain has effectively proven a powerful inspiration for the development of computing architectures in which processing is tightly integrated with memory, communication is event-driven, and analog computation can be performed at scale. These neuromorphic systems increasingly show an ability to improve the efficiency and speed of scientific computing and artificial intelligence applications. Herein, it is proposed that the brain’s ubiquitous stochasticity represents an additional source of inspiration for expanding the reach of neuromorphic computing to probabilistic applications. To date, many efforts exploring probabilistic computing have focused primarily on one scale of the microelectronics stack, such as implementing probabilistic algorithms on deterministic hardware or developing probabilistic devices and circuits with the expectation that they will be leveraged by eventual probabilistic architectures. A co-design vision is described by which large numbers of devices, such as magnetic tunnel junctions and tunnel diodes, can be operated in a stochastic regime and incorporated into a scalable neuromorphic architecture that can impact a number of probabilistic computing applications, such as Monte Carlo simulations and Bayesian neural networks. Finally, a framework is presented to categorize increasingly advanced hardware-based probabilistic computing technologies.
Keywords: magnetic tunnel junctions; neuromorphic computing; probabilistic computing; stochastic computing; tunnel diodes.
© 2022 The Authors. Advanced Materials published by Wiley-VCH GmbH.