In the world around us, many things exist in the context of time: a bird’s path through the sky is understood as different positions over a period of time, and conversations as a series of words occurring one after another.In the world around us, many things exist in the context of time: a bird’s path through the sky is understood as different positions over a period of time, and conversations as a series of words occurring one after another.[#item_full_content]

Researchers have found that giving AI “peers” in virtual reality (VR) a body that can interact with the virtual environment can help students learn programming. Specifically, the researchers found students were more willing to accept these “embodied” AI peers as partners, compared to voice-only AI, helping the students better engage with the learning experience.Researchers have found that giving AI “peers” in virtual reality (VR) a body that can interact with the virtual environment can help students learn programming. Specifically, the researchers found students were more willing to accept these “embodied” AI peers as partners, compared to voice-only AI, helping the students better engage with the learning experience.[#item_full_content]

When a machine fails, our first reaction is often frustration. A computer freezing at the worst possible moment, a navigation app leading us straight into a traffic jam, or a washing machine suddenly stopping mid-cycle—all are everyday situations that trigger anger and irritation. But a new study by two Israeli researchers offers a different perspective: it turns out that we also apply mechanisms of forgiveness toward technology, much like we do with people.When a machine fails, our first reaction is often frustration. A computer freezing at the worst possible moment, a navigation app leading us straight into a traffic jam, or a washing machine suddenly stopping mid-cycle—all are everyday situations that trigger anger and irritation. But a new study by two Israeli researchers offers a different perspective: it turns out that we also apply mechanisms of forgiveness toward technology, much like we do with people.[#item_full_content]

A new framework for generative diffusion models was developed by researchers at Science Tokyo, significantly improving generative AI models. The method reinterpreted Schrödinger bridge models as variational autoencoders with infinitely many latent variables, reducing computational costs and preventing overfitting. By appropriately interrupting the training of the encoder, this approach enabled development of more efficient generative AI, with broad applicability beyond standard diffusion models.A new framework for generative diffusion models was developed by researchers at Science Tokyo, significantly improving generative AI models. The method reinterpreted Schrödinger bridge models as variational autoencoders with infinitely many latent variables, reducing computational costs and preventing overfitting. By appropriately interrupting the training of the encoder, this approach enabled development of more efficient generative AI, with broad applicability beyond standard diffusion models.[#item_full_content]

Bar-Ilan University announced today that a team from its Department of Computer Science has developed a breakthrough in video processing that significantly simplifies the separation of foreground objects from their backgrounds, without the need for extensive training or optimization. The new method, called OmnimatteZero, was developed by Dr. Dvir Samuel and Prof. Gal Chechik, who also serves as a senior director of AI research at NVIDIA. The paper is published on the arXiv preprint server.Bar-Ilan University announced today that a team from its Department of Computer Science has developed a breakthrough in video processing that significantly simplifies the separation of foreground objects from their backgrounds, without the need for extensive training or optimization. The new method, called OmnimatteZero, was developed by Dr. Dvir Samuel and Prof. Gal Chechik, who also serves as a senior director of AI research at NVIDIA. The paper is published on the arXiv preprint server.[#item_full_content]

It’s a sunny June day in southeast England. I’m driving along a quiet, rural road that stretches through the Kent countryside. The sun flashes through breaks in the hedgerow, offering glimpses of verdant crop fields and old farmhouses.It’s a sunny June day in southeast England. I’m driving along a quiet, rural road that stretches through the Kent countryside. The sun flashes through breaks in the hedgerow, offering glimpses of verdant crop fields and old farmhouses.[#item_full_content]

You’re neck-deep in IKEA assembly instructions. Furniture parts lie strewn across the floor. Your new purchase sits half-complete in front of you, mocking your fruitless hours. As an uninterested partner walks in, you let the frustration out:You’re neck-deep in IKEA assembly instructions. Furniture parts lie strewn across the floor. Your new purchase sits half-complete in front of you, mocking your fruitless hours. As an uninterested partner walks in, you let the frustration out:[#item_full_content]

A new study has uncovered the remarkable potential of biomolecular computing that could provide innovative solutions in next generation of integrated circuits. In their article published in Materials Today Nano, the authors review existing “biological integrated circuits” that utilize DNA or proteins for clinical analysis tasks and also explore the prospects of their integration with classical integrated circuits.A new study has uncovered the remarkable potential of biomolecular computing that could provide innovative solutions in next generation of integrated circuits. In their article published in Materials Today Nano, the authors review existing “biological integrated circuits” that utilize DNA or proteins for clinical analysis tasks and also explore the prospects of their integration with classical integrated circuits.[#item_full_content]

Probabilistic Ising machines (PIMs) are advanced and specialized computing systems that could tackle computationally hard problems, such as optimization or integer factorization tasks, more efficiently than classical systems. To solve problems, PIMs rely on interacting probabilistic bits (p-bits), networks of interacting units of digital information with values that randomly fluctuate between 0 and 1, but that can be biased to converge to yield desired solutions.Probabilistic Ising machines (PIMs) are advanced and specialized computing systems that could tackle computationally hard problems, such as optimization or integer factorization tasks, more efficiently than classical systems. To solve problems, PIMs rely on interacting probabilistic bits (p-bits), networks of interacting units of digital information with values that randomly fluctuate between 0 and 1, but that can be biased to converge to yield desired solutions.[#item_full_content]

Train delays can cascade into stalled commutes, economic losses, and vacation snags. Scheduling trains is computationally complex, though: It can take hours or days to solve large transportation networks on traditional computers, when disruptions like train breakdowns or traffic accidents demand much quicker solutions.Train delays can cascade into stalled commutes, economic losses, and vacation snags. Scheduling trains is computationally complex, though: It can take hours or days to solve large transportation networks on traditional computers, when disruptions like train breakdowns or traffic accidents demand much quicker solutions.[#item_full_content]