Getting to the heart of causality is central to understanding the world around us. What causes one variable—be it a biological species, a voting region, a company stock, or a local climate—to shift from one state to another can inform how we might shape that variable in the future.
A tool that can watermark text generated by large language models, improving the ability for it to identify and trace synthetic content, is described in Nature this week.
Nearly 200 years after Beethoven's death, a team of musicians and computer scientists created a generative artificial intelligence (AI) that completed his Tenth Symphony so convincingly that music scholars could not differentiate the music originating from the AI or from the composer's handwritten notes.
As technology advances, the limitations of conventional electronic computers are becoming increasingly apparent, especially when tackling complex computational challenges. NP-complete problems, which grow exponentially with size, represent some of the toughest puzzles in computer science. These issues have significant implications across various fields, including biomedicine, transportation, and manufacturing. In the quest for more effective solutions, researchers are exploring alternatives to traditional computing methods, with optical computing emerging as a promising avenue.
Two experts with the OpenAI team have developed a new kind of continuous-time consistency model (sCM) that they claim can generate video media 50 times faster than models currently in use. Cheng Lu and Yang Song have published a paper describing their new model on the arXiv preprint server. They have also posted an introductory paper on the company's website.
While artificial intelligence (AI) bots can serve a legitimate purpose on social media—such as marketing or customer service—some are designed to manipulate public discussion, incite hate speech, spread misinformation or enact fraud and scams. To combat potentially harmful bot activity, some platforms have published policies on using bots and created technical mechanisms to enforce those policies.
Penn Engineers have developed a new algorithm that allows robots to react to complex physical contact in real time, making it possible for autonomous robots to succeed at previously impossible tasks, like controlling the motion of a sliding object.
Researchers at Apple Computer Company have found evidence, via testing, showing that the seemingly intelligent responses given by AI-based LLMs are little more than an illusion. In their paper posted on the arXiv preprint server, the researchers argue that after testing several LLMs, they found that they are not capable of performing genuine logical reasoning.
University of Virginia School of Engineering and Applied Science professor Nikolaos Sidiropoulos has introduced a breakthrough in graph mining with the development of a new computational algorithm.
A team of microchip engineers at Pragmatic Semiconductor, working with a pair of colleagues from Harvard University and another from Qamcom, has developed a bendable, programmable, non-silicon 32-bit RISC-V microprocessor. Their research is published in the journal Nature.
Artificial intelligence (AI) has opened new interesting opportunities for the music industry, for instance, enabling the development of tools that can automatically generate musical compositions or specific instrument tracks. Yet most existing tools are designed to be used by musicians, composers and music producers, as opposed to non-expert users.
Imaging microscopic samples requires capturing multiple, sequential measurements, then using computational algorithms to reconstruct a single, high-resolution image. This process can work well when the sample is static, but if it's moving—as is common with live, biological specimens—the final image may be blurry or distorted.
Imagine you're tasked with sending a team of football players onto a field to assess the condition of the grass (a likely task for them, of course). If you pick their positions randomly, they might cluster together in some areas while completely neglecting others. But if you give them a strategy, like spreading out uniformly across the field, you might get a far more accurate picture of the grass condition.
Integrating post-quantum security algorithms into hardware has long been considered a challenge. But a research team at TU Graz has now developed hardware for NIST post-quantum cryptography standards with additional security measures for this purpose.
Neural networks have a remarkable ability to learn specific tasks, such as identifying handwritten digits. However, these models often experience "catastrophic forgetting" when taught additional tasks: They can successfully learn the new assignments, but "forget" how to complete the original. For many artificial neural networks, like those that guide self-driving cars, learning additional tasks thus requires being fully reprogrammed.
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