Lifeboat Foundation

Training AI models today isn’t just about designing better architectures—it’s also about managing data efficiently. Modern models require vast datasets and need those datasets delivered quickly to GPUs and other accelerators. The problem? Traditional data loading systems often lag behind, slowing everything down. These older systems rely heavily on process-based methods that struggle to keep up with the demand, leading to GPU downtime, longer training sessions, and higher costs. This becomes even more frustrating when you’re trying to scale up or work with a mix of data types.

To tackle these issues, Meta AI has developed SPDL (Scalable and Performant Data Loading), a tool designed to improve how data is delivered during AI training. SPDL uses thread-based loading, which is a departure from the traditional process-based approach, to speed things up. It handles data from all sorts of sources—whether you’re pulling from the cloud or a local storage system—and integrates it seamlessly into your training workflow.

SPDL was built with scalability in mind. It works across distributed systems, so whether you’re training on a single GPU or a large cluster, SPDL has you covered. It’s also designed to work well with PyTorch, one of the most widely used AI frameworks, making it easier for teams to adopt. And since it’s open-source, anyone can take advantage of it or even contribute to its improvement.

The hallmark of a good AI model is its ability to work the same in different groups, settings, and situations. See how these NCI researchers used in-house and external images to test their prostate model’s generalizability.

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Scientific literature is growing rapidly, meaning scientists are increasingly unable to keep up with all of the latest developments in research. AI large language models, though, can read and “digest” information much more quickly than their human counterparts, making them the perfect tools to conduct massive literature reviews. Recent research shows they’re also very accurate at predicting the results of studies that they’ve never read before. Let’s take a look.

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However, while Google’s achievements have been noted for advancing the field, experts say that quantum computing still has no real-world uses — yet.

“We need a ChatGPT moment for quantum,” Francesco Ricciuti, associate at venture capital firm Runa Capital, told CNBC on Tuesday, referencing OpenAI’s chatbot that has been credited with driving the boom in artificial intelligence. “This is probably not that.”

Proponents of quantum computing claim it will be able to solve problems that current computers can’t.

AI can predict human intelligence by looking at the connections of a working human brain. Neuroscientists can predict intelligence from brain structure and function—to a point. Previous studies have suggested that intelligence is widely distributed across the brain.

Kirsten Hilger and colleagues used machine learning models to predict multiple kinds of intelligence from brain connections of 806 healthy adults while resting and while completing tasks. The article is published in PNAS Nexus.

Fluid intelligence includes inductive and deductive reasoning abilities that do not rely on context, while crystallized intelligence reflects the ability to apply knowledge from individual experience and culture. General intelligence captures both types of intelligence.

OpenAI has released Sora Turbo, an AI video generator, to the public. It offers faster, high-definition video creation up to 22 seconds. Available to ChatGPT Plus and Pro subscribers, it includes safeguards against misuse. Currently, it is not available in European and British markets.

OpenAI on Monday released the latest version of its highly anticipated Sora video generator to the public, stepping into an increasingly crowded field of AI tools that has raised concerns about disruption to creative industries.

The company behind ChatGPT said its latest version, dubbed Sora Turbo, offers significant speed improvements over the February preview model and can create high-definition videos lasting up to 22 seconds.

Continue reading “OpenAI releases Sora AI video generator to public” »

Just as the metaverse industry finally began its breakthrough, ChatGPT’s launch in November 2022 proved a technological avalanche. Amid post-pandemic economic pressures, companies pivoted away from metaverse aspirations to AI adoption, seeking immediate returns through automation and virtualization.

Apple’s 2023 entry into the space with Vision Pro VR headset has similarly faced challenges. At $3,499, with limited content and a sparse developer ecosystem, Apple struggles to find its market. Early projections suggest initial production runs of fewer than 400,000 units.

What if we’ve been looking for the metaverse in the wrong places? While Meta, HTC and Sony have so far struggled to establish their vision of a VR-first digital world, gaming platforms like Roblox quietly built what might be the actual metaverse.

Researchers at the University of Reading and University College London have developed a new artificial intelligence model that can predict how atoms arrange themselves in crystal structures. Called CrystaLLM, the technology works similarly to AI chatbots, by learning the “language” of crystals by studying millions of existing crystal structures. It could lead to faster discovery of new materials for everything from solar panels to computer chips.

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To leverage the power of AI for data analytics, companies need to have systems in place that bring all relevant data points together on one platform. Siloed systems prevent AI from conducting a comprehensive evaluation. Companies that want to conduct predictive analysis, such as demand forecasting, will need to give AI platforms access to historical data.

Optimizing user interfaces for both customers and employees is also an important step toward empowering efficient operations. Consumer-facing interfaces should provide transparency, flexibility and a high level of user control, enabling potential customers to explore a variety of options and providing the information needed to ensure moves are handled correctly. Employee-facing interfaces should make it easy for drivers and customer service representatives to receive and respond to notifications such as route updates or customer concerns.

For commercial moving, achieving maximum efficiency requires much more than transportation. Solutions should consider every aspect of the move, leveraging technology and informed strategies to empower a seamless transition. When approached strategically, the result is reduced downtime and the peace of mind that all equipment is transported with precision and moved in accordance with predetermined timelines.