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EPFL scientists show that even a few simple examples are enough for a quantum machine-learning model, the “quantum neural networks,” to learn and predict the behavior of quantum systems, bringing us closer to a new era of quantum computing.

Imagine a world where computers can unravel the mysteries of , enabling us to study the behavior of complex materials or simulate the intricate dynamics of molecules with unprecedented accuracy.

Thanks to a pioneering study led by Professor Zoe Holmes and her team at EPFL, we are now closer to that becoming a reality. Working with researchers at Caltech, the Free University of Berlin, and the Los Alamos National Laboratory, they have found a new way to teach a quantum computer how to understand and predict the behavior of quantum systems. The research has been published in Nature Communications.

As Nvidia’s recent surge in market capitalization clearly demonstrates, the AI industry is in desperate need of new hardware to train large language models (LLMs) and other AI-based algorithms. While server and HPC GPUs may be worthless for gaming, they serve as the foundation for data centers and supercomputers that perform highly parallelized computations necessary for these systems.

When it comes to AI training, Nvidia’s GPUs have been the most desirable to date. In recent weeks, the company briefly achieved an unprecedented $1 trillion market capitalization due to this very reason. However, MosaicML now emphasizes that Nvidia is just one choice in a multifaceted hardware market, suggesting companies investing in AI should not blindly spend a fortune on Team Green’s highly sought-after chips.

The AI startup tested AMD MI250 and Nvidia A100 cards, both of which are one generation behind each company’s current flagship HPC GPUs. They used their own software tools, along with the Meta-backed open-source software PyTorch and AMD’s proprietary software, for testing.

Generative AI’s potential to unleash creativity, accelerate discovery, and enhance efficiency could add trillions to Asian economies.

When it comes to the ability to generate, arrange, and analyze content, generative AI is a gamechanger—one with transformative social and economic potential.

As a technology that is democratized—one that doesn’t simply exist in a faraway lab or tech community in Silicon Valley, for instance—generative AI lowers the barriers to participation. In the age of generative AI, anyone can be a creator. But this also entails a profound workforce shift, changing the processes of production within the economy and, in turn, the types of tasks that are undertaken and the… More.

Meta is taking on Twitter with a new app. Instagram today announced the anticipated launch of its text-based social networking app, Threads, which allows Instagram users to authenticate with their existing credentials in order to post short updates, including text up to 500 characters; links; photos; and videos up to 5 minutes in length.

At launch, Threads is available on iOS and Android in 100 countries, though not in the EU, reportedly due to concerns around adhering to local data privacy regulations. Users can log in with their Instagram credentials, where their username and verification status will carry over. However, Threads profiles can be customized independently as well.

The app’s existence was first scooped by MoneyControl this March and later confirmed by Platformer. In June, Meta previewed the app to employees during a company-wide meeting. Further leaks offered more details about the app’s target market of high-profile celebrities, influencers and artists, and its planned feature set.

Usual weather prediction systems have the capacity to generate around 50 predictions for the week ahead. FourCastNet can instead predict thousands of possibilities, accurately capturing the risk of rare but deadly disasters and thereby giving vulnerable populations valuable time to prepare and evacuate.

The hoped-for revolution in climate modeling is just the beginning. With the advent of AI, science is about to become much more exciting—and in some ways unrecognizable. The reverberations of this shift will be felt far outside the lab; they will affect us all.