Lifeboat Foundation

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Humans and animals can use diverse decision-making strategies to maximize rewards in uncertain environments, but previous studies have not investigated the use of multiple strategies that involve distinct latent switching dynamics in reward-guided behavior. Here, using a reversal learning task, we showed that mice displayed a much more variable behavior than would be expected from a uniform strategy, suggesting that they mix between multiple behavioral modes in the task. We develop a computational method to dissociate these learning modes from behavioral data, addressing the challenges faced by current analytical methods when agents mix between different strategies. We found that the use of multiple strategies is a key feature of rodent behavior even in the expert stages of learning, and applied our tools to quantify the highly diverse strategies used by individual mice in the task. We further mapped these behavioral modes to two types of underlying algorithms, model-free Q-learning and inference-based behavior. These rich descriptions of underlying latent states form the basis of detecting abnormal patterns of behavior in reward-guided decision-making.

Citation: Le NM, Yildirim M, Wang Y, Sugihara H, Jazayeri M, Sur M (2023) Mixtures of strategies underlie rodent behavior during reversal learning. PLoS Comput Biol 19: e1011430. https://doi.org/10.1371/journal.pcbi.

Editor: Alireza Soltani, Dartmouth College, UNITED STATES

In work that could lead to more robust quantum computing, Princeton researchers have succeeded in forcing molecules into quantum entanglement.

For the first time, a team of Princeton physicists has been able to link together individual molecules into special states that are quantum mechanically “entangled.” In these bizarre states, the molecules remain correlated with each other—and can interact simultaneously—even if they are miles apart, or indeed, even if they occupy opposite ends of the universe. This research was published in the journal Science.

Molecular entanglement: a breakthrough for practical applications.

Cerebras introduces gigaGPT: GPT-3 sized models in 565 lines of code.

GigaGPT is Cerebras’ implementation of Andrei Karpathy’s nanoGPT – the simplest and most compact code base to train and fine-tune GPT models. Whereas nanoGPT can train models in the 100M parameter range, gigaGPT trains models well over 100B parameters. We do this without introducing additional code or relying on third party frameworks – the entire repo is just 565 lines of code. Instead gigaGPT utilizes the large memory and compute capacity of Cerebras hardware to enable large scale training on vanilla torch.nn code. With no modifications, gigaGPT supports long context lengths and works with a variety of optimizers.

Why gigaGPT

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Q-day (the day when quantum computers will successfully actually break the internet) may be some time away yet. However, that does not mean that companies — and states — shouldn’t hop on the qubit bandwagon now so as not to be left behind in the race for a technology that could potentially alter how we think about life, the Universe, and well… everything.

Spurred on by a discourse that more and more revolves around the concept of “digital sovereignty,” 11 EU member states this week signed the European Declaration on Quantum Technologies.

The signatories have agreed to align, coordinate, engage, support, monitor, and all those other international collaboration verbs, on various parts of the budding quantum technology ecosystem. They include France, Belgium, Croatia, Greece, Finland, Slovakia, Slovenia, Czech Republic, Malta, Estonia, and Spain. However, the coalition is still missing some quantum frontrunners, such as the Netherlands, Ireland, and Germany, who reportedly opted out due to the short time frame.

Rigetti announced the launch of its Novera™ QPU — a 9-qubit quantum processing unit.

Rigetti Computing announced the launch and sale of the company’s Novera™ QPU, a 9-qubit quantum processing unit.

Admiral Grace Hopper was one of the first programmers of the Harvard Mark I computer. She developed the first compiler for a computer programming language…

A small taste of the Core Ultra 9 185H’s flagship performance.

The team at EPFL’s Photonic Systems Laboratory (PHOSL) has developed a chip-scale laser source that enhances the performance of semiconductor lasers while enabling the generation of shorter wavelengths.

This pioneering work, led by Professor Camille Brès and postdoctoral researcher Marco Clementi from EPFL’s School of Engineering represents a significant advance in the field of photonics, with implications for telecommunications, metrology, and other high-precision applications.

The study, published in the journal Light: Science & Applications, reveals how the PHOSL researchers, in collaboration with the Laboratory of Photonics and Quantum Measurements, have successfully integrated semiconductor lasers with silicon nitride photonic circuits containing microresonators. This integration results in a hybrid device that emits highly uniform and precise light in both near-infrared and visible ranges, filling a technological gap that has long challenged the industry.