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Researchers from the Institute for Molecules and Materials at Radboud University, Netherlands, have demonstrated that a complex self-organizing chemical reaction network can perform various computational tasks, such as nonlinear classification and complex dynamics prediction.

Entegris will build the $600 million facility.

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Continue reading “Daniel Dennett on the Mind as Computer” »

The 56-qubit H2-1 computer has broken the previous record in the ‘quantum supremacy’ benchmark first set by Google in 2019.

Oxford Ionics reported that the company’s newest chips can provide over twice the performance of previous records.

One of the variables in TD algorithms is called reward prediction error (RPE), which is the difference between the discounted predicted reward at the current state and the discounted predicted reward plus the actual reward at the next state. TD learning theory gained traction in neuroscience once it was demonstrated that firing patterns of dopaminergic neurons in the ventral tegmental area (VTA) during reinforcement learning resemble RPE^5,9,10.

Implementations of TD using computer algorithms are straightforward, but are more complex when they are mapped onto plausible neural machinery^11,12,13. Current implementations of neural TD assume a set of temporal basis-functions^13,14, which are activated by external cues. For this assumption to hold, each possible external cue must activate a separate set of basis-functions, and these basis-functions must tile all possible learnable intervals between stimulus and reward.

In this paper, we argue that these assumptions are unscalable and therefore implausible from a fundamental conceptual level, and demonstrate that some predictions of such algorithms are inconsistent with various established experimental results. Instead, we propose that temporal basis functions used by the brain are themselves learned. We call this theoretical framework: Flexibly Learned Errors in Expected Reward, or FLEX for short. We also propose a biophysically plausible implementation of FLEX, as a proof-of-concept model. We show that key predictions of this model are consistent with actual experimental results but are inconsistent with some key predictions of the TD theory.

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39,785 views • Jul 12, 2024 • #ASUSCopilotPlusPC #ASUS #Vivobook

Scientists from Trinity College Dublin have created a computer program that “paints” the structure of molecules in the style of famous Dutch artist, Piet Mondrian, whose beautiful artworks will be instantly recognizable to many.

Entanglement, Einstein’s “spooky action at a distance,” today is THE tool of quantum information science. It is the essential resource for quantum computers and used to transmit quantum information in a future quantum network. But it is highly sensitive. It is therefore an enormous challenge to entangle resting quantum bits (qubits) with flying qubits in the form of photons “at the push of a button.”