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A team of mechanical engineers from Chung-Ang University, Massachusetts General Hospital, LS Materials and Yonsei University has found that a hand-held cylinder containing crumpled aluminum foil balls is capable of producing enough electricity when shaken to light a small LED grid. In their paper published in the journal Advanced Science, the group describes other materials used in the cylinder and possible uses for such a device.

Prior research has shown that a wide variety of materials can be used to generate , and that some constructions can capture that . Researchers have suggested such devices could be useful as the power needs of personal electronics decrease. In this new effort, the researchers have looked to aluminum foil as a material for generating static electricity and capturing it to power an external device.

The device the team built is shaped as a with a cap on the top and bottom—about the size of a Pringle’s can. The tube was made using an acrylic substrate covered with a polytetrafluoroethylene layer. The caps, which serve as electrodes, were made of aluminum. The team then crumpled three wads of into balls and placed them inside the tube.

A groundbreaking theoretical proof reveals that using a technique called overparametrization enhances performance in quantum machine learning.

Machine learning is a subset of artificial intelligence (AI) that deals with the development of algorithms and statistical models that enable computers to learn from data and make predictions or decisions without being explicitly programmed to do so. Machine learning is used to identify patterns in data, classify data into different categories, or make predictions about future events. It can be categorized into three main types of learning: supervised, unsupervised and reinforcement learning.

Part of the reason that these chips are becoming so much more efficient and getting so many more cores on a similar die size to the chips that have come before is the 3nm process. Apple is heavily invested in the new chip creation tech, having ordered almost the entirety of the first printing of the process, making sure that it has an advantage over the competition. The first device that we might see with a 3nm process chip is the iPhone 15 Pro, and then it’s likely that the M3 chips will follow.

Whatever happens with these new chips, it’s looking like it’s going to be an exciting time for Apple silicon and the latest Apple hardware to use it.

We’re seeing major advancements in tech that can decode brain signals, interpreting neural activity to reveal what’s on someone’s mind, what they want to say, or – in the case of a new study – which song they’re listening to.

US researchers have been able to reconstruct a “recognizable version” of a Pink Floyd song based on the pulses of activity moving through a specific part of the brain’s temporal lobe in volunteers as they listened to the hit Another Brick in the Wall Part 1.

While the tune in question did go through some initial processing into a spectrogram form to be more compatible with the brain’s audio processing techniques, the reverse process is impressive in terms of its fidelity.

In this video I discuss New Cerebras Supercomputer with Cerebras’s CEO Andrew Feldman.
Timestamps:
00:00 — Introduction.
02:15 — Why such a HUGE Chip?
02:37 — New AI Supercomputer Explained.
04:06 — Main Architectural Advantage.
05:47 — Software Stack NVIDIA CUDA vs Cerebras.
06:55 — Costs.
07:51 — Key Applications & Customers.
09:48 — Next Generation — WSE3
10:27 — NVIDIA vs Cerebras Comparison.

Mentioned Papers:
Massively scalable stencil algorithm: https://arxiv.org/abs/2204.03775
https://www.cerebras.net/blog/harnessing-the-power-of-sparsi…-ai-models.
https://www.cerebras.net/press-release/cerebras-wafer-scale-…ge-models/
Programming at Scale:
https://8968533.fs1.hubspotusercontent-na1.net/hubfs/8968533…tScale.pdf.
Massively Distributed Finite-Volume Flux Computation: https://arxiv.org/abs/2304.

Mentioned Video:
New CPU Technology: https://youtu.be/OcoZTDevwHc.

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