Lifeboat Foundation

During a demonstration, a Motorola representative showed how the phone could bend in various ways to wrap around a wrist or stand up on a table.

When the phone is wrapped around the wrist, the way information is displayed changes. For example, the apps appear at the top of the screen.

The representative said the phone is “contextually aware” so adapts depending on how it has been bent.

Fast and cheap for AI inference (responding to chat prompts with very low latency at very high speeds.)

Discussing how it works, benchmarks, how it compares to other AI accelerators and the future outlook!

Continue reading “The Fastest AI Chip in the World Explained” »

The effort to give robots AI brains is revealing big practical challenges—and bigger ethical concerns.

By David Berreby

Panprotopsychism instantiated.

Shared with Dropbox.

Alan Thompson is an AI expert who is closely tracking progress to AGI.

His definition is:

Artificial general intelligence (AGI) is a machine capable of understanding the world as well as—or better than—any human, in practically every field, including the ability to interact with the world via physical embodiment.

A process that leverages capillary interactions between oligomers in an elastomeric polydimethylsiloxane substrate and deposited Ga enables the formation of Ga nanodroplets with nanoscale gaps in a single step. Gap-plasmon resonances excited within the nanogaps give rise to structural colours that can be tuned by changing the oligomer content in the substrate or by mechanical stretching.

Understanding Neuromorphic Engineering.

Neuromorphic Engineering draws inspiration from the human brain’s architecture and functioning, aiming to create electronic systems that mimic the brain’s ability to process information in a parallel, energy-efficient, and adaptable manner. Unlike traditional computing, which relies on sequential processing, neuromorphic systems leverage neural networks to enable faster and more efficient computation.

Mimicking the Human Brain.

Kurihara, Y., Takahashi, T. & Osu, R. The topology of interpersonal neural network in weak social ties. Sci Rep 14, 4,961 (2024). https://doi.org/10.1038/s41598-024-55495-7

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Popular Summary.

Unequivocally demonstrating that a quantum computer can significantly outperform any existing classical computers will be a milestone in quantum science and technology. Recently, groups at Google and at the University of Science and Technology of China (USTC) announced that they have achieved such quantum computational advantages. The central quantity of interest behind their claims is the linear cross-entropy benchmark (XEB), which has been claimed and used to approximate the fidelity of their quantum experiments and to certify the correctness of their computation results. However, such claims rely on several assumptions, some of which are implicitly assumed. Hence, it is critical to understand when and how XEB can be used for quantum advantage experiments. By combining various tools from computer science, statistical physics, and quantum information, we critically examine the properties of XEB and show that XEB bears several intrinsic vulnerabilities, limiting its utility as a benchmark for quantum advantage.

Concretely, we introduce a novel framework to identify and exploit several vulnerabilities of XEB, which leads to an efficient classical algorithm getting comparable XEB values to Google’s and USTC’s quantum devices (2% 12% of theirs) with just one GPU within 2 s. Furthermore, its performance features better scaling with the system size than that of a noisy quantum device. We observe that this is made possible because the XEB can highly overestimate the fidelity, which implies the existence of “shortcuts” to achieve high XEB values without simulating the system. This is in contrast to the intuition of the hardness of achieving high XEB values by all possible classical algorithms.