What’s Possible: 7th October, 2022

A weekly curation of what’s possible in frontier tech.

What’s Possible: 7th October, 2022

We hope you enjoy this week's hand-picked selection of important and interesting stories from the frontiers of tech.

The Aussie tech company helping make EV planes a reality

An Australian company working with a newly invented magnetic material says it should be able to halve the size and weight of electric motors by the end of the decade, a development that could have huge implications for the airline industry as it struggles to decarbonise. (Financial Review)

Kite Magnetics electric motors will use a new magnetic material called Aeroperm™ - a nanocrystalline microstructure that has an energy loss one-tenth that of existing magnetic core materials used in today's electric motors.

This is GMO

Collaborating with bioengineer Marcus Walker to explore the future of synthetic biology in fashion, Modern Synthesis employed genetically engineered (GMO) bacteria to ‘weave’ a sneaker with the colour grown-in. (Modern Synthesis)

Defining Circulating Supply

To date, there has been no industry standard to define ‘circulating supply’ for ERC-20 tokens. Specifically, there is no clear consensus on how to treat tokens that have unlocked but have not been used (tokens which have not been transferred after being unlocked). Immutable is adopting the gold standard for coin trackers and many major tokens. (Immutable Tokenomics)

Discovering novel algorithms with AlphaTensor

This paper is a stepping stone in DeepMind’s mission to advance science and unlock the most fundamental problems using AI. Our system, AlphaTensor, builds upon AlphaZero, an agent that has shown superhuman performance on board games, like chess, Go and shogi, and this work shows the journey of AlphaZero from playing games to tackling unsolved mathematical problems for the first time. (Deepmind)

AlphaTensor with an objective corresponding to the runtime of the algorithm. When a correct matrix multiplication algorithm is discovered, it's benchmarked on the target hardware, which is then fed back to AlphaTensor, in order to learn more efficient algorithms on the target hardware.

Interesting adjacencies

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