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Molecules from mucus can be used to produce synthetic bone graft material and help with the healing of larger bone loss, a new study found.

Researchers at KTH Royal Institute of Technology report the development of a bioactive gel which they say could replace the clinical gold standard of autografting, in which lost is replaced with healthy bone taken from another part of the patient’s body.

Hongji Yan, a researcher at KTH Royal Institute of Technology, says the gel contains mucins, molecules which were derived from cow . The mucins are processed into gels which are combined with monetite granules, a commonly-used synthetic bone graft material. The synthetic gel can be injected to the site of the bone loss.

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The coming of artificial general intelligence (AGI) — the ability of an artificial intelligence to understand or learn any intellectual task that a human can — is inevitable. Despite the predictions of many experts that AGI might never be achieved or will take hundreds of years to emerge, I believe it will be here within the next decade.

How can I be so certain? We already have the know-how to produce massive programs with the capacity for processing and analyzing reams of data faster and more accurately than a human ever could. And in truth, massive programs may not be necessary anyway. Given the structure of the neocortex (the part of the human brain we use to think) and the amount of DNA needed to define it, we may be able to create a complete AGI in a program as small as 7.5 megabytes.

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2022 has been a dynamic year for quantum computing. With commercial breakthroughs such as the UK Ministry of Defence (MoD) investing in its first quantum computer, the launch of the world’s first quantum computer capable of advantage over the cloud and the Nobel Prize in Physics awarded for ground-breaking experiments with entangled photons, the industry is making progress.

At the same time, 2022 saw the tremendous accomplishment of the exaflop barrier broken with the Frontier supercomputer. At a cost of roughly $600 million and requiring more than 20 megawatts of power, we are approaching the limits of what classical computing approaches can do on their own. Often for practical business reasons, many companies are not able to fully exploit the increasing amount of data available to them. This hampers digital transformation across areas most reliant on high-performance computing (HPC): healthcare, defense, energy and finance.

Im still w/ Kurzweil at 2029, but:


+1. While I will also respect the request to not state them in the comments, I would bet that you could sample 10 ICML/NeurIPS/ICLR/AISTATS authors and learn about 10 well-defined, not entirely overlapping obstacles of this sort.

We don’t have any obstacle left in mind that we don’t expect to get overcome in more than 6 months after efforts are invested to take it down.

I don’t want people to skim this post and get the impression that this is a common view in ML.