The predicted shapes still need to be confirmed in the lab, Ellis told Technology Review. If the results hold up, they will rapidly push forward the study of the proteome, or the proteins in a given organism. DeepMind researchers published their open-source code and laid out the method in two peer-reviewed papers published in Nature last week.
And in 20 other animals often studied by science, too.
CORVALLIS, Ore. – A two-legged robot invented at Oregon State University completed a 5K in just over 52 minutes. Cassie the robot, created by OSU spinout company Agility Robotics, made history with the successful trot. “Cassie, the first bipedal robot to use machine learning to control a running gait on outdoor terrain, completed the 5K on Oregon State’s campus untethered and on a single battery charge,” according to OSU. But it didn’t go off without a hitch.
AlphaFold 2 paper and code is finally released. This post aims to inspire new generations of Machine Learning (ML) engineers to focus on foundational biological problems.
This post is a collection of core concepts to finally grasp AlphaFold2-like stuff. Our goal is to make this blog post as self-complete as possible in terms of biology. Thus in this article, you will learn about:
Only a few years ago, this might have sounded crazy, but it’s here now – the first head-to-head, high-speed race without the actual racing drivers. Autonomous vehicles will soon be competing against each other at the Indy Autonomous Challenge, an event that will probably be remembered for years to come.
A transformative artificial intelligence (AI) tool called AlphaFold, which has been developed by Google’s sister company DeepMind in London, has predicted the structure of nearly the entire human proteome (the full complement of proteins expressed by an organism). In addition, the tool has predicted almost complete proteomes for various other organisms, ranging from mice and maize (corn) to the malaria parasite.
The more than 350000 protein structures, which are available through a public database, vary in their accuracy. But researchers say the resource — which is set to grow to 130 million structures by the end of the year — has the potential to revolutionize the life sciences.
“Killer Robots” may seem far fetched, but as @AlexGatopoulos explains, the use of autonomous machines and other military applications of artificial intelligence are a growing reality of modern warfare.
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Experimental facilities around the globe are facing a challenge: their instruments are becoming increasingly powerful, leading to a steady increase in the volume and complexity of the scientific data they collect. At the same time, these tools demand new, advanced algorithms to take advantage of these capabilities and enable ever-more intricate scientific questions to be asked—and answered. For example, the ALS-U project to upgrade the Advanced Light Source facility at Lawrence Berkeley National Laboratory (Berkeley Lab) will result in 100 times brighter soft X-ray light and feature superfast detectors that will lead to a vast increase in data-collection rates.
To make full use of modern instruments and facilities, researchers need new ways to decrease the amount of data required for scientific discovery and address data acquisition rates humans can no longer keep pace with. A promising route lies in an emerging field known as autonomous discovery, where algorithms learn from a comparatively little amount of input data and decide themselves on the next steps to take, allowing multi-dimensional parameter spaces to be explored more quickly, efficiently, and with minimal human intervention.
“More and more experimental fields are taking advantage of this new optimal and autonomous data acquisition because, when it comes down to it, it’s always about approximating some function, given noisy data,” said Marcus Noack, a research scientist in the Center for Advanced Mathematics for Energy Research Applications (CAMERA) at Berkeley Lab and lead author on a new paper on Gaussian processes for autonomous data acquisition published July 28 in Nature Reviews Physics. The paper is the culmination of a multi-year, multinational effort led by CAMERA to introduce innovative autonomous discovery techniques across a broad scientific community.
Wiliot — the IoT startup that has developed a new kind of processor that is ultra thin and light and runs on ambient power but possesses all the power of a “computer” — has picked up a huge round of growth funding on the back of strong interest in its technology, and a strategy aimed squarely at scale.
The company has raised $200 million, a Series C that it will use toward its next steps as a business. In the coming months, it will make a move into a SaaS model — which Wiliot likes to say refers not to “software as a service,” but “sensing as a service,” using its AI to read and translate different signals on the object attached to the chip — to run and sell its software. This will be combined with a shift to a licensing model for its chip hardware, so that they can be produced by multiple third parties. Wiliot says that it already has several agreements in place for the chip licensing part. The plan is for this, in turn, to lead to a new range of sizes and form factors for the chips down the line.
Softbank’s Vision Fund 2 led the financing, with previous backers — it’s a pretty illustrious list that speaks of the opportunities ahead — including 83North, Amazon Web Services, Inc. (AWS), Avery Dennison, Grove Ventures, M Ventures, the corporate VC of Merck KGaA, Maersk Growth, Norwest Venture Partners, NTT DOCOMO Ventures, Qualcomm Ventures LLC, Samsung Venture Investment Corp., Vintage Investment Partners and Verizon Ventures.