DeepMind’s AI predicts structures for a vast trove of proteins

AlphaFold neural network produced a ‘totally transformative’ database of more than 350,000 structures from Homo sapiens and 20 model organisms.

The human genome holds the instructions for more than 20,000 proteins. But only about one-third of those have had their 3D structures determined experimentally. And in many cases, those structures are only partially known.

Now, 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 (see ‘Folding options’).

The more than 350,000 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.

7 Open Source Libraries for Deep Learning Graphs

In this article, we introduce Deep Learning Graphs and go through 7 up-and-coming open-source libraries for graph deep learning, ranked in order of increasing popularity.

Introducing Deep Learning on Graphs
If you’re a deep learning enthusiast you’re probably already familiar with some of the basic mathematical primitives that have been driving the impressive capabilities of what we call deep neural networks. Although we like to think of a basic artificial neural network as some nodes with some weighted connections, it’s more efficient computationally to think of neural networks as matrix multiplication all the way down. We might draw a cartoon of an artificial neural network like the figure below, with information traveling from left to right from inputs to outputs (ignoring recurrent networks for now).

Obviously AI, a no code startup for data analysts, increases its seed round to $4.7M

Nirman Dave’s two startups are very different, but both have a DIY spirit. The first, called CircuiTricks and founded during his gap year after high school, created kits to teach students about electronics and physics. Now Dave is chief executive officer of Obviously AI, a no code AI/ML platform that enables people without technical backgrounds to build and train machine learning models. The Berkeley-based company has raised a seed extension that brings the round’s total to $4.7 million, up from the $3.6 million it announced two months ago. The extension was led by the University of Tokyo Edge Capital Partners (UTEC), a deep tech investment firm, with participation from Trail Mix Ventures and B-Capital.

UTEC principal Kiran Mysore told TechCrunch that he found Obviously AI on Product Hunt while helping a friend without an AI/ML or coding background build machine learning models. After using Obviously AI and benchmarking it against other AutoML products, Mysore was so impressed that he reached out to the startup and led the investment round.

Germany aims to get self-driving cars on the roads in 2022

German lawmakers greenlit a bill that would allow for some autonomous vehicles to hit public roads as early as next year. But those looking for a driverless joyride on the Autobahn will still have to wait.

Driverless busses and other autonomous vehicles could soon hit the streets of Germany after lawmakers in the lower house of parliament approved new rules for self-driving cars. The measure now passes to the upper chamber or parliament, the Bundesrat, for approval before it can take effect. Once approved, it would be the world’s first legal framework for integrating autonomous vehicles in regular traffic, according to the German government.
What will be allowed?
The bill, passed by Bundestag lawmakers in a late-night session on Thursday, changes traffic regulations to allow for autonomous vehicles to be put into regular use across Germany. The bill specifically concerns vehicles with fully autonomous systems that fall under the “Level 4” classification — where a computer is in complete control of the car and no human driver is needed to control or monitor it.

イオンリテールが店舗のスマートストア化に向けてAI映像解析ソリューション「GREENAGES Citywide Surveillance」を採用

「Citywide Surveillance」のAIによる店舗分析の特長

1. AIを活用した人物検知、年代推定機能による安心・安全な店舗運営や法令順守の支援
店内の人数をリアルタイムにカウントし混雑状況を検知することで、3密を避けた店舗運営を支援します。
レジ前のカメラで取得した映像をもとにAIでお客様の年代情報を分析し、未成年者と推定した場合、レジ従業員に通知します。従業員はAIからの通知を元にお客様に声掛けし、未成年者への酒類販売を防止します。
一般的な映像解析で用いる顔の特徴を取得せずに、体格や服装などの特徴から人物検知や属性(性別、年代)を推定できるため、マスク着用時でも高精度な解析が可能です。また個人情報を取得しないため、お客様のプライバシーに配慮した上で、収集データを活用することができます。