Lila Ibrahim is the first ever chief operating officer of DeepMind, one of the world's best known artificial intelligence companies.

In brief OpenAI has disbanded its AI robotics team and is no longer trying to apply machine learning to physical machines. Wojciech Zaremba, co-founder of OpenAI, who led the robotics group confirmed that the company recently broke up the team to focus working on more promising areas of artificial general intelligence research. "Here's a reveal ... as of recently we changed the focus at OpenAI, and I actually disbanded the robotics team," he said during an episode of the Weights & Biases podcast. Zaremba said a lack of training data was holding the robotics research back: there wasn't enough information on hand to teach the systems to the level of intelligence desired. "From the perspective of what we want to achieve, which is to build AGI, I think there was actually some components missing," he added.

I asked OpenAI's now-famous algorithm GPT-3 to write me a story. GPT-3 is likely one of the most powerful natural language processing (NLP) algorithms in the world. It can be used for a wide range of tasks, such as summarizing articles, powering video game dialogue, and even writing programming code.

Last year DeepMind presented AlphaFold v2, which predicts 3D structures of proteins down to atomic accuracy. Today they share the methods in their latest paper at Nature along with open source codes. It is inspiring to see the research this enables. This new model, AlphaFold v2.0 has been published in Nature and entered into the CASP14 competition. Deepmind has pushed the boundaries of computing.

I got my invitation to the technical preview of GitHub Copilot two days ago and have been pair programming a bit with my new AI buddy. Check out our app Bamboozled, a minimal quiz (find the repository here). Please note: Copilot is currently not available publicly, so if you want to test and review it, you have to join the waitlist. GitHub Copilot is "your AI pair programmer". It's an AI-powered tool that can write code by itself, generating quite impressive programming functions, comments and more based on your directions.

Join live for the final day of Transform 2021, including the AI Innovation & Women in AI Awards. OpenAI has disbanded its robotics team after years of research into machines that can learn to perform tasks like solving a Rubik's Cube. Company cofounder Wojciech Zaremba quietly revealed on a podcast hosted by startup Weights & Biases that OpenAI has shifted its focus to other domains, where data is more readily available. "So it turns out that we can make a gigantic progress whenever we have access to data, and all our machine learning, unsupervised, and reinforcement learning -- they work extremely well, and there [are] actually plenty of domains that are very, very rich with data. And ultimately that was holding us back in terms of robotics," Zaremba said.

Scientists have waited months for access to high-accuracy protein structure prediction since DeepMind presented remarkable progress in this area at the 2020 Critical Assessment of Structure Prediction, or CASP14, conference. The wait is now over. Researchers at the Institute for Protein Design at the University of Washington School of Medicine in Seattle have largely recreated the performance achieved by DeepMind on this important task. These results will be published by the journal Science. Unlike DeepMind, the UW Medicine team has already made their method, dubbed RoseTTAFold, freely available.

This article is part of our reviews of AI research papers, a series of posts that explore the latest findings in artificial intelligence. In a new paper, researchers at OpenAI have revealed details about Codex, a deep learning model that generates software source code. Codex powers Copilot, an "AI pair programmer" tool developed jointly by OpenAI and GitHub. Copilot is currently available in beta test mode to a limited number of users. The paper is a fascinating read that explains the process through which the scientists at OpenAI managed to repurpose their flagship language model GPT-3 to create Codex.

Proteins are the minions of life, working alone or together to build, manage, fuel, protect, and eventually destroy cells. To function, these long chains of amino acids twist and fold and intertwine into complex shapes that can be slow, even impossible, to decipher. Scientists have dreamed of simply predicting a protein's shape from its amino acid sequence—an ability that would open a world of insights into the workings of life. “This problem has been around for 50 years; lots of people have broken their head on it,” says John Moult, a structural biologist at the University of Maryland, Shady Grove. But a practical solution is in their grasp. Several months ago, in a result hailed as a turning point, computational biologists showed that artificial intelligence (AI) could accurately predict protein shapes. Now, David Baker and Minkyung Baek at the University of Washington, Seattle, and their colleagues have made AI-based structure prediction more powerful and accessible. Their method, described online in Science this week, works on not just simple proteins, but also complexes of proteins, and its creators have made their computer code freely available. Since the method was posted online last month, the team has used it to model more than 4500 protein sequences submitted by other researchers. Savvas Savvides, a structural biologist at Ghent University, had tried six times to model a problematic protein. He says Baker's and Baek's program, called RoseTTAFold, “paved the way to a structure solution.” In fall of 2020, DeepMind, a U.K.-based AI company owned by Google, wowed the field with its structure predictions in a biennial competition ( Science , 4 December 2020, p. [1144][1]). Called Critical Assessment of Protein Structure Prediction (CASP), the competition uses structures newly determined using laborious lab techniques such as x-ray crystallography as benchmarks. DeepMind's program, AlphaFold2, did “really extraordinary things [predicting] protein structures with atomic accuracy,” says Moult, who organizes CASP. But for many structural biologists, AlphaFold2 was a tease: “Incredibly exciting but also very frustrating,” says David Agard, a structural biophysicist at the University of California, San Francisco. DeepMind has yet to publish its method and computer code for others to take advantage of. In mid-June, 3 days after the Baker lab posted its RoseTTAFold preprint, Demis Hassabis, DeepMind's CEO, tweeted that AlphaFold2's details were under review at a publication and the company would provide “broad free access to AlphaFold for the scientific community.” DeepMind's 30-minute presentation at CASP was enough to inspire Baek to develop her own approach. Like AlphaFold2, it uses AI's ability to discern patterns in vast databases of examples, generating ever more informed and accurate iterations as it learns. When given a new protein to model, RoseTTAFold proceeds along multiple “tracks.” One compares the protein's amino acid sequence with all similar sequences in protein databases. Another predicts pairwise interactions between amino acids within the protein, and a third compiles the putative 3D structure. The program bounces among the tracks to refine the model, using the output of each one to update the others. DeepMind's approach, although still under wraps, involves just two tracks, Baek and others believe. Gira Bhabha, a cell and structural biologist at New York University School of Medicine, says both methods work well. “Both the DeepMind and Baker lab advances are phenomenal and will change how we can use protein structure predictions to advance biology,” she says. A DeepMind spokesperson wrote in an email, “It's great to see examples such as this where the protein folding community is building on AlphaFold to work towards our shared goal of increasing our understanding of structural biology.” But AlphaFold2 solved the structures of only single proteins, whereas RoseTTAFold has also predicted complexes, such as the structure of the immune molecule interleukin-12 latched onto its receptor. Many biological functions depend on protein-protein interactions, says Torsten Schwede, a computational structural biologist at the University of Basel. “The ability to handle protein-protein complexes directly from sequence information makes it extremely attractive for many questions in biomedical research.” Baker concedes that, in general, AlphaFold2's structures are more accurate. But Savvides says the Baker lab's approach better captures “the essence and particularities of protein structure,” such as identifying strings of atoms sticking out of the sides of the protein—features key to interactions between proteins. Agard adds that Baker's and Baek's approach is faster and requires less computing power than DeepMind's, which relied on Google's massive servers. However, the DeepMind spokesperson wrote that its latest algorithm is more than 16 times as fast as the one it used at CASP in 2020. As a result, she wrote, “It's not clear to us that the system being described is an advance in speed.” Beginning on 1 June, Baker and Baek began to challenge their method by asking researchers to send in their most baffling protein sequences. Fifty-six head scratchers arrived in the first month, all of which have now predicted structures. Agard's group sent in an amino acid sequence with no known similar proteins. Within hours, his group got a protein model back “that probably saved us a year of work,” Agard says. Now, he and his team know where to mutate the protein to test ideas about how it functions. Because Baek's and Baker's group has released its computer code on the web, others can improve on it; the code has been downloaded 250 times since 1 July. “Many researchers will build their own structure prediction methods upon Baker's work,” says Jinbo Xu, a computational structural biologist at the Toyota Technological Institute at Chicago. Moult agrees: “When there's a breakthrough like this, 2 years later, everyone is doing it as well if not better than before.” [1]: http://www.sciencemag.org/content/370/6521/1144

After Apple, Microsoft recently became the only publicly traded American company to hit the $2 trillion market cap. The company has reached the milestone just two years after it crossed the $1 trillion mark. In this article, we list major AI projects and initiatives the company undertook post-2019. In 2019, Microsoft said it would invest $1 billion in OpenAI to build artificial general intelligence. The partnership is directed at developing a hardware and software platform within Azure geared towards AGI.