Unreasonably Effective AI with Demis Hassabis Google DeepMind 2024-10-22 | Unreasonably Effective AI with Demis Hassabis
How AI is transforming our computing and software Google DeepMind 2023-06-07 | As part of our effort to build increasingly capable and general AI systems, we’re making computing more powerful and sustainable. Our AI models AlphaZero and MuZero are expanding their capabilities to help optimise data centres, streamline video compression, and discover new algorithms to accelerate software. But this is just the beginning: we envision a future where more general-purpose AI tools can help optimise the entire computing ecosystem that powers our digital world.Read our case study blog: deepmind.com/blog/optimising-computer-systems-with-more-generalised-ai-tools
How #AI can help find trending videos. 🤯 #shorts Google DeepMind 2023-05-24 | 🤳 @YouTube Shorts are being viewed more than 50 billion times a day - and growing. How can AI help you find more relevant content?Enter Flamingo, our powerful visual language model generating metadata descriptions for hundreds of millions of videos.
Using machine learning to optimise agriculture in Brazil | AI by you - Vitor’s story Google DeepMind 2022-11-07 | Vitor Gonçalves da Silva lives in Campinas, Brazil. He is studying agricultural engineering at the University of Campinas. For Vitor, machine learning and artificial intelligence provide the means to optimise agriculture in his home country. Every day, DeepMind scholars like Vitor are making their mark in artificial intelligence. In the AI by you series, they express what artificial intelligence means to them, in their own words. Learn what drives them and how they'll use artificial intelligence technology to help build a better future for everyone.Watch the full series here: youtube.com/playlist?list=PLqYmG7hTraZDoNy8TLPk-uB4FyaZ23xon And visit deepmind.com/scholarships to learn more.
Using AI to manage resources in Africa | AI by you - Arnol’s story Google DeepMind 2022-11-07 | Arnol Fokam was born in Cameroon and now lives in South Africa. Arnol studies at the University of Witwatersrand and he believes that AI could be the key to resource management across his home continent. Every day, DeepMind scholars like Arnol are making their mark in artificial intelligence. In the AI by you series, they express what artificial intelligence means to them, in their own words. Learn what drives them and how they'll use artificial intelligence technology to help build a better future for everyone.Watch the full series here: youtube.com/playlist?list=PLqYmG7hTraZDoNy8TLPk-uB4FyaZ23xon And visit deepmind.com/scholarships to learn more.
Can AI help to unlock the mysteries of the mind? | AI by you - Weronika’s story Google DeepMind 2022-11-07 | Weronika Skibicka was born in Poland and has recently moved to Germany to continue her study. She arrived at AI through her passion for neuroscience and believes artificial intelligence provides an exciting opportunity to better understand the human brain. Every day, DeepMind scholars like Weronika are making their mark in artificial intelligence. In the AI by you series, they express what artificial intelligence means to them, in their own words. Learn what drives them and how they'll use artificial intelligence technology to help build a better future for everyone.Watch the full series here: youtube.com/playlist?list=PLqYmG7hTraZDoNy8TLPk-uB4FyaZ23xon And visit deepmind.com/scholarships to learn more.
Transforming medicine with AI | AI by you - Sneha’s story Google DeepMind 2022-11-07 | Sneha Naik studied at Imperial College, London, before moving to New York to undertake a PhD. Her own experience with Crohn’s disease has played a significant role in her focus on health care. She hopes to utilise artificial intelligence to better diagnose and treat diseases that cause hidden disabilities. Every day, DeepMind scholars like Sneha are making their mark in artificial intelligence. In the AI by you series, they express what artificial intelligence means to them, in their own words. Learn what drives them and how they'll use artificial intelligence technology to help build a better future for everyone.Watch the full series here: youtube.com/playlist?list=PLqYmG7hTraZDoNy8TLPk-uB4FyaZ23xon Visit deepmind.com/scholarships to learn more.
How can AI help us fight climate change? | AI by you - Julia’s story Google DeepMind 2022-11-07 | Julia Kaltenborn was born in Germany, grew up in Switzerland and now lives in Canada. She has a passion for climate science and hopes artificial intelligence can help provide solutions to one of the biggest challenges we face. Every day, DeepMind scholars like Julia are making their mark in artificial intelligence. In the AI by you series, they express what artificial intelligence means to them, in their own words. Learn what drives them and how they'll use artificial intelligence technology to help build a better future for everyone.Watch the full series here: youtube.com/playlist?list=PLqYmG7hTraZDoNy8TLPk-uB4FyaZ23xon And visit deepmind.com/scholarships to learn more.
AI for everyone needs AI by you Google DeepMind 2022-11-07 | AI could be one of humanity’s most useful inventions. But only if it’s made by people representing our whole society. Including you. We need a stronger, more inclusive AI community. A community that brings diverse values, concerns, and hopes to the table. Only then, can the whole of humanity truly benefit from this technology. AI for everyone needs AI by you.We established the DeepMind scholarship programme in 2017 to help build a stronger and more inclusive AI community, who can bring a wider range of experiences to the fields of AI and computer science. The scholarships provide financial support to students from underrepresented groups seeking to graduate studies in AI-related and adjacent fields. Scholars are also offered support from a DeepMind mentor, and have opportunities to attend leading AI academic conferences and DeepMind events.Over the last five years, the scholarship programme has expanded internationally, and now supports scholars in many more countries currently underrepresented in AI – including Bulgaria, Colombia, Greece, Poland, Romania, South Africa, and Turkey, as well as the UK, USA, Canada, and France.Watch our scholars stories film series here: youtube.com/playlist?list=PLqYmG7hTraZDoNy8TLPk-uB4FyaZ23xonAnd visit deepmind.com/scholarships to learn more.
Unlocking the mystery of the demon-duck of doom - Unfolded Google DeepMind 2022-09-29 | Could burn marks on eggshells explain the disappearance of the giant flightless bird Genyornis newtoni? This huge ostrich-sized “thunderbird” - nicknamed “the demon-duck of doom” for its huge head - disappeared from Australia’s fossil record around 50,000 years ago. The discovery of burned eggshells led scientists, including Gifford Miller of the University of Colorado, Boulder, to propose that their extinction was caused by early humans eating the bird’s eggs.But the evidence was not clear cut. The burned eggshells seemed far too thin to come from such a large bird. Were they not from something much smaller, more the size of a large turkey?To determine whether Genyornis became extinct through human intervention, scientists needed to prove that the burnt eggshells were indeed from Genyornis. But that led to a new problem. The DNA in these eggshells was never going to survive 50,000 years in the hot sands of the Australian desert. So, the researchers turned to proteins and artificial intelligence to help fill in the gaps.It took a genuinely multidisciplinary team including specialists in palaeoproteomics, bird genetics, archaeology and more to crack the eggshell code and answer the question of what led to the demise of the thunderbird.Read the full paper by Beatrice, Josefin, Matthew and colleagues in Proceedings of the National Academy of Sciences.
Welcome to DeepMind: Embarking on one of the greatest adventures in scientific history Google DeepMind 2022-09-29 | At DeepMind, we’re embarking on one of the greatest adventures in scientific history. Our mission is to solve intelligence, to advance science and benefit humanity. To make this possible, we bring together scientists, designers, engineers, ethicists, and more, to research and build safe artificial intelligence systems that can help transform society for the better.By combining creative thinking with our dedicated, scientific approach, we’re unlocking new ways of solving complex problems and working to develop a more general and capable problem-solving system, known as artificial general intelligence (AGI). Guided by safety and ethics, this invention could help society find answers to some of the most important challenges facing society today. We regularly partner with academia and nonprofit organisations, and our technologies are used across Google devices by millions of people every day. From solving a 50-year-old grand challenge in biology with AlphaFold and synthesising voices with WaveNet, to mastering complex games with AlphaZero and preserving wildlife in the Serengeti, our novel advances make a positive and lasting impact. Incredible ideas thrive when diverse people join together. With headquarters in London and research labs in Paris, New York, Montreal, Edmonton, and Mountain View, CA, we’re always looking for great people from all walks of life to join our mission.Learn more at deepmind.com/about and apply for open roles at deepmind.com/careers.#LifeAtDeepMind #artificialintelligence #AGI #socialimpact
Meet John and Rosie, fighting plastic pollution with proteins - Unfolded Google DeepMind 2022-07-28 | It all started with an email.The world produces around 400m tonnes of plastic waste each year. Much of it ends up in landfill; a significant portion is polluting the world’s oceans. Conventional plastic recycling only degrades the material – in fact, most plastic items never fully disappear.John McGeehan, Rosie Graham, and their colleagues at the Centre for Enzyme Innovation at the University of Portsmouth, are developing a different solution: a fully circular plastic economy, using enzymes to break plastic polymers down so they can be 100% recycled back to their initial state – or even upcycling degraded material back to the quality of virgin plastic. As part of Unfolded, a series documenting the scientists using AlphaFold, we met John and Rosie at their labs in Portsmouth to find out how a chance email has accelerated this work. Find more Unfolded stories at unfolded.deepmind.com
How Marcelo and Megan solved a ten-year problem in minutes - Unfolded Google DeepMind 2022-07-28 | Most people who have access to a modern healthcare system would not consider a disease like bubonic plague to be a threat. Such bacterial infections are usually dealt with easily through modern antibiotics. Yet antibiotic resistance, where bacteria evolve the ability to defeat these drugs, is fast becoming a growing global problem - and new antibiotics are thin on the ground.Marcelo Sousa and Megan Mitchell at the University of Colorado Boulder are looking at a different approach: targeting the resistance mechanism itself, controlled in this case with enzymes. As part of Unfolded, a series meeting the scientists using AlphaFold, Marcelo and Megan explain how they solved the structure of one such enzyme - a challenge that a decade of experimentation couldn’t complete - in minutes.Find more Unfolded stories at unfolded.deepmind.com
How EMBL-EBI make AlphaFold’s 200m predictions available - Unfolded Google DeepMind 2022-07-28 | Prof Dame Janet Thornton and Mihaly Varadi are helping transform biology at the European Bioinformatics Institute of the European Molecular Biology Laboratory (EMBL-EBI), an intergovernmental research organisation funded by over 20 member states. EMBL-EBI champions open data in the life sciences, with data sets spanning genomics, proteins, small molecules, ontologies and more. They were a natural partner for DeepMind to host the AlphaFold Protein Structure Database, now making over 200m protein structure predictions freely available to scientists globally.As part of Unfolded, a series meeting the scientists using AlphaFold, we travelled to EMBL-EBI’s home at the Wellcome Genome Campus in Hinxton, UK, to meet Janet and Mihaly.Find more Unfolded stories at unfolded.deepmind.com
Welcome to DeepMind: Embarking on one of the greatest adventures in scientific history Google DeepMind 2022-05-05 | At DeepMind, we’re embarking on one of the greatest adventures in scientific history. Our mission is to solve intelligence, to advance science and benefit humanity. To make this possible, we bring together scientists, designers, engineers, ethicists, and more, to research and build safe artificial intelligence systems that can help transform society for the better.By combining creative thinking with our dedicated, scientific approach, we’re unlocking new ways of solving complex problems and working to develop a more general and capable problem-solving system, known as artificial general intelligence (AGI). Guided by safety and ethics, this invention could help society find answers to some of the most important challenges facing society today. We regularly partner with academia and nonprofit organisations, and our technologies are used across Google devices by millions of people every day. From solving a 50-year-old grand challenge in biology with AlphaFold and synthesising voices with WaveNet, to mastering complex games with AlphaZero and preserving wildlife in the Serengeti, our novel advances make a positive and lasting impact. Incredible ideas thrive when diverse people join together. With headquarters in London and research labs in Paris, New York, Montreal, and Mountain View, CA, we’re always looking for great people from all walks of life to join our mission.Learn more at deepmind.com/about and apply for open roles at deepmind.com/careers.#LifeAtDeepMind #artificialintelligence #AGI #socialimpact
Match 2: 90 Second Summary - Google DeepMind Challenge Match Google DeepMind 2022-03-28 | Summary by Michael Redmond, 9-dan professional, and Chris Garlock on today's historic second match between DeepMind's program AlphaGo take on the legendary Lee Sedol (9-dan pro), the top Go player of the past decade, in a $1M 5-game challenge match in Seoul.In October 2015, AlphaGo became the first computer program ever to beat a professional Go player by winning 5-0 against the reigning 3-times European Champion Fan Hui (2-dan pro). That work was featured in a front cover article in the science journal Nature in January 2016.
Match 5: 90 Second Summary - Google DeepMind Challenge Match 2016 Google DeepMind 2022-03-28 | 90 Second Summary by Michael Redmond, 9-dan professional and, Chris Garlock on today's amazing fifth and final match between DeepMind's program AlphaGo take on the legendary Lee Sedol (9-dan pro), the top Go player of the past decade, in a $1M 5-game challenge match in Seoul.In October 2015, AlphaGo became the first computer program ever to beat a professional Go player by winning 5-0 against the reigning 3-times European Champion Fan Hui (2-dan pro). That work was featured in a front cover article in the science journal Nature in January 2016.
Match 4: 90 Second Summary - Google DeepMind Challenge Match 2016 Google DeepMind 2022-03-28 | 90 Second Summary by Michael Redmond, 9-dan professional, and Chris Garlock on today's spectacular fourth match between DeepMind's program AlphaGo take on the legendary Lee Sedol (9-dan pro), the top Go player of the past decade, in a $1M 5-game challenge match in Seoul.In October 2015, AlphaGo became the first computer program ever to beat a professional Go player by winning 5-0 against the reigning 3-times European Champion Fan Hui (2-dan pro). That work was featured in a front cover article in the science journal Nature in January 2016.
Match 3: 90 Second Summary - Google DeepMind Challenge Match 2016 Google DeepMind 2022-03-28 | 90 Second Summary by Michael Redmond, 9-dan professional, and Chris Garlock on today's momentous third match between DeepMind's program AlphaGo take on the legendary Lee Sedol (9-dan pro), the top Go player of the past decade, in a $1M 5-game challenge match in Seoul.In October 2015, AlphaGo became the first computer program ever to beat a professional Go player by winning 5-0 against the reigning 3-times European Champion Fan Hui (2-dan pro). That work was featured in a front cover article in the science journal Nature in January 2016.
Match 1: 90 Second Summary - Google DeepMind Challenge Match 2016 Google DeepMind 2022-03-28 | Summary by Michael Redmond, 9-dan professional, and Chris Garlock on today's historic match between DeepMind's program AlphaGo take on the legendary Lee Sedol (9-dan pro), the top Go player of the past decade, in a $1M 5-game challenge match in Seoul.In October 2015, AlphaGo became the first computer program ever to beat a professional Go player by winning 5-0 against the reigning 3-times European Champion Fan Hui (2-dan pro). That work was featured in a front cover article in the science journal Nature in January 2016.
The promise of AI with Demis Hassabis - DeepMind: The Podcast (S2, Ep9) Google DeepMind 2022-03-15 | Hannah wraps up the series by meeting DeepMind co-founder and CEO, Demis Hassabis. In an extended interview, Demis describes why he believes AGI is possible, how we can get there, and the problems he hopes it will solve. Along the way, he highlights the important role of consciousness and why he’s so optimistic that AI can help solve many of the world’s major challenges. As a final note, Demis shares the story of a personal meeting with Stephen Hawking to discuss the future of AI and discloses Hawking’s parting message.For questions or feedback on the series, message us on Twitter @DeepMind or email podcast@deepmind.com. Interviewee: Deepmind co-founder and CEO, Demis HassabisCreditsPresenter: Hannah FrySeries Producer: Dan HardoonProduction support: Jill AchinekuSounds design: Emma BarnabyMusic composition: Eleni ShawSound Engineer: Nigel AppletonEditor: David PrestCommissioned by DeepMindThank you to everyone who made this season possible! Further reading: DeepMind, The Podcast: deepmind.com/blog/article/welcome-to-the-deepmind-podcastDeepMind’s Demis Hassabis on its breakthrough scientific discoveries, WIRED: youtube.com/watch?v=2WRow9FqUbwRiemann hypothesis, Wikipedia: en.wikipedia.org/wiki/Riemann_hypothesisUsing AI to accelerate scientific discovery by Demis Hassabis, Kendrew Lecture 2021: youtube.com/watch?v=sm-VkgVX-2oProtein Folding & the Next Technological Revolution by Demis Hassabis, Bloomberg: youtube.com/watch?v=vhd4ENh5ON4The Algorithm, MIT Technology Review: forms.technologyreview.com/newsletters/ai-the-algorithmMachine learning resources, The Royal Society: royalsociety.org/topics-policy/education-skills/teacher-resources-and-opportunities/resources-for-teachers/resources-machine-learningHow to get empowered, not overpowered, by AI, TED: youtube.com/watch?v=2LRwvU6gEbAYouTube addition: Find Seasons 1 & 2 on YouTube: http://dpmd.ai/3geDPmL Or search “DeepMind: The Podcast” and subscribe on your favourite podcast app:Apple Podcasts: http://dpmd.ai/2RzlmcuGoogle Podcasts: http://dpmd.ai/3geDjp5Spotify: http://dpmd.ai/3w29cb4Pocket Casts: https://pca.st/30m1
Using AI to accelerate scientific discovery - Demis Hassabis (Crick Insight Lecture Series) Google DeepMind 2022-03-04 | Using AI to accelerate scientific discoveryDemis Hassabis, co-founder and CEO, DeepMindAt The Francis Crick Institute in King's Cross, LondonAbstract:The past decade has seen incredible advances in the field of Artificial Intelligence (AI). DeepMind has been in the vanguard of many of these big breakthroughs, pioneering the development of self-learning systems like AlphaGo, the first program to beat the world champion at the complex game of Go. Games have proven to be a great training ground for developing and testing AI algorithms, but the aim at DeepMind has always been to build general learning systems ultimately capable of solving important problems in the real world. Excitingly, I believe we are on the cusp of a new era in science with AI poised to be a powerful tool for accelerating scientific discovery itself. We recently demonstrated this potential with our AlphaFold system, a solution to the 50-year grand challenge of protein structure prediction, culminating in the release of the most accurate and complete picture of the human proteome.The speaker:Demis Hassabis is the Founder and CEO of DeepMind, the world's leading AI research company that aims to solve intelligence to advance science and benefit humanityIn 2016, DeepMind developed AlphaGo, the first program to beat a world champion at the complex game of Go. In 2020, its Alphafold program was heralded as a solution to the 50-year grand challenge of protein structure prediction and in 2021, DeepMind launched the AlphaFold Protein Structure Database, which offers the most complete and accurate picture of the human proteome to date. A chess prodigy, Demis reached master standard aged 13, and went on to program the multi-million selling simulation game Theme Park aged 17. After graduating from Cambridge University in computer science, he founded pioneering videogames company Elixir Studios, and completed a PhD in cognitive neuroscience at University College London. Science listed his neuroscience research on imagination as one of 2007’s top ten breakthroughs, and in 2021, AlphaFold2 was selected as the Breakthrough of the Year.He is a Fellow of the Royal Society and the Royal Academy of Engineering. In 2017 he featured in the Time 100 list of most influential people, and in 2018 he was awarded a CBE for services to science and technology.
Fair for all - DeepMind: The Podcast (S2, Ep8) Google DeepMind 2022-03-01 | AI needs to benefit everyone, not just those who build it. But fulfilling this promise requires careful thought before new technologies are built and released into the world. In this episode, Hannah delves into some of the most pressing and difficult ethical and social questions surrounding AI today. She explores complex issues like racial and gender bias and the misuse of AI technologies, and hears why diversity and representation is vital for building technology that works for all.For questions or feedback on the series, message us on Twitter @DeepMind or email podcast@deepmind.com. Interviewees: DeepMind’s Sasha Brown, William Isaac, Shakir Mohamed, Kevin Mckee & Obum EkekeCreditsPresenter: Hannah FrySeries Producer: Dan HardoonProduction support: Jill AchinekuSounds design: Emma BarnabyMusic composition: Eleni ShawSound Engineer: Nigel AppletonEditor: David PrestCommissioned by DeepMindThank you to everyone who made this season possible! Further reading: What a machine learning tool that turns Obama white can (and can’t) tell us about AI bias, The Verge: theverge.com/21298762/face-depixelizer-ai-machine-learning-tool-pulse-stylegan-obama-biasTuskegee Syphilis Study, Wikipedia: en.wikipedia.org/wiki/Tuskegee_Syphilis_StudyEthics & Society, DeepMind: deepmind.com/about/ethics-and-societyRow over AI that 'identifies gay faces', BBC: bbc.co.uk/news/technology-41188560The Trevor Project: thetrevorproject.orgAI takes root, helping farmers identify diseased plants, Google: https://www.blog.google/technology/ai/ai-takes-root-helping-farmers-identity-diseased-plants/How Can You Use Technology to Support a Culture of Inclusion and Diversity?, myHRfuture: myhrfuture.com/blog/2019/7/16/how-can-you-use-technology-to-support-a-culture-of-inclusion-and-diversityScholarships at DeepMind: deepmind.com/scholarshipsAI, Ain’t I a Woman? Joy Buolamwini, YouTube: youtube.com/watch?v=QxuyfWoVV98How to be Human in the Age of the Machine, Hannah Fry: royalsociety.org/grants-schemes-awards/book-prizes/science-book-prize/2018/hello-worldFind Seasons 1 & 2 on YouTube: http://dpmd.ai/3geDPmL Or search “DeepMind: The Podcast” and subscribe on your favourite podcast app:Apple Podcasts: http://dpmd.ai/2RzlmcuGoogle Podcasts: http://dpmd.ai/3geDjp5Spotify: http://dpmd.ai/3w29cb4Pocket Casts: https://pca.st/30m1
Me, myself and AI - DeepMind: The Podcast (S2, Ep7) Google DeepMind 2022-02-22 | AI doesn’t just exist in the lab, it’s already solving a range of problems in the real world. In this episode, Hannah encounters a realistic recreation of her voice by WaveNet, the voice synthesising system that powers the Google Assistant and helps people with speech difficulties and illnesses regain their voices. Hannah also discovers how ‘deepfake’ technology can be used to improve weather forecasting and how DeepMind researchers are collaborating with Liverpool Football Club, aiming to take sports to the next level.For questions or feedback on the series, message us on Twitter @DeepMind or email podcast@deepmind.com. Interviewees: DeepMind’s Demis Hassabis, Raia Hadsell, Karl Tuyls, Zach Gleicher & Jackson Broshear; Niall Robinson of the UK Met OfficeCreditsPresenter: Hannah FrySeries Producer: Dan HardoonProduction support: Jill AchinekuSounds design: Emma BarnabyMusic composition: Eleni ShawSound Engineer: Nigel AppletonEditor: David PrestCommissioned by DeepMindThank you to everyone who made this season possible! Further reading: A generative model for raw audio, DeepMind: deepmind.com/blog/article/wavenet-generative-model-raw-audioWaveNet case study, DeepMind: deepmind.com/research/case-studies/wavenetUsing WaveNet technology to reunite speech-impaired users with their original voices, DeepMind:| deepmind.com/blog/article/Using-WaveNet-technology-to-reunite-speech-impaired-users-with-their-original-voicesProject Euphonia, Google Research: https://sites.research.google/euphonia/about/Nowcasting the next hour of rain, DeepMind: deepmind.com/blog/article/nowcastingNow DeepMind is using AI to transform football, WIRED: wired.co.uk/article/deepmind-football-liverpool-aiAdvancing sports analytics through AI, DeepMind: deepmind.com/blog/article/advancing-sports-analytics-through-aiMetOffice, BBC: metoffice.gov.ukThe village ‘washed on to the map’, BBC: bbc.co.uk/news/uk-england-cornwall-28523053Michael Fish got the storm of 1987 wrong, Sky News: news.sky.com/story/michael-fish-got-the-storm-of-1987-wrong-but-modern-supercomputers-may-have-missed-it-too-11076659#:~:text=In%20a%20lunchtime%20broadcast%20on,%2C%22%20he%20confidently%20told%20viewers.Find Seasons 1 & 2 on YouTube: http://dpmd.ai/3geDPmL Or search “DeepMind: The Podcast” and subscribe on your favourite podcast app:Apple Podcasts: http://dpmd.ai/2RzlmcuGoogle Podcasts: http://dpmd.ai/3geDjp5Spotify: http://dpmd.ai/3w29cb4Pocket Casts: https://pca.st/30m1
AI for science - DeepMind: The Podcast (S2, Ep6) Google DeepMind 2022-02-16 | Step inside DeepMind's laboratories and you'll find researchers studying DNA to understand the mysteries of life, seeking new ways to use nuclear energy, or putting AI to the test in mind-bending areas of maths. In this episode, Hannah meets Pushmeet Kohli, the head of science at DeepMind, to understand how AI is accelerating scientific progress. Listeners also join Hannah on a [virtual] safari in the Serengeti in East Africa to find out how researchers are using AI to conserve wildlife in one of the world’s most spectacular ecosystems. For questions or feedback on the series, message us on Twitter @DeepMind or email podcast@deepmind.com. Interviewees: DeepMind’s Demis Hassabis, Pushmeet Kohli & Sarah Jane Dunn; Meredith Palmer of the Princeton UniversityCreditsPresenter: Hannah FrySeries Producer: Dan HardoonProduction support: Jill AchinekuSounds design: Emma BarnabyMusic composition: Eleni ShawSound Engineer: Nigel AppletonEditor: David PrestCommissioned by DeepMindThank you to everyone who made this season possible! Further reading:Using AI for scientific discovery, DeepMind: deepmind.com/blog/article/AlphaFold-Using-AI-for-scientific-discoveryDeepMind’s Demis Hassabis on its breakthrough scientific discoveries, WIRED: youtube.com/watch?v=2WRow9FqUbwThe AI revolution in scientific research, The Royal Society: royalsociety.org/-/media/policy/projects/ai-and-society/AI-revolution-in-science.pdfDOE Explains...Tokamaks, Office of Science: energy.gov/science/doe-explainstokamaksHow AI Accidentally Learned Ecology by Playing StarCraft, Discover: discovermagazine.com/technology/how-ai-accidentally-learned-ecology-by-playing-starcraftGoogle AI can identify wildlife from trap-camera footage, VentureBeat:venturebeat.com/2019/12/17/googles-ai-can-identify-wildlife-from-trap-camera-footage-with-up-to-98-6-accuracySnapshot Serengeti, Zooniverse:zooniverse.org/projects/zooniverse/snapshot-serengetiThe Human Genome Project, National Human Genome Research Institute: genome.gov/human-genome-projectExploring the beauty of pure mathematics in novel ways, DeepMind: deepmind.com/blog/article/exploring-the-beauty-of-pure-mathematics-in-novel-waysPredicting gene expression with AI, DeepMind: deepmind.com/blog/article/enformerUsing machine learning to accelerate ecological research, DeepMind: deepmind.com/blog/article/using-machine-learning-to-accelerate-ecological-researchAccelerating fusion science through learned plasma control, DeepMind: deepmind.com/blog/article/Accelerating-fusion-science-through-learned-plasma-controlSimulating matter on the quantum scale with AI, DeepMind: deepmind.com/blog/article/Simulating-matter-on-the-quantum-scale-with-AIHow AI is helping the natural sciences, Nature: nature.com/articles/d41586-021-02762-6Inside DeepMind's epic mission to solve science's trickiest problem, WIRED: wired.co.uk/article/deepmind-protein-foldingHow Artificial Intelligence Is Changing Science, Quanta: quantamagazine.org/how-artificial-intelligence-is-changing-science-20190311Find Seasons 1 & 2 on YouTube: http://dpmd.ai/3geDPmL Or search “DeepMind: The Podcast” and subscribe on your favourite podcast app:Apple Podcasts: http://dpmd.ai/2RzlmcuGoogle Podcasts: http://dpmd.ai/3geDjp5Spotify: http://dpmd.ai/3w29cb4Pocket Casts: https://pca.st/30m1
The road to AGI - DeepMind: The Podcast (S2, Ep5) Google DeepMind 2022-02-15 | Hannah meets DeepMind co-founder and chief scientist Shane Legg, the man who coined the phrase ‘artificial general intelligence’, and explores how it might be built. Why does Shane think AGI is possible? When will it be realised? And what could it look like? Hannah also explores a simple theory of using trial and error to reach AGI and takes a deep dive into MuZero, an AI system which mastered complex board games from chess to Go, and is now generalising to solve a range of important tasks in the real world.For questions or feedback on the series, message us on Twitter @DeepMind or email podcast@deepmind.com. Interviewees: DeepMind’s Shane Legg, Doina Precup, Dave Silver & Jackson BroshearCreditsPresenter: Hannah FrySeries Producer: Dan HardoonProduction support: Jill AchinekuSounds design: Emma BarnabyMusic composition: Eleni ShawSound Engineer: Nigel AppletonEditor: David PrestCommissioned by DeepMindThank you to everyone who made this season possible! Further reading: Real-world challenges for AGI, DeepMind: deepmind.com/blog/article/real-world-challenges-for-agiAn executive primer on artificial general intelligence, McKinsey: mckinsey.com/business-functions/operations/our-insights/an-executive-primer-on-artificial-general-intelligenceMastering Go, chess, shogi and Atari without rules, DeepMind: deepmind.com/blog/article/muzero-mastering-go-chess-shogi-and-atari-without-rulesWhat is AGI?, Medium: medium.com/intuitionmachine/what-is-agi-99cdb671c88eA Definition of Machine Intelligence by Shane Legg, arXiv: arxiv.org/abs/0712.3329Reward is enough by David Silver, ScienceDirect: sciencedirect.com/science/article/pii/S0004370221000862Find Seasons 1 & 2 on YouTube: http://dpmd.ai/3geDPmL Or search “DeepMind: The Podcast” and subscribe on your favourite podcast app:Apple Podcasts: http://dpmd.ai/2RzlmcuGoogle Podcasts: http://dpmd.ai/3geDjp5Spotify: http://dpmd.ai/3w29cb4Pocket Casts: https://pca.st/30m1
Lets get physical - DeepMind: The Podcast (S2, Ep4) Google DeepMind 2022-02-08 | Do you need a body to have intelligence? And can one exist without the other? Hannah takes listeners behind the scenes of DeepMind's robotics lab in London where she meets robots that are trying to independently learn new skills, and explores why physical intelligence is a necessary part of intelligence. Along the way, she finds out how researchers trained their robots at home during lockdown, uncovers why so many robotics demonstrations are faking it, and what it takes to train a robotic football team. For questions or feedback on the series, message us on Twitter @DeepMind or email podcast@deepmind.com. Interviewees: DeepMind’s Raia Hadsell, Viorica Patraucean, Jan Humplik, Akhil Raju & Doina PrecupCreditsPresenter: Hannah FrySeries Producer: Dan HardoonProduction support: Jill AchinekuSounds design: Emma BarnabyMusic composition: Eleni ShawSound Engineer: Nigel AppletonEditor: David PrestCommissioned by DeepMindThank you to everyone who made this season possible! Further reading: Stacking our way to more general robots, DeepMind: deepmind.com/blog/article/stacking-our-way-to-more-general-robotsResearchers Propose Physical AI As Key To Lifelike Robots, Forbes: forbes.com/sites/simonchandler/2020/11/11/researchers-propose-physical-ai-as-key-to-lifelike-robotsThe robots going where no human can, BBC: bbc.co.uk/news/av/technology-41584738The Robot Assault On Fukushima, WIRED: wired.com/story/fukushima-robot-cleanupLeaps, Bounds, and Backflips, Boston Dynamics: http://blog.bostondynamics.com/atlas-leaps-bounds-and-backflipsNow DeepMind is using AI to transform football, WIRED: wired.co.uk/article/deepmind-football-liverpool-aiFind Seasons 1 & 2 on YouTube: http://dpmd.ai/3geDPmL Or search “DeepMind: The Podcast” and subscribe on your favourite podcast app:Apple Podcasts: http://dpmd.ai/2RzlmcuGoogle Podcasts: http://dpmd.ai/3geDjp5Spotify: http://dpmd.ai/3w29cb4Pocket Casts: https://pca.st/30m1
Better together - DeepMind: The Podcast (S2, Ep3) Google DeepMind 2022-02-01 | Cooperation is at the heart of our society. Inventing the railway, giving birth to the Renaissance, and creating the Covid-19 vaccine all required people to combine efforts. But cooperation is so much more. It governs our education systems, healthcare, and food production. In this episode, Hannah meets the researchers working on cooperative AI, and hears about their work and influences from the famous American psychologist - and pigeon trainer - BF Skinner to the strategic board game Diplomacy.For questions or feedback on the series, message us on Twitter @DeepMind or email podcast@deepmind.com. Interviewees: DeepMind’s Thore Graepel, Kevin Mckee, Doina Precup & Laura WeidingerCreditsPresenter: Hannah FrySeries Producer: Dan HardoonProduction support: Jill AchinekuSounds design: Emma BarnabyMusic composition: Eleni ShawSound Engineer: Nigel AppletonEditor: David PrestCommissioned by DeepMindThank you to everyone who made this season possible! Further reading: Machines must learn to find common ground, Nature: nature.com/articles/d41586-021-01170-0Introduction to Reinforcement Learning, DeepMind: youtube.com/watch?v=2pWv7GOvuf0B.F. Skinner, Wikipedia: en.wikipedia.org/wiki/B._F._SkinnerThe Tragedy of the Commons, Wikipedia: en.wikipedia.org/wiki/Tragedy_of_the_commonsStaving Off The Ultimate Tragedy Of The Commons, Forbes: forbes.com/sites/georgebradt/2021/11/02/staving-off-the-ultimate-tragedy-of-the-commons-by-making-better-complex-decisions-cooperatively-in-glasgowUnderstanding Agent Cooperation, DeepMind: deepmind.com/blog/article/understanding-agent-cooperationThe emergence of complex cooperative agents, DeepMind: deepmind.com/blog/article/capture-the-flag-science Find Seasons 1 & 2 on YouTube: http://dpmd.ai/3geDPmL Or search “DeepMind: The Podcast” and subscribe on your favourite podcast app:Apple Podcasts: http://dpmd.ai/2RzlmcuGoogle Podcasts: http://dpmd.ai/3geDjp5Spotify: http://dpmd.ai/3w29cb4Pocket Casts: https://pca.st/30m1
A breakthrough unfolds - DeepMind: The Podcast (S2, Ep1) Google DeepMind 2022-01-25 | In December 2019, DeepMind’s AI system, AlphaFold, solved a 50-year-old grand challenge in biology, known as the protein-folding problem. A headline in the journal Nature read, “It will change everything” and the President of the UK's Royal Society called it a “stunning advance [that arrived] decades before many in the field would have predicted”. In this episode, Hannah uncovers the inside story of AlphaFold from the people who made it happen and finds out how it could help transform the future of healthcare and medicine.For questions or feedback on the series, message us on Twitter @DeepMind or email podcast@deepmind.com. Interviewees: DeepMind’s Demis Hassabis, John Jumper, Kathryn Tunyasunakool and Sasha Brown; Charles Mowbray and Monique Wasuna of the Drugs for Neglected Diseases initiative (DNDi]) & John McGeehan of the Centre for Enzyme Innovation at the University of PortsmouthCreditsPresenter: Hannah FrySeries Producer: Dan HardoonProduction support: Jill AchinekuSounds design: Emma BarnabyMusic composition: Eleni ShawSound Engineer: Nigel AppletonEditor: David PrestCommissioned by DeepMindThank you to everyone who made this season possible! Further reading: AlphaFold blog, DeepMind: deepmind.com/blog/article/alphafold-a-solution-to-a-50-year-old-grand-challenge-in-biologyAlphaFold case study, DeepMind: deepmind.com/research/case-studies/alphafoldIt will change everything, Nature: nature.com/articles/d41586-020-03348-4AlphaFold Is The Most Important Achievement In AI—Ever, Forbes: forbes.com/sites/robtoews/2021/10/03/alphafold-is-the-most-important-achievement-in-ai-ever/?sh=359278426e0aBacteria found to eat PET plastics, NewScientist: newscientist.com/article/2080279-bacteria-found-to-eat-pet-plastics-could-help-do-the-recyclingProtein Structure Prediction Center: predictioncenter.orgAn interview with Professor John McGeehan, BBSRC: bbsrc.ukri.org/news/features/enzyme-science/an-interview-with-professor-john-mcgeehanJohn McGeehan profile, University of Portsmouth: researchportal.port.ac.uk/en/persons/john-mcgeehanDrugs for Neglected Diseases initiative (DNDi): dndi.orgA doctor’s dream, DNDi: youtube.com/watch?v=Tk31iucWYdEThe Curious Cases of Rutherford and Fry, BBC: bbc.co.uk/programmes/b07dx75g/episodes/downloadsHannah Fry: hannahfry.co.ukFind Seasons 1 & 2 on YouTube: http://dpmd.ai/3geDPmLOr search “DeepMind: The Podcast” and subscribe on your favourite podcast app:Apple Podcasts: http://dpmd.ai/2RzlmcuGoogle Podcasts: http://dpmd.ai/3geDjp5Spotify: http://dpmd.ai/3w29cb4Pocket Casts: https://pca.st/30m1
Speaking of intelligence - DeepMind: The Podcast (S2, Ep2) Google DeepMind 2022-01-25 | Hannah explores the potential of language models, the questions they raise, and if teaching a computer about language is enough to create artificial general intelligence (AGI). Beyond helping us communicate ideas, language plays a crucial role in memory, cooperation, and thinking – which is why AI researchers have long aimed to communicate with computers using natural language. Recently, there has been extraordinary progress using large-language models (LLM), which learn how to speak by processing huge amounts of data from the internet. The results can be very convincing, but pose significant ethical challenges. For questions or feedback on the series, message us on Twitter @DeepMind or email podcast@deepmind.com. Interviewees: DeepMind’s Geoffrey Irving, Chris Dyer, Angeliki Lazaridou, Lisa-Anne Hendriks & Laura Weidinger CreditsPresenter: Hannah FrySeries Producer: Dan HardoonProduction support: Jill AchinekuSounds design: Emma BarnabyMusic composition: Eleni ShawSound Engineer: Nigel AppletonEditor: David PrestCommissioned by DeepMindThank you to everyone who made this season possible! Further reading: GPT-3 Powers the Next Generation of Apps, OpenAI: openai.com/blog/gpt-3-appshttps://web.stanford.edu/class/linguist238/p36-weizenabaum.pdfNever Mind the Computer 1983 about the ELIZA program, BBC: bbc.co.uk/programmes/p023kpf8How Large Language Models Will Transform Science, Society, and AI, Stanford University: https://hai.stanford.edu/news/how-large-language-models-will-transform-science-society-and-aiChallenges in Detoxifying Language Models, DeepMind: deepmind.com/research/publications/2021/Challenges-in-Detoxifying-Language-ModelsExtending Machine Language Models toward Human-Level Language Understanding, DeepMind: deepmind.com/research/publications/2020/Extending-Machine-Language-Models-toward-Human-Level-Language-UnderstandingLanguage modelling at scale, DeepMind: deepmind.com/blog/article/language-modelling-at-scaleArtificial general intelligence, Technology Review: technologyreview.com/2020/10/15/1010461/artificial-general-intelligence-robots-ai-agi-deepmind-google-openaiA Definition of Machine Intelligence by Shane Legg, arXiv: arxiv.org/abs/0712.3329Stuart Russell - Living With Artificial Intelligence, BBC: bbc.co.uk/programmes/m001216k/episodes/playerFind Seasons 1 & 2 on YouTube: http://dpmd.ai/3geDPmL Or search “DeepMind: The Podcast” and subscribe on your favourite podcast app:Apple Podcasts: http://dpmd.ai/2RzlmcuGoogle Podcasts: http://dpmd.ai/3geDjp5 Spotify: http://dpmd.ai/3w29cb4Pocket Casts: https://pca.st/30m1
DeepMind: The Podcast (S2 trailer) Google DeepMind 2022-01-10 | The multi-award winning podcast which uncovers the extraordinary ways artificial intelligence (AI) is transforming our world is back for a second season. Join mathematician and broadcaster Professor Hannah Fry behind the scenes of world-leading AI research lab DeepMind to get the inside story of how AI is being created – and how it can benefit our lives and the society we live in. Recorded over six months and featuring over 30 original interviews, including DeepMind co-founders Demis Hassabis and Shane Legg, the podcast gives listeners exclusive access to the brilliant people building the technology of the future. Throughout nine original episodes, Hannah discovers how DeepMind is using AI to advance science in critical areas, like solving a 50-year-old grand challenge in biology and developing nuclear fusion. Listeners hear stories of teaching robots to walk at home during lockdown, as well as using AI to forecast weather, help people regain their voices, and enhance game strategies with Liverpool Football Club. Hannah also takes an in-depth look at the challenges and potential of building artificial general intelligence (AGI) and explores what it takes to ensure AI is built to benefit society.“I hope this series gives people a better understanding of AI and a feeling for just how exhilarating an endeavour it is.” – Demis Hassabis, CEO and Co-Founder of DeepMindFor questions or feedback on the series, message us on Twitter @DeepMind or email podcast@deepmind.com. CreditsPresenter: Hannah FrySeries Producer: Dan HardoonProduction support: Jill AchinekuSounds design: Emma BarnabyMusic composition: Eleni ShawSound Engineer: Nigel AppletonEditor: David PrestCommissioned by DeepMindFind Seasons 1 & 2 on YouTube: http://dpmd.ai/3geDPmLOr search “DeepMind: The Podcast” and subscribe on your favourite podcast app:Apple Podcasts: http://dpmd.ai/2RzlmcuGoogle Podcasts: http://dpmd.ai/3geDjp5Spotify: http://dpmd.ai/3w29cb4Pocket Casts: https://pca.st/30m1
DeepMind x UCL RL Lecture Series - Introduction to Reinforcement Learning [1/13] Google DeepMind 2021-09-09 | Research Scientist Hado van Hasselt introduces the reinforcement learning course and explains how reinforcement learning relates to AI.Slides: dpmd.ai/introslides Full video lecture series: dpmd.ai/DeepMindxUCL21
DeepMind x UCL RL Lecture Series - Exploration & Control [2/13] Google DeepMind 2021-09-09 | Research Scientist Hado van Hasselt looks at why it's important for learning agents to balance exploring and exploiting acquired knowledge at the same time.Slides: dpmd.ai/explorationcontrolFull video lecture series: dpmd.ai/DeepMindxUCL21
DeepMind x UCL RL Lecture Series - MDPs and Dynamic Programming [3/13] Google DeepMind 2021-09-09 | Research Scientist Diana Borsa explains how to solve MDPs with dynamic programming to extract accurate predictions and good control policies.Slides: dpmd.ai/MDPsFull video lecture series: dpmd.ai/DeepMindxUCL21
DeepMind x UCL RL Lecture Series - Theoretical Fund. of Dynamic Programming Algorithms [4/13] Google DeepMind 2021-09-09 | Research Scientist Diana Borsa explores dynamic programming algorithms as contraction mappings, looking at when and how they converge to the right solutions.Slides: dpmd.ai/dynamicprogrammingFull video lecture series: dpmd.ai/DeepMindxUCL21
DeepMind x UCL RL Lecture Series - Model-free Prediction [5/13] Google DeepMind 2021-09-09 | Research Scientist Hado van Hasselt takes a closer look at model-free prediction and its relation to Monte Carlo and temporal difference algorithms.Slides: dpmd.ai/modelfreepredictionFull video lecture series: dpmd.ai/DeepMindxUCL21
DeepMind x UCL RL Lecture Series - Model-free Control [6/13] Google DeepMind 2021-09-09 | Research Scientist Hado van Hasselt covers prediction algorithms for policy improvement, leading to algorithms that can learn good behaviour policies from sampled experience.Slides: dpmd.ai/modelfreecontrolFull video lecture series: dpmd.ai/DeepMindxUCL21
DeepMind x UCL RL Lecture Series - Function Approximation [7/13] Google DeepMind 2021-09-09 | Research Scientist Hado van Hasselt explains how to combine deep learning with reinforcement learning for "deep reinforcement learning".Slides: dpmd.ai/functionapproximationFull video lecture series: dpmd.ai/DeepMindxUCL21
DeepMind x UCL RL Lecture Series - Planning & models [8/13] Google DeepMind 2021-09-09 | Research Engineer Matteo Hessel explains how to learn and use models, including algorithms like Dyna and Monte-Carlo tree search (MCTS).Slides: dpmd.ai/planningmodelsFull video lecture series: dpmd.ai/DeepMindxUCL21
DeepMind x UCL RL Lecture Series - Policy-Gradient and Actor-Critic methods [9/13] Google DeepMind 2021-09-09 | Research Scientist Hado van Hasselt covers policy algorithms that can learn policies directly and actor critic algorithms that combine value predictions for more efficient learning.Slides: dpmd.ai/policygradientFull video lecture series: dpmd.ai/DeepMindxUCL21
DeepMind x UCL RL Lecture Series - Approximate Dynamic Programming [10/13] Google DeepMind 2021-09-09 | Research Scientist Diana Borsa introduces approximate dynamic programming, exploring what we can say theoretically about the performance of approximate algorithms.Slides: dpmd.ai/approximatedynamicFull video lecture series: dpmd.ai/DeepMindxUCL21
DeepMind x UCL RL Lecture Series - Multi-step & Off Policy [11/13] Google DeepMind 2021-09-09 | Research Scientist Hado van Hasselt discusses multi-step and off policy algorithms, including various techniques for variance reduction.Slides: dpmd.ai/offpolicyFull video lecture series: dpmd.ai/DeepMindxUCL21
DeepMind x UCL RL Lecture Series - Deep Reinforcement Learning #1 [12/13] Google DeepMind 2021-09-09 | Research Engineer Matteo Hessel talks practical considerations and algorithms for deep reinforcement learning, including how to implement them using auto-differentiation (i.e Jax).Slides: dpmd.ai/deeprl1Full video lecture series: dpmd.ai/DeepMindxUCL21
DeepMind x UCL RL Lecture Series - Deep Reinforcement Learning #2 [13/13] Google DeepMind 2021-09-09 | Research Engineer Matteo Hessel covers general value functions, GVFs as auxiliary tasks, and explains how to deal with scaling issues in algorithms.Slides: dpmd.ai/deeprl2Full video lecture series: dpmd.ai/DeepMindxUCL21
Open-Ended Learning Leads to Generally Capable Agents | Results Showreel Google DeepMind 2021-07-27 | This is the accompanying video for the paper "Open-Ended Learning Leads to Generally Capable Agents" (DeepMind 2021). All results shown are with the same agent and on hand-authored probe tasks that were held out of training.Further reading blog: deepmind.com/blog/article/generally-capable-agents-emerge-from-open-ended-playPaper: deepmind.com/research/publications/open-ended-learning-leads-to-generally-capable-agentsTimestamps of Results:00:00 Intro00:24 Tag Fiesta01:23 King of the Hill02:18 Hide and Seek04:22 Capture the Flag06:57 Catch 'em All07:53 Choose Wisely08:47 Nowhere to Hide09:33 Stop Roll10:43 Near not Near11:53 See not See12:42 Build Ramp14:26 Outro
AlphaFold Protein Structure Database Google DeepMind 2021-07-22 | With EMBL-EBI, we're proud to launch the AlphaFold Protein Structure Database, which offers the most complete and accurate picture of the human proteome, doubling humanity’s accumulated knowledge of high-accuracy human protein structures - for free. Try it today: dpmd.ai/alphafolddb
NeurIPS 2020: JAX Ecosystem Meetup Google DeepMind 2021-01-05 | Learn more about JAX and why it's effective for research in reinforcement learning, GANs, meta-gradients and more.Date: 09/12/20Slides: bit.ly/3pQB0vl
AlphaFold: The making of a scientific breakthrough Google DeepMind 2020-11-30 | The inside story of the DeepMind team of scientists and engineers who created AlphaFold, an AI system that is recognised as a solution to "protein folding", a grand scientific challenge for more than 50 years. Find out more: deepmind.com/alphafoldProtein references: TBP = To be published1BYI: Sandalova, T., et al. (1999) Structure of dethiobiotin synthetase at 0.97 A resolution. Acta Crystallographica Section D 55: 610-624.3NPD: Das, D. et al. (2014) Crystal structure of a putative quorum sensing-regulated protein (PA3611) from the Pseudomonas-specific DUF4146 family. Proteins 82: 1086-1092.5AOZ: Bule, P., et al. Structural Characterization of the Third Cohesin from Ruminococcus Flavefaciens Scaffoldin Protein, Scab. (TBP)5ERE: Joachimiak, A. A novel extracellular ligand receptor. (TBP)5L8E: Dharadhar, S., et al. (2016) A conserved two-step binding for the UAF1 regulator to the USP12 deubiquitinating enzyme. Journal of Structural Biology 196: 437-447.5M20: Liauw, P., et al. Structure of Thermosynechococcus elongatus Psb32 fused to sfGFP. (TBP)5W9F: Buchko, G.W., et al. (2018) Cytosolic expression, solution structures, and molecular dynamics simulation of genetically encodable disulfide-rich de novo designed peptides. Protein Science 27: 1611-1623.6BTC: Mir-Sanchis, I., et al. (2018) Crystal Structure of an Unusual Single-Stranded DNA-Binding Protein Encoded by Staphylococcal Cassette Chromosome Elements. Structure 26: 1144.6CL6: Buth, S.A., et al. (2018) Structure and Analysis of R1 and R2 Pyocin Receptor-Binding Fibers. Viruses 10.6CP9: Gucinski, G.C., et al. (2019) Convergent Evolution of the Barnase/EndoU/Colicin/RelE (BECR) Fold in Antibacterial tRNase Toxins. Structure 27: 1660.6CVZ: Loppnau, P., et al. Crystal structure of the WD40-repeat of RFWD3. (TBP)6D2V: Clinger, J.A., et al. Structure and Function of Terfestatin Biosynthesis Enzymes TerB and TerC. (TBP)6E4B: Tan, K., et al. The crystal structure of a putative alpha-ribazole-5'-P phosphatase from Escherichia coli str. K-12 substr. MG1655 (CASP target). (TBP)6EK4: Brauning, B., et al. (2018) Structure and mechanism of the two-component alpha-helical pore-forming toxin YaxAB. Nature Communications 9: 1806-1806.6F45: Dunne, M., et al. (2018) Salmonella Phage S16 Tail Fiber Adhesin Features a Rare Polyglycine Rich Domain for Host Recognition. Structure 26: 1573-1582.e4.6M9T: Audet, M., et al. (2019) Crystal structure of misoprostol bound to the labor inducer prostaglandin E2receptor. Nature Chemical Biology 15: 11-17.6MSP: Koepnick, B., et al. (2019) De novo protein design by citizen scientists. Nature 570: 390-394.6N64: Birkinshaw, R.W., et al. Structure of SMCHD1 hinge domain. (TBP)6N9Y: Kerviel, A., et al. (2019) Atomic structure of the translation regulatory protein NS1 of bluetongue virus. Nature Microbiology 4: 837-845.6ORI: Spiegelman, L., et al. Enterococcal surface protein, partial N-terminal region (CASP target). (TBP)6PX4: Krieger, I.V., et al. (2020) The Structural Basis of T4 Phage Lysis Control: DNA as the Signal for Lysis Inhibition. Journal of Molecular Biology 432: 4623-4636.6QVM: Osipov, E.M., et al. Crystal structure of native O-glycosylated multiheme cytochrome cf with S-layer binding domain. (TBP)6T1Z: Debruycker, V., et al. (2020) An embedded lipid in the multidrug transporter LmrP suggests a mechanism for polyspecificity. Nature Structural & Molecular Biology 27: 829-835.6TRI: Rasmussen, K.K., et al. (2020) Revealing the mechanism of repressor inactivation during switching of a temperate bacteriophage. PNAS 117: 20576-20585.6U7L: Minasov, G., et al. 2.75 Angstrom Crystal Structure of Galactarate Dehydratase from Escherichia coli. (TBP)6UBL: Kosgei, A.J., et al. Structure of DynF from the Dynemicin Biosynthesis Pathway of Micromonospora chersina. (TBP)6UK5: Alvarado, S.K., et al. Structure of SAM bound CalS10, an amino pentose methyltransferase from Micromonospora echinaspora involved in calicheamicin biosynthesis. (TBP)6VR4: Leiman, P.G., et al. Virion-packaged DNA-dependent RNA polymerase of crAss-like phage phi14:2 (CASP target). (TBP)6X6O: Shi, K., et al. (2020) Crystal structure of bacteriophage T4 Spackle as determined by native SAD phasing. Acta Crystallographica Section D 76: 899-904.6XBD: Coudray, N., et al. Structure of MlaFEDB lipid transporter reveals an ABC exporter fold and two bound phospholipids. (TBP)6YA2: Bahat, Y., et al. First structure of a glycoprotein from enveloped plant virus. (TBP)6YFN: Rumnieks, J., et al. Expansion of the structural diversity of single-stranded RNA bacteriophages. (TBP)6YJ1: Sobieraj, A., et al. (CASP target) Crystal structure of the M23 peptidase domain of Staphylococcal phage 2638A endolysin. (TBP)7JTL: Flower, T.G., et al. (2020) Structure of SARS-CoV-2 ORF8, a rapidly evolving coronavirus protein implicated in immune evasion. Biorxiv.
Protein folding explained Google DeepMind 2020-11-30 | Join DeepMind Science Engineer Kathryn Tunyasuvunakool to explore the hidden world of proteins.These tiny molecular machines underpin every biological process in every living thing and each one has a unique 3D shape that determines how it works and what it does.But figuring out the exact structure of a protein is an expensive and often time-consuming process, meaning we only know the exact 3D structure of a tiny fraction of the 200m proteins known to science.Being able to accurately predict the shape of proteins could accelerate research in every field of biology. That could lead to important breakthroughs like finding new medicines or finding proteins and enzymes that break down industrial and plastic waste or efficiently capture carbon from the atmosphere. Join Kathryn as she explains what protein folding is, why it's important and how our Artificial Intelligence system AlphaFold offers a solution to this grand scientific challenge.Find out more: deepmind.com/alphafold
DeepMind Scholars: Benedettas story Google DeepMind 2020-11-05 | The DeepMind Scholars programme gives talented students from underrepresented backgrounds the support they need to study at leading universities, and connect with our researchers and engineers. Scholars get their Masters' fees paid in full, as well as guidance and support from personal DeepMind mentors. Find out more on our website: deepmind.com/scholarships
DeepMind x UCL | Deep Learning Lectures | 12/12 | Responsible Innovation Google DeepMind 2020-07-09 | What can we do to build algorithms that are safe, reliable and robust? And what are the responsibilities of technologists who work in this area? In this talk, Chongli Qin and Iason Gabriel explore these questions — connected through the lens of responsible innovation — in two parts. In the first part, Chongli explores the question of why and how we can design algorithms that are safe, reliable and trustworthy through the lens of specification driven machine learning. In the second part, Iason looks more closely at ethical dimensions of machine learning, at the responsibility of researchers, and at processes that can structure ethical deliberation in this domain. Taken together, they suggest that there are important measures that we can, and should, put in place — if we want to build systems that are beneficial to society.Download the slides here: storage.googleapis.com/deepmind-media/UCLxDeepMind_2020/L12%20-%20UCLxDeepMind%20DL2020.pdfFind out more about how DeepMind increases access to science here: deepmind.com/about#access_to_scienceSpeaker Bios: Chongli Qin is a research scientist at DeepMind, her primary interest is in building safer, more reliable and more trustworthy machine learning algorithms. Over the past several years, she has contributed in developing algorithms to make neural networks more robust to noise. Key parts of her research focuses on functional analysis: properties of neural network that can naturally enhance robustness. She has also contributed in building mathematical frameworks to verify/guarantee that certain properties hold for neural networks. Prior to DeepMind, Chongli studied in Cambridge, where she studied the mathematics tripos and scientific computing before doing a PhD in bioinformatics. Iason Gabriel is a Senior Research Scientist at DeepMind where he works in the ethics research team. His work focuses on the applied ethics of artificial intelligence, human rights, and the question of how to align technology with human values. Before joining DeepMind, Iason was a Fellow in Politics at St John’s College, Oxford, and a member of the Centre for the Study of Social Justice (CSSJ). He holds a doctorate in Political Theory from the University of Oxford and spent a number of years working for the United Nations in post-conflict environments.About the lecture series: The Deep Learning Lecture Series is a collaboration between DeepMind and the UCL Centre for Artificial Intelligence. Over the past decade, Deep Learning has evolved as the leading artificial intelligence paradigm providing us with the ability to learn complex functions from raw data at unprecedented accuracy and scale. Deep Learning has been applied to problems in object recognition, speech recognition, speech synthesis, forecasting, scientific computing, control and many more. The resulting applications are touching all of our lives in areas such as healthcare and medical research, human-computer interaction, communication, transport, conservation, manufacturing and many other fields of human endeavour. In recognition of this huge impact, the 2019 Turing Award, the highest honour in computing, was awarded to pioneers of Deep Learning.In this lecture series, research scientists from leading AI research lab, DeepMind, deliver 12 lectures on an exciting selection of topics in Deep Learning, ranging from the fundamentals of training neural networks via advanced ideas around memory, attention, and generative modelling to the important topic of responsible innovation.
