DeepMind
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DeepMind Technologies is a British artificial intelligence subsidiary of Alphabet Inc. and research laboratory founded in 2010. DeepMind was acquired by Google in 2014. The company is based in London, with research centres in Canada, France, and the United States. In 2015, it became a wholly owned subsidiary of Alphabet Inc, Google's parent company.
DeepMind has created a neural network that learns how to play video games in a fashion similar to that of humans, as well as a Neural Turing machine,[8] or a neural network that may be able to access an external memory like a conventional Turing machine, resulting in a computer that mimics the short-term memory of the human brain. Source
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Recent Articles
Search ArticlesEvaluating social and ethical risks from generative AI
Introducing a context-based framework for comprehensively evaluating the social and ethical risks of AI systems Generative AI systems are already being used to write books, create graphic designs, assist medical practitioners, and are becoming increasingly capable. Ensuring these systems are developed and deployed responsibly requires carefully evaluating the potential ethical and social risks they may pose.
Scaling up learning across many different robot types
Notes We would like to thank the co-authors of this work: Abhishek Padalkar, Acorn Pooley, Ajinkya Jain, Alex Bewley, Alex Herzog, Alex Irpan, Alexander Khazatsky, Anant Rai, Anikait Singh, Anthony Brohan, Antonin Raffin, Ayzaan Wahid, Ben Burgess-Limerick, Beomjoon Kim, Bernhard Schölkopf, Brian Ichter, Cewu Lu, Charles Xu, Chelsea Finn, Chenfeng Xu, Cheng Chi, Chenguang Huang, Christine Chan, Chuer Pan, Chuyuan Fu, Coline Devin, Danny Driess, Deepak Pathak, Dhruv Shah, Dieter Büchler,...
A catalogue of genetic mutations to help pinpoint the cause of diseases
New AI tool classifies the effects of 71 million ‘missense’ mutations Uncovering the root causes of disease is one of the greatest challenges in human genetics. With millions of possible mutations and limited experimental data, it’s largely still a mystery which ones could give rise to disease. This knowledge is crucial to faster diagnosis and developing life-saving treatments.
SynthID
We’re beta launching SynthID, a tool for watermarking and identifying AI-generated images. SynthID is being released to a limited number of Vertex AI customers using Imagen, one of our latest text-to-image models that uses input text to create photorealistic images. With this tool, users can embed an imperceptible digital watermark into their AI-generated images and identify if Imagen was used for generating the image, or even part of the image.
Identifying AI-generated images with SynthID
New tool helps watermark and identify synthetic images created by Imagen AI-generated images are becoming more popular every day. But how can we better identify them, especially when they look so realistic? Today, in partnership with Google Cloud, we’re launching a beta version of SynthID, a tool for watermarking and identifying AI-generated images.
RT-2: New model translates vision and language into action
Robotic Transformer 2 (RT-2) is a novel vision-language-action (VLA) model that learns from both web and robotics data, and translates this knowledge into generalised instructions for robotic control. High-capacity vision-language models (VLMs) are trained on web-scale datasets, making these systems remarkably good at recognising visual or language patterns and operating across different languages.
Using AI to fight climate change
How we’re applying the latest AI developments to help fight climate change and build a more sustainable, low-carbon world AI is a powerful technology that will transform our future, so how can we best apply it to help combat climate change and find sustainable solutions? Our climate & sustainability lead, Sims Witherspoon, who recently spoke about how AI can accelerate our transition to renewables at TED Countdown, explains, “Climate change is a multifaceted problem with no single solution.
Google DeepMind’s latest research at ICML 2023
Next week marks the start of the 40th International Conference on Machine Learning (ICML 2023), taking place 23-29 July in Honolulu, Hawai'i. ICML brings together the artificial intelligence (AI) community to share new ideas, tools, and datasets, and make connections to advance the field. From computer vision to robotics, researchers from around the world will be presenting their latest advances.
Developing reliable AI tools for healthcare
New research proposes a system to determine the relative accuracy of predictive AI in a hypothetical medical setting, and when the system should defer to a human clinician Artificial intelligence (AI) has great potential to enhance how people work across a range of industries. But to integrate AI tools into the workplace in a safe and responsible way, we need to develop more robust methods for understanding when they can be most useful. So when is AI more accurate, and when is a human?
Exploring institutions for global AI governance
New white paper investigates models and functions of international institutions that could help manage opportunities and mitigate risks of advanced AI Growing awareness of the global impact of advanced artificial intelligence (AI) has inspired public discussions about the need for international governance structures to help manage opportunities and mitigate risks involved.