Cambridge Quantum Appoints Professor Stephen Clark as Head of Artificial Intelligence

Cambridge Quantum Computing

Cambridge Quantum Computing (CQC) are pleased to announce the appointment of Prof. Stephen Clark as Head of Artificial Intelligence. Prof. Clark joins CQC from DeepMind where he was a Senior Staff Research Scientist and led a team working on grounded language learning in virtual environments. He also holds an Honorary Professorship at Queen Mary University of London.

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Researchers from DeepMind and Alberta University propose policy-guided Heuristic search Algorithm

DeepMind’s AlphaGo and its successors previously demonstrated that the policy and heuristic function is formulated upon the PUCT (Polynomial Upper Confidence Trees) search algorithm. This algorithm can be quite effective for guiding search in adversarial games. However, PUCT is computationally inefficient and lacks guarantees on its search effort. Though other methods such as LevinTS provide guarantees on search steps, they do not use a heuristic function.

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Google AI, DeepMind and the University of Toronto introduce DreamerV2

dreamer-v2

It is the first Reinforcement Learning (RL) agent based on the world model to attain human-level success on the Atari benchmark. It includes the second generation of the Dreamer agent who learns behaviors entirely within a world model’s latent space […]

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DeepMind & UCL’s Alchemy Is a ‘Best-of-Both-Worlds’ 3D Video Game for Meta-RL

Deepmind Alchemy

n recent years, reinforcement learning (RL) has garnered much attention in the field of machine learning. The approach does not require labelled data and has yielded remarkable successes on a wide variety of specific tasks. RL unfortunately continues to struggle with issues such as sample efficiency, generalization, and transfer learning. To address these drawbacks, researchers have been exploring meta-reinforcement learning (meta-RL), in which learning strategies can quickly adapt to novel tasks by using experience gained on a large set of tasks that have a shared structure.

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DeepMind’s big losses, and the questions around running an AI lab

Last week, on the heels of DeepMind’s breakthrough in using AI to predict protein-folding came the news that the UK-based AI company is still costing its parent company Alphabet Inc hundreds of millions of dollars in losses each year. A tech company losing money is nothing new. The tech industry is replete with examples of companies who burned investor money long before becoming profitable. But DeepMind is not a normal company seeking to grab a share of a specific market. It is an AI research lab that has had to repurpose itself into a semi-commercial outfit to ensure its survival.

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DeepMind’s annual report: Why it’s hard to run a commercial AI lab

Google

A tech company losing money is nothing new. The tech industry is replete with examples of companies who burned investor money long before becoming profitable. But DeepMind is not a normal company seeking to grab a share of a specific market. It is an AI research lab that has had to repurpose itself into a semi-commercial outfit to ensure its survival.

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DeepMind, Google’s AI unit, reports big loss

DeepMind requires deep pockets. Reports surfaced this week that the London-based AI lab acquired in 2014 by Google parent Alphabet for about $600 million lost a staggering $649 million in the last year. The Financial Times and other media outlets also reported Thursday (Dec. 17) that Alphabet (NASDAQ: GOOGL) has written off roughly $1.5 billion in debt associated with loan and interest repayments due from DeepMind.

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Researchers suggest AI can learn common sense from animals

AI researchers developing reinforcement learning agents could learn a lot from animals. That’s according to recent analysis by Google’s DeepMind, Imperial College London, and University of Cambridge researchers assessing AI and non-human animals.

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DeepMind open-sources the FermiNet, a neural network that simulates electron behaviors

In September, Alphabet’s DeepMind published a paper in the journal Physical Review Research detailing Fermionic Neural Network (FermiNet), a new neural network architecture that’s well-suited to modeling the quantum state of large collections of electrons. The FermiNet, which DeepMind claims is one of the first demonstrations of AI for computing atomic energy, is now available in open source on GitHub — and ostensibly remains one of the most accurate methods to date.

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All we need to know about Google’s DeepMind

Google Deepmind

DeepMind was a small startup company focused on Artificial Intelligence and Machine Learning that received recognition from Google. Subsequently, this multibillion company acquired DeepMind and now owns it. You may be wondering what Google’s intentions for this company are and what impact it will make. Here is all the information you need to know about Google’s DeepMind and how it is trying to make AI work for your business.e.

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AI researchers share data on how we sort options, make decisions

scientists studying the brain and the emerging field of artificial intelligence (AI) have been homing in how that happens. A new paper in Nature, “A distributional code for value in dopamine-based reinforcement learning,” goes further in exploring how experience and options are sorted in making decisions.

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