(testing signal)

Tag: DeepMind

The Architecture Behind DeepMind’s Model for Near Real-Time Weather Forecasts

Deep Generative Model of Rain (DGMR) is the newest creation from DeepMind which can predict precipitation in short-term intervals.

Source: https://technohubnewzz.blogspot.com/2021/10/artificial-intelligence-can-forecast-if.html
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DeepMind’s Collect & Infer: A Fresh Look at Data-Efficient Reinforcement Learning | Synced

In recent years there has been growing interest in reinforcement learning (RL) algorithms that can learn entirely from fixed datasets without interaction (offline RL). A number of relatively unexplored challenges remain in this research field, such as how to get the most out of the collected data, how to work with growing datasets, and how to compose the most effective datasets.

In a new paper, a DeepMind research team proposes a clear conceptual separation of the RL process into data-collection and inference of knowledge to improve RL data efficiency. The team introduces a “Collect and Infer” (C&I) paradigm and provides insights on how to interpret RL algorithms from the C&I perspective; while also showing how it could guide future research into more data-efficient RL.

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DeepMind’s New Super Model: Perceiver IO is a Transformer that can Handle Any Dataset

Source: https://www.zdnet.com/article/googles-supermodel-deepmind-perceiver-is-a-step-on-the-road-to-an-ai-machine-that-could-process-everything/

I recently started a new newsletter focus on AI education and already has over 50,000 subscribers. TheSequence is a no-BS( meaning no hype, no news etc) AI-focused newsletter that takes 5 minutes to read. The goal is to keep you up to date with machine learning projects, research papers and concepts. Please give it a try by subscribing below:

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Most deep learning models we build these days are highly optimized for a specific type of dataset. Architectures that are good at processing textual data cant be applied to computer vision or audio analysis. That level of specialization naturally influences the creation of models highly specialized in a given task and that are not able to adapt to other tasks.

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How DeepMind Trains Agents to Play Any Game Without Intervention

Image Credit: DeepMind

I recently started a new newsletter focus on AI education and already has over 50,000 subscribers. TheSequence is a no-BS( meaning no hype, no news etc) AI-focused newsletter that takes 5 minutes to read. The goal is to keep you up to date with machine learning projects, research papers and concepts. Please give it a try by subscribing below:

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Gaming have been at the center of some of the biggest deep learning in the recent years. The sputnik moment of deep learning and gaming came when DeepMind’s reinforcement learning agent AlphaGo beating go world champion Lee Sedol.

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Geometric foundations of Deep Learning

By Michael Bronstein (Imperial College), Joan Bruna (NYU), Taco Cohen (Qualcomm), and Petar Veličković (DeepMind).

This blog post is based on the new “proto-book” M. M. Bronstein, J. Bruna, T. Cohen, and P. Veličković, Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges (2021), Petar’s talk at Cambridge, and Michael’s keynote talk at ICLR 2021.

 

In October 1872, the philosophy faculty of a small university in the Bavarian city of Erlangen appointed a new young professor. As customary, he was requested to deliver an inaugural research program, which he published under the somewhat long and boring title Vergleichende Betrachtungen über neuere geometrische Forschungen (“A comparative review of recent researches in geometry”).

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