In a new paper, our team tackles a fundamental AI problem: how can we simultaneously parse the world into objects and properties, while simultaneously inducing the rules explaining how objects change over time: dpmd.ai/3fmrxsn (1/)
Work by @LittleBimble with @pfau, @pushmeet, Matko Bosnjak, Lars Buesing, Kevin Ellis, and Marek Sergot. (2/)
This system combines the Apperception Engine with a binary neural network to learn a provably 100% accurate model of non-trivial environments (e.g. Sokoban) from noisy raw pixel data. (3/)
The ability to learn the world dynamics sample efficiently is a key component of intelligence, according to @MelMitchell1, @GaryMarcus, @fchollet, @mpshanahan, among many others. (4/4)

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More from @DeepMind

7 May
Multimodal transformers achieve impressive results on many tasks like Visual Question Answering and Image Retrieval, but what contributes most to their success? dpmd.ai/3h8u23Z (1/)
This work explores how different architecture variations, pretraining datasets, and losses impact multimodal transformers’ performance on image retrieval: dpmd.ai/3eENAtF

(By Lisa Anne Hendricks, John Mellor, Rosalia Schneider, @jalayrac & @aidanematzadeh) (2/)
Multimodal transformers outperform simpler dual encoder architectures when the amount of data is held constant. Interestingly, larger datasets don’t always improve performance. (3/)
Read 4 tweets
10 Dec 20
For #NeurIPS2020, we spoke with @wojczarnecki about Spinning Tops, advice he wish he received as a student, and his goals for next year! #PeopleBehindThePapers Image
AI has been extremely successful in real world games (GO, DOTA, StarCraft) with results coming from relatively simple multi-agent algorithms. In this paper, we hypothesise that they share a common geometry - Spinning Tops. Learn more: bit.ly/3qI8RrD #NeurIPS2020 Image
I’ve always loved biology. During my masters I decided to take a handful of neurophysiology courses - which I found to be interesting. But eventually I realised that my true strengths were in mathematical sciences. A career in ML and AI became a natural way to combine the two. Image
Read 5 tweets
1 Dec 20
Yesterday we shared the news that #AlphaFold has been recognised as a solution to the ‘protein folding problem’ by #CASP14, the biennial Critical Assessment of Protein Structure Prediction. But what exactly is protein folding, and why is it important? A thread… (1/6)
Proteins are the building blocks of life - they underpin the biological processes in every living thing. If you could unravel a protein you would see that it’s like a string of beads made of a sequence of different chemicals known as amino acids. (2/6)
Interactions between these amino acids make the protein fold, as it finds its shape out of almost limitless possibilities. For decades, scientists have been trying to find a method to reliably determine a protein’s structure just from its sequence of amino acids. (3/6)
Read 6 tweets
9 Jun 20
We have research scientist @seb_ruder up next with more #AtHomeWithAI recommendations!

He suggests the Deep Learning Book from @mitpress for a comprehensive introduction to the fundamentals of DL: bit.ly/351qMzb (1/7)
Overwhelmed with the number of available machine learning courses? @seb_ruder recommends taking a look through @venturidb’s curated - and ranked - list available on @freeCodeCamp.

bit.ly/3erZEN4 #AtHomeWithAI
Do you have a technical background? Are you looking for an introduction to natural language processing?

Sebastian recommends the @fastdotai course, “A Code-First Introduction to Natural Language Processing”.

bit.ly/3esFtP8 #AtHomeWithAI
Read 7 tweets
8 Jun 20
We’re back with more #AtHomeWithAI researcher recommendations. Next up is research scientist @csilviavr with suggestions for resources to learn about causal inference! (1/5) Image
Her first suggestion is “The Book of Why” by @yudapearl & Dana Mackenzie.

According to Silvia, this is best for those looking for an introduction to the topic: bit.ly/30isGej #AtHomeWithAI
Need a more in-depth look at causal inference? Silvia suggests reading through “Causal Inference in Statistics: A Primer” by @yudapearl, @MadelynTheRose & @NP_Jewell.

bit.ly/36xdvza #AtHomeWithAI
Read 5 tweets
27 May 20
Looking for a few more favourite resources from the team? Today’s #AtHomeWithAI picks are from research scientist @TaylanCemgilML! (1/6)
His first recommendation is for those looking to learn about the basics of probabilistic reasoning and modelling.

He suggests “Bayesian Reasoning and Machine Learning” [longer read] by @davidobarber. Read it for free here: bit.ly/3cG99rS #AtHomeWithAI
Are you a beginner looking for a lesson on the Monte Carlo method?

Taylan’s own, “A Tutorial Introduction to Monte Carlo methods, Markov Chain Monte Carlo and Particle Filtering” is available here: bit.ly/3cAQ8XG #AtHomeWithAI
Read 6 tweets

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