A look into the happenings of the Machine Learning world.
OUTLINE: 0:00 - Intro 0:25 - Facebook AI trains rapidly adapting robots 3:05 - Baidu presents autonomous excavator system 4:45 - EleutherAI turns 1 6:05 - Elon Musk says FSD harder than expected 8:10 - AI interview tools still fall short 11:10 - RunwayML AI-powered cloud video editor 11:55 - MineRL BASALT competition to learn from human feedback 13:15 - The Myth of the Expert Reviewer 15:55 - NVIDIA unveils Cambridge-1 supercomputer 17:10 - CLIP art sees rapid improvements 19:00 - AI demystifies boiling 21:20 - AI avatars for easier language learning 23:20 - Outro
Dreamer is a new RL agent by DeepMind that learns a continuous control task through forward-imagination in latent space.
https://arxiv.org/abs/1912.01603
Videos: https://dreamrl.github.io/
Abstract:
Learned world models summarize an agent's experience to facilitate learning complex behaviors. While learning world models from high-dimensional sensory inputs is becoming feasible through deep learning, there are many potential ways for deriving behaviors from them. We present Dreamer, a reinforcement learning agent that solves long-horizon tasks from images purely by latent imagination. We efficiently learn behaviors by propagating analytic gradients of learned state values back through trajectories imagined in the compact state space of a learned world model. On 20 challenging visual control tasks, Dreamer exceeds existing approaches in data-efficiency, computation time, and final performance.
Authors: Danijar Hafner, Timothy Lillicrap, Jimmy Ba, Mohammad Norouzi
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https://www.youtube.com/watch?v=awyuuJoHawo
#cvpr #socialmedia #machinelearning
In this week's ML news we look at CVPR's controversial action to ban paper promotions on social media during the review phase, among other things!
OUTLINE:
0:00 - Intro & Overview
0:25 - CVPR bans social media paper discussions
5:10 - WalMart uses AI to suggest substitutions
6:05 - NVIDIA releases Alias-Free GAN
7:30 - Confession Video in Myanmar possibly a DeepFake
8:50 - AI restores Rembrandt painting
10:40 - AI for healthcare not problem-free yet
11:50 - ML interviews book
12:15 - NVIDIA canvas turns sketches into paintings
13:00 - GPU prices down after crypto shock
13:30 - Facebook AI improves shopping experience
14:05 - DeepLab2 released on GitHub
14:35 - Toxic Language Models: Nobody cares
16:55 - Does AI have common sense?
References:
CVPR forbids social media promotion
https://twitter.com/wjscheirer/status/1408507154219384834
WalMart uses AI to substitute out-of-stock products
https://www.supermarketnews.com/technology/walmart-enlists-artificial-intelligence-online-grocery-substitutions
NVIDIA releases Alias-Free GAN
https://nvlabs.github.io/alias-free-gan/
Myanmar Politician's confession could be DeepFake
https://www.wired.com/story/opinion-the-world-needs-deepfake-experts-to-stem-this-chaos/
Rembrandt restored using AI
https://www.smithsonianmag.com/smart-news/lost-edges-rembrandts-night-watch-are-restored-using-artificial-intelligence-180978056/
AI in healthcare still shaky
http://www.greenvillebusinessmag.com/2021/06/22/360303/prisma-health-announces-artificial-intelligence-partnership
https://www.theverge.com/2021/6/22/22545044/algorithm-hospital-sepsis-epic-prediction
ML interviews book
https://huyenchip.com/ml-interviews-book/
NVIDIA Canvas Beta available
https://blogs.nvidia.com/blog/2021/06/23/studio-canvas-app/
GPU prices down as China cracks down on Crypto
https://www.theregister.com/2021/06/22/as_china_shutters_cryptomining_plants/
Facebook AI's big goal of improving shopping
https://ai.facebook.com/blog/advancing-ai-to-make-shopping-easier-for-everyone/
GoogleAI releases DeepLab2
https://github.com/google-research/deeplab2
Toxic Language Model: Nobody cares
https://arxiv.org/pdf/2105.03023.pdf
AI has no common sense
https://www.analyticsinsight.net/incapable-yes-artificial-intelligence-cant-do-these-things/
https://6b.eleuther.ai/
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If
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https://www.youtube.com/watch?v=tDk10VTHwNo
#mlnews #alphacode #openai
The latest and greatest from the world of Machine Learning!
Merch: http://store.ykilcher.com
Sponsor: Weights & Biases
https://wandb.me/yannic
OUTLINE:
0:00 - Intro
0:15 - Sponsor: Weights & Biases
3:15 - DeepMind's AlphaCode: AI competitive programmer
11:30 - OpenAI uses language models to prove math theorems
14:30 - StyleGAN XL: Scaling StyleGAN to diverse datasets
16:10 - ar5iv.org displays papers as HTML5
17:40 - Helpful Things
19:30 - ICML22 Review process changes
21:15 - Meta AI tackles harmful content classification using few-shot learning
23:55 - Company claims to produce face images from DNA
References:
https://deepmind.com/blog/article/Competitive-programming-with-AlphaCode
https://alphacode.deepmind.com/#layer=18,problem=34,heads=11111111111
https://storage.googleapis.com/deepmind-media/AlphaCode/competition_level_code_generation_with_alphacode.pdf
https://twitter.com/DBahdanau/status/1489009994007674881?utm_source=pocket_mylist
https://openai.com/blog/formal-math/
https://arxiv.org/pdf/2202.01344.pdf
https://blog.eleuther.ai/announcing-20b/?utm_source=pocket_mylist
https://sites.google.com/view/stylegan-xl/
https://arxiv.org/pdf/2202.00273.pdf
https://ar5iv.org/
https://ar5iv.org/html/1910.06709
https://twitter.com/YiTayML/status/1488556619256328192?utm_source=pocket_mylist
https://ffcv.io/
https://github.com/ott-jax/ott
https://twitter.com/soumithchintala/status/1488206868573040641?utm_source=pocket_mylist
https://github.com/facebookresearch/dietgpu
https://www.reddit.com/r/MachineLearning/comments/shazv1/n_changes_in_the_icml_2022_review_process/?utm_source=pocket_mylist
https://icml.cc/Conferences/2022/ReviewForm
https://icml.cc/Conferences/2022/CallForPapers
https://ai.facebook.com/blog/harmful-content-can-evolve-quickly-our-new-ai-system-adapts-to-tackle-it/?utm_source=pocket_mylist
https://www.technologyreview.com/2022/01/31/1044576/corsight-face-recognition-from-dna/
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https://www.youtube.com/watch?v=McpjrsHrEY4
#saycan #robots #ai
This is an interview with the authors Brian Ichter, Karol Hausman, and Fei Xia.
Original Paper Review Video: https://youtu.be/Ru23eWAQ6_E
Large Language Models are excellent at generating plausible plans in response to real-world problems, but without interacting with the environment, they have no abilities to estimate which of these plans are feasible or appropriate. SayCan combines the semantic capabilities of language models with a bank of low-level skills, which are available to the agent as individual policies to execute. SayCan automatically finds the best policy to execute by considering a trade-off between the policy's ability to progress towards the goal, given by the language model, and the policy's probability of executing successfully, given by the respective value function. The result is a system that can generate and execute long-horizon action sequences in the real world to fulfil complex tasks.
OUTLINE:
0:00 - Introduction & Setup
3:40 - Acquiring atomic low-level skills
7:45 - How does the language model come in?
11:45 - Why are you scoring instead of generating?
15:20 - How do you deal with ambiguity in language?
20:00 - The whole system is modular
22:15 - Going over the full algorithm
23:20 - What if an action fails?
24:30 - Debunking a marketing video :)
27:25 - Experimental Results
32:50 - The insane scale of data collection
40:15 - How do you go about large-scale projects?
43:20 - Where did things go wrong?
45:15 - Where do we go from here?
52:00 - What is the largest unsolved problem in this?
53:35 - Thoughts on the Tesla Bot
55:00 - Final thoughts
Paper: https://arxiv.org/abs/2204.01691
Website: https://say-can.github.io/
Abstract:
Large language models can encode a wealth of semantic knowledge about the world. Such knowledge could be extremely useful to robots aiming to act upon high-level, temporally extended instructions expressed in natural language. However, a significant weakness of language models is that they lack real-world experience, which makes it difficult to leverage them for decision making within a given embodiment. For example, asking a language model to describe how to clean a spill might result in a reasonable narrative, but it may not be applicable to a particular agent, such as a robot, that needs to perform this task in a particular environment. We propose to provide real-world grounding by means of pretrained skills, which are used to constrain the model to propose natural language actions that are both feasible and contextually appropriate. The robot can act as the language model's "hands and eyes," while the language model supplies high-level semantic knowledge about the task. We show how low-level skills can be combined with large language models so that the language model prov
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https://www.youtube.com/watch?v=X4S8F3bwuuw
#dreamcoder #programsynthesis #symbolicreasoning
Classic Machine Learning struggles with few-shot generalization for tasks where humans can easily generalize from just a handful of examples, for example sorting a list of numbers. Humans do this by coming up with a short program, or algorithm, that explains the few data points in a compact way. DreamCoder emulates this by using neural guided search over a language of primitives, a library, that it builds up over time. By doing this, it can iteratively construct more and more complex programs by building on its own abstractions and therefore solve more and more difficult tasks in a few-shot manner by generating very short programs that solve the few given datapoints. The resulting system can not only generalize quickly but also delivers an explainable solution to its problems in form of a modular and hierarchical learned library. Combining this with classic Deep Learning for low-level perception is a very promising future direction.
OUTLINE:
0:00 - Intro & Overview
4:55 - DreamCoder System Architecture
9:00 - Wake Phase: Neural Guided Search
19:15 - Abstraction Phase: Extending the Internal Library
24:30 - Dreaming Phase: Training Neural Search on Fictional Programs and Replays
30:55 - Abstraction by Compressing Program Refactorings
32:40 - Experimental Results on LOGO Drawings
39:00 - Ablation Studies
39:50 - Re-Discovering Physical Laws
42:25 - Discovering Recursive Programming Algorithms
44:20 - Conclusions & Discussion
Paper: https://arxiv.org/abs/2006.08381
Code: https://github.com/ellisk42/ec
Abstract:
Expert problem-solving is driven by powerful languages for thinking about problems and their solutions. Acquiring expertise means learning these languages -- systems of concepts, alongside the skills to use them. We present DreamCoder, a system that learns to solve problems by writing programs. It builds expertise by creating programming languages for expressing domain concepts, together with neural networks to guide the search for programs within these languages. A ``wake-sleep'' learning algorithm alternately extends the language with new symbolic abstractions and trains the neural network on imagined and replayed problems. DreamCoder solves both classic inductive programming tasks and creative tasks such as drawing pictures and building scenes. It rediscovers the basics of modern functional programming, vector algebra and classical physics, including Newton's and Coulomb's laws. Concepts are built compositionally from those learned earlier, yielding multi-layered symbolic representations that are interpretable and transferrable to new tasks, while still growing scalably and flexibly with experience.
Authors: Kevin Ellis, Catherine Wong, Maxwell Nye, Mathias Sable-Meyer, Luc Cary, Lucas Mora
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https://www.youtube.com/watch?v=qtu0aSTDE2I
#ai #research #attention
Transformers, having already captured NLP, have recently started to take over the field of Computer Vision. So far, the size of images as input has been challenging, as the Transformers' Attention Mechanism's memory requirements grows quadratic in its input size. LambdaNetworks offer a way around this requirement and capture long-range interactions without the need to build expensive attention maps. They reach a new state-of-the-art in ImageNet and compare favorably to both Transformers and CNNs in terms of efficiency.
OUTLINE:
0:00 - Introduction & Overview
6:25 - Attention Mechanism Memory Requirements
9:30 - Lambda Layers vs Attention Layers
17:10 - How Lambda Layers Work
31:50 - Attention Re-Appears in Lambda Layers
40:20 - Positional Encodings
51:30 - Extensions and Experimental Comparisons
58:00 - Code
Paper: https://openreview.net/forum?id=xTJEN-ggl1b
Lucidrains' Code: https://github.com/lucidrains/lambda-networks
Abstract:
We present a general framework for capturing long-range interactions between an input and structured contextual information (e.g. a pixel surrounded by other pixels). Our method, called the lambda layer, captures such interactions by transforming available contexts into linear functions, termed lambdas, and applying these linear functions to each input separately. Lambda layers are versatile and may be implemented to model content and position-based interactions in global, local or masked contexts. As they bypass the need for expensive attention maps, lambda layers can routinely be applied to inputs of length in the thousands, en-abling their applications to long sequences or high-resolution images. The resulting neural network architectures, LambdaNetworks, are computationally efficient and simple to implement using direct calls to operations available in modern neural network libraries. Experiments on ImageNet classification and COCO object detection and instance segmentation demonstrate that LambdaNetworks significantly outperform their convolutional and attentional counterparts while being more computationally efficient. Finally, we introduce LambdaResNets, a family of LambdaNetworks, that considerably improve the speed-accuracy tradeoff of image classification models. LambdaResNets reach state-of-the-art accuracies on ImageNet while being ∼4.5x faster than the popular EfficientNets on modern machine learning accelerators.
Authors: Anonymous
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https://www.youtube.com/watch?v=3qxJ2WD8p4w
#aiia #ai #art
A trip report from the AiiA Festival in Geneva organized by the ImpactAI foundation.
OUTLINE:
0:00 - Intro
1:50 - Laura Tocmacov: The Festival
4:10 - Timothy O'Hear: The Tech
6:50 - Jonathan O'Hear: The Robot
11:50 - Cléa Chopard: The Artist
17:45 - Final Words
Website: https://aiiafestival.org/en/
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https://www.youtube.com/watch?v=xrYhDMqaa4U
#alphatensor #deepmind #ai
Matrix multiplication is the most used mathematical operation in all of science and engineering. Speeding this up has massive consequences. Thus, over the years, this operation has become more and more optimized. A fascinating discovery was made when it was shown that one actually needs less than N^3 multiplication operations to multiply to NxN matrices. DeepMind goes a step further and creates AlphaTensor, a Deep Reinforcement Learning algorithm that plays a single-player game, TensorGame, in order to find even more optimized algorithms for matrix multiplication. And it turns out, there exists a plethora of undiscovered matrix multiplication algorithms, which not only will make everything from computers to smart toasters faster, but also bring new insights into fundamental math and complexity theory.
Sponsor: Assembly AI
Link: https://www.assemblyai.com/?utm_source=youtube&utm_medium=social&utm_campaign=yannic_sentiment
OUTLINE:
0:00 - Intro
1:50 - Sponsor: Assembly AI (link in description)
3:25 - What even is Matrix Multiplication?
6:10 - A very astounding fact
8:45 - Trading multiplications for additions
12:35 - Matrix Multiplication as a Tensor
17:30 - Tensor Decompositions
20:30 - A formal way of finding multiplication algorithms
31:00 - How to formulate this as a game?
39:30 - A brief primer on AlphaZero / MCTS
45:40 - The Results
48:15 - Optimizing for different hardware
52:40 - Expanding fundamental math
53:45 - Summary & Final Comments
Paper: https://www.nature.com/articles/s41586-022-05172-4
Title: Discovering faster matrix multiplication algorithms with reinforcement learning
Abstract:
Improving the efficiency of algorithms for fundamental computations can have a widespread impact, as it can affect the overall speed of a large amount of computations. Matrix multiplication is one such primitive task, occurring in many systems—from neural networks to scientific computing routines. The automatic discovery of algorithms using machine learning offers the prospect of reaching beyond human intuition and outperforming the current best human-designed algorithms. However, automating the algorithm discovery procedure is intricate, as the space of possible algorithms is enormous. Here we report a deep reinforcement learning approach based on AlphaZero1 for discovering efficient and provably correct algorithms for the multiplication of arbitrary matrices. Our agent, AlphaTensor, is trained to play a single-player game where the objective is finding tensor decompositions within a finite factor space. AlphaTensor discovered algorithms that outperform the state-of-the-art complexity for many matrix sizes. Particularly relevant is the case of 4 × 4 matrices in a finite field, where AlphaTensor’s algorithm improves on Strassen’s two-level algorithm for the first time, to our knowledge, since its discovery 50 years ago2. We further showcase the flexibility of AlphaTensor through different use-cases: algorithms with state-of-the-art complexity for structured matrix multiplication and improved practical efficiency by optimizing matrix multiplication for runtime on specific hardware. Our results highlight AlphaTensor’s ability to accelerate the process of algorithmic discovery on a range of problems, and to optimize for different criteria.
Authors: Alhussein Fawzi, Matej Balog, Aja Huang, Thomas Hubert, Bernardino Romera-Paredes, Mohammadamin Barekatain, Alexander Novikov, Francisco J. R. Ruiz, Julian Schrittwieser, Grzegorz Swirszcz, David Silver, Demis Hassabis & Pushmeet Kohli
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https://www.youtube.com/watch?v=3N3Bl5AA5QU