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[ML News] AI learns to search the Internet | Drawings come to life | New ML journal launches
#webgpt #aiart #mlnews
The latest and greatest from the Machine Learning world.
OUTLINE:
0:00 - Intro
0:20 - Sponsor: Weights & Biases
2:40 - WebGPT: When GPT-3 can search the Internet
15:45 - MetaAI brings children's drawings to life
17:15 - OpenAI lets anyone fine-tune GPT-3
18:15 - New Journal: Transactions on Machine Learning Research
21:20 - Hugging Face buys Gradio
22:45 - Helpful Things
28:35 - NetHack Challenge winners announced
29:20 - Characters for good, created by AI
Sponsor: Weights & Biases
https://wandb.me/yannic
References:
WebGPT: When GPT-3 can search the Internet
https://openai.com/blog/improving-factual-accuracy/
https://cdn.openai.com/WebGPT.pdf
MetaAI brings children's drawings to life
https://ai.facebook.com/blog/using-ai-to-bring-childrens-drawings-to-life
https://sketch.metademolab.com/canvas
https://tech.fb.com/ai-childrens-drawings/?utm_source=Twitter&utm_medium=organic_social&utm_campaign=TECH2021H2
OpenAI lets anyone fine-tune GPT-3
https://openai.com/blog/customized-gpt3/
https://openai.com/api/pricing/
New Journal: Transactions on Machine Learning Research
https://medium.com/@hugo_larochelle_65309/announcing-the-transactions-on-machine-learning-research-3ea6101c936f
https://jmlr.org/tmlr/
Hugging Face buys Gradio
https://gradio.app/joining-huggingface/
Helpful Things
https://github.com/kakaobrain/minDALL-E
https://github.com/borisdayma/dalle-mini
https://github.com/deepmind/arnheim
https://colab.research.google.com/github/deepmind/arnheim/blob/master/arnheim_3.ipynb
http://duebenchmark.com/leaderboard
https://github.com/due-benchmark
http://duebenchmark.com/data
https://datasets-benchmarks-proceedings.neurips.cc/paper/2021/hash/069059b7ef840f0c74a814ec9237b6ec-Abstract-round2.html
https://github.com/nyu-mll/quality
https://github.com/nyu-mll/quality/blob/main/quality_preprint.pdf
https://huggingface.co/blog/perceiver
https://arxiv.org/pdf/2112.05682.pdf
https://towardsdatascience.com/deriving-convolution-from-first-principles-4ff124888028
https://ai.googleblog.com/2021/12/training-machine-learning-models-more.html
https://github.com/huawei-noah/HEBO
https://www.sberbank.com/news-and-media/press-releases/article?newsID=a26a208d-6c72-4f8a-a3b7-aefe1112cbae&blockID=7®ionID=77&lang=en&type=NEWS
https://sbercloud.ru/ru/datahub/rugpt3family/rudall-e-12b?_ga=2.169749668.48600719.1639868013-1523472348.1639868013
NetHack Challenge winners announced
https://nethackchallenge.com/report.html
Characters for good, created by AI
https://news.mit.edu/2021/ai-generated-characters-for-good-1216
Links:
TabNine Code Completion (Referral): http://bit.ly/tabnine-yannick
YouTube: https://www.youtube.com/c/yannickilcher
Twitter: https://twitter.com/ykilcher
Discord: https://discord.gg/4H8xxDF
BitChute: https://www.bitchute.com/c
...
https://www.youtube.com/watch?v=GgHXGpQ60x0
The latest and greatest from the Machine Learning world.
OUTLINE:
0:00 - Intro
0:20 - Sponsor: Weights & Biases
2:40 - WebGPT: When GPT-3 can search the Internet
15:45 - MetaAI brings children's drawings to life
17:15 - OpenAI lets anyone fine-tune GPT-3
18:15 - New Journal: Transactions on Machine Learning Research
21:20 - Hugging Face buys Gradio
22:45 - Helpful Things
28:35 - NetHack Challenge winners announced
29:20 - Characters for good, created by AI
Sponsor: Weights & Biases
https://wandb.me/yannic
References:
WebGPT: When GPT-3 can search the Internet
https://openai.com/blog/improving-factual-accuracy/
https://cdn.openai.com/WebGPT.pdf
MetaAI brings children's drawings to life
https://ai.facebook.com/blog/using-ai-to-bring-childrens-drawings-to-life
https://sketch.metademolab.com/canvas
https://tech.fb.com/ai-childrens-drawings/?utm_source=Twitter&utm_medium=organic_social&utm_campaign=TECH2021H2
OpenAI lets anyone fine-tune GPT-3
https://openai.com/blog/customized-gpt3/
https://openai.com/api/pricing/
New Journal: Transactions on Machine Learning Research
https://medium.com/@hugo_larochelle_65309/announcing-the-transactions-on-machine-learning-research-3ea6101c936f
https://jmlr.org/tmlr/
Hugging Face buys Gradio
https://gradio.app/joining-huggingface/
Helpful Things
https://github.com/kakaobrain/minDALL-E
https://github.com/borisdayma/dalle-mini
https://github.com/deepmind/arnheim
https://colab.research.google.com/github/deepmind/arnheim/blob/master/arnheim_3.ipynb
http://duebenchmark.com/leaderboard
https://github.com/due-benchmark
http://duebenchmark.com/data
https://datasets-benchmarks-proceedings.neurips.cc/paper/2021/hash/069059b7ef840f0c74a814ec9237b6ec-Abstract-round2.html
https://github.com/nyu-mll/quality
https://github.com/nyu-mll/quality/blob/main/quality_preprint.pdf
https://huggingface.co/blog/perceiver
https://arxiv.org/pdf/2112.05682.pdf
https://towardsdatascience.com/deriving-convolution-from-first-principles-4ff124888028
https://ai.googleblog.com/2021/12/training-machine-learning-models-more.html
https://github.com/huawei-noah/HEBO
https://www.sberbank.com/news-and-media/press-releases/article?newsID=a26a208d-6c72-4f8a-a3b7-aefe1112cbae&blockID=7®ionID=77&lang=en&type=NEWS
https://sbercloud.ru/ru/datahub/rugpt3family/rudall-e-12b?_ga=2.169749668.48600719.1639868013-1523472348.1639868013
NetHack Challenge winners announced
https://nethackchallenge.com/report.html
Characters for good, created by AI
https://news.mit.edu/2021/ai-generated-characters-for-good-1216
Links:
TabNine Code Completion (Referral): http://bit.ly/tabnine-yannick
YouTube: https://www.youtube.com/c/yannickilcher
Twitter: https://twitter.com/ykilcher
Discord: https://discord.gg/4H8xxDF
BitChute: https://www.bitchute.com/c
...
https://www.youtube.com/watch?v=GgHXGpQ60x0
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VIDEO
JOIN
join-me-for-the-neurips-2020-flatland
Join me to solve the NeurIPS 2020 challenge on multi-agent reinforcement learning in the flatland environment. This challenge has participants optimize a complex train scheduling system, subject to accidents, delays and re-routing. The plan is to solve this as a community with no expectations of winning and fully in the open.
Discord: https://discord.gg/4H8xxDF
Community GitHub Repo: https://github.com/yk/youtube-flatland
Neurips 2020 Flatland Challenge: https://www.aicrowd.com/challenges/neurips-2020-flatland-challenge
Flatland Environment: https://gitlab.aicrowd.com/flatland/flatland
OUTLINE:
0:00 - Intro
1:00 - The Flatland Environment
2:00 - The NeurIPS 2020 Flatland Challenge
3:20 - Let's do this as a Community
4:10 - Ground Rules
6:15 - Conclusion
Links:
YouTube: https://www.youtube.com/c/yannickilcher
Twitter: https://twitter.com/ykilcher
Discord: https://discord.gg/4H8xxDF
BitChute: https://www.bitchute.com/channel/yannic-kilcher
Minds: https://www.minds.com/ykilcher
...
https://www.youtube.com/watch?v=cvkeWwDQr0A
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VIDEO
JUST
just-chatting-(openassistant-goodbye
...
https://www.youtube.com/watch?v=S7JJ91sElQU
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video/mp4
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VIDEO
REPNE
repnet-counting-out-time-class-agnostic
Counting repeated actions in a video is one of the easiest tasks for humans, yet remains incredibly hard for machines. RepNet achieves state-of-the-art by creating an information bottleneck in the form of a temporal self-similarity matrix, relating video frames to each other in a way that forces the model to surface the information relevant for counting. Along with that, the authors produce a new dataset for evaluating counting models.
OUTLINE:
0:00 - Intro & Overview
2:30 - Problem Statement
5:15 - Output & Loss
6:25 - Per-Frame Embeddings
11:20 - Temporal Self-Similarity Matrix
19:00 - Periodicity Predictor
25:50 - Architecture Recap
27:00 - Synthetic Dataset
30:15 - Countix Dataset
31:10 - Experiments
33:35 - Applications
35:30 - Conclusion & Comments
Paper Website: https://sites.google.com/view/repnet
Colab: https://colab.research.google.com/github/google-research/google-research/blob/master/repnet/repnet_colab.ipynb
Abstract:
We present an approach for estimating the period with which an action is repeated in a video. The crux of the approach lies in constraining the period prediction module to use temporal self-similarity as an intermediate representation bottleneck that allows generalization to unseen repetitions in videos in the wild. We train this model, called RepNet, with a synthetic dataset that is generated from a large unlabeled video collection by sampling short clips of varying lengths and repeating them with different periods and counts. This combination of synthetic data and a powerful yet constrained model, allows us to predict periods in a class-agnostic fashion. Our model substantially exceeds the state of the art performance on existing periodicity (PERTUBE) and repetition counting (QUVA) benchmarks. We also collect a new challenging dataset called Countix (~90 times larger than existing datasets) which captures the challenges of repetition counting in real-world videos.
Authors: Debidatta Dwibedi, Yusuf Aytar, Jonathan Tompson, Pierre Sermanet, Andrew Zisserman
Links:
YouTube: https://www.youtube.com/c/yannickilcher
Twitter: https://twitter.com/ykilcher
Discord: https://discord.gg/4H8xxDF
BitChute: https://www.bitchute.com/channel/yannic-kilcher
Minds: https://www.minds.com/ykilcher
...
https://www.youtube.com/watch?v=qSArFEIoSbo
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video/mp4
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Controlling
VIDEO
[RANT
rant-the-male-only-history-of-deep
This casting of our field in terms of ideological narrow-sighted group-think is disgusting. Keep Science about ideas!
https://twitter.com/timnitGebru/status/1252752743942328321
Links:
YouTube: https://www.youtube.com/c/yannickilcher
Twitter: https://twitter.com/ykilcher
BitChute: https://www.bitchute.com/channel/yannic-kilcher
Minds: https://www.minds.com/ykilcher
...
https://www.youtube.com/watch?v=yPjuAo53uNI
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VIDEO
[ML N
ml-news-stanford-hai-coins-foundation
#plagiarism #foundationmodels #tesla
The best place to keep up to date with the latest and greatest from the ML world!
OUTLINE:
0:00 - Intro & Sponsor
3:15 - A high-profile case of plagiarism shocks the ML world
11:55 - Stanford AI releases paper on "Foundation Models"
19:45 - Updates on Apple's NeuralHash
20:45 - RL control for two-player splorts
21:45 - Tesla's AI Day
23:55 - COMMA THREE announced
24:40 - Intel winding down RealSense cameras
25:20 - IBM unveils Telum Processor
25:50 - Lux AI Challenge & Neural MMO Challenge
26:50 - Dribnet's CLIP PixelArt
27:40 - Multi-Agent RL papers are mostly fake
28:50 - I can't even come up with a segment title
29:25 - AI News Questions
31:20 - Frameworks & Libraries
Sponsor: Weights & Biases
https://wandb.ai
References:
Plagiarism case shocks ML world
https://arxiv.org/abs/2102.07870v1
https://arxiv.org/pdf/2102.07870v1.pdf
https://arxiv.org/abs/2108.05862
https://arxiv.org/pdf/2108.05862v1.pdf
https://www.reddit.com/r/MachineLearning/comments/p59pzp/d_imitation_is_the_sincerest_form_of_flattery/
https://michaelsdr.github.io/momentumnet/plagiarism/
https://www.zhihu.com/question/480075870/answer/2065820430?utm_source=pocket_mylist
https://zhuanlan.zhihu.com/p/400351960?utm_source=pocket_mylist
https://finance.sina.com.cn/tech/2021-08-17/doc-ikqciyzm1956801.shtml?utm_source=pocket_mylist
https://duoli.org/
https://web.archive.org/web/20210816025239/http://duoli.org/
https://twitter.com/shaohua0116/status/1427324015723487256/photo/1
Stanford AI targets Foundation Models
https://arxiv.org/abs/2108.07258
https://arxiv.org/pdf/2108.07258.pdf
https://ieeexplore.ieee.org/document/5206848
https://xgboost.readthedocs.io/en/latest/
https://en.wikipedia.org/wiki/Support-vector_machine
https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html
https://syncedreview.com/2019/06/27/the-staggering-cost-of-training-sota-ai-models/
https://openai.com/blog/better-language-models/
NeuralHash Saga Continues
https://www.reddit.com/r/MachineLearning/comments/p8q27o/p_run_neuralhash_in_your_browser/?utm_source=pocket_mylist
https://blog.roboflow.com/neuralhash-collision/
https://www.kron4.com/news/bay-area/bay-area-doctor-had-2000-child-pornography-images-and-videos-federal-complaint-alleges/
RL Control for competitive sports
https://ai.facebook.com/research/publications/control-strategies-for-physically-simulated-characters-performing-two-player-competitive-sports?utm_source=pocket_mylist
Tesla AI Day
https://www.youtube.com/watch?v=ABbDB6xri8o
https://spectrum.ieee.org/elon-musk-robot
https://www.youtube.com/watch?v=j0z4FweCy4M&t=4057s
George Hotz announces COMMA THREE
https://www.youtube.com/watch?v=jJn2OzOLIzo
https://comma.ai/shop/products/three
Intel abandons RealSense cameras
http
...
https://www.youtube.com/watch?v=tunf2OunOKg
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VIDEO
OPEN
open-assistant-inference-backend
#ai #huggingface #coding
Join me as I build streaming inference into the Hugging Face text generation server, going through cuda, python, rust, grpc, websockets, server-sent events, and more...
Original repo is here: https://github.com/huggingface/text-generation-inference
OpenAssistant repo is here: https://github.com/LAION-AI/Open-Assistant (see inference/)
Check out https://www.wandb.courses/ for free MLOps courses!
Links:
Homepage: https://ykilcher.com
Merch: https://ykilcher.com/merch
YouTube: https://www.youtube.com/c/yannickilcher
Twitter: https://twitter.com/ykilcher
Discord: https://ykilcher.com/discord
LinkedIn: https://www.linkedin.com/in/ykilcher
If you want to support me, the best thing to do is to share out the content :)
If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this):
SubscribeStar: https://www.subscribestar.com/yannickilcher
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VIDEO
LEDEE
ledeepchef-deep-reinforcement-learning
The AI cook is here! This agent learns to play a text-based game where the goal is to prepare a meal according to a recipe. Challenges? Many! The number of possible actions is huge, ingredients change and can include ones never seen before, you need to navigate rooms, use tools, manage an inventory and sequence everything correctly and all of this from a noisy textual description that the game engine throws at you. This paper mixes supervised explicit training with reinforcement learning in order to solve this task.
Abstract:
While Reinforcement Learning (RL) approaches lead to significant achievements in a variety of areas in recent history, natural language tasks remained mostly unaffected, due to the compositional and combinatorial nature that makes them notoriously hard to optimize. With the emerging field of Text-Based Games (TBGs), researchers try to bridge this gap. Inspired by the success of RL algorithms on Atari games, the idea is to develop new methods in a restricted game world and then gradually move to more complex environments. Previous work in the area of TBGs has mainly focused on solving individual games. We, however, consider the task of designing an agent that not just succeeds in a single game, but performs well across a whole family of games, sharing the same theme. In this work, we present our deep RL agent--LeDeepChef--that shows generalization capabilities to never-before-seen games of the same family with different environments and task descriptions. The agent participated in Microsoft Research's "First TextWorld Problems: A Language and Reinforcement Learning Challenge" and outperformed all but one competitor on the final test set. The games from the challenge all share the same theme, namely cooking in a modern house environment, but differ significantly in the arrangement of the rooms, the presented objects, and the specific goal (recipe to cook). To build an agent that achieves high scores across a whole family of games, we use an actor-critic framework and prune the action-space by using ideas from hierarchical reinforcement learning and a specialized module trained on a recipe database.
Authors: Leonard Adolphs, Thomas Hofmann
https://arxiv.org/abs/1909.01646
...
https://www.youtube.com/watch?v=69IjNZaoeao
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VIDEO
POPUL
population-based-search-and-open-ended
Comments on the ICML2019 tutorial on population-based search and open-ended learning.
Talk: https://www.facebook.com/icml.imls/videos/481758745967365/
Slides: http://www.cs.uwyo.edu/~jeffclune/share/2019_06_10_ICML_Tutorial.pdf
Book: https://www.amazon.com/dp/B00X57B4JG/
Event: https://icml.cc/Conferences/2019/ScheduleMultitrack?event=4336
...
https://www.youtube.com/watch?v=TFiZYA_JfJs
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VIDEO
I BUI
i-built-the-most-expensive-cpu-ever!
#gpt4 #promptengineering #compiler
I have built the most expensive CPU ever by compiling arbitrary C code to LLVM intermediate representation (LLVM-IR), then sending every single instruction to OpenAI's GPT API.
A true marvel of engineering.
Check out course: https://wandb.me/yannic-course
Repo: https://github.com/yk/llmvm
OUTLINE:
0:00 - Intro
0:55 - Sponsor Time
2:20 - FizzBuzz
4:05 - Computers 101
6:30 - Building a CPU on top of a large language model
7:40 - Compilers & LLVM
10:05 - From parsers to virtual machines
12:10 - GPTVM - A VM powered by GPT
15:55 - Snek!
18:40 - Going beyond AGI - Introducing Chad GPT VM
Links:
Homepage: https://ykilcher.com
Merch: https://ykilcher.com/merch
YouTube: https://www.youtube.com/c/yannickilcher
Twitter: https://twitter.com/ykilcher
Discord: https://ykilcher.com/discord
LinkedIn: https://www.linkedin.com/in/ykilcher
If you want to support me, the best thing to do is to share out the content :)
If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this):
SubscribeStar: https://www.subscribestar.com/yannickilcher
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Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq
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Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n
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