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We got lot's of fine messages and feedback, let's discuss most notable papers and news of 2021 to assemble Community 2021 WrapUp in our chat:

https://t.iss.one/datascience_chat

2021 WrapUps and Summaries


Those are two technical posts summarizing the progress which were published during 2021.

Papers with Code 2021 : A Year in Review post by Papers with Code

Medium: https://medium.com/paperswithcode/papers-with-code-2021-a-year-in-review-de75d5a77b8b

Post on KDNuggers
Post: https://www.kdnuggets.com/2021/12/2021-year-review-amazing-ai-papers.html

#summary

The Illustrated Retrieval Transformer
by @jayalammar

The latest batch of language models can be much smaller yet achieve GPT-3 like performance by being able to query a database or search the web for information. A key indication is that building larger and larger models is not the only way to improve performance.


https://jalammar.github.io/illustrated-retrieval-transformer/

#nlp #gpt3 #retro #deepmind

🦜 Hi!

We are the first Telegram Data Science channel.

Channel was started as a collection of notable papers, news and releases shared for the members of Open Data Science (ODS) community. Through the years of just keeping the thing going we grew to an independent online Media supporting principles of Free and Open access to the information related to Data Science.


Ultimate Posts

* Where to start learning more about Data Science. https://github.com/open-data-science/ultimate_posts/tree/master/where_to_start
* @opendatascience channel audience research. https://github.com/open-data-science/ods_channel_stats_eda


Open Data Science

ODS.ai is an international community of people anyhow related to Data Science.

Website: https://ods.ai



Hashtags

Through the years we accumulated a big collection of materials, most of them accompanied by hashtags.

#deeplearning #DL — post about deep neural networks (> 1 layer)
#cv — posts related to Computer Vision. Pictures and videos
#nlp #nlu — Natural Language Processing and Natural Language Understanding. Texts and sequences
#audiolearning #speechrecognition — related to audio information processing
#ar — augmeneted reality related content
#rl — Reinforcement Learning (agents, bots and neural networks capable of playing games)
#gan #generation #generatinveart #neuralart — about neural artt and image generation
#transformer #vqgan #vae #bert #clip #StyleGAN2 #Unet #resnet #keras #Pytorch #GPT3 #GPT2 — related to special architectures or frameworks
#coding #CS — content related to software engineering sphere
#OpenAI #microsoft #Github #DeepMind #Yandex #Google #Facebook #huggingface — hashtags related to certain companies
#productionml #sota #recommendation #embeddings #selfdriving #dataset #opensource #analytics #statistics #attention #machine #translation #visualization


Chats

- Data Science Chat https://t.iss.one/datascience_chat
- ODS Slack through invite form at website

ODS resources

* Main website: https://ods.ai
* ODS Community Telegram Channel (in Russian): @ods_ru
* ML trainings Telegram Channel: @mltrainings
* ODS Community Twitter: https://twitter.com/ods_ai

Feedback and Contacts

You are welcome to reach administration through telegram bot: @opendatasciencebot

Highly accurate protein structure prediction with AlphaFold
Anna Potapenko @DeepMind

Predicting a protein’s structure from its primary sequence has been a grand challenge in biology for the past 50 years, holding the promise to bridge the gap between the pace of genomics discovery and resulting structural characterization. In this talk, we will describe work at DeepMind to develop AlphaFold, a new deep learning-based system for structure prediction that achieves high accuracy across a wide range of targets. We demonstrated our system in the 14th biennial Critical Assessment of Protein Structure Prediction (CASP14) across a wide range of difficult targets, where the assessors judged our predictions to be at an accuracy “competitive with experiment” for approximately 2/3rds of proteins. The talk will cover both the underlying machine learning ideas and the implications for biological research.


https://youtu.be/oD34Q1qeMII

Using public datasets in commercial software

Great paper: Can I use this publicly available dataset to build commercial AI software? Most likely not.

An extremely important question for the Data Science community: it turns out (wat?) that not all publicly available datasets can be used to build commercial solutions 💣💣💣.

Authors examine license agreements of 6 popular datasets used in Computer Vision (CIFAR-10, ImageNet, Cityscapes, FFHQ, VGGFaces2 and MS COCO) and conclude that the models trained on these data can not be commercialized at least.

An example of the results of the CIFAR-10 dataset's license analysis is shown in the screenshot.

It was logical to assume this, but for many community members this may be the opening of the century.

Prepared by @codemining for ods.ai. Subscribe!

​​There had been less posts than usual as you might have noticed, only because editor-in-chief's (mine) attention been directed to DeFi space in general and NFT in particular.

However once involved with the beauty of AI and art, one can't just exit it, so I've been working on an NFT-collection drop as an exercise to study the field. Because as Feynman taught us 'What I cannot create, I do not understand'.

After getting some early results with #StyleGAN2 I realised that I need to study how drop mechanics and everything else works and asked a very talented friend to help me with generating the art for the collection. On my humble scale, he is top 0.1% researcher in the field (though he might argue with that, or you might, but let's wait for his scientific papers published here to judge on that), so I was sure that the art will be dope.

And it was so great we decided to make a real NFT drop instead of purely experimental one. Soon we will publish all the intersting technical details on the architecture and approache we used to generate beautiful toadz. So stay tuned for the post on how to generate astonishing art and subsribe to the collection twitter not to miss any details.

Twitter: https://twitter.com/toadverseNFT

Scholarships in UAE

MBZUAI in Abu Dhabi is a modern and innovative university in Artificial Intelligence. All admitted candidates are granted a full scholarship, which includes: tuition fee, accommodation, health insurance, round air transportation. Additionally students get a monthly stipend of 8,000 DH (USD $2200) for MSc and 10,000 DH (USD $2700) for PhD.

So, if you are interested, welcome to join MBZUAI info session with current MBZUAI students from Ukraine and Kazakhstan: Friday, Jan 21 at 5pm Moscow time / 4pm of Kyiv time. (Session is today)

MBZUAI website: https://mbzuai.ac.ae/
Info session Zoom: https://tinyurl.com/MBZUAI-Ukraine

ML and NLP Research Highlights of 2021

by Sebastian Ruder

https://ruder.io/ml-highlights-2021/

what we know about the beginning of february

* alpha-code -- a system that can compete at average human level in competitive coding competitions like codeforces. an exciting leap in ai problem-solving capabilities, combining many advances in machine learning

link: https://deepmind.com/blog/article/Competitive-programming-with-AlphaCode

* solving some formal mathematics statement curriculum learning -- neural network that solved two problems from the international math olympiad

link: https://openai.com/blog/formal-math/

* "hyperdetailed render of my bizarro acid trip, detailed architectural render. I can't believe how detailed this is"

link: https://twitter.com/Somnai_dreams/status/1489108710962384896

AIModel-Mutator: Finding Vulnerabilities in TensorFlow

Another current study on the security of machine learning models, and information on how framework bugs (such as Tensorflow) can affect it. For example, from 2019 to 2021, the number of CVEs for TF increased 15 times.

Qian Feng, a senior security researcher at Baidu Security talks about the important work they did with their colleagues.

As we know, it's pretty easy to corrupt models, they freely distributed and without any additional checks, so short deep dive into the problem in this video: https://www.youtube.com/watch?v=7QqbJRZ6CxU.

Prepared by @codemining.

GLIDE for image augmentation aka ToadVerse technical details

Technical details on how we used GLIDE for image augmentation.

Article Link: https://mirror.xyz/kefirski.eth/XN1cV27uHcAjN_tPSc_ckgSz4B3Nfh5l5HH9lRs9xEE

#GAN #StyleGAN2 #GLIDE #art #art_generation

​​Simple book about #ML — Machine Learning Simplified

The main purpose of the book is to build an intuitive understanding of how algorithms work through basic examples. In order to understand the presented material, it is enough to know basic mathematics and linear algebra.

After reading this book, you will know the basics of supervised learning, understand complex mathematical models, understand the entire pipeline of a typical ML project, and also be able to share your knowledge with colleagues from related industries and with technical professionals.

And for those who find the theoretical part not enough - the book is supplemented with a repository on GitHub, which has Python implementation of all methods and algorithms described in chapters.

Book is absolutely free to read.

Link: themlsbook.com

#wheretostart #book

AlphaCode Explained: AI Code Generation

AlphaCode is DeepMind's new massive language model for generating code. It is similar to OpenAI Codex, except for in the paper they provide a bit more analysis. The field of NLP within AI and ML has exploded get a lot more papers all the time. This video can help you understand how AlphaCode works and what some of the key takeaways are.


youtube: https://www.youtube.com/watch?v=t3Yh56efKGI
blog post: https://deepmind.com/blog/article/Competitive-programming-with-AlphaCode
paper: https://storage.googleapis.com/deepmind-media/AlphaCode/competition_level_code_generation_with_alphacode.pdf

🐙 OCTIS : Optimizing and Comparing Topic Models is Simple!

Github: https://github.com/mind-Lab/octis

Paper: https://arxiv.org/abs/2202.07631v1

Dataset: https://paperswithcode.com/dataset/20-newsgroups

@ai_machinelearning_big_data

One Voice Detector to Rule Them All

A brief English article about our VAD got released on The Gradient!

Please follow the link to learn:

- Which values we did pursue;
- Why we decided to create our own VAD;
- Which criteria and metrics we optimized;
- A brief overview of what is available in general;
- How it compares with well-established and similar class solutions;

Links:

- The article https://thegradient.pub/one-voice-detector-to-rule-them-all/
- The VAD is always available on Github (please give us a ⭐️) here - https://github.com/snakers4/silero-vad

PS

- Also new features probably will be reserved for later quarters, but you can vote here
- Also you can find a Russian article here

How Useful is Self-Supervised Pretraining for Visual Tasks?

A relatively old paper (CVPR2020), by our fast life standards. Nevertheless, it has a pair of practical takeaways.

Authors created a synthetic dataset with several degrees of freedom to vary difficulty. It varies from almost monochrome objects to randomized textures and positioning on image.

The target was to compare how good different self-supervised approaches help to tune for different downstream tasks. From classification to depth estimation.

Two practical takeways are:
1. The self-supervised method utility is wildly dependent on task, markup amount and even data complexity.
2. A linear evaluation score, so popular in papers, has almost no correlation with actual fine-tuning results.

Authors found out, that there is no improvement by self-supervised training when lots of labeled data presented (which became kinda well known since then). Based on this, they hypothesise, that improvement of SSL pre-training is rather kind of a regularization than optimization. That is, SSL pre-training helps to find wider optimum, not better. Though, to claim this, some kind of loss plane investigation would be more helpful.

Source: here

Models based on graphs are quite important for a lot of tasks in NLP. There is an overview from Michael Bronstein about what he is expecting for upcoming year for the Graph ML field:

1. Geometry becomes increasingly important in ML.
2. Message passing is still the dominant paradigm in GNNs.
3. Differential equations give rise to new GNN architectures.
4. Old ideas from Signal Processing, Neuroscience, and Physics get a new life.
5. Modeling complex systems requires going beyond graphs.
6. Reasoning, axiomatisation, and generalisation are still big open questions in Graph ML.
7. Graphs become increasingly popular in Reinforcement Learning, but probably still have a way to go.
8. AlphaFold 2 is a triumph of Geometric ML and a paradigm shift in structural biology.
9. Drug discovery and design benefits from GNNs and their confluence with Transformers.
10. AI-first drug discovery is increasingly using Geometric and Graph ML.
11. Quantum ML benefits from graph-based methods.

[link]