RL + NLP + Minecraft = Awesomeness

The video from Data Fest Online 2021 about IGLU Competition which was accepted at competition track of NeurIPS 2021

Link: https://youtu.be/mbDY8uxk9bs

New Coding Assistant Tool From OpenAI and Microsoft

Github announced new tool for improving coding experience: Github's copilot, developed with Microsoft and OpenAI's help. This looks really promosing, at least from the announce perspective: imaging just typing convert_datetime_to_date and getting function for that. Looking forward to the actual demo.

Project: https://copilot.github.com
Blog entry: https://github.blog/2021-06-29-introducing-github-copilot-ai-pair-programmer/
CNBC news post: https://www.cnbc.com/2021/06/29/microsoft-github-copilot-ai-offers-coding-suggestions.html

#OpenAI #microsoft #coding #CS #computerlanguageunderstanding #CLU #Github

GitHub's Copilot #demo

@opendatascience

MMPX Style-Preserving Pixel Art Magnification

Work on #pixel graphics resolution upscale. Hopefully we will get all the classic games auto-remastered someday.

Publication: https://www.jcgt.org/published/0010/02/04/
Article: https://www.jcgt.org/published/0010/02/04/paper.pdf

#CV #superresolution #upscale

Habitat 2.0: Training home assistant robots with faster simulation and new benchmarks

Facebook released a new simulation platform to train robots in. Yeah, virtual robots in virtual environment, which can be a real space replica. This work brings us closer to domestic use of assistive robots.

Project website: https://ai.facebook.com/blog/habitat-20-training-home-assistant-robots-with-faster-simulation-and-new-benchmarks
Paper: https://ai.facebook.com/research/publications/habitat-2.0-training-home-assistants-to-rearrange-their-habitat

#Facebook #DigitalTwin #VR #RL #assistiverobots

Cloud-Native MLOps Framework

In this video, Artem Koval, Big Data and Machine Learning Practice Lead at Clear Scale, will analyse the requirements for modern MLOps and the main trends: Human-Centered AI, Fairness, Explainability, Model Monitoring, Human Augmented AI.

Link: https://youtu.be/K8s6dD7TPH4

FEDOT - AutoML framework for composite pipelines

FEDOT is an open-source framework for automated modeling and machine learning (AutoML). It can build custom modeling pipelines for different real-world processes in an automated way using an evolutionary approach. FEDOT supports classification (binary and multiclass), regression, clustering, and time series prediction tasks, as well as different data types and multi-modal cases. Also, sensitivity analysis of the pipelines, custom pipelines design as the initial assumption of optimization, domain-specific objective functions, and other interesting features are implemented.

Github: https://github.com/nccr-itmo/FEDOT

Preprint: https://arxiv.org/abs/2106.15397

Intro: https://www.youtube.com/watch?v=RjbuV6i6de4

Thank all of you 44 666 for your support!

Under the Boot of Google and Facebook and How to Crack it for better Performance

In this video, Alex Farseev from SoMin.ai will shed the light into the complex Digital Advertising ecosystem and will show you techniques, such as Long-Tail targeting, that we use in to crack the Ad Performance.

Link: https://youtu.be/p7wT_4Lf3Ks

New Language Classifier For 116 Languages

- 116 languages (83% accuracy), 77 language groups (87% accuracy)
- Mutually intelligible languages are united into language groups (i.e. Serbian + Croatian + Bosnian)
- Trained on approx 20k hours of data (10k of which are for 5 most popular languages)
- 1.7M params

Shortcomings

- Predictably, related and mutually intelligible languages are hard to tell apart
- The confusion matrix mostly makes sense, except for low resource languages and English
- English has the lowest accuracy
- Dataset needs some further curation (i.e. remove hardly spoken or artificial languages)
- Make a model larger

Link

- https://github.com/snakers4/silero-vad

Automated Machine Learning Library

Simple but powerful Automated Machine Learning library for tabular data. It uses efficient in-memory SAP HANA algorithms to automate routine Data Science tasks. Beats built-in solution in HANA, database from SAP. Written by 2 students as diploma project.

Features:
• Easy to use Python interface
• Automates most Machine Learning steps
• Complete documentation
• Intuitive web client
• Supports Regression and Binary Classification tasks

Roadmap:
• Text classification
• Multi class classification
• Forecasting
• Automate all ML steps
• Beat other libraries in accuracy
• More hyperparameter tuning methods


GitHub: https://github.com/dan0nchik/SAP-HANA-AutoML
Web app: https://share.streamlit.io/dan0nchik/sap-hana-automl/main/web.py
Docs: https://sap-hana-automl.readthedocs.io/en/latest/index.html#
Authors: @dan0nchik, @m_whiskas

#automl