Data Science by ODS.ai 🦜

08 Apr 2023 07:00

​​Segment Anything

The Segment Anything project aims to democratize image segmentation in computer vision, a core task used across various applications such as scientific imagery analysis and photo editing. Traditionally, accurate segmentation models require specialized expertise, AI training infrastructure, and large amounts of annotated data. This project introduces a new task, dataset, and model for image segmentation to overcome these challenges and make segmentation more accessible.

The researchers are releasing the Segment Anything Model (SAM) and the Segment Anything 1-Billion mask dataset (SA-1B), the largest segmentation dataset to date. These resources will enable a wide range of applications and further research into foundational models for computer vision. The SA-1B dataset is available for research purposes, while the SAM is provided under the permissive Apache 2.0 open license. Users can explore the demo to try SAM with their own images.

Paper link: https://arxiv.org/abs/2304.02643

Code link: https://github.com/facebookresearch/segment-anything

Demo link: https://segment-anything.com/demo

Blogpost link: https://ai.facebook.com/blog/segment-anything-foundation-model-image-segmentation/

Dataset link: https://ai.facebook.com/datasets/segment-anything/

A detailed unofficial overview of the paper: https://andlukyane.com/blog/paper-review-sam

#deeplearning #cv #pytorch #imagesegmentation #dataset

Data Science by ODS.ai 🦜

06 Apr 2023 17:08

Hey, let’s see how many of us have some Data Science-related vacancies to share. Please submit them through Google Form.

Best vacancies may be published in this channel.

Google Form: link.

#ds_jobs

Data Science by ODS.ai 🦜

04 Apr 2023 19:42

Kandinsky 2.1
by Sber & AIRI

The main features:

- 3.3B parameters
- generation resolution - 768x768
- image prior transformer
- new MoVQ image autoencoder
- doing a cleaner set of 172M text-image pairs
- work modes: generate by text, blend image, generate images by pattern, change images by text, inpainting/outpainting

The FID on the COCO_30k dataset reaches 8.21

Few posts where compare Kandinsky 2.1 with another similar models

- /channel/dushapitona/643
- /channel/antidigital/6153


Habr: https://habr.com/ru/companies/sberbank/articles/725282/
Telegram-bot: /channel/kandinsky21_bot
ruDALL-E: https://rudalle.ru/
MLSpace: https://sbercloud.ru/ru/datahub/rugpt3family/kandinsky-2-1
FusionBrain: https://fusionbrain.ai/diffusion

Data Science by ODS.ai 🦜

03 Apr 2023 23:53

Stanford 2023 AI Index Report is published!

The section on machine translation is based on Intento data as usual :)

https://aiindex.stanford.edu/report/

Data Science by ODS.ai 🦜

03 Apr 2023 15:01

Reliable ML track at Data Fest Online 2023
Call for Papers

Friends, we are glad to inform you that the largest Russian-language conference on Data Science - Data Fest - from the Open Data Science community will take place in 2023 (at the end of May).

And it will again have a section from Reliable ML community. We are waiting for your applications for reports: write directly to me or Dmitry.

Track Info

The concept of Reliable ML is about what to do so that the result of the work of data teams would be, firstly, applicable in the business processes of the customer company and, secondly, brought benefits to this company.

For this you need to be able to:

- correctly build a portfolio of projects (#business)
- think over the system design of each project (#ml_system_design)
- overcome various difficulties when developing a prototype (#tech #causal_inference #metrics)
- explain to the business that your MVP deserves a pilot (#interpretable_ml)
- conduct a pilot (#causal_inference #ab_testing)
- implement your solution in business processes (#tech #mlops #business)
- set up solution monitoring in the productive environment (#tech #mlops)

If you have something to say on the topics above, write to us! If in doubt, write anyway. Many of the coolest reports of previous Reliable ML tracks have come about as a result of discussion and collaboration on the topic.

If you are not ready to make a report but want to listen to something interesting, you can still help! Repost to a relevant community / forward to a friend = participate in the creation of good content.

Registration and full information about Data Fest 2023 is here.

@Reliable ML

Data Science by ODS.ai 🦜

02 Apr 2023 10:13

Complexity Explorables

Another collection of interactive explorable explanations of complex systems in biology, physics, mathematics, social sciences, epidemiology, ecology

Link: https://www.complexity-explorables.org
The emergence of communities in weighted networks: https://www.complexity-explorables.org/explorables/jujujajaki-networks/

#interactive #demo #systems #explanations

Data Science by ODS.ai 🦜

31 Mar 2023 21:38

🕊Twitter Recommendation Algorithm

#Twitter disclosed the sources of its recommendation engine.

GitHub: https://github.com/twitter/the-algorithm
Blog post: https://blog.twitter.com/engineering/en_us/topics/open-source/2023/twitter-recommendation-algorithm

#recommenders #recsys #recommendation

Data Science by ODS.ai 🦜

30 Mar 2023 09:56

Adobe does image generation

> Adobe announced a beta of Firefly, a generative ML tool for making images, Unlike MidJourney or Stable Diffusion (or Bing) this looks a lot more like an actual product - instead of typing 50-100 works into a box trying to refine your results, there are GUI tools and settings. It also has a much more clearly-defined set of training data - note that Getty is suing Stable Diffusion for training on its images without permission. In more normal times this would be a huge story - now it’s only half way down the page.

https://firefly.adobe.com/?ref=lore.ghost.io

This really looks like a product. Also numerous tags and knobs are probably sourced from internal Adobe data.

Lots of networks here - upscaling, cycle-gan like domain transfers, inpainting, editing, plain generation, etc

I understand that their demos are probably cherry picked af, but proper product work is evident. Also probably this shows the real niche these tools are meant to occupy. Not the "AGI".

Also evident that the data requirements and scale to pull this off are huge.

Data Science by ODS.ai 🦜

28 Mar 2023 23:45

My experience with PyTorch 2.0 so far:

[1] - packaging?
[2] - compilation errors

We will test other models as well.

Data Science by ODS.ai 🦜

27 Mar 2023 09:07

Do large language models need sensory grounding for meaning and understanding?

TLDR: Yes

Slides from philosophical debate by Yann LeCun, who claimed Auto-Regressive LLMs are exponentially diverging diffusion processes.


#LLM #YanLeCun

Data Science by ODS.ai 🦜

20 Mar 2023 14:04

Tracking the Fake GitHub Star Black Market with Dagster, dbt and BigQuery

This is a simple Dagster project to analyze the number of fake GitHub stars on any GitHub repository:
https://github.com/dagster-io/fake-star-detector

Data Science by ODS.ai 🦜

17 Mar 2023 20:47

In the meantime, some slides from my talks on NLP in 2022

https://docs.google.com/presentation/d/1m7Wpzaowbvi2je6nQERXyfQ0bzzS0dD0OArWznfOjHE/edit

Data Science by ODS.ai 🦜

14 Mar 2023 00:06

⏩ OpenOccupancy: A Large Scale Benchmark for Surrounding Semantic Occupancy Perception.

OpenOccupancy first surrounding semantic occupancy perception benchmar.

🖥 Github: https://github.com/jeffwang987/openoccupancy

⏩ Paper: https://arxiv.org/abs/2303.03991v1

⭐️ Dataset: https://paperswithcode.com/dataset/synthcity

💨 Project: https://www.mmlab-ntu.com/project/styleganex/

ai_machinelearning_big_data

Data Science by ODS.ai 🦜

09 Mar 2023 05:28

​​PaLM-E: An Embodied Multimodal Language Model

In this paper, the authors introduce the concept of "embodied language models," which integrate real-world sensory information with language processing. This integration enables the models to perform tasks related to robotics and perception seamlessly.

To achieve this, the models are trained end-to-end using a large language model and multiple sensory inputs, including visual and textual information. These models can tackle complex tasks such as sequential robotic manipulation planning, visual question answering, and captioning. The results of evaluations demonstrate the effectiveness of this approach, including positive transfer across different domains.

The flagship model, PaLM-E-562B, is the crown jewel of this research. It excels in robotics tasks and delivers state-of-the-art performance on OK-VQA. Despite its specialization in robotics, this model maintains its generalist language capabilities.

Paper: https://arxiv.org/abs/2303.03378

Project link: https://palm-e.github.io/

A detailed unofficial overview of the paper: https://andlukyane.com/blog/paper-review-palme

#deeplearning #nlp #transformer #sota #languagemodel #robotics

Data Science by ODS.ai 🦜

06 Mar 2023 10:29

​​In-Context Instruction Learning

The authors introduce a novel approach called In-Context Instruction Learning (ICIL), which greatly enhances zero-shot task generalization performance for both pretrained and instruction-fine-tuned models. ICIL employs a single fixed prompt to evaluate all tasks, which is a concatenation of cross-task demonstrations. The authors demonstrate that even the most powerful instruction-fine-tuned baseline (text-davinci-003) benefits from ICIL by 9.3%, indicating that the effect of ICIL is complementary to instruction-based fine-tuning.

Paper: https://arxiv.org/abs/2302.14691

Code: https://github.com/seonghyeonye/ICIL

A detailed unofficial overview of the paper: https://andlukyane.com/blog/paper-review-icil

#deeplearning #nlp #transformer #sota #languagemodel

Data Science by ODS.ai 🦜

07 Apr 2023 22:05

Tabby: Self-hosted AI coding assistant

Self-hosted AI coding assistant. An opensource / on-prem alternative to GitHub Copilot.

- Self-contained, with no need for a DBMS or cloud service
- Web UI for visualizing and configuration models and MLOps.
- OpenAPI interface, easy to integrate with existing infrastructure.
- Consumer level GPU supports (FP-16 weight loading with various optimization).

Data Science by ODS.ai 🦜

04 Apr 2023 21:38

Rask — service for AI-supported video localization

TLDR: Service which allows to translate video end-to-end between languages.

Rask AI offers voice cloning capabilities to make your voice part of your brand, although it has a library of natural and human-like voices to choose from. They currently support the output of videos in the following languages: German, French, Spanish, Chinese, English, and Portuguese, regardless of the source language.

In the near future, a team plans to offer additional services such as captions and subtitles and increase the number of supported languages up to 60 languages.

They haven’t raised any funds for the current setup and currently are launched on the Product Hunt. You are welcome to support them via link below (we all know how important it is for founders, right?).

Website: https://www.rask.ai/
ProductHunt: https://www.producthunt.com/posts/rask-ai-video-localization-dubbing-app

#producthunt #aiproduct #localization

Data Science by ODS.ai 🦜

04 Apr 2023 14:50

Pandas v2.0.0

The main enhancements:

- installing optional dependencies with pip extras
- index can now hold numpy numeric dtypes
- argument dtype_backend, to return pyarrow-backed or numpy-backed nullable dtypes
- copy-on-write improvements
- ..
+ other notable bug fixes

Full list of changes: https://pandas.pydata.org/docs/whatsnew/v2.0.0.html

Data Science by ODS.ai 🦜

03 Apr 2023 16:18

​​BloombergGPT: A Large Language Model for Finance

The realm of financial technology involves a wide range of NLP applications, such as sentiment analysis, named entity recognition, and question answering. Although Large Language Models (LLMs) have demonstrated effectiveness in various tasks, no LLM specialized for the financial domain has been reported so far. This work introduces BloombergGPT, a 50-billion-parameter language model trained on an extensive range of financial data. The researchers have created a massive 363-billion-token dataset using Bloomberg's data sources, supplemented with 345 billion tokens from general-purpose datasets, potentially creating the largest domain-specific dataset to date.

BloombergGPT has been validated on standard LLM benchmarks, open financial benchmarks, and a suite of internal benchmarks that accurately reflect its intended usage. The mixed dataset training results in a model that significantly outperforms existing models on financial tasks without sacrificing performance on general LLM benchmarks. The paper also discusses modeling choices, training processes, and evaluation methodology. As a next step, the researchers plan to release training logs (Chronicles) detailing their experience in training BloombergGPT.

Paper: https://arxiv.org/abs/2303.17564

A detailed unofficial overview of the paper: https://andlukyane.com/blog/paper-review-bloomberggpt

#deeplearning #nlp #transformer #sota #languagemodel #finance

Data Science by ODS.ai 🦜

02 Apr 2023 18:28

When you stack enough layers, them can explain the meme about stacking more layers.

#memelearning

Data Science by ODS.ai 🦜

01 Apr 2023 18:18

CodeGeeX: A Pre-Trained Model for Code Generation with Multilingual Evaluations on HumanEval-X

CodeGeeX is a multilingual model with 13 billion parameters for code generation. It is pre-trained on 850 billion tokens of 23 programming languages.

- Multilingual Code Generation: CodeGeeX has good performance for generating executable programs in several mainstream programming languages, including Python, C++, Java, JavaScript, Go, etc.
- Crosslingual Code Translation: CodeGeeX supports the translation of code snippets between different languages.
- Customizable Programming Assistant: CodeGeeX is available in the VS Code extension marketplace for free. It supports code completion, explanation, summarization and more, which empower users with a better coding experience.
- Open-Source and Cross-Platform: All codes and model weights are publicly available for research purposes. CodeGeeX supports both Ascend and NVIDIA platforms. It supports inference in a single Ascend 910, NVIDIA V100 or A100.

GitHub

Data Science by ODS.ai 🦜

31 Mar 2023 17:31

An AST-based Code Change Representation and its Performance in Just-in-time Vulnerability Prediction

Authors propose a novel way of representing changes in source code, the Code Change Tree, a form that is designed to keep only the differences between two abstract syntax trees of Java source code. The appoach was evaluated in predicting if a code change introduces a vulnerability against multiple representation types and evaluated them by a number of machine learning models as a baseline. The evaluation is done on a novel dataset VIC.

RQ. 1 Can a vulnerability introducing database generated from a vulnerability fixing commit database be used for vulnerability prediction?
RQ. 2 How effective are Code Change Trees in representing source code changes?
RQ. 3 Are source code metrics sufficient to represent code changes?

dataset paper
VIC dataset

Data Science by ODS.ai 🦜

29 Mar 2023 15:42

Sparks of Artificial General Intelligence: Early experiments with GPT-4

TLDR: Paper from #Microsoft research about #GPT4 showing something which can be considered signs of #AGI.


ArXiV: https://arxiv.org/abs/2303.12712

Data Science by ODS.ai 🦜

27 Mar 2023 13:08

​​ReBotNet: Fast Real-time Video Enhancement

The authors introduce a novel Recurrent Bottleneck Mixer Network (ReBotNet) method, designed for real-time video enhancement in practical scenarios, such as live video calls and video streams. ReBotNet employs a dual-branch framework, where one branch focuses on learning spatio-temporal features, and the other aims to enhance temporal consistency. A common decoder combines the features from both branches to generate the improved frame. This method incorporates a recurrent training approach that utilizes predictions from previous frames for more efficient enhancement and superior temporal consistency.

To assess ReBotNet, the authors use two new datasets that simulate real-world situations and show that their technique surpasses existing methods in terms of reduced computations, decreased memory requirements, and quicker inference times.

Paper: https://arxiv.org/abs/2303.13504
Project link: https://jeya-maria-jose.github.io/rebotnet-web/

A detailed unofficial overview of the paper: https://andlukyane.com/blog/paper-review-rebotnet

#deeplearning #cv #MachineLearning #VideoEnhancement #AI #Innovation #RealTimeVideo

Data Science by ODS.ai 🦜

26 Mar 2023 10:11

Interview of Ilya Sutskver

TLDR: thereotically #chatgpt can learn a lot and eventually converge to #AGI given the proper dataset and help of #RLHF (Reinforcement Learning from Human Feedback).

Video provides valuable insights into the current state and future of artificial intelligence. The conversation explores the progress of AI, its limitations, and the importance of reinforcement learning and ethics in AI development. Ilia also discusses the potential benefits of AI in democracy and its potential role in helping humans manage society. This interview offers a comprehensive and thought-provoking overview of the AI landscape, making it a must-watch for anyone interested in understanding the impact of AI on our lives and the world at large.

Youtube: https://www.youtube.com/watch?v=SjhIlw3Iffs

#youtube #Sutskever #OpenAI #GPTEditor

Data Science by ODS.ai 🦜

20 Mar 2023 11:34

​​Hyena Hierarchy: Towards Larger Convolutional Language Models

Attention has been a cornerstone of deep learning, but it comes at a steep cost: quadratic expense in sequence length. This can limit the amount of context accessible, making it challenging for subquadratic methods like low-rank and sparse approximations to achieve comparable performance. That's where Hyena comes in!

Hyena is a revolutionary subquadratic drop-in replacement for attention that combines implicitly parametrized long convolutions and data-controlled gating. And the results speak for themselves! Hyena significantly improves accuracy in recall and reasoning tasks on long sequences, matching attention-based models.

In fact, Hyena sets a new state-of-the-art for dense-attention-free architectures in language modeling, reaching Transformer quality with 20% less training compute at sequence length 2K. And that's not all! Hyena operators are twice as fast as optimized attention at sequence length 8K and 100x faster at sequence length 64K.

Paper: https://arxiv.org/abs/2302.10866
Code link: https://github.com/HazyResearch/safari
Project link: https://hazyresearch.stanford.edu/blog/2023-03-07-hyena

A detailed unofficial overview of the paper: https://andlukyane.com/blog/paper-review-hyena

#deeplearning #nlp #cv #languagemodel #convolution

Data Science by ODS.ai 🦜

17 Mar 2023 13:07

Software Vulnerability Prediction Knowledge Transferring Between Programming Languages

One of the biggest challenges in this area is the lack of code samples for all different programming languages. In this study, authors address this issue by proposing a transfer learning technique to leverage available datasets and generate a model to detect common vulnerabilities in different programming languages. They use C source code samples to train a CNN model, then, they use Java source code samples to adopt and evaluate the learned model. The authors use code samples from two benchmark datasets: NIST Software Assurance Reference Dataset (SARD) and Draper VDISC dataset. The results show that proposed model detects vulnerabilities in both C and Java codes with average recall of 72%.

Data Science by ODS.ai 🦜

13 Mar 2023 08:15

​​Visual ChatGPT: Talking, Drawing and Editing with Visual Foundation Models

ChatGPT is a language interface with distinctive conversational competency and reasoning capabilities across many domains. However, it is currently unable to process or generate images from the visual world. To address this limitation, the authors propose a system called Visual ChatGPT that incorporates different Visual Foundation Models to enable users to interact with ChatGPT using both language and images. The system is capable of handling complex visual questions or instructions that require multiple AI models and steps. Additionally, it allows for feedback and corrections.

Rather than creating a new multimodal ChatGPT from scratch, the authors propose building Visual ChatGPT by incorporating various (22) Visual Foundation Models (VFMs) directly into ChatGPT. To facilitate the integration of these VFMs, the authors introduce a Prompt Manager that supports several functions. These include specifying the input-output formats of each VFM, converting visual information to language format, and managing the histories, priorities, and conflicts of different VFMs. With the Prompt Manager's help, ChatGPT can use these VFMs iteratively and receive their feedback until it satisfies the users' requirements or reaches the end condition.

Paper: https://arxiv.org/abs/2303.04671

Code link: https://github.com/microsoft/visual-chatgpt

A detailed unofficial overview of the paper: https://andlukyane.com/blog/paper-review-palme

#deeplearning #nlp #transformer #sota #languagemodel #visual

Data Science by ODS.ai 🦜

07 Mar 2023 20:33

ChatML

OpenAI released ChatGPT API with Chat Markup Language. The basic idea behind ChatML is ensure the LLM model inputs are sent in structured format following ChatML and not as unstructured text.

https://github.com/openai/openai-python/blob/main/chatml.md

Data Science by ODS.ai 🦜

05 Mar 2023 23:36

Hot news: https://ai.facebook.com/blog/large-language-model-llama-meta-ai/

Training smaller foundation models like LLaMA is desirable in the large language model space because it requires far less computing power and resources to test new approaches, validate others’ work, and explore new use cases. Foundation models train on a large set of unlabeled data, which makes them ideal for fine-tuning for a variety of tasks. We are making LLaMA available at several sizes (7B, 13B, 33B, and 65B parameters) and also sharing a LLAMA model card that details how we built the model in keeping with our approach to Responsible AI practices.

In particular, LLaMA-13B outperforms GPT-3 (175B) on most benchmarks, and LLaMA-65B is competitive with the best models, Chinchilla70B and PaLM-540B. We release all our models to the research community.

Model card: https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md

Paper: https://research.facebook.com/publications/llama-open-and-efficient-foundation-language-models/

Form to apply: https://docs.google.com/forms/d/e/1FAIpQLSfqNECQnMkycAp2jP4Z9TFX0cGR4uf7b_fBxjY_OjhJILlKGA/viewform

Unfortunately, it's only for non-commercial purposes :(

"You will not, and will not permit, assist or cause any third party to:

a. use, modify, copy, reproduce, create derivative works of, or distribute the Software Products (or any derivative works thereof, works incorporating the Software Products, or any data produced by the Software), in whole or in part, for (i) any commercial or production purposes ... "