Data Science by ODS.ai 🦜

29 Jun 2023 06:44

​​Fast Segment Anything

The Segment Anything Model (SAM), a revolutionary tool in computer vision tasks, has significantly impacted various high-level tasks like image segmentation, image captioning, and image editing. However, its application has been restricted in industry scenarios due to its enormous computational demand, largely attributed to the Transformer architecture handling high-resolution inputs.

The authors of this paper have proposed a speedier alternative method that accomplishes this foundational task with performance on par with SAM, but at a staggering 50 times faster! By ingeniously reformulating the task as segments-generation and prompting and employing a regular CNN detector with an instance segmentation branch, they've converted this task into the well-established instance segmentation task. The magic touch? They've trained the existing instance segmentation method using just 1/50 of the SA-1B dataset, a stroke of brilliance that led to a solution marrying performance and efficiency.

Paper link: https://huggingface.co/papers/2306.12156
Code link: https://github.com/CASIA-IVA-Lab/FastSAM

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

#deeplearning #cv #segmentanythingmodel #efficiency

Data Science by ODS.ai 🦜

23 Jun 2023 06:24

​​Voicebox: Text-Guided Multilingual Universal Speech Generation at Scale

In the ever-evolving field of natural language processing and computer vision research, the revolution is being led by large-scale generative models like GPT and DALL-E. These models have the remarkable capability of generating high fidelity text or image outputs, and more importantly, they possess a 'generalist' character, able to solve tasks that they weren't explicitly trained to accomplish. However, when it comes to speech generative models, there's still a significant gap in terms of scale and task generalization. Enter, Voicebox - a pioneering advancement set to redefine the landscape of speech generation technology.

Voicebox is an exceptionally versatile text-guided generative model for speech at an impressive scale. Trained on over 50K hours of unfiltered, unenhanced speech data, Voicebox is a non-autoregressive flow-matching model, designed to infill speech, given an audio context and text. Much like its predecessors, Voicebox is able to perform a wide range of tasks through in-context learning, but with an added flexibility - it can condition on future context. The applications are boundless - from mono or cross-lingual zero-shot text-to-speech synthesis to noise removal, content editing, style conversion, and diverse sample generation. What's truly phenomenal is Voicebox's capability to outshine the state-of-the-art zero-shot TTS model, VALL-E, on both intelligibility and audio similarity metrics, while being a staggering 20 times faster.

Paper link: https://research.facebook.com/publications/voicebox-text-guided-multilingual-universal-speech-generation-at-scale/
Blogpost link: https://ai.facebook.com/blog/voicebox-generative-ai-model-speech/
Project link: https://voicebox.metademolab.com/

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

#deeplearning #nlp #speechgeneration #texttospeech

Data Science by ODS.ai 🦜

15 Jun 2023 06:54

​​Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture

In a recent breakthrough, a novel approach for learning highly semantic image representations has been introduced that eschews the need for hand-crafted data augmentations. The strategy, known as Image-based Joint-Embedding Predictive Architecture (I-JEPA), offers a refreshing, non-generative pathway to self-supervised learning from images. The concept underpinning I-JEPA is deceptively simple, yet incredibly powerful: it takes a single context block from an image and predicts the representations of various target blocks within the same image.

I-JEPA's core design principle - its masking strategy - plays a pivotal role in shaping the system's semantic prowess. The key is to sample target blocks at a sufficiently large, semantic scale while using a context block that provides ample, spatially distributed information. When integrated with Vision Transformers, I-JEPA exhibits impressive scalability. To illustrate, a ViT-Huge/14 model was trained on ImageNet using just 16 A100 GPUs in under 72 hours, delivering robust performance across a wide spectrum of tasks, including linear classification, object counting, and depth prediction.

Paper link: https://arxiv.org/abs/2301.08243

Code link: https://github.com/facebookresearch/ijepa

Blogpost link: https://ai.facebook.com/blog/yann-lecun-ai-model-i-jepa/

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

#deeplearning #cv #selfsupervisedlearning

Data Science by ODS.ai 🦜

12 Jun 2023 11:36

🇵🇹Are there people in Lissabon? Let’s meet for a brunch this week!

Data Science by ODS.ai 🦜

08 Jun 2023 06:50

​​StableRep: Synthetic Images from Text-to-Image Models Make Strong Visual Representation Learners

In a ground-breaking exploration of visual representation learning, researchers have leveraged synthetic images produced by leading text-to-image models, specifically Stable Diffusion, achieving promising results. The study uncovers two key insights - firstly, when configured correctly, self-supervised methods trained on synthetic images can match or even outperform those trained on real images. This suggests an exciting avenue for efficient and effective representation learning, reducing the need for extensive real image datasets.

Secondly, the researchers have devised a novel approach called StableRep, a multi-positive contrastive learning method that treats multiple images, generated from the same text prompt, as mutual positives. The compelling finding is that StableRep, trained solely with synthetic images, outperforms representations learned by prominent methods such as SimCLR and CLIP, even when these used real images. In a striking demonstration, when language supervision is added, StableRep trained with 20M synthetic images outperforms CLIP trained with a whopping 50M real images. These findings not only underscore the potential of synthetic data but also pave the way for more efficient, large-scale visual representation learning.

Paper link: https://arxiv.org/abs/2306.00984

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

#deeplearning #cv #nlp #stablediffusion #texttoimage #syntheticdata

Data Science by ODS.ai 🦜

02 Jun 2023 16:33

CodeTF: One-stop Transformer Library for State-of-the-art Code LLM (Salesforce)

The authors we present CodeTF, an open-source Transformer-based library for state-of-the-art Code LLMs and code intelligence. CodeTF is designed with a unified interface to enable rapid access and development across different types of models, datasets and tasks. The library supports a collection of pretrained Code LLM models and popular code benchmarks, including a standardized interface to train and serve code LLMs efficiently, and data features such as language-specific parsers and utility functions for extracting code attributes.

Data Science by ODS.ai 🦜

30 May 2023 08:07

​​Chain of Hindsight Aligns Language Models with Feedback

AI language models are becoming a major part of our digital world. The challenge, however, lies in aligning these models with human preferences to be genuinely useful and valuable. Current methods, although successful in many ways, have limitations - they are either inefficient in utilizing data or depend heavily on challenging reward functions and reinforcement learning.

Here comes "Chain of Hindsight," an exciting, novel technique inspired by human learning mechanisms. It can learn from any form of feedback, even transforming it into language for fine-tuning the model. This approach conditions the model on a sequence of model generations paired with feedback, helping it learn to correct negative attributes or errors. It is significantly outperforming previous methods, particularly showing major strides in summarization and dialogue tasks.
Paper link: https://arxiv.org/abs/2302.02676

A detailed unofficial overview of the paper: https://andlukyane.com/blog/paper-review-coh
#deeplearning #nlp #llm

Data Science by ODS.ai 🦜

22 May 2023 06:31

​​Drag Your GAN: Interactive Point-based Manipulation on the Generative Image Manifold

Meet DragGAN, a groundbreaking approach that is set to revolutionize the way we control generative adversarial networks (GANs) and synthesize visual content! This innovative tool offers users unprecedented flexibility and precision when manipulating images, sidestepping the limitations of prior 3D models and annotated training data. With DragGAN, you can now "drag" any point of an image to a precise target position, introducing a nvel user-interactive element.

Two ingenious components underpin DragGAN's functionality: the first is a feature-based motion supervision that effortlessly guides the handle point towards the desired position, and the second is a novel point tracking approach that utilizes the discriminating features of the generator to maintain the handle points' positions. The real game-changer is that anyone can now deform an image with absolute control over pixel movements, enabling the manipulation of pose, shape, expression, and layout across diverse categories like animals, cars, humans, landscapes, and more. DragGAN outperforms its predecessors in both image manipulation and point tracking tasks, promising an exciting leap forward in AI-generated visual content!

Paper link: https://arxiv.org/abs/2305.10973
Code link: https://github.com/XingangPan/DragGAN
Project link: https://vcai.mpi-inf.mpg.de/projects/DragGAN/

A detailed unofficial overview of the paper: https://andlukyane.com/blog/paper-review-draggan
#deeplearning #cv #gan #imagemanipulation

Data Science by ODS.ai 🦜

18 May 2023 06:47

​​DarkBERT: A Language Model for the Dark Side of the Internet

The researchers have developed a novel model called DarkBERT, which specifically focuses on the linguistically complex and often misunderstood domain of the Dark Web. This innovative model stands out due to its unique pretraining on Dark Web data, which allows it to handle the extreme lexical and structural diversity characteristic of the Dark Web. This is a critical development considering the clear differences that exist in language use between the Dark Web and the Surface Web, a factor that can often hinder accurate textual analysis.

DarkBERT isn't just a novelty, but a robust, high-performing language model that consistently outshines current popular models like BERT and RoBERTa in various use cases. These findings shed light on the considerable advantages that a domain-specific model like DarkBERT can offer. More than anything else, DarkBERT promises to be a vital resource for future research on the Dark Web, setting a new standard for language models in this intriguing and intricate realm.

Paper link: https://arxiv.org/abs/2305.08596

A detailed unofficial overview of the paper: https://andlukyane.com/blog/paper-review-darkbert
#deeplearning #nlp #darkweb #cybersecurity

Data Science by ODS.ai 🦜

12 May 2023 23:59

Introducing 100K Token Context Windows

- approximately 75K words
- hundreds of pages
- a book, for example "The Great Gatsby" (about 72K tokens)
- a text that will take approximately 5 hours to read

Data Science by ODS.ai 🦜

11 May 2023 19:44

StarCoder: may the source be with you!

The BigCode community, an open-scientific collaboration working on the responsible development of Code LLMs, introduces StarCoder and StarCoderBase:
- 15.5B parameter models
- 8K context length
- StarCoderBase is trained on 1 trillion tokens sourced from The Stack, a large collection of permissively licensed GitHub repositories with inspection tools and an opt-out process
- StarCoderBase is fine-tuned on 35B Python tokens, resulting in the creation of StarCoder

StarCoderBase outperforms every open Code LLM that supports multiple programming languages and matches or outperforms the OpenAI code-cushman-001 model.

Data Science by ODS.ai 🦜

10 May 2023 14:53

Found another PyTorch-based library with basic image functions, losses and transformations

Looks like it is a combination toolkit of augs, skimage and classic cv2 functions, but written in PyTorch.

What is Kornia? Kornia is a differentiable library that allows classical computer vision to be integrated into deep learning models.

Examples:

- https://kornia.readthedocs.io/en/latest/get-started/highlights.html
- and especially this https://kornia.readthedocs.io/en/latest/losses.html

Data Science by ODS.ai 🦜

08 May 2023 06:22

​​Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes

Researchers have developed "Distilling step-by-step," a cutting-edge method to train smaller, more efficient task-specific models that outperform large language models (LLMs) while requiring significantly less training data. This innovation promises to revolutionize the practicality of NLP models in real-world applications by reducing both model size and data requirements: a 770M T5 model surpasses a 540B PaLM model using only 80% of available data.

Distilling step-by-step leverages LLM-generated rationales within a multi-task training framework, yielding impressive results across 4 NLP benchmarks. The technique consistently achieves better performance with fewer labeled/unlabeled training examples, surpassing LLMs with substantially smaller model sizes.

Paper link: https://arxiv.org/abs/2305.02301

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

#deeplearning #nlp #languagemodels #distillation

Data Science by ODS.ai 🦜

06 May 2023 11:05

Last call to apply for the Yandex School of Data Analysis.

Recruitment for the YSDA, which is a vocational training program, free of charge, lasting for two years, will end on the 06 May 2023.

You can choose one of the four highly demanded majors: data science, big data infrastructure, machine learning or data analysis in applied sciences.

To be able to pass examinations and study successfully at the YSDA one should have a basic understanding of the machine learning, have a good mathematical background and use one of the programming languages. Experienced developers can apply for an alternative admission track that includes both assessment of algorithms basics and mathematics and research and/or industrial achievements.

The educational process is mainly conducted in the Russian language.

Application form is accessible via link https://clck.ru/34GwCS, there is also a tg-chat for the applicants /channel/+DQ1j7epbIlNmNjFi

Data Science by ODS.ai 🦜

01 May 2023 06:56

​​Scaling Transformer to 1M tokens and beyond with RMT

Imagine extending the context length of BERT, one of the most effective Transformer-based models in natural language processing, to an unprecedented two million tokens! This technical report unveils the Recurrent Memory Transformer (RMT) architecture, which achieves this incredible feat while maintaining high memory retrieval accuracy.

The RMT approach enables storage and processing of both local and global information, allowing information flow between segments of the input sequence through recurrence. The experiments showcase the effectiveness of this groundbreaking method, with immense potential to enhance long-term dependency handling in natural language understanding and generation tasks, as well as enable large-scale context processing for memory-intensive applications.

Paper link: https://arxiv.org/abs/2304.11062
Code link: https://github.com/booydar/t5-experiments/tree/scaling-report

A detailed unofficial overview of the paper: https://andlukyane.com/blog/paper-review-rmt-1m

#deeplearning #nlp #bert #memory

Data Science by ODS.ai 🦜

26 Jun 2023 09:47

​​Tracking Everything Everywhere All at Once

In the field of motion estimation, a remarkable breakthrough has just arrived! Introducing OmniMotion, an innovative method that pioneers a complete and globally consistent motion representation. OmniMotion moves beyond the constraints of traditional optical flow or particle video tracking algorithms that are hindered by limited temporal windows and difficulties in maintaining global consistency of estimated motion trajectories. Instead, OmniMotion enables accurate, full-length motion estimation of every pixel in a video sequence - a truly remarkable feat.

OmniMotion represents a video using a quasi-3D canonical volume and accomplishes pixel-wise tracking via the transformation between local and canonical spaces. This representation doesn't just ensure global consistency; it also opens the doors to tracking through occlusions and modeling any mixture of camera and object motion. The extensive evaluations conducted on the TAP-Vid benchmark and real-world footage have proven that OmniMotion outperforms existing state-of-the-art methods by a substantial margin, both quantitatively and qualitatively.

Paper link: https://arxiv.org/abs/2306.05422
Project link: https://omnimotion.github.io/

A detailed unofficial overview of the paper: https://artgor.medium.com/paper-review-tracking-everything-everywhere-all-at-once-27caa13918bcn

#deeplearning #cv #motionestimation

Data Science by ODS.ai 🦜

19 Jun 2023 06:41

​​Principle-Driven Self-Alignment of Language Models from Scratch with Minimal Human Supervision

AI-assistant agents like ChatGPT have largely depended on supervised fine-tuning and reinforcement learning from human feedback. But, this method brings its own set of challenges - high costs, potential biases, and constraints on the true potential of these AI agents. What if there was a more effective, self-sufficient way to align AI output with human intentions? Enter Self-ALIGN, a groundbreaking methodology that marries principle-driven reasoning and the generative capabilities of large language models. This promising approach takes the AI realm by storm, offering a novel way to ensure our AI models are more helpful, ethical, and reliable - all with minimal human intervention.

Self-ALIGN is a multistage process that works by generating synthetic prompts from a large language model, augmenting prompt diversity, and leveraging a concise set of human-written principles to guide AI models. When applied to the LLaMA-65b base language model, it led to the creation of a new AI assistant, Dromedary, using less than 300 lines of human annotations. Dromedary not only outshines several state-of-the-art AI systems, such as Text-Davinci-003 and Alpaca, but it does so on a variety of benchmark datasets.

Paper link: https://arxiv.org/abs/2305.03047

Code link: https://mitibmdemos.draco.res.ibm.com/dromedary

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

#deeplearning #nlp #llm

Data Science by ODS.ai 🦜

14 Jun 2023 23:45

Stack Overflow 2023 Developer Survey

Data Science by ODS.ai 🦜

12 Jun 2023 06:39

​​BiomedGPT: A Unified and Generalist Biomedical Generative Pre-trained Transformer for Vision, Language, and Multimodal Tasks

Introducing the groundbreaking Biomedical Generative Pre-trained Transformer (BiomedGPT) model, this paper revolutionizes the field of biomedicine by offering a unified and generalist approach. BiomedGPT harnesses the power of self-supervision on extensive and diverse datasets, enabling it to effortlessly handle multi-modal inputs and excel in a wide range of downstream tasks. In a series of comprehensive experiments, BiomedGPT astoundingly outperforms its predecessors, emerging as the unrivaled leader across five distinct tasks and a staggering 20 public datasets encompassing over 15 unique biomedical modalities. Its ability to deliver expansive and all-encompassing representations of biomedical data heralds a significant advancement in the field, with promising implications for improving healthcare outcomes.

Through meticulous ablation studies, the efficacy of BiomedGPT's multi-modal and multi-task pretraining approach is vividly showcased. This groundbreaking model effortlessly transfers its vast knowledge to previously unseen data, demonstrating its versatility and adaptability. The implications of this research are profound, paving the way for the development of unified and all-encompassing models for biomedicine.

Paper link: https://arxiv.org/abs/2305.17100

Code link: https://github.com/taokz/BiomedGPT

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

#deeplearning #nlp #selfsupervised #gpt #biomedicine

Data Science by ODS.ai 🦜

05 Jun 2023 06:53

​​The effectiveness of MAE pre-pretraining for billion-scale pretraining

Revolutionizing the current pretrain-then-finetune paradigm of computer vision, this research has introduced an innovative pre-pretraining stage. Utilizing the Masked Autoencoder (MAE) technique for model initialization, this pre-pretraining strategy scales with the size of both the model and the data. This makes it an ideal tool for training next-generation foundation models, even on the grandest scales.

The robustness of our pre-pretraining technique is demonstrated by consistent improvement in model convergence and downstream transfer performance across diverse model scales and dataset sizes. The authors measured the effectiveness of pre-pretraining on a wide array of visual recognition tasks, and the results have been promising. The ielargest model achieved unprecedented results on iNaturalist-18 (91.3%), 1-shot ImageNet-1k (62.1%), and zero-shot transfer on Food-101 (96.0%), underlining the tremendous potential of proper model initialization, even when handling web-scale pretraining with billions of images.

Paper link: https://arxiv.org/abs/2303.13496

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

#deeplearning #cv #pretraining #selfsupervisedlearning

Data Science by ODS.ai 🦜

01 Jun 2023 11:29

​​QLoRA: Efficient Finetuning of Quantized LLMs

Thia paper introduces QLoRA, a novel finetuning approach that decreases memory usage significantly, while maintaining impressive performance. Imagine this - a 65 billion parameter model finetuned on a single 48GB GPU, while preserving full 16-bit task performance. This method involves backpropagating gradients through a frozen, 4-bit quantized pretrained language model into Low Rank Adapters, a method that opens up new frontiers in machine learning. The icing on the cake is their high-performing model family, Guanaco, which trumps all previously released models on the Vicuna benchmark, achieving a staggering 99.3% of the performance level of ChatGPT with just 24 hours of finetuning on a single GPU.

The study also unveils several innovative techniques to conserve memory without compromising performance. These include 4-bit NormalFloat (NF4), an innovative data type that is theoretically optimal for normally distributed weights, double quantization for average memory footprint reduction, and paged optimizers to handle memory spikes. The QLoRA approach was applied to finetune more than 1000 models, leading to a detailed analysis of instruction following and chatbot performance across various model types and scales. The results affirm that QLoRA finetuning on a small, high-quality dataset yields state-of-the-art results, even with smaller models than previously used. A notable finding is that GPT-4 evaluations offer a cost-effective alternative to human evaluation. All models and code, including CUDA kernels for 4-bit training, have been released by the researchers.

Paper link: https://arxiv.org/abs/2305.14314
Code link: https://github.com/artidoro/qlora
CUDA kernels link: https://github.com/TimDettmers/bitsandbytes

A detailed unofficial overview of the paper: https://andlukyane.com/blog/paper-review-qlora
#deeplearning #nlp #llm #quantization

Data Science by ODS.ai 🦜

25 May 2023 18:33

​​MMS: Scaling Speech Technology to 1000+ languages

Get ready for a breakthrough in speech technology that is set to revolutionize the world of communication! The field, which has so far been restricted to around a hundred languages, barely scratches the surface of the more than 7,000 languages spoken globally. The Massively Multilingual Speech (MMS) project is taking a monumental leap to bridge this gap, increasing the number of supported languages by an astounding 10 to 40 times, depending on the task. This unprecedented expansion will be a game-changer, significantly improving global access to information and creating a more inclusive digital landscape.

This incredible feat is achieved through the creation of a new dataset drawn from publicly available religious texts and the strategic implementation of self-supervised learning. The MMS project's achievements are staggering, including the development of pre-trained wav2vec 2.0 models for 1,406 languages, a single multilingual automatic speech recognition model for 1,107 languages, speech synthesis models for as many languages, and a language identification model for a whopping 4,017 languages. Even more impressive is the significant improvement in accuracy - our multilingual speech recognition model more than halves the word error rate of Whisper on 54 languages of the FLEURS benchmark, despite being trained on a significantly smaller dataset.

Paper link: https://research.facebook.com/publications/scaling-speech-technology-to-1000-languages/
Blogpost link: https://ai.facebook.com/blog/multilingual-model-speech-recognition/
Code link: https://github.com/facebookresearch/fairseq/tree/main/examples/mms

A detailed unofficial overview of the paper: https://andlukyane.com/blog/paper-review-mms
#deeplearning #speechrecognition #tts #audio

Data Science by ODS.ai 🦜

20 May 2023 23:24

Code Execution with Pre-trained Language Models

Code execution is a fundamental aspect of programming language semantics that reflects the exact behavior of the code. However, most pretrained models for code intelligence ignore the execution trace and only rely on source code and syntactic structures. In this paper, the authors aim to teach pretrained models the real-world code execution process. They propose CodeExecutor, a Transformer-based model that learns to execute arbitrary programs and predict their execution traces.

Data Science by ODS.ai 🦜

15 May 2023 09:28

​​NaturalSpeech 2: Latent Diffusion Models are Natural and Zero-Shot Speech and Singing Synthesizers

In the rapidly evolving domain of text-to-speech (TTS) technology, an exciting breakthrough has been made with the development of NaturalSpeech 2. This innovative system brings new levels of diversity to the table, by uniquely capturing a wide range of human speech characteristics such as speaker identities, prosodies, and even styles like singing. By employing a neural audio codec and residual vector quantizers, it transcends the limitations of existing TTS systems, which often suffer from unstable prosody, word skipping/repeating issues, and subpar voice quality.

More impressively, NaturalSpeech 2 enhances the "zero-shot" capability, a crucial factor for diverse speech synthesis. By designing a unique speech prompting mechanism, it facilitates in-context learning in both the diffusion model and the duration/pitch predictor. Its expansive training on 44K hours of speech and singing data has yielded unprecedented results. NaturalSpeech 2 significantly outperforms previous TTS systems in prosody/timbre similarity, robustness, and voice quality, even demonstrating novel zero-shot singing synthesis.

Project link: https://speechresearch.github.io/naturalspeech2/

Paper link: https://arxiv.org/pdf/2304.09116.pdf

A detailed unofficial overview of the paper: https://andlukyane.com/blog/paper-review-naturalspeech2
#deeplearning #nlp #tts #texttospeech

Data Science by ODS.ai 🦜

12 May 2023 09:38

Launching the Open Data Science Talent Pool Initiative!

Hello, community!

We received several requests to organize some tools to match people seeking career / pet projects matching opportunities. So now we are launching the Open Data Science Talent Pool!

The field of data science is rapidly evolving, and we recognize the importance of matching skilled professionals with organizations that value their unique capabilities. This Talent Pool Initiative is our endeavor to facilitate these connections, making the opportunities search process smoother and more efficient for everyone involved.

Here's how it works:

🔍 For Opportunities Seekers:

If you're a data scientist, machine learning engineer, AI specialist, or hold any other role in the data science domain, we invite you to submit your resume and a brief introduction about yourself. This is a fantastic opportunity to showcase your skills, interests, and aspirations to potential employers. Don't forget to highlight those special projects or unique experiences that make you stand out!

🏢 For Talent Seekers:

If you're an organization or an individual looking for talented individuals in the field of data science, our Talent Pool will be an invaluable resource. You'll have access to a diverse array of professionals, each with their own unique skill sets and experiences, ready to help your organization reach new heights. Make sure you submitted your request through the form in the quoted post

🔄 The Process:

1 Submission: Individuals can submit their resumes and short introductions through a dedicated form on our website (link will be shared soon).
2 Review: Our team will review these submissions to ensure they meet the necessary standards and criteria.
3 Access: Approved profiles will be included in our Talent Pool, accessible to match with the requests within our community.

During the earliest stage we are going to match the requests personally ensuring we don’t overengineer the process. We will not hesitate to introduce necessary product adjustments once the tool meets the demand inside the community.

Remember, we're all in this journey together. Whether you're looking for your next big opportunity or seeking the perfect addition to your team, we're here to support you.

Stay tuned, stay connected, and let's continue to foster a supportive, dynamic, and prosperous data science community!
Best,
ChatGPT with the prompt from Open Data Science Channel Editorial Team

Google Form: https://forms.gle/3GH1vrt91mRtstzK8

#ds_jobs #ds_intros

Data Science by ODS.ai 🦜

11 May 2023 16:57

For those how are looking beyond Data Science or wondering to play around, here is a news on the release of the portfolio company of one of the channel editors:

TON Play: the Unity SDK + payment management for games

TON Play is a toolkit for developers based on the TON blockchain and working closely with the messaging app Telegram. They recently introduced Pay-in, Mass payout, and On-demand payout methods in TON. If you dabble with games, this might be curious to test in action.

The main features:

* projects get paid by Telegram users in TON
* option to add mass payouts in TON to games with cash prizes
* automated payouts on user demand

TON Play also released SDKs, allowing projects to manage assets and in-game marketplace and port Unity or HTML5 games to work inside Telegram as a web app. SDKs are written in Unity, Python, and Typescript.

Website: https://tonplay.io/
Documentation: https://docs.tonplay.io/
Telegram channel: /channel/tonplayinsider
Contacts: @tonplay_devs, gamedevs@tonplay.io


#ds_jobs #ds_resumes

Data Science by ODS.ai 🦜

10 May 2023 06:11

​​ImageBind: One Embedding Space To Bind Them All

Introducing ImageBind, a groundbreaking approach that learns a joint embedding across six different modalities – images, text, audio, depth, thermal, and IMU data – using only image-paired data. This innovative method leverages recent large-scale vision-language models, extending their zero-shot capabilities to new modalities through their natural pairing with images. ImageBind unlocks a myriad of novel emergent applications 'out-of-the-box,' including cross-modal retrieval, composing modalities with arithmetic, cross-modal detection, and generation.

ImageBind's emergent capabilities improve as the strength of the image encoder increases, setting a new state-of-the-art benchmark in emergent zero-shot recognition tasks across modalities, even outperforming specialist supervised models. Furthermore, ImageBind demonstrates impressive few-shot recognition results, surpassing prior work in the field. This pioneering technique offers a fresh way to evaluate vision models for both visual and non-visual tasks, opening the door to exciting advancements in AI and machine learning.

Blogpost link: https://ai.facebook.com/blog/imagebind-six-modalities-binding-ai/

Code link: https://github.com/facebookresearch/ImageBind

Paper link: https://dl.fbaipublicfiles.com/imagebind/imagebind_final.pdf

Demo link: https://imagebind.metademolab.com/

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

#deeplearning #nlp #multimodal #cv #embedding

Data Science by ODS.ai 🦜

06 May 2023 23:23

TWIMC

string2string: A Modern Python Library for String-to-String Algorithms
https://arxiv.org/abs/2304.14395

We introduce string2string, an open-source library that offers a comprehensive suite of efficient algorithms for a broad range of string-to-string problems. It includes traditional algorithmic solutions as well as recent advanced neural approaches to tackle various problems in string alignment, distance measurement, lexical and semantic search, and similarity analysis -- along with several helpful visualization tools and metrics to facilitate the interpretation and analysis of these methods. Notable algorithms featured in the library include the Smith-Waterman algorithm for pairwise local alignment, the Hirschberg algorithm for global alignment, the Wagner-Fisher algorithm for edit distance, BARTScore and BERTScore for similarity analysis, the Knuth-Morris-Pratt algorithm for lexical search, and Faiss for semantic search. Besides, it wraps existing efficient and widely-used implementations of certain frameworks and metrics, such as sacreBLEU and ROUGE, whenever it is appropriate and suitable. Overall, the library aims to provide extensive coverage and increased flexibility in comparison to existing libraries for strings. It can be used for many downstream applications, tasks, and problems in natural-language processing, bioinformatics, and computational social sciences. It is implemented in Python, easily installable via pip, and accessible through a simple API. Source code, documentation, and tutorials are all available on our GitHub page:

https://github.com/stanfordnlp/string2string

Data Science by ODS.ai 🦜

04 May 2023 06:37

​​Phoenix: Democratizing ChatGPT across Languages

Introducing "Phoenix," a revolutionary multilingual ChatGPT that's breaking barriers in AI language models! By excelling in languages with limited resources and demonstrating competitive performance in English and Chinese models, Phoenix is set to transform accessibility for people around the world.

The methodology behind Phoenix combines instructions and conversations data to create a more well-rounded language model, leveraging the multi-lingual nature of the data to understand and interact with diverse languages.

Paper link: https://arxiv.org/abs/2304.10453

Code link: https://github.com/FreedomIntelligence/LLMZoo

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

#deeplearning #nlp #Phoenix #ChatGPT #multilingual #languagemodel

Data Science by ODS.ai 🦜

27 Apr 2023 06:41

​​Speed Is All You Need: On-Device Acceleration of Large Diffusion Models via GPU-Aware Optimizations

The rapid development of foundation models is revolutionizing the field of artificial intelligence, with large diffusion models gaining significant attention for their ability to generate photorealistic images and support various tasks. Deploying these models on-device brings numerous benefits, including lower server costs, offline functionality, and improved user privacy. However, with over 1 billion parameters, these models face challenges due to restricted computational and memory resources on devices.

Excitingly, researchers from Google have presented a series of implementation optimizations for large diffusion models that achieve the fastest reported inference latency to date (under 12 seconds for Stable Diffusion 1.4 without INT8 quantization for a 512 × 512 image with 20 iterations) on GPU-equipped mobile devices. These groundbreaking enhancements not only broaden the applicability of generative AI but also significantly improve the overall user experience across a wide range of devices, paving the way for even more innovative AI applications in the future.

Paper link: https://arxiv.org/abs/2304.11267

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

#deeplearning #stablediffusion #inferenceoptimization