Artificial Intelligence

Today I would like to discuss the games Chess and Go, the world's champions, algorithms and Al.

In 1997, a computer program developed by IBM Deep Blue defeated the world Chess champion Garry Kasparov. Go remained the last board game in which humans were still better than machines.

Why is that?

Chess is primarily distinguished from Go by the number of variations for each move. Chess, the game is more predictable with more structured rules: we have value for each figure (e.g bishop = 3 pawns, rook = 5 pawns -> rook > bishop), some kind of openings and strategies. Go, in turn, has incredibly simple rules, which creates the complexity of the game for the machine. Go is one of the oldest board games. Until recently, it was assumed that a machine was not capable of playing on an equal footing with a professional player due to the high level of abstraction and the inability to sort through all possible scenarios - exactly as many valid combinations in a game on a standard 19×19 go-ban are 10¹⁸⁰ (greater than the number of atoms in the visible universe).

However, almost 20 years later, in 2015, there was a breakthrough. Google's Deep Mind company enhanced AlphaGo, which was the last step for the computer to defeat the world champions in board games. The AlphaGo program defeated the European champion and then, in March 2016 demonstrated a high level of play by defeating Lee Sedol, one of the strongest go players in the world, with a score of 4:1 in favour of the machine. A year later, Google introduced to the world a new version of AlphaGo - AlphaGoZero.

Every day a new neural network appears and every day more opportunities are opened to designers to simplify their workflow. Someone fundamentally refuses to use them, because “there is no life in machinex and technologies”, and someone is only happy to find a way to reduce the amount of work. Personally, I belong to the second type and want to share the most detailed gait on neurons I have acquired lately. 

Everything that follows from this point forward input prompts, followed by ChatCGP’s responses, complete with sample code in Swift.

> Hey ChatGPT, can you make a SwiftUI registration form with name, address and city fields?

Online sports betting is a vertical of the gambling industry that has witnessed a massive surge in recent years. It is a great source of entertainment and thrill for online punters and bettors. Moreover, it also provides monetary benefits that are enough to entice the average layman. This is one of the most prominent reasons why people are gravitating towards these online sports betting platforms.

Additionally, the growing popularity of these platforms is urging sports betting software development companies to innovate and upgrade their platforms to cater to the growing needs of the user base. This is where Artificial Intelligence comes into the equation, AI has been at the forefront of innovations and development and has been offering users an enhanced user experience across multiple platforms. 

Artificial Intelligence technology has allowed Sports betting platforms to evolve with time and streamline their operations for better efficiency and enhanced productivity. This is why sports betting platforms all over the world are adopting this technology to offer better features and functionality to users and also increase their productivity and revenue. 

In this article, we will highlight how Artificial Intelligence has influenced the sports betting industry. So without further delay, let’s get started. 

Video recording of our webinar about dstack and reproducible ML workflows, AVL binary tree operations, Ultralytics YOLOv8, training XGBoost, productionize ML models, introduction to forecasting ensembles, domain expansion of image generators, Muse, X-Decoder, Box2Mask, RoDynRF, AgileAvatar and more.

Unlock the power of Transformer Neural Networks and learn how to build your own GPT-like model from scratch. In this in-depth guide, we will delve into the theory and provide a step-by-step code implementation to help you create your own miniGPT model. The final code is only 400 lines and works on both CPUs as well as on the GPUs. If you want to jump straight to the implementation here is the GitHub repo.

Transformers are revolutionizing the world of artificial intelligence. This simple, but very powerful neural network architecture, introduced in 2017, has quickly become the go-to choice for natural language processing, generative AI, and more. With the help of transformers, we've seen the creation of cutting-edge AI products like BERT, GPT-x, DALL-E, and AlphaFold, which are changing the way we interact with language and solve complex problems like protein folding. And the exciting possibilities don't stop there - transformers are also making waves in the field of computer vision with the advent of Vision Transformers.

Most people assume crypto and NFT are the same but both are different. NFTs are based on blockchain platforms that allow the minting and exchange of cryptocurrencies of a specific type. The basic difference between crypto and NFTs is that two NFTs can not have equal value. Meanwhile one 1 crypto coin will be equal to one coin.

In this article, we will discuss NFT games, like why they are trending and what features and functions are making them more advanced than traditional games. As the demand for NFT based is increasing day by day, then you can also churn this opportunity by developing your own game with the help of a crypto app development company. 

Before diving in, let’s know about the blockchain and NFTs.

From language translation and virtual assistants to self-driving cars and personalized recommendations, AI has been a buzzword for a while now, but it seems that it is only now with the new ChatGPT 3 being released to the public that it is so close to revolutionizing the educational technology field as well. In this article, I would like to give my first impressions, test results, and insights on the new technology.

ChatGPT is a chatbot by OpenAI that can write texts, code, answer questions, and solve various problems. It can even write college essays that, although lacking heart and personal touch, are still pretty good.

It somehow reminds me of the times when distance learning started captivating different fields and what started as a tool for kids with special needs (about 15 years ago, it was a major theme in pedagogical universities, at least) turned into massive online open courses from top universities available to anyone with access to the internet. In corporate learning culture, it went from "e-learning is a cheap and less effective replacement for offline trainings" to being a part of a complicated educational system where we can have the best qualities of offline and online learning for employees.

Right away, serious discussions emerged on the threats to the usage of ChatGPT. Since the beginning of December, many educators have been giving their opinion on its ability to write essays, code, and find correct answers for tests and on the studying culture that will probably need to change.

InvokeAI 2.2 is now available to everyone. This update brings in exciting features, like UI Outpainting, Embedding Management and more. See highlighted updates below, or the full release notes for everything included in the release.

Great for seamless patterns, abstract drawings, and watercolor-styled images. How to use it and train a neural network on your own pictures?

Download the model here: https://huggingface.co/netsvetaev/netsvetaev-free

The InvokeAI team is excited to share our latest feature release, with a set of new features, UI enhancements, and CLI capabilities.

Toloka is a crowdsourcing platform and microtasking project launched by Yandex to quickly markup large amounts of data. But how can such a simple concept play a crucial role in improving the work of neural networks?

FL_PyTorch: Optimization Research Simulator for Federated Learning is publicly available on GitHub.

FL_PyTorch is a suite of open-source software written in python that builds on top of one of the most popular research Deep Learning (DL) frameworks PyTorch. We built FL_PyTorch as a research simulator for FL to enable fast development, prototyping, and experimenting with new and existing FL optimization algorithms. Our system supports abstractions that provide researchers with sufficient flexibility to experiment with existing and novel approaches to advance the state-of-the-art. The work is in proceedings of the 2nd International Workshop on Distributed Machine Learning DistributedML 2021. The paper, presentation, and appendix are available in DistributedML’21 Proceedings (https://dl.acm.org/doi/abs/10.1145/3488659.3493775).

The project is distributed in open source form under Apache License Version 2.0. Code Repository: https://github.com/burlachenkok/flpytorch.

To become familiar with that tool, I recommend the following sequence of steps:

Alexander Volchek, IT entrepreneur, CEO educational platform GeekBrains

Pretty much everyone in the IT community is talking metaverses, NFTs, blockchain and cryptocurrency. This time we will discuss metaverses, and come back to everything else in the letters to follow. Entrepreneurs and founders of tech giants are passionate about this idea, and investors are allocating millions of dollars for projects dealing with metaverses. Let's start with the basics.

Most people fear of artificial intelligence (AI) for the unpredictability of its possible actions and impact [1], [2]. In regard to this technology concerns are voiced also by AI experts themselves - scientists, engineers, among whom are the foremost faces of their professions [3], [4], [5]. And you possibly share these concerns because it's like leaving a child alone at home with a loaded gun on the table - in 2021, AI was first used on the battlefield in completely autonomous way: with an independent determination of a target and a decision to defeat it without operator participation [6]. But let’s be honest, since humanity has taken in the opportunities this new tool could give us, there is already no way back – this is how the law of gengle works [7].

Imagine the feeling of a caveman observing our modern routine world: electricity, Internet, smartphones, robots... etc. In the next two hundred years in large part thankfully to AI humankind will undergo the number of transformations it has since the moment we have learned to control the fire [8]. The effect of this technology will surpass all our previous changes as a civilization. And even as a species, because our destiny is not to create AI, but to literally become it.

The first text-based CAPTCHA ( we’ll call it just CAPTCHA for the sake of brevity ) was used in 1997 by AltaVista search engine. It prevented bots from adding Uniform Resource Locator (URLs) to their web search engine.

Back then it was a decent defense measure. However the progress can't be stopped, and this defense was bypassed using OCR available at those times (for example FineReader).

CAPTCHA became more complex, noise was added to it, along with distortions, so the popular OCRs couldn’t recognize this text. And then OCRs custom made for this task appeared. It costed extra money and knowledge for the attacking side. The CAPTCHA developers were required to understand the challenges the attackers met, what distortions to add, in order to make the automation of the CAPTCHA recognition more complex.

The misunderstanding of the principles the OCRs were based on, some CAPTCHAs were given such distortions, that they were more of a hassle for regular users than for a machine.

OCRs for different types of CAPTCHAs were made using heuristics, and the most complicated part of it was the CAPTCHA segmentation for the stand along symbols, that subsequently could be easily recognized by the CNN (for example LeNet-5), also SVM showed a good result even on the raw pixels.

In this article I’ll try to grasp the whole history of CAPTCHA recognition, from heuristics to the contemporary automated recognition systems. We’ll figure out, if a CAPTCHA is still alive.

I’ll review the yandex.com CAPTCHA. The Russian version of the same CAPTCHA is more complex.

Multimodality has led the pack in machine learning in 2021. Neural networks are wolfing down images, text, speech and music all at the same time.  OpenAI is, as usual, top dog, but as if in defiance of their name, they are in no hurry to share their models openly.  At the beginning of the year, the company presented the DALL-E neural network, which generates 256x256 pixel images in answer to a written request.  Descriptions of it can be found as articles on arXiv and examples on their blog.  

As soon as DALL-E flushed out of the bushes, Chinese researchers got on its tail.  Their open-source CogView neural network does the same trick of generating images from text.  But what about here in Russia? One might say that “investigate, master, and train” is our engineering motto.  Well, we caught the scent, and today we can say that we created from scratch a complete pipeline for generating images from descriptive textual input written in Russian.

In this article we present the ruDALL-E XL model, an open-source text-to-image transformer with 1.3 billion parameters as well as ruDALL-E XXL model, an text-to-image transformer with 12.0 billion parameters which is available in DataHub SberCloud, and several other satellite models.

We at Data Science Digest have always strived to ignite the fire of knowledge in the AI community. We’re proud to have helped thousands of people to learn something new and give you the tools to push ahead. And we’ve not been standing still, either.

Please meet Data Phoenix, a Data Science Digest rebranded and risen anew from our own flame. Our mission is to help everyone interested in Data Science and AI/ML to expand the frontiers of knowledge. More news, more updates, and webinars(!) are coming. Stay tuned!

The new issue of the new Data Phoenix Digest is here! AI that helps write code, EU’s ban on biometric surveillance, genetic algorithms for NLP, multivariate probabilistic regression with NGBoosting, alias-free GAN, MLOps toys, and more…

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The impact of NLP and the growing budgets to drive AI transformations. How Airbnb standardized metric computation at scale. Cross-Validation, MASA-SR, AgileGAN, EfficientNetV2, and more.

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Machine learning in healthcare, the top 10 TED talks on AI, fraud detection in Uber, DatasetGAN, Text-to-Image generation via transformers, and more…