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ruDALL-E: Generating Images from Text. Facing down the biggest computational challenge in Russia

Reading time11 min
Views10K

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.

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Total votes 3: ↑3 and ↓0+3
Comments4

Lingtrain Aligner. How to make parallel books for language learning. Part 1. Python and Colab version

Reading time8 min
Views3.1K

title


If you're interested in learning new languages or teaching them, then you probably know such a way as parallel reading. It helps to immerse yourself in the context, increases the vocabulary, and allows you to enjoy the learning process. When it comes to reading, you most likely want to choose your favorite author, theme, or something familiar and this is often impossible if no one has published such a variant of a parallel book. It's becoming even worse when you're learning some cool language like Hungarian or Japanese.


Today we are taking a big step forward toward breaking this situation.


We will use the lingtrain_aligner tool. It's an open-source project on Python which aims to help all the people eager to learn foreign languages. It's a part of the Lingtrain project, you can follow us on Telegram, Facebook and Instagram. Let's start!


Find the texts


At first, we should find two texts we want to align. Let's take two editions of "To Kill a Mockingbird" by Harper Lee, in Russian and the original one.

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Total votes 5: ↑5 and ↓0+5
Comments0

Using the Machine Learning model to detect credit card fraud

Reading time3 min
Views1.6K

When we move towards the digital world, we shouldn’t forget that cybersecurity has been playing a major role in our life. Talks about digital security have been stiff. The main challenge we would face is abnormality.

During an online transaction, most of the product-lovers prefer credit cards. The credit limit available in credit cards would allow us to purchase even when our bank balance is insufficient. But this is great news for cyber attackers eyeing your money.

For tackling this problem, we should depend upon a system to make hardpressed transactions effortless.

This is where we need a system to track the transaction patterns. With AI, we can abort any abnormal transaction, precisely for credit card fraud detection AI.

As of now, we will come across a number of machine learning algorithms to classify unusual transactions where Artificial Intelligence detect fraud. We only need past data and the right algorithm to fit the data in the right form in case of credit card fraud detection ai.

How do we make this happen? Let’s look into the process of credit card fraud detection AI:

Import the needed libraries

The best step to detect credit card fraud detection with AI is to import the libraries. The best practice would be to import the necessary libraries in a single section for the purpose of quick modification. To use the credit card data, we can use the PCA’s transformed version or RFECV, RFE, VIF and SelectKBest to get the best model features.

Import Dataset

Machine learning helps with fraud detection. It’s quite simple to import the dataset when you use the pandas module in python. You can run the run command for importing your data. 

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Comments2

We have published a model for text repunctuation and recapitalization for four languages

Reading time7 min
Views6.1K


Open In Colab


Working with speech recognition models we often encounter misconceptions among potential customers and users (mostly related to the fact that people have a hard time distinguishing substance over form). People also tend to believe that punctuation marks and spaces are somehow obviously present in spoken speech, when in fact real spoken speech and written speech are entirely different beasts.


Of course you can just start each sentence with a capital letter and put a full stop at the end. But it is preferable to have some relatively simple and universal solution for "restoring" punctuation marks and capital letters in sentences that our speech recognition system generates. And it would be really nice if such a system worked with any texts in general.


For this reason, we would like to share a system that:


  • Inserts capital letters and basic punctuation marks (dot, comma, hyphen, question mark, exclamation mark, dash for Russian);
  • Works for 4 languages (Russian, English, German, Spanish) and can be extended;
  • By design is domain agnostic and is not based on any hard-coded rules;
  • Has non-trivial metrics and succeeds in the task of improving text readability;

To reiterate — the purpose of such a system is only to improve the readability of the text. It does not add information to the text that did not originally exist.

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Total votes 4: ↑3 and ↓1+2
Comments0

Mode on: Comparing the two best colorization AI's

Reading time11 min
Views3.3K

This article continues a series of notes about colorization. During today's experiment, we’ll be comparing a recent neural network with the good old Deoldify to gauge the rate at which the future is approaching.

This is a practical project, so we won’t pay extra attention to the underlying philosophy of the Transformer architecture. Besides, any attempt to explain the principles of its operation to a wide public in hand waving terms would become misguiding.

A lecturer: Mr. Petrov! How does a transformer work?
Petrov with a bass voice: Hum-m-m-m.


Google Colorizing Transformer vs Deoldify

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Total votes 17: ↑17 and ↓0+17
Comments0

Data Phoenix Digest — 01.07.2021

Reading time5 min
Views1.9K

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…

If you’re more used to getting updates every day, subscribe to our Telegram channel or follow us on social media: TwitterFacebook.

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Total votes 1: ↑0 and ↓1-1
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DataScience Digest — 24.06.21

Reading time5 min
Views1.9K

The new issue of DataScienceDigest is here!

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.

If you’re more used to getting updates every day, subscribe to our Telegram channel or follow us on social media: Twitter, LinkedIn, Facebook.

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Total votes 2: ↑1 and ↓10
Comments0

Flitter Your Business With AI Integrated Flutter App Development

Reading time5 min
Views2.9K

As we all are aware of the fact that the digital market is heavily leaning towards a reliable UX-driven process, app development has become quite complex, especially for targeting the industry for mobile platforms.

For every organization, creating a product that is beneficial for their customer needs always comes up with a plethora of challenges. 

From the technical point of time, there are various challenges that every business faces, including selecting the right platform for the app, the right technology stack or framework, and creating an app that fulfills the needs and expectations of customers.  

Similarly, there are more challenges that every business faces and needs to cope with while creating its dream product. 

So, what to do??

Well, what if I say that the answer to all your queries and questions is Flutter app development with Artificial Intelligence (AI)  integration……

Surprised? Wondering how? 

Well, AI in Flutter app development is one of the best advancements in the software market. The concept of AI was first introduced during the 20th century with loads of innovations and advancements that we are still integrating into our mobile app development. 

But, what are Artificial Intelligence and Flutter app development? 

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Data Science Digest — 21.04.21

Reading time3 min
Views987

Hi All,

I’m pleased to invite you all to enroll in the Lviv Data Science Summer School, to delve into advanced methods and tools of Data Science and Machine Learning, including such domains as CV, NLP, Healthcare, Social Network Analysis, and Urban Data Science. The courses are practice-oriented and are geared towards undergraduates, Ph.D. students, and young professionals (intermediate level). The studies begin July 19–30 and will be hosted online. Make sure to apply — Spots are running fast!

If you’re more used to getting updates every day, follow us on social media:

Telegram
Twitter
LinkedIn
Facebook

Regards,
Dmitry Spodarets.

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Total votes 3: ↑2 and ↓1+1
Comments0

Neural network Telegram bot with StyleGAN and GPT-2

Reading time3 min
Views5K

The Beginning


So we have already played with different neural networks. Cursed image generation using GANs, deep texts from GPT-2 — we have seen it all.


This time I wanted to create a neural entity that would act like a beauty blogger. This meant it would have to post pictures like Instagram influencers do and generate the same kind of narcissistic texts. \


Initially I planned to post the neural content on Instagram but using the Facebook Graph API which is needed to go beyond read-only was too painful for me. So I reverted to Telegram which is one of my favorite social products overall.


The name of the entity/channel (Aida Enelpi) is a bad neural-oriented pun mostly generated by the bot itself.


One of the first posts generated by Aida

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Data Science Digest — We Are Back

Reading time5 min
Views1.1K

Hi All,

I have some good news for you…

Data Science Digest is back! We’ve been “offline” for a while, but no worries — You’ll receive regular digest updates with top news and resources on AI/ML/DS every Wednesday, starting today.

If you’re more used to getting updates every day, follow us on social media:

Telegram - https://t.me/DataScienceDigest
Twitter - https://twitter.com/Data_Digest
LinkedIn - https://www.linkedin.com/company/data-science-digest/
Facebook - https://www.facebook.com/DataScienceDigest/

And finally, your feedback is very much appreciated. Feel free to share any ideas with me and the team, and we’ll do our best to make Data Science Digest a better place for all.

Regards,
Dmitry Spodarets.

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Distributed Artificial Intelligence with InterSystems IRIS

Reading time7 min
Views970

Author: Sergey Lukyanchikov, Sales Engineer at InterSystems

What is Distributed Artificial Intelligence (DAI)?

Attempts to find a “bullet-proof” definition have not produced result: it seems like the term is slightly “ahead of time”. Still, we can analyze semantically the term itself – deriving that distributed artificial intelligence is the same AI (see our effort to suggest an “applied” definition) though partitioned across several computers that are not clustered together (neither data-wise, nor via applications, not by providing access to particular computers in principle). I.e., ideally, distributed artificial intelligence should be arranged in such a way that none of the computers participating in that “distribution” have direct access to data nor applications of another computer: the only alternative becomes transmission of data samples and executable scripts via “transparent” messaging. Any deviations from that ideal should lead to an advent of “partially distributed artificial intelligence” – an example being distributed data with a central application server. Or its inverse. One way or the other, we obtain as a result a set of “federated” models (i.e., either models trained each on their own data sources, or each trained by their own algorithms, or “both at once”).

Distributed AI scenarios “for the masses”

We will not be discussing edge computations, confidential data operators, scattered mobile searches, or similar fascinating yet not the most consciously and wide-applied (not at this moment) scenarios. We will be much “closer to life” if, for instance, we consider the following scenario (its detailed demo can and should be watched here): a company runs a production-level AI/ML solution, the quality of its functioning is being systematically checked by an external data scientist (i.e., an expert that is not an employee of the company). For a number of reasons, the company cannot grant the data scientist access to the solution but it can send him a sample of records from a required table following a schedule or a particular event (for example, termination of a training session for one or several models by the solution). With that we assume, that the data scientist owns some version of the AI/ML mechanisms already integrated in the production-level solution that the company is running – and it is likely that they are being developed, improved, and adapted to concrete use cases of that concrete company, by the data scientist himself. Deployment of those mechanisms into the running solution, monitoring of their functioning, and other lifecycle aspects are being handled by a data engineer (the company employee).

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High-Quality Text-to-Speech Made Accessible, Simple and Fast

Reading time8 min
Views9.3K

image


There is a lot of commotion in text-to-speech now. There is a great variety of toolkits, a plethora of commercial APIs from GAFA companies (based both on new and older technologies). There are also a lot of Silicon Valley startups trying to ship products akin to "deep fakes" in speech.


But despite all this ruckus we have not yet seen open solutions that would fulfill all of these criteria:


  • Naturally sounding speech;
  • A large library of voices in many languages;
  • Support for 16kHz and 8kHz out of the box;
  • No GPUs / ML engineering team / training required;
  • Unique voices not infringing upon third-party licenses;
  • High throughput on slow hardware. Decent performance on one CPU thread;
  • Minimalism and lack of dependencies. One-line usage, no builds or coding in C++ required;
  • Positioned as a solution, not yet another toolkit / compilation of models developed by other people;
  • Not affiliated by any means with ecosystems of Google / Yandex / Sberbank;

We decided to share our open non-commercial solution that fits all of these criteria with the community. Since we have published the whole pipeline we do not focus much on cherry picked examples and we encourage you to visit our project GitHub repo to test our TTS for yourself.

Total votes 5: ↑5 and ↓0+5
Comments8

Doing «Data Science» even if you have never heard the words before

Reading time12 min
Views1.4K

There’s a lot of talk about machine learning nowadays. A big topic – but, for a lot of people, covered by this terrible layer of mystery. Like black magic – the chosen ones’ art, above the mere mortal for sure. One keeps hearing the words “numpy”, “pandas”, “scikit-learn” - and looking each up produces an equivalent of a three-tome work in documentation.

I’d like to shatter some of this mystery today. Let’s do some machine learning, find some patterns in our data – perhaps even make some predictions. With good old Python only – no 2-gigabyte library, and no arcane knowledge needed beforehand.

Interested? Come join us.

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CLIP from OpenAI: what is it and how you can try it out yourself

Reading time7 min
Views12K

Neural networks (NN) and computer vision models in particular are known to perform well in specific tasks, but often fail to generalize to tasks they have not been trained on. A model that performs well on a food data may perform poorly on satellite images. 

A new model from OpenAI named CLIP claims to close this gap by a large margin. The paper Open AI wrote presenting CLIP demonstrates how the model may be used on a various classification datasets in a zero-shot manner. 

In this article, I will explain the key ideas of the model they proposed and show you the code to use it. 

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Total votes 3: ↑3 and ↓0+3
Comments4

Modern Portable Voice Activity Detector Released

Reading time3 min
Views4.8K

image


Currently, there are hardly any high quality / modern / free / public voice activity detectors except for WebRTC Voice Activity Detector (link). WebRTC though starts to show its age and it suffers from many false positives.


Also in some cases it is crucial to be able to anonymize large-scale spoken corpora (i.e. remove personal data). Typically personal data is considered to be private / sensitive if it contains (i) a name (ii) some private ID. Name recognition is a highly subjective matter and it depends on locale and business case, but Voice Activity and Number Detection are quite general tasks.


Key features:


  • Modern, portable;
  • Low memory footprint;
  • Superior metrics to WebRTC;
  • Trained on huge spoken corpora and noise / sound libraries;
  • Slower than WebRTC, but fast enough for IOT / edge / mobile applications;
  • Unlike WebRTC (which mostly tells silence from voice), our VAD can tell voice from noise / music / silence;
  • PyTorch (JIT) and ONNX checkpoints;

Typical use cases:


  • Spoken corpora anonymization;
  • Can be used together with WebRTC;
  • Voice activity detection for IOT / edge / mobile use cases;
  • Data cleaning and preparation, number and voice detection in general;
  • PyTorch and ONNX can be used with a wide variety of deployment options and backends in mind;
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