Generative large language models (LLMs) have become crucial for modern NLP research and applications across various languages. However, the development of foundational models specifically tailored to the Russian language has been limited, primarily due to the significant computational resources required. This paper introduces the GigaChat family of Russian LLMs, available in various sizes, including base models and instruction-tuned versions. We provide a detailed report on the model architecture, pre-training process, and experiments to guide design choices. In addition, we evaluate their performance on Russian and English benchmarks and compare GigaChat with multilingual analogs. The paper presents a system demonstration of the top-performing models accessible via an API, a Telegram bot, and a Web interface. Furthermore, we have released three open GigaChat models in open-source (https://huggingface.co/ai-sage), aiming to expand NLP research opportunities and support the development of industrial solutions for the Russian language.

In early 2020, deepfake expert Henry Ajder uncovered one of the first Telegram bots built to "undress" photos of women using artificial intelligence. At the time, Ajder recalls, the bot had been used to generate more than 100,000 explicit photos--including those of children--and its development marked a "watershed" moment for the horrors deepfakes could create. Since then, deepfakes have become more prevalent, more damaging, and easier to produce. Now, a WIRED review of Telegram communities involved with the explicit nonconsensual content has identified at least 50 bots that claim to create explicit photos or videos of people with only a couple of clicks. The bots vary in capabilities, with many suggesting they can "remove clothes" from photos while others claim to create images depicting people in various sexual acts.

This article was published as a part of the Data Science Blogathon. In addition to socializing with pals, humans are addicted to their smartphones. We primarily communicate via social media, such as WhatsApp, Facebook, Telegram, Instagram, and many others. It's always interesting to see how people start using technology on social media platforms. Especially because it is a very simple pattern to understand.

The Dataset we are going to use is the Loan prediction dataset. The loan prediction dataset is a unique dataset that contains 12 columns. The data was gathered to predict if a customer is eligible for a loan. The Dataset is publicly available on Kaggle and can be accessed using this link. Let's Start with the bottom-up approach and build a simple Machine learning model.

In this article, using Telegram and Python, I will show how to build a friendly Bot with multiple functions that can chat with question-answering conversations (short-term information) and store user data to recall in the future (long-term information). All this started because a friend of mine yelled at me for not remembering her birthday. I don't know if that has ever happened to you. So I thought I could pretend I remember birthdays while I actually have a Bot doing it for me. Now I know what you're thinking, why building something from scratch instead of using one of the millions of calendar apps around?

Around 104,852 women had their photos uploaded to a bot, on the WhatsApp-like text messaging app Telegram, which were then used to generate computer-generated fake nudes of them without their knowledge or consent, researchers revealed on Tuesday. The text messaging service Telegram was used to generate and share these fake nudes. These so-called "deepfake" images were created by an ecosystem of bots on the messaging app Telegram that could generate fake nudes on request, according to a report released by Sensity, an intelligence firm that specializes in deepfakes. The report found that users interacting with these bots were mainly creating fake nudes of women they know from images taken from social media, which is then shared and traded on other Telegram channels. The Telegram channels the researchers examined were made up of 101,080 members worldwide, with 70% coming from Russia and other eastern European countries.

Pornographic deepfakes are being weaponized at an alarming scale with at least 104,000 women targeted by a bot operating on the messaging app Telegram since July. The bot is used by thousands of people every month who use it to create nude images of friends and family members, some of whom appear to be under the age of 18. This story originally appeared on WIRED UK. The still images of nude women are generated by an AI that'removes' items of clothing from a non-nude photo. Every day the bot sends out a gallery of new images to an associated Telegram channel which has almost 25,000 subscribers.

Jovo is a cross-platform framework that you can use to build and run voice experiences that work across devices and platforms, including Amazon Alexa, Google Assistant, mobile phones, Raspberry Pi, and more. It also easily connects to bot platforms like Facebook Messenger, Slack, Telegram and more. Now that we have our bot ready to talk to, and the Dialogflow Agent to understand the conversation, we need to catch the intents spoken by the user and return a response. I have prepared a simple TelegramJovoHelloWorld project that you can use as a template for your bot. Go ahead and clone the repository.

Every Marvel fan must have at some point of time in his fandom read or watched Ironman and wish if he had Jarvis at his disposal. I went through the same crisis once and that is where it all began. I started exploring how feasible developing my own Virtual Assistant was, and that is how MEERA was born. MEERA stands for Multifunctional Event-driven Expert in Real-time Assistance. It started as a general purpose scalable virtual assistant backed by the mystic power of machine learning and artificial intelligence.

Let's see how to build a Telegram Bot which checks the emotions in your text with a quantitative approach through sentiment analysis. As already said, the weapon of choice is a Telegram Bot for the APIs ease of use, but any other programmable messenger system will work. A lot of companies offer a good text analysis API (IBM Watson, Google, Api.ai), but this time I decided to use Indico for the ease of use, the nice dashboard and mostly for the node module we can easily integrate in our webtask. We have a running bot, a running webtask which is not getting any message so far () and our AI ready to analyse our emotions and sentiments ().