What Makes Telegram Chatbots So Useful? In this digital era, we prefer almost anything to be speedier and more convenient, don’t we? The examples of our life can be many, from setting events as reminders to searching for information and as simple as just chatting. Here’s the thing, instead of having to have an app for the many tasks we do, we can also use one single app for all of them. Can I reveal to you that you can perform all of these tasks in one place only? That is great, isn’t it? Right, this is how the chatbot Telegram works for you. The multifunctionality of your daily life, the complexity of the educational process or the very process of staying organized are all well within the capabilities of a chatbot Telegram. Also, it’s very cool that the feature is embedded in the Telegram app, an app that is very popular and is used by many of us daily. By deploying a Telegram bot such as Yatter, which is based on ChatGPT-4, you can not only make your work easier but also you will get answers instantly and there’s even a chance to talk to your assistant. Yeah, too...

We all leave traces behind: emails, text messages, photos, voice notes. But what if you could go one step further? What if your loved ones could still talk to you after you’re gone? Thanks to advances in artificial intelligence, digital immortality is no longer the stuff of science fiction. It’s becoming technically possible to create custom AIs that mimic a person’s personality, tone, and behavior, and eventually their voice and even their face. But like any AI system, your future digital twin will only be as good as the data it’s trained on. If you want to live on as an AI that your family and friends can talk to, here’s what you should start collecting today. Text – The Foundation of Digital Memory Text is still the easiest and richest form of data for training AI. Personal emails Chat logs and text message threads Social media posts and comments Journals, blog posts, essays, poems Any long-form writing in your own voice The more text, the better. Aim for diversity in topics and tone: serious, funny, emotional, factual. This gives the AI a fuller range of expression to learn from. Voice – The Next Level of Presence Once you’ve built a convincing...

 Hi everyone, There are a ton of language models out there today! Many of which have their unique way of learning “self-supervised” language representations that can be used by other downstream tasks.  In this article, I decided to summarize the current trends and share some key insights to glue all these novel approaches together.  😃 (Slide credits: Delvin et. al. Stanford CS224n) Problem: Context-free/Atomic Word Representations We started with context-free approaches like word2vec, GloVE embeddings in my previous post. The drawback of these approaches is that they do not account for syntactic context. e.g. “open a bank account” v/s “on the river bank“. The word bank has different meanings depending on the context the word is used in. Solution #1: Contextual Word Representations With ELMo the community started building forward (left to right) and backward (right to left) sequence language models, and used embeddings extracted from both (concatenated) these models as pre-trained embeddings for downstream modeling tasks like classification (Sentiment etc.) Potential drawback: ELMo can be considered a “weakly bi-directional model” as they trained 2 separate models here. Solution #2: Truly bi-directional Contextual Representations To solve the drawback of “weakly bi-directional” approach and the information bottleneck that comes with LSTMs...

Research by FTI Consulting and Relativity has found that inhouse lawyers now view genAI technology as very much within their ‘comfort zone’ – and that’s great to see. It also has several far-reaching implications, (see below). The data collected covers how inhouse lawyers view the use of genAI internally, or by law firms advising them.   In this case, when it came to AI tools for reviewing contracts, 85% said they were comfortable to some degree, with 60% saying they were ‘very’ or ‘extremely’ comfortable with using the tech, or their advisers doing so. After that came privilege review, doc review in general (i.e. not necessarily just contracts), then e-discovery, and then all the way through to compliance monitoring – which even in this ‘least comfortable’ task position saw almost 40% say they were ‘very’ or ‘extremely’ comfortable with an AI solution. In short…..what an incredible rate of uptake and familiarisation. Back in 2023 there was plenty of commentary about how inhouse lawyers were nervous about using AI tools, or even their advisers doing so. Well, that nervousness has largely evaporated. From Artificial Lawyer’s perspective there are several key takeaways here: If clients are happy to use this tech internally...

Despite significant advancements in artificial intelligence, a concerning trend is emerging: the newest and most sophisticated AI models, particularly those employing complex “reasoning” capabilities, are demonstrating a significant increase in inaccurate and fabricated information. This is a phenomenon commonly referred to as “hallucinations.” This development is puzzling to industry leaders and posing considerable challenges for the widespread and reliable application of AI technologies. Recent testing of the latest models from major players like OpenAI and DeepSeek reveals a surprising reality: these supposedly more intelligent systems are generating incorrect information at higher rates than their predecessors. OpenAI’s own evaluations, detailed in a recent research paper, showed that their latest o3 and o4-mini models, released in April, suffered from significantly elevated hallucination rates compared to their earlier o1 model from late 2024. For instance, when summarizing questions about public figures, o3 hallucinated 33% of the time, while o4-mini did so a staggering 48% of the time. In stark contrast, the older o1 model had a hallucination rate of just 16%. The issue is not isolated to OpenAI. Independent testing by Vectara, which ranks AI models, indicates that several “reasoning” models, including DeepSeek’s R1, have experienced significant increases in hallucination rates compared to...

In the context of artificial intelligence (AI), information is the building block used for training and operating models. The diversity, quality, and pertinence of data directly affect how fair and precise AI systems are. But gathering such data is no small feat—it requires ensuring diversity, maintaining high standards, and staying compliant with regulations. A data collection partner is a company that provides specialized data services to improve AI model training, accuracy, and compliance. How AI Training Data Collection Partners Help Train AI AI Training Data Collection Partners specialize in sourcing, curating, and managing datasets for specific AI use cases. Their strengths include: Tailored Data Solutions: Designing data collection strategies that align with unique project goals. Resource Efficiency: Using proven infrastructures to collect data effectively and at scale. By working with a partner, organizations overcome typical data hurdles and ensure their AI is trained on high-quality, representative datasets. Improving Data Quality Great AI models are powered by great data. Here’s how partners enhance data quality: Ensuring Relevance: Collecting data suited to specific use-case scenarios. Comprehensive Coverage: Capturing a wide range of real-world situations. Data Labeling and Cleanup: Removing duplicates, correcting errors, and accurately tagging data for better training. 📌 Example: A...

Exploring the Machine Learning Periodic Table Exploring the Machine Learning Periodic Table opens a gateway to simplifying complex algorithms and concepts into a structured, visual framework. Imagine having a guide that not only organizes machine learning tools, techniques, and models, but also helps you choose the right ones based on your problem type and data characteristics. This is where Microsoft’s innovative concept shines. If you’re navigating the fast-evolving world of artificial intelligence and machine learning, this table can save time, reduce confusion, and bring clarity to your ML workflow. It’s engineered to drive curiosity, and built for practitioners who want actionable insights. Also Read: Microsoft 365 Copilot Adds New AI Models What Is the Machine Learning Periodic Table? The Machine Learning Periodic Table is a curated chart, inspired by the classic chemical periodic table. Created by Microsoft researchers, it organizes more than 100 machine learning methods, tools, and concepts in a way that makes them intuitive to explore and apply. Each “element” in the table represents a component such as an algorithm, objective, or process that is vital in the ML development lifecycle. Grouped into thematic categories such as learning types, optimization methods, fairness, interpretability, and evaluation metrics, this table...

This article accompanies a visual roadmap which you can view and download here. Roadmapping is a useful tool to allow us to look into the future, predict different possible pathways, and identify areas that might present opportunities or problems. The aim is to visualise different scenarios in order to prepare, and avoid scenarios that might lead to an undesirable future or even worse, disaster. It is also an exercise for visualizing a desired future and finding the optimal path towards achieving it. This roadmap depicts three hypothetical scenarios in the development of an artificial general intelligence (AGI) system, from the perspective of an imaginary company (C1). The main focus is on the AI race, where stakeholders strive to reach powerful AI, and its implications on safety. It maps out possible decisions made by key actors in various “states of affairs”, which lead to diverse outcomes. Traffic-light color coding is used to visualise the potential outcomes with green showing positive outcomes, red — negative and orange — in-between. The aim of this roadmap is not to present the viewer with all possible scenarios, but with a few vivid examples. The roadmap is primarily focusing on AGI, which presumably will have a...