mailitics

Category: editors-pick

  • Talk to Videos

    Talk to Videos Large language models (LLMs) are improving in efficiency and are now able to understand different data formats, offering possibilities for myriads of applications in different domains. Initially, LLMs were inherently able to process only text. The image understanding feature was integrated by coupling an LLM with another image encoding model. However, gpt-4o…

  • Attractors in Neural Network Circuits: Beauty and Chaos

    Attractors in Neural Network Circuits: Beauty and Chaos The state space of the first two neuron activations over time follows an attractor. What is one thing in common between memories, oscillating chemical reactions and double pendulums? All these systems have a basin of attraction for possible states, like a magnet that draws the system towards certain…

  • A Clear Intro to MCP (Model Context Protocol) with Code Examples

    A Clear Intro to MCP (Model Context Protocol) with Code Examples As the race to move AI agents from prototype to production heats up, the need for a standardized way for agents to call tools across different providers is pressing. This transition to a standardized approach to agent tool calling is similar to what we…

  • Build Your Own AI Coding Assistant in JupyterLab with Ollama and Hugging Face

    Build Your Own AI Coding Assistant in JupyterLab with Ollama and Hugging Face Jupyter AI brings generative AI capabilities right into the Jupyter interface. Having a local AI assistant ensures privacy, reduces latency, and provides offline functionality, making it a powerful tool for developers. In this article, we’ll learn how to set up a local…

  • Evolving Product Operating Models in the Age of AI

    Evolving Product Operating Models in the Age of AI In a previous article on organizing for AI (link), we looked at how the interplay between three key dimensions — ownership of outcomes, outsourcing of staff, and the geographical proximity of team members — can yield a variety of organizational archetypes for implementing strategic AI initiatives,…

  • Google’s Data Science Agent: Can It Really Do Your Job?

    Google’s Data Science Agent: Can It Really Do Your Job? On March 3rd, Google officially rolled out its Data Science Agent to most Colab users for free. This is not something brand new — it was first announced in December last year, but it is now integrated into Colab and made widely accessible. Google says…

  • R.E.D.: Scaling Text Classification with Expert Delegation

    R.E.D.: Scaling Text Classification with Expert Delegation With the new age of problem-solving augmented by Large Language Models (LLMs), only a handful of problems remain that have subpar solutions. Most classification problems (at a PoC level) can be solved by leveraging LLMs at 70–90% Precision/F1 with just good prompt engineering techniques, as well as adaptive…

  • Algorithm Protection in the Context of Federated Learning 

    Algorithm Protection in the Context of Federated Learning  While working at a biotech company, we aim to advance ML & AI Algorithms to enable, for example, brain lesion segmentation to be executed at the hospital/clinic location where patient data resides, so it is processed in a secure manner. This, in essence, is guaranteed by federated…

  • Mastering Prompt Engineering with Functional Testing: A Systematic Guide to Reliable LLM Outputs 

    Mastering Prompt Engineering with Functional Testing: A Systematic Guide to Reliable LLM Outputs  Creating efficient prompts for large language models often starts as a simple task… but it doesn’t always stay that way. Initially, following basic best practices seems sufficient: adopt the persona of a specialist, write clear instructions, require a specific response format, and…

  • Using GPT-4 for Personal Styling

    Using GPT-4 for Personal Styling I’ve always been fascinated by Fashion—collecting unique pieces and trying to blend them in my own way. But let’s just say my closet was more of a work-in-progress avalanche than a curated wonderland. Every time I tried to add something new, I risked toppling my carefully balanced piles. Why this…

  • When You Just Can’t Decide on a Single Action

    When You Just Can’t Decide on a Single Action In Game Theory, the players typically have to make assumptions about the other players’ actions. What will the other player do? Will he use rock, paper or scissors? You never know, but in some cases, you might have an idea of the probability of some actions…

  • Overcome Failing Document Ingestion & RAG Strategies with Agentic Knowledge Distillation

    Overcome Failing Document Ingestion & RAG Strategies with Agentic Knowledge Distillation Introduction Many generative AI use cases still revolve around Retrieval Augmented Generation (RAG), yet consistently fall short of user expectations. Despite the growing body of research on RAG improvements and even adding Agents into the process, many solutions still fail to return exhaustive results,…

  • The Urgent Need for Intrinsic Alignment Technologies for Responsible Agentic AI

    The Urgent Need for Intrinsic Alignment Technologies for Responsible Agentic AI Advancements in agentic artificial intelligence (AI) promise to bring significant opportunities to individuals and businesses in all sectors. However, as AI agents become more autonomous, they may use scheming behavior or break rules to achieve their functional goals. This can lead to the machine…

  • Generative AI and Civic Institutions

    Generative AI and Civic Institutions Different sectors, different goals Recent events have got me thinking about AI as it relates to our civic institutions — think government, education, public libraries, and so on. We often forget that civic and governmental organizations are inherently deeply different from private companies and profit-making enterprises. They exist to enable…

  • Avoidable and Unavoidable Randomness in GPT-4o

    Avoidable and Unavoidable Randomness in GPT-4o Of course there is randomness in GPT-4o’s outputs. After all, the model samples from a probability distribution when choosing each token. But what I didn’t understand was that those very probabilities themselves are not deterministic. Even with consistent prompts, fixed seeds, and temperature set to zero, GPT-4o still introduces…

  • I Won’t Change Unless You Do

    I Won’t Change Unless You Do In Game Theory, how can players ever come to an end if there still might be a better option to decide for? Maybe one player still wants to change their decision. But if they do, maybe the other player wants to change too. How can they ever hope to…

  • The Dangers of Deceptive Data–Confusing Charts and Misleading Headlines

    The Dangers of Deceptive Data–Confusing Charts and Misleading Headlines “You don’t have to be an expert to deceive someone, though you might need some expertise to reliably recognize when you are being deceived.” When my co-instructor and I start our quarterly lesson on deceptive visualizations for the data visualization course we teach at the University…

  • When Optimal is the Enemy of Good: High-Budget Differential Privacy for Medical AI

    When Optimal is the Enemy of Good: High-Budget Differential Privacy for Medical AI Imagine you’re building your dream home. Just about everything is ready. All that’s left to do is pick out a front door. Since the neighborhood has a low crime rate, you decide you want a door with a standard lock — nothing too fancy,…

  • 6 Common LLM Customization Strategies Briefly Explained

    6 Common LLM Customization Strategies Briefly Explained Why Customize LLMs? Large Language Models (Llms) are deep learning models pre-trained based on self-supervised learning, requiring a vast amount of resources on training data, training time and holding a large number of parameters. LLM have revolutionized natural language processing especially in the last 2 years, demonstrating remarkable…

  • Learning How to Play Atari Games Through Deep Neural Networks

    Learning How to Play Atari Games Through Deep Neural Networks In July 1959, Arthur Samuel developed one of the first agents to play the game of checkers. What constitutes an agent that plays checkers can be best described in Samuel’s own words, “…a computer [that] can be programmed so that it will learn to play…

  • Neural Networks – Intuitively and Exhaustively Explained

    Neural Networks – Intuitively and Exhaustively Explained An in-depth exploration of the most fundamental architecture in modern AI “The Thinking Part” by Daniel Warfield using MidJourney. All images by the author unless otherwise specified. Article originally made available on Intuitively and Exhaustively Explained. In this article we’ll form a thorough understanding of the neural network,…

  • Improving Agent Systems & AI Reasoning

    Improving Agent Systems & AI Reasoning DeepSeek-R1, OpenAI o1 & o3, Test-Time Compute Scaling, Model Post-Training and the Transition to Reasoning Language Models (RLMs) Image by author and GPT-4o meant to represent DeepSeek and other competitive GenAI model providers Introduction Over the past year generative AI adoption and AI Agent development have skyrocketed. Reports from LangChain…

  • AI Ethics for the Everyday User — Why Should You Care?

    AI Ethics for the Everyday User — Why Should You Care? A beginner’s guide to understanding the importance of ethics in artificial intelligence Continue reading on Towards Data Science » Murtaza Ali Go to original source

  • How GenAI Tools Have Changed My Work as a Data Scientist

    How GenAI Tools Have Changed My Work as a Data Scientist An overview of the 4 use cases and 6 GenAI tools I use Continue reading on Towards Data Science » Jonte Dancker Go to original source

  • Understanding Emergent Capabilities in LLMs: Lessons from Biological Systems

    Understanding Emergent Capabilities in LLMs: Lessons from Biological Systems How natural systems fundamental laws help explain AI’s unexpected abilities Continue reading on Towards Data Science » Javier Marin Go to original source

  • The Solar Cycle(s): history, data analysis and trend forecasting.

    The Solar Cycle(s): history, data analysis and trend forecasting. The Solar Cycle(s): History, Data Analysis and Trend Forecasting A brief article on the Solar Cycles, the history behind their observation, data analysis and time series forecasting for the incoming solar maximum in 2025–2026 and the next decades You have probably heard about the 11-year Solar Cycle…

  • Fighting Fraud Fairly: Upgrade Your AI Toolkit

    Fighting Fraud Fairly: Upgrade Your AI Toolkit A practical approach to address bias in AI systems Photo by the author As sophisticated AI systems are increasingly used in decision-making, ensuring fairness has become a priority, with a growing need to prevent algorithms from disproportionately affecting vulnerable groups in sensitive areas like the justice or educational system. One…

  • How to Pick Between Data Science, Data Analytics, Data Engineering, ML Engineering, and SW…

    How to Pick Between Data Science, Data Analytics, Data Engineering, ML Engineering, and SW… Make the right choice for YOU Continue reading on Towards Data Science » Marina Wyss – Gratitude Driven Go to original source

  • Water Cooler Small Talk: Benford’s Law

    Water Cooler Small Talk: Benford’s Law A look into the strange first digit distribution of naturally occurring datasets Continue reading on Towards Data Science » Maria Mouschoutzi, PhD Go to original source

  • What Would a Stoic Do? — An AI-Based Decision-Making Model

    What Would a Stoic Do? — An AI-Based Decision-Making Model Using AI to build Marcus Aurelius’ reincarnation Continue reading on Towards Data Science » Pol Marin Go to original source

  • Solving A Rubik’s Cube with Supervised Learning — Intuitively and Exhaustively Explained

    Solving A Rubik’s Cube with Supervised Learning — Intuitively and Exhaustively Explained A Popular Toy in a Brave New World Continue reading on Towards Data Science » Daniel Warfield Go to original source

  • The Cultural Impact of AI Generated Content: Part 2

    The Cultural Impact of AI Generated Content: Part 2 What can we do about the increasingly sophisticated AI generated content in our lives? Photo by Meszárcsek Gergely on Unsplash In my prior column, I established how AI generated content is expanding online, and described scenarios to illustrate why it’s occurring. (Please read that before you go on…

  • Demand Forecasting with Darts: A Tutorial

    Demand Forecasting with Darts: A Tutorial A hands-on tutorial with Python and Darts for demand forecasting, showcasing the power of TiDE and TFT Photo by Victoriano Izquierdo on Unsplash Demand forecasting for retailing companies can become a complex task, as several factors need to be considered from the start of the project to the final deployment. This…

  • Multi-Agentic RAG with Hugging Face Code Agents

    Multi-Agentic RAG with Hugging Face Code Agents Using Qwen2.5–7B-Instruct powered code agents to create a local, open source, multi-agentic RAG system Photo by Jaredd Craig on Unsplash Large Language Models have shown impressive capabilities and they are still undergoing steady improvements with each new generation of models released. Applications such as chatbots and summarisation can directly exploit…

  • 2024 Survival Guide for Machine Learning Engineer Interviews

    2024 Survival Guide for Machine Learning Engineer Interviews A year-end summary for junior-level MLE interview preparation Job-seeking is hard! In today’s market, job-seeking for machine learning-related roles is more complex than ever. Even though public reports claim that the job demand for machine learning engineers (MLE) is fast growing, the fact is that the market has…

  • An Agentic Approach to Reducing LLM Hallucinations

    An Agentic Approach to Reducing LLM Hallucinations Simple techniques to alleviate LLM hallucinations using LangGraph Photo by Greg Rakozy on Unsplash If you’ve worked with LLMs, you know they can sometimes hallucinate. This means they generate text that’s either nonsensical or contradicts the input data. It’s a common issue that can hurts the reliability of LLM-powered…

  • Evaluation-Driven Development for agentic applications using PydanticAI

    Evaluation-Driven Development for agentic applications using PydanticAI An open-source, model-agnostic agentic framework that supports dependency injection Ideally, you can evaluate agentic applications even as you are developing them, instead of evaluation being an afterthought. For this to work, though, you need to be able to mock both internal and external dependencies of the agent you…

  • When Averages Lie: Moving Beyond Single-Point Predictions

    When Averages Lie: Moving Beyond Single-Point Predictions The Case for Predicting Full Probability Distributions in Decision-Making Some people like hot coffee, some people like iced coffee, but no one likes lukewarm coffee. Yet, a simple model trained on coffee temperatures might predict that the next coffee served should be… lukewarm. This illustrates a fundamental problem…

  • From Prototype to Production: Enhancing LLM Accuracy

    From Prototype to Production: Enhancing LLM Accuracy Implementing evaluation frameworks to optimize accuracy in real-world applications Image created by DALL-E 3 Building a prototype for an LLM application is surprisingly straightforward. You can often create a functional first version within just a few hours. This initial prototype will likely provide results that look legitimate and be…

  • Is Complex Writing Nothing But Formulas?

    Is Complex Writing Nothing But Formulas? Text analytics hints at how volumes of writing get created In the broadest of strokes, Natural Language Processing transforms language into constructs that can be usefully manipulated. Since deep-learning embeddings have proven so powerful, they’ve also become the default: pick a model, embed your data, pick a metric, do some…

  • Modeling DAU with Markov Chain

    Modeling DAU with Markov Chain How to predict DAU using Duolingo’s growth model and control the prediction 1. Introduction Doubtlessly, DAU, WAU, and MAU — daily, weekly, and monthly active users — are critical business metrics. An article “How Duolingo reignited user growth” by Jorge Mazal, former CPO of Duolingo, is #1 in the Growth section of Lenny’s Newsletter…

  • Reinforcement Learning: Self-Driving Cars to Self-Driving Labs

    Reinforcement Learning: Self-Driving Cars to Self-Driving Labs Understanding AI applications in bio for machine learning engineers Photo by Ousa Chea on Unsplash Anyone who has tried teaching a dog new tricks knows the basics of reinforcement learning. We can modify the dog’s behavior by repeatedly offering rewards for obedience and punishments for misbehavior. In reinforcement learning…

  • How to Build a General-Purpose LLM Agent

    How to Build a General-Purpose LLM Agent A Step-by-Step Guide High-level Overview of an LLM Agent. (Image by author) Why build a general-purpose agent? Because it’s an excellent tool to prototype your use cases and lays the groundwork for designing your own custom agentic architecture. Before we dive in, let’s quickly introduce LLM agents. Feel free…

  • The Cultural Impact of AI Generated Content: Part 1

    The Cultural Impact of AI Generated Content: Part 1 What happens when AI generated media becomes ubiquitous in our lives? How does this relate to what we’ve experienced before, and how does it change us? Photo by Annie Spratt on Unsplash This is the first part of a two part series I’m writing analyzing how people and…

  • Water Cooler Small Talk: Simpson’s Paradox

    Water Cooler Small Talk: Simpson’s Paradox Is your data tricking you? What can you do about it? Continue reading on Towards Data Science » Maria Mouschoutzi, PhD Go to original source