Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Ever since Mira Murati departed OpenAI last fall, many have wondered about the former CTO’s next move. Well, now we know (or, at least have a rough idea).  Murati took to X today to announce her new venture Thinking Machines Lab, an AI research and product company with a goal to “advance AI by making it broadly useful and understandable through solid foundations, open science and practical applications.” Murati posted: “We’re building three things: Helping people adapt AI systems to work for their specific needs; Developing strong foundations to build more capable AI systems; Fostering a culture of open science that helps the whole field understand and improve these systems.” Thinking Machine’s team of roughly two dozen engineers and scientists is stacked with other OpenAI alums — including co-founder and deep reinforcement learning pioneer John Schulman and ChatGPT co-creator Barret Zoph — which could position the startup to make significant strides in AI research and development.  As of the posting of this article, the company’s newly-launched X account @thinkymachines had already amassed roughly 14,000 followers.  Model intelligence, multimodality, strong infrastructure  Thinking Machines — not to be mistaken with the now defunct supercomputer and AI firm of the 1980s — isn’t yet offering specific examples of intended projects, but suggests a broad focus on multimodal capabilities, human-AI collaboration (as opposed to purely agentic systems) and strong infrastructure. The goal is to build more “flexible, adaptable and personalized AI systems,” the company writes on its new website.  Multimodality is “critical” to the future of AI, Thinking Machines says, as it allows for more natural and efficient communication that captures intent and supports deeper integration. “Instead of focusing solely on making fully autonomous AI systems, we

The customer support teams were drowning with the overwhelming volume of customer inquiries at every company I’ve worked at. Have you had similar experiences? What if I told you that you could use AI to automatically identify, categorize, and even resolve the most common issues? By fine-tuning a transformer model like BERT, you can build an automated system that tags tickets by issue type and routes them to the right team. In this tutorial, I’ll show you how to fine-tune a transformer model for emotion classification in five steps: By the end, you’ll have a fine-tuned model that classifies emotions from text inputs with high accuracy, and you’ll also learn how to interpret these predictions using SHAP. This same approach can be applied to real-world use cases beyond emotion classification, such as customer support automation, sentiment analysis, content moderation, and more. Let’s dive in! Choosing the Right Transformer Model When selecting a transformer model for Text Classification, here’s a quick breakdown of the most common models: For this tutorial, I chose to fine-tune DistilBERT because it offers the best balance between performance and efficiency. Step 1: Setup and Installing Dependencies Ensure you have the required libraries installed: Step 2: Load and Preprocess Data I used the Emotions dataset for NLP by Praveen Govi, available on Kaggle and licensed for commercial use. It contains text labeled with emotions. The data comes in three .txt files: train, validation, and test. Each line contains a sentence and its corresponding emotion label, separated by a semicolon: Parsing the Dataset into a Pandas DataFrame Let’s load the dataset: Understanding the Label Distribution This dataset contains 16k training examples and 2k examples for the validation and testing. Here’s the label distribution breakdown: The bar chart above shows that the dataset is imbalanced, with the majority of

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Large language models (LLMs) may have changed software development, but enterprises will need to think twice about entirely replacing human software engineers with LLMs, despite OpenAI CEO Sam Altman’s claim that models can replace “low-level” engineers. In a new paper, OpenAI researchers detail how they developed an LLM benchmark called SWE-Lancer to test how much foundation models can earn from real-life freelance software engineering tasks. The test found that, while the models can solve bugs, they can’t see why the bug exists and continue to make more mistakes.  The researchers tasked three LLMs — OpenAI’s GPT-4o and o1 and Anthropic’s Claude-3.5 Sonnet — with 1,488 freelance software engineer tasks from the freelance platform Upwork amounting to $1 million in payouts. They divided the tasks into two categories: individual contributor tasks (resolving bugs or implementing features), and management tasks (where the model roleplays as a manager who will choose the best proposal to resolve issues).  “Results indicate that the real-world freelance work in our benchmark remains challenging for frontier language models,” the researchers write.  The test shows that foundation models cannot fully replace human engineers. While they can help solve bugs, they’re not quite at the level where they can start earning freelancing cash by themselves.  Benchmarking freelancing models The researchers and 100 other professional software engineers identified potential tasks on Upwork and, without changing any words, fed these to a Docker container to create the SWE-Lancer dataset. The container does not have internet access and cannot access GitHub “to avoid the possible of models scraping code diffs or pull request details,” they explained. The team identified 764 individual contributor tasks, totaling about $414,775, ranging from 15-minute bug fixes to weeklong feature

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 a better game of checkers than can be played by the person who wrote the program” [1]. The checkers’ agent tries to follow the idea of simulating every possible move given the current situation and selecting the most advantageous one i.e. one that brings the player closer to winning. The move’s “advantageousness” is determined by an evaluation function, which the agent improves through experience. Naturally, the concept of an agent is not restricted to the game of checkers, and many practitioners have sought to match or surpass human performance in popular games. Notable examples include IBM’s Deep Blue (which managed to defeat Garry Kasparov, a chess world champion at the time), and Tesauro’s TD-Gammon, a temporal-difference approach, where the evaluation function was modelled using a neural network. In fact, TD-Gammon’s playing style was so uncommon that some experts even adopted some strategies it conjured up [2]. Unsurprisingly, research into creating such ‘agents’ only skyrocketed, with novel approaches able to reach peak human performance in complex games. In this post, we explore one such approach: the DQN approach introduced in 2013 by Mnih et al, in which playing Atari games is approached through a synthesis of Deep Neural Networks and TD-Learning (NB: the original paper came out in 2013, but we will focus on the 2015 version which comes with some technical improvements) [3, 4]. Before we continue, you should note that in the ever-expanding space of new approaches, DQN has been superseded by faster and more refined state-of-the-art methods. Yet, it remains an ideal stepping

This is the second in a two-part series on using SQLite for Machine Learning. In my last article, I dove into how SQLite is rapidly becoming a production-ready database for web applications. In this article, I will discuss how to perform retrieval-augmented-generation using SQLite. If you’d like a custom web application with generative AI integration, visit losangelesaiapps.com The code referenced in this article can be found here. When I first learned how to perform retrieval-augmented-generation (RAG) as a budding data scientist, I followed the traditional path. This usually looks something like: In fact I actually wrote an article about my experience building a RAG system in LangChain with Pinecone. There is nothing terribly wrong with using a RAG framework with a cloud vector database. However, I would argue that for first time learners it overcomplicates the situation. Do we really need an entire framework to learn how to do RAG? Is it necessary to perform API calls to cloud vector databases? These databases act as black boxes, which is never good for learners (or frankly for anyone).  In this article, I will walk you through how to perform RAG on the simplest stack possible. In fact, this ‘stack’ is just Sqlite with the sqlite-vec extension and the OpenAI API for use of their embedding and chat models. I recommend you read part 1 of this series to get a deep dive on SQLite and how it is rapidly becoming production ready for web applications. For our purposes here, it is enough to understand that SQLite is the simplest kind of database possible: a single file in your repository.  So ditch your cloud vector databases and your bloated frameworks, and let’s do some RAG. SQLite-Vec One of the powers of the SQLite database is the use of extensions. For those of

In the past few years, technology and AI have evolved more than ever. As I read about the new concepts in tech and learn new skills and techniques each day, I feel in a state of limbo — there is so much content to consume and yet, very little content that I could create.  In the rapidly expanding technological world of today, AI is taking over as it gets increasingly integrated into the various aspects of our lives. With the growing concerns around AI usage, it is quintessential to have tools and applications that build trust in Artificial Intelligence, support business automation, and drive faster, more accurate decision-making. That is exactly where Decision Intelligence (DI) comes into the picture. In this blog, I want to introduce the readers to the concept of Decision Intelligence — a very nuanced and rapidly growing concept. This will be your guide to knowing the what, why, and how of Decision Intelligence. Introduction to Decision Intelligence A good decision is often defined by its outcome. But how do you ensure that you’re making the best choice? This is where Decision Intelligence (DI) becomes important for organizations making focused, contextual, and personalized decisions. Decision Intelligence is an interdisciplinary field that uses AI to enhance all aspects of decision-making across all areas of a Business. It blends concepts of Data Science (statistics, machine learning, AI, analytics) with Behavioral Sciences (psychology, neuroscience, economics, and managerial sciences) to understand how decisions are made and how outcomes are measured.  What is the difference between Decision Intelligence and Artificial Intelligence? Artificial Intelligence is about creating systems or machines that can do tasks that usually need human intelligence, like learning from experience, recognizing patterns, solving problems, understanding language, and making decisions. Decision Intelligence (DI) can be considered a subset where it uses AI to build a