Beyond Market Intelligence/parameters

parameters

parameters on Beyond Market Intelligence: a running collection of 10 stories we have gathered and hand-picked because they are worth your time. Every post here touches on parameters in some way — the news, the analysis, the deep dives, and the occasional surprise find. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work with data. New stories are added to this page as we find them, so check back if you want to keep up with what is happening around parameters, or subscribe to the RSS feed to get them as soon as they are published. Browse the collection below, or head back to the homepage to see everything Beyond Market Intelligence is covering right now.

Machine Learning

First A submission (AAMAS): how much theory is enough when your experiments went sideways? [D]

Navigating the complexities of empirical MARL research, particularly under A* submission deadlines like AAMAS, often demands a careful balance between experimental rigor and theoretical grounding. A 2nd-year PhD candidate currently facing this challenge highlights a common predicament: experiments yielding nuanced results and a subsequent struggle to formulate robust theory. Recognizing the potential pitfalls of HARKing and data anomalies, the post seeks advice on acceptable theory depth at A* venues and strategies for salvaging a project timeline.

Machine Learning

Bart- A vintage llm [R]

Unbounded Labs proudly introduces Bart, a 2.82B parameter LLM meticulously trained from scratch on a unique corpus of 20.1B tokens of English text predating 1931. After three months and a modest $800 investment, we’ve achieved a significant milestone: the best-performing vintage base model at its scale on Vintage CORE. Our research, detailed in a comprehensive article, explores the potential for LLMs to replicate historical scientific reasoning—a crucial step toward understanding AI originality. Explore Bart and our methodology at the links provided.

Machine Learning

Imagenet-1k Classifier trained entirely on an Android [P]

Introducing a surprisingly capable Imagenet-1k classifier, trained entirely on an Android device using a compact MLP architecture with approximately 500K parameters. Despite utilizing a downscaled 32x32 dataset and training for just 5 epochs, the model achieves a Top-1 accuracy of 4.59% and a Top-5 accuracy of 12.68%. This project, executed within Termux on a Dimensity 9300+ CPU, demonstrates the potential for accessible AI development, training in roughly 30 minutes. As noted in a related discussion, "Non-Physical Intelligence Has A Ceiling," even efficient models require a

Machine Learning

Transformers are famously bad at arithmetic, so I set one's weights by hand (no training) and it multiplies with 100% accuracy [P]

Researchers have demonstrated a surprising feat: achieving 100% accuracy in arithmetic calculations within a Phi-3 transformer model, entirely without training. By meticulously hand-crafting the model's weights to implement a grade-school multiplication algorithm, they’ve created a functional three-digit calculator—and extended it to support up to 12-digit multiplication via Hugging Face checkpoints. This experiment highlights a stark contrast in performance compared to frontier models, revealing limitations in their ability to handle precise calculations.

Machine Learning

Improved compression of Bad Apple into a Neural Network [P]

Recent experimentation with SIREN networks has yielded significant improvements in compressing the "Bad Apple" video. By employing a novel batch generation technique that incorporates pixels across the entire video, we’ve achieved a more faithful reproduction while maintaining the original model architecture—4 x 512 wide sine layers totaling 792,257 parameters. While a full framerate version proved challenging due to increased temporal data demands, the low-rate version demonstrates compelling compression capabilities. This reimplementation, built using GPT5.

Machine Learning

I have trained a model to predict my blood sugar [P]

A novel AI model for blood sugar prediction has been released, offering a future-focused approach to diabetes management. This encoder-only transformer, leveraging a BERT-style architecture, accurately forecasts blood glucose levels up to two hours ahead by analyzing past and future data (glucose, carbs, insulin), conditioned on announced meals and boluses. Four model sizes exist, ranging from a compact nano version (<40K parameters) to a 17-million-parameter large model. As discussed in "Conference Reviews: Asking Too Much?

VentureBeat

Thinking Machines debuts Inkling Small open source AI model nearing performance of predecessor at about 1/4 size

Thinking Machines has unveiled Inkling-Small, a groundbreaking open-source AI model demonstrating remarkable efficiency. Nearing the performance of its predecessor, Inkling, this new model achieves this at roughly one-quarter the size, surpassing it on several key benchmarks. Released under a permissive Apache 2.0 license, Inkling-Small offers enterprises a compelling blend of power and practicality, reducing compute requirements and deployment complexities. Explore this transformative solution and discover how it can empower your data journey—a clear signal that enterprise AI is rapidly evolving.

Towards Data Science

How to Decode the Temperature Parameter in LLMs

Large Language Models (LLMs) offer remarkable generative capabilities, but understanding how to control their output is key. A crucial parameter is "temperature," which governs the balance between deterministic and creative responses. This post delves into the physics behind temperature, revealing how it dictates the transition from predictable outputs to the generation of novel text. Explore how statistical mechanics illuminates this core element of LLM behavior, empowering you to fine-tune your AI interactions.

Analytics Vidhya

Complete Guide to Thinking Machines Inkling

Thinking Machines Lab’s Inkling represents a significant advancement in AI foundation models. This open-weights model, boasting 975B parameters and a 1M-token context window, prioritizes adaptability over benchmark scores. Designed as a customizable base for diverse applications—from multimodal reasoning and agentic AI to coding and audio-visual tasks—Inkling empowers developers to build specialized solutions. Explore the complete guide to understand Inkling's architecture and potential. For broader context on the evolving AI landscape, consider "What to watch for after Jensen Huang’s Japan visit."

VentureBeat

China’s Moonshot AI releases Kimi K3, the largest open-source model ever, rivaling top U.S. systems

Moonshot AI has unveiled Kimi K3, a 2.8-trillion-parameter model now recognized as the world’s largest open-source AI, rivaling top proprietary systems from Anthropic and OpenAI. This release, timed before the 2026 World Artificial Intelligence Conference, marks a significant moment in the global AI race and a remarkable comeback for the Beijing-based startup. Full model weights will be released July 27th, allowing users to explore its capabilities—and potentially reshape their data strategies—at kimi.com.