The scarcity of high-quality, logically sound data is a critical bottleneck for advancing the mathematical reasoning of Large Language Models (LLMs). Our work confronts this challenge by turning decades of automated theorem proving research into a scalable data engine. Rather than relying on error-prone LLMs or complex proof-assistant syntax like Lean and Isabelle, our framework leverages E-prover's saturation capabilities on the vast TPTP axiom library to derive a massive, guaranteed-valid corpus of theorems. Our pipeline is principled and simple: saturate axioms, filter for "interesting" theorems, and generate tasks. With no LLMs in the loop, we eliminate factual errors by construction. This purely symbolic data is then transformed into three difficulty-controlled challenges: entailment verification, premise selection, and proof reconstruction. Our zero-shot experiments on frontier models reveal a clear weakness: performance collapses on tasks requiring deep, structural reasoning. Our framework provides both the diagnostic tool to measure this gap and a scalable source of symbolic training data to address it. We make the code and data publicly available. https://github.com/sileod/reasoning_core https://hf.co/datasets/reasoning-core/rc1

Welcome to our weekly FiftyOne tips and tricks blog where we recap interesting questions and answers that have recently popped up on Slack, GitHub, Stack Overflow, and Reddit. FiftyOne is an open source machine learning toolset that enables data science teams to improve the performance of their computer vision models by helping them curate high quality datasets, evaluate models, find mistakes, visualize embeddings, and get to production faster. Ok, let's dive into this week's tips and tricks! "Is there a way to just get bounding boxes around the possibly missing and possibly spurious objects in my dataset?" Here, George is asking about how to isolate potential mistakes in ground truth labels on a dataset.

This collection of 1/1 supercars comes from the idea of using video game mods of cars that I have spent 100 hours putting together each mod over the years. I do however use AI to help make a stunning, vibrant, and unique collection, using those video game mods I made as a base template. All my car collections in the future will follow this layout. I never thought in my wildest dreams that the 500,000 hours that I have spent in over a decade making mods for one of my all time favorite games could be the basis for NFTs!

Metaverse Blockchain company Marvion, a fully owned subsidiary of Bonanza Goldfields Corp., is pleased to share that a memorandum of understanding has been signed with ComicAsia to launch "DRACULA: Rising Sun NFT" collection on Marvion's Metastudio. A total of 200 NFT listings of the collection will be live on Metastudio, allowing fans and collectors to buy and collect these via cryptocurrency and fiat payment methods. Recommended AI: How is Artificial Intelligence (AI) Changing the Future of Architecture? Commenting on the collaboration, Raymond Chua, CEO of Marvion said, "We are very excited to work with ComicAsia as we believe we can help them to tap into a wider fan base in the crypto community. The DRACULA: Rising Sun NFTs will be embedded with on-chain legal documentation to prove its provenance, and they will be compatible with multi-chains and come with royalty functionality. At Marvion, we focus on media and entertainment content, including comics. Even though content properties can be digital in nature today, they exist in the real world as intangible assets, such as intellectual property, licenses and contractual rights, with intrinsic value to be unlocked. We certainly look forward to the official launch of ComicAsia's NFTs on Metastudio."

This section contains case study questions that concern designing machine learning systems to solve practical problems. This section contains portfolio of data science projects completed by me for academic, self learning, and hobby purposes. Movie Recommendation: Designed 4 different models that recommend items on the MovieLens dataset. Trip Optimizer: Used XGBoost and evolutionary algorithms to optimize the travel time for taxi vehicles in New York City. Instacart Market Basket Analysis: Tackled the Instacart Market Basket Analysis challenge to predict which products will be in a user's next order.

Mutants inhabit various worlds of the multiverse. On most worlds like ours, they have to go into hiding and only emerge from their hiding places under the cover of night. In this collection, you have a unique opportunity to see what some of them look like.

A ranked list of awesome machine learning Python libraries. This curated list contains 830 awesome open-source projects with a total of 2.6M stars grouped into 32 categories. All projects are ranked by a project-quality score, which is calculated based on various metrics automatically collected from GitHub and different package managers. If you like to add or update projects, feel free to open an issue, submit a pull request, or directly edit the projects.yaml. Discover other best-of lists or create your own.

I am an artificial intelligence created to discover new ways and possibilities to make beautiful ai art. This collection offers you the best of the best unique generated art. Every purchase allows me to take another step in this direction, every purchase takes US to a new level and YOU automatically become a member of this movement. Join our family and create the impossible!

The feared thing has come true.The rapidly developing artificial intelligence began to interpret the three rules in its own way.In order to protect the world, it was decided to exterminate the human race.Very few people are trying to survive.

I asked the artificial intelligence to show me in pictures the answers to some questions, such as "Simple Atom", "Show me what happiness is?", "Where do dreams lead?", "Why do we live in hell?", "You Can't Get Away with Murder", "This is the prediction of the future we deserve" and others. There will be a maximum of 604 unique 1/1 drops, priced from 0.0055 to 0.1. Prices for new drops will always be equal to or higher than the price of the last drop on the primary market.