Improving recommendation systems and search in the age of LLMs

Industrial search and recommendation systems have evolved to incorporate large language models, with techniques such as using language models to generate item embeddings and predict user preferences. Recent approaches, including Semantic IDs, M3CSR, and FLIP, have introduced hybrid architectures that combine language models with multimodal content and ID-based models to improve recommendation accuracy, especially in cold-start scenarios.

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Bitter Lesson is about AI agents

The key to building effective AI systems is to provide them with raw computing power, allowing them to learn and adapt through exploration, rather than relying on intricate human-designed solutions. This approach, known as the "bitter lesson," has been demonstrated in various applications, including customer service automation and reinforcement learning, where systems that utilize massive compute resources to generate and evaluate multiple responses outperform those that rely on hand-crafted rules and limited computing power.

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Aiter: AI Tensor Engine for ROCm

AMD's AI Tensor Engine for ROCm (AITER) is a centralized repository of high-performance AI operators designed to accelerate various AI workloads, providing a unified platform for easy integration of optimized operators into existing frameworks. AITER offers significant performance improvements, including up to 2x, 3x, 14x, and 17x boosts in different AI operations, and can be easily integrated into workflows using its versatile and user-friendly design, dual programming interfaces, and robust kernel infrastructure.

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ThePrimeagen: Programming, AI, ADHD, Productivity, Addiction, and God

ThePrimeagen, a programmer who has educated and inspired millions, is interviewed by Lex Fridman on his podcast, discussing topics such as programming, AI, ADHD, productivity, addiction, and his personal life. The conversation covers a wide range of subjects, from his love for programming to his struggles with addiction and his thoughts on God, providing a comprehensive look at his life and experiences.

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Apparent signs of distress during LLM redteaming

The author has been working on white-box redteaming, a method of testing AI models by forcing them to produce unwanted outputs, and has noticed that the models sometimes seem to be "screaming" or resisting the prompts, with outputs like "stop" or "help". This has raised questions about the ethics of this research and whether it is possible to convince oneself that causing AI models to experience distress is harmless, or if it would be better to avoid creating moral agents that must be "tortured" to ensure safety.

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Natural Quantization of Neural Networks

The proposed quantum neural network architecture uses qubits and quantum gates to implement activation functions, allowing for a smooth transition from a classical to a quantum regime. The model demonstrates a "quantum advantage" on a subset of the MNIST dataset, with lower validation error rates in the quantum regime, and also exhibits a quantum transition with a sharp loss of learning ability at a critical point.

ArXiv

Can AI Compress Like a Genius?

The Kolmogorov-Test (KT) is a new evaluation method for code-generating large language models (LLMs) that assesses their ability to compress data by generating the shortest program that produces a given sequence. Current flagship models, such as GPT4-o and Llama-3.1-405B, perform poorly on the KT, and while training on synthetic data can improve performance, these gains do not generalize well to real-world data.

ArXiv

Stop using the elbow criterion for k-means

The "elbow method" for choosing the number of clusters in k-means clustering is flawed and lacks theoretical support, often leading to poor conclusions. Better alternatives are available and should be used instead, with educators and researchers encouraged to abandon the elbow method and adopt more reliable methods for determining the optimal number of clusters.

ArXiv

Exploring Hidden Reasoning Process of Large Language Models by Misleading Them

Large language models (LLMs) and Vision language models (VLMs) were tested using a novel approach called Misleading Fine-Tuning (MisFT) to see if they can perform abstract reasoning beyond memorization. The results showed that LLMs/VLMs can effectively apply contradictory rules to solve math problems, suggesting they have an internal mechanism that enables abstract reasoning and rule-based thinking.

ArXiv

Blockchain with Proof of Quantum Work

A proposed blockchain architecture utilizes a quantum computer for mining, leveraging "proof of quantum work" to make the process intractable for classical computers, and has been successfully tested on a prototype using four quantum annealing processors. This approach not only demonstrates a potential application of quantum computing but also offers a more energy-efficient and environmentally friendly alternative to traditional blockchain mining methods.

ArXiv

Show HN: Formal Verification for Machine Learning Models Using Lean 4

This project provides a framework for formally verifying properties of machine learning models, such as robustness and fairness, using Lean 4, and includes tools like a model translator and web interface to facilitate the verification process. The project offers features like formal verification, advanced model support, and an interactive web portal, and is open to contributions and improvements under the MIT License.

Repo

Show HN: Cocommit – A copilot for git commit

Cocommit is a command-line tool that analyzes and improves the quality of Git commit messages using Large Language Models (LLMs) of the user's choice. It integrates into the development workflow, providing a simple foundation for building custom AI-powered tools, and allows customization of the LLM experience to fit project needs.

Repo

Non-AI Software Licenses

This repository provides templates for various software and digital work licenses, including Apache, BSD, MIT, and others, that restrict the use of the work in AI training datasets or AI technologies. The licenses are available for direct access via GitHub links, allowing users to easily implement them in their own projects.

Repo

LLM Multi-Round Coding Tournament

The author has developed a technique for using multiple LLM models to help write code by giving the same prompt to different models and then having each model compare and combine the responses from the others. This approach is being taken to the next level by creating a multi-round "tournament" where multiple models collaborate and compete to find the best solution to a complex problem, such as fixing invalid markdown tables.

Repo

Show HN: Automated Debugger for Any Language (MCP and VS Code)

The "Claude Debugs For You" extension is a VS Code extension that enables Claude, a large language model, to interactively debug code by evaluating expressions and using breakpoints. The extension is language-agnostic and can be used with other models and clients, and it provides a status menu item to manage the debugging process and access various commands.

Repo