Wednesday, Computer Vision History Series: we discuss ImageNet, a dataset that enabled AlexNet, which in turn led to the emergence of GenAI;

Friday: An interview with Eli Hooten, Director of Engineering at Sentry, about AI and coding

As GPT-4o is not enough, OpenAI has introduced the Model Spec, a new initiative aimed at guiding AI behavior to ensure transparency and public engagement in shaping AI responses. This adaptive document will evolve with community feedback and research, balancing ethical considerations and practical uses. The Model Spec also integrates aspects of RLHF transparency and personalization.

If you considered to become a prompt engineer, maybe don’t. Microsoft’s latest Copilot AI update transforms everyday users into prompt engineers, helping them craft better prompts.

Anthropic has released Metaprompt, a tool designed to enhance performance in Claude-powered applications by converting brief task descriptions into optimized prompt templates. It utilizes a few-shot prompt method with variables like subject, length, and tone.

Databricks eats more of FMOps space. They just launched Vector Search, integrating it into their platform with governance tools. This feature optimizes embedding storage and retrieval, supporting real-time similarity searches using the HNSW algorithm and managing sync with Delta tables. Academics rule!

It just unveiled eight open-source Granite code models, ranging from 3 to 34 billion parameters, trained on 116 programming languages. Available on Hugging Face in both base and instruct modes, these models excel in code generation, bug fixes, and documentation, even translating COBOL to modern languages. Despite their excellence, a limitation is their smaller context lengths (2k to 8k tokens), which may hinder detailed tasks in large codebases.

Kolmogorov–Arnold Networks (KAN) made a splash last week. The ML community discusses it nonstop. This blog explains why KANs are a potential alternative to multi-layer perceptrons (MLPs) and the current landscape of machine learning. This blog argues that KAN is just MLP.

Google DeepMind CEO on Drug Discovery, Hype, Isomorphic | Bloomberg

Google CEO Sundar Pichai and the Future of AI | Bloomberg

A Conversation with Elon Musk | Milken Institute

How Diffusion Models are Improving AI by Artificial Made SImple

The Illustrated Word2vec by Jay Alammar

How Good Are the Latest Open LLMs? And Is DPO Better Than PPO? by Sebastian Raschka

ChatBotArena: The peoples’ LLM evaluation, the future of evaluation, the incentives of evaluation, and gpt2chatbot by interconnects.ai

Researchers from ELLIS Unit, LIT AI Lab, Institute for Machine Learning, JKU Linz, Austria introduced the xLSTM, enhancing traditional LSTMs by integrating exponential gating and novel memory structures. xLSTM improves over standard LSTM and offers competitive performance to state-of-the-art Transformers in various language modeling tasks. Modifications include scalar and matrix memory models, allowing both better parallelization and efficiency, particularly in handling longer sequences and larger datasets effectively →read the paper

Researchers from Google DeepMind have developed AlphaFold 3, a new model that significantly advances the prediction of complex biomolecular structures. This version introduces a diffusion-based architecture capable of accurately modeling interactions across proteins, nucleic acids, small molecules, and more. AlphaFold 3 outperforms specialized tools in predicting protein-ligand, protein-nucleic acid, and antibody-antigen interactions, demonstrating its effectiveness across a broad spectrum of biomolecular research within a unified deep learning framework →read the paper

Researchers from the University of Pennsylvania and NVIDIA have developed DrEureka, an algorithm that leverages LLMs to automate reward design and domain randomization for robot training. DrEureka enhances the sim-to-real transfer by automating tasks that were manually intensive, showing competitive results in tasks like quadruped locomotion and novel challenges such as balancing a quadruped on a yoga ball. This approach reduces human labor and could accelerate the development of robotic skills adaptable to dynamic real-world applications →read the paper

Generalization in Diffusion Models Arises from Geometry-Adaptive Harmonic Representations by Zahra Kadkhodaie, Florentin Guth, Eero P Simoncelli, Stéphane Mallat →read the paper
ICLR Presentation

Learning Interactive Real-World Simulators by Sherry Yang, Yilun Du, Seyed Kamyar Seyed Ghasemipour, Jonathan Tompson, Leslie Pack Kaelbling, Dale Schuurmans, Pieter Abbeel →read the paper
ICLR Presentation

Never Train from Scratch: Fair Comparison of Long-Sequence Models Requires Data-Driven Priors by Ido Amos, Jonathan Berant, Ankit Gupta
→read the paper
ICLR Presentation

Protein Discovery with Discrete Walk-Jump Sampling by Nathan C. Frey, Dan Berenberg, Karina Zadorozhny, Joseph Kleinhenz, Julien Lafrance-Vanasse, Isidro Hotzel, Yan Wu, Stephen Ra, Richard Bonneau, Kyunghyun Cho, Andreas Loukas, Vladimir Gligorijevic, Saeed Saremi to→read the paper
ICLR Presentation

Vision Transformers Need Registers by Timothée Darcet, Maxime Oquab, Julien Mairal, Piotr Bojanowski →read the paper
ICLR Presentation

DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model: Introduces an advanced Mixture-of-Experts language model that significantly reduces training costs and improves computational efficiency while maintaining high performance →read the paper

Characterizing the Accuracy - Efficiency Trade-off of Low-rank Decomposition in Language Models: Explores the balance between model compression and performance, demonstrating that Tucker decomposition can effectively reduce model size with minimal accuracy loss →read the paper

Can LLMs Deeply Detect Complex Malicious Queries? A Framework for Jailbreaking via Obfuscating Intent: Examines vulnerabilities in LLMs' ability to detect complex malicious queries, introducing a method that successfully evades security mechanisms →read the paper

A Survey on RAG Meets LLMs: Towards Retrieval-Augmented Large Language Models: Reviews the integration of Retrieval-Augmented Generation techniques with LLMs, addressing challenges such as hallucinations and enhancing model robustness and relevance →read the paper

Position: Leverage Foundational Models for Black-Box Optimization: Proposes the integration of LLMs with black-box optimization strategies, suggesting potential enhancements in diverse fields through improved strategy engineering →read the paper