Wednesday, AI 101: a dive deep into Jepa, a framework for AI that emphasizes joint embedding predictive architecture, designed to improve unsupervised learning by predicting future observations from current embeddings.;

Friday: A new investigation into the world if AI Unicorns.

Timing: Apple isn't early, but it's not late either. The company is entering the AI landscape at a time when it can learn from others' mistakes and focus on delivering AI features that genuinely enhance the user experience.

Hardware Boost: AI is computationally intensive, and Apple's latest devices are well-equipped to handle it. This could drive a wave of upgrades, benefiting Apple's bottom line.

Ecosystem Lock-In: By integrating AI into its ecosystem, Apple makes its devices even more indispensable to users. This strengthens its platform and makes it harder for users to switch to competitors.

Privacy Focus: Apple has a strong reputation for privacy, and its AI features are designed with this in mind.

California Laws and the risks to open source

AMD is keeping up with Nvidia

Microsoft: Recall’s backlash and the introduction of Aurora

Google uses AI in software engineering

Misinformation is more manageable than expected

Sam Altman’s web

A video overview of the three stages of developing an LLM: Building, Training, and Finetuning. Building, Training, Finetuning by Sebastian Raschka

The Rise of AI Agent Infrastructure by Madrona

Brilliant Max Read’s take on Leopold Aschenbrenner: A new A.I. influencer is producing some of the most criminal charts I've ever seen

The interview with Lisa Su, CEO of AMD, by Stratechery

“The idea here is that just as we've built foundation models to help us better classify and generate human language, we might seek to do the same with animal,” – Researchers from the Max Planck Institute of Animal Behavior and their collaborators introduced animal2vec and MeerKAT: A self-supervised transformer for rare-event raw audio input and a large-scale reference dataset for bioacoustics. Animal2vec, a self-supervised transformer model, addresses the challenges of analyzing sparse and unbalanced bioacoustic data by learning from raw audio waveforms. The MeerKAT dataset, the largest publicly available labeled dataset on non-human terrestrial mammals, contains over 1068 hours of audio, including 184 hours with detailed vocalization labels. Isn’t that incredible? I told my dog that I would soon understand his language, and he rolled his eyes.

Researchers from OpenAI introduced “Scaling and Evaluating Sparse Autoencoders” paper, which presents a methodology how to interpret and extract concepts from GPT-4. Using scalable techniques, they decomposed its internal representations into 16 million patterns. Despite challenges in capturing the full behavior of large models, their sparse autoencoders identify human-interpretable features. This research aims to enhance model trustworthiness and steerability. OpenAI is open-sourcing their findings, including papers, code, and visualizations.

In the paper “Open-Endedness is Essential for Artificial Superhuman Intelligence,” researchers from Google DeepMind argue that achieving artificial superhuman intelligence (ASI) necessitates open-ended AI systems – those capable of continuous self-improvement and discovery. They provide a formal definition of open-endedness based on novelty and learnability. The paper illustrates the potential of foundation models combined with open-ended systems to create human-relevant discoveries and highlights the safety implications of developing such AI.

Advancing DRL Agents in Commercial Fighting Games: Training, Integration, and Agent-Human Alignment – Explores developing and deploying a DRL agent system in a commercial game using Heterogeneous League Training to balance competence and efficiency, aligning agent behavior with human expectations. Read the paper

Artificial Generational Intelligence: Cultural Accumulation in Reinforcement Learning investigates how RL agents can accumulate culture across generations, enhancing capabilities through episodic in-context learning and in-weights learning, inspired by human cultural evolution. Read the paper

Self-Improving Robust Preference Optimization iIntroduces an offline RLHF framework to enhance alignment with human preferences, treating learning as a self-improvement process optimized through a min-max objective. Read the paper

AGENTGYM: Evolving LLM-based Agents across Diverse Environments presents a framework for training LLM-based agents to handle diverse tasks, integrating behavioral cloning and a novel evolution method, achieving performance comparable to state-of-the-art models. Read the paper

MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark presents an enhanced benchmark to evaluate LLMs with more difficult questions, showcasing a significant drop in model accuracy, highlighting its effectiveness in distinguishing model capabilities. Read the paper

Show, Don’t Tell: Aligning Language Models with Demonstrated Feedback introduces Demonstration Iterated Task Optimization to align LLMs with user preferences using fewer examples, significantly outperforming other methods. Read the paper

To Believe or Not to Believe Your LLM explores uncertainty quantification in LLMs, developing an information-theoretic metric to detect high epistemic uncertainty, identifying unreliable responses and hallucinations. Read the paper

Buffer of Thoughts: Thought-Augmented Reasoning with Large Language Models proposes a framework enhancing LLMs' reasoning abilities using a meta-buffer to store high-level thought-templates, achieving significant performance improvements across diverse tasks. Read the paper

PLaD: Preference-based Large Language Model Distillation with Pseudo-Preference Pairs introduces a framework for distilling LLMs using preference data, leveraging quality discrepancy for ranking loss, improving model performance and generation quality. Read the paper

Item-Language Model for Conversational Recommendation combines a text-aligned item encoder with a frozen LLM to integrate user interaction signals, enhancing recommendation performance and maintaining language and reasoning capabilities. Read the paper

Will we run out of data? Limits of LLM scaling based on human-generated data analyzes constraints on LLM scaling due to finite human-generated text data, suggesting synthetic data, transfer learning, and improving data efficiency to sustain progress. Read the paper

µLO: Compute-Efficient Meta-Generalization of Learned Optimizers develops a method for improving generalization of learned optimizers using Maximal Update Parametrization, enabling zero-shot generalization of optimizer hyperparameters. Read the paper

Block Transformer: Global-to-Local Language Modeling for Fast Inference introduces an architecture optimizing inference speed by separating global and local context modeling, drastically reducing key-value cache retrieval needs and increasing throughput. Read the paper

Scaling Laws for Reward Model Overoptimization in Direct Alignment Algorithms analyzes reward overoptimization in DAAs used in training LLMs, proposing solutions for mitigating performance degradation at higher KL divergence budgets. Read the paper