Gonzo ML

Gonzo ML focuses on the latest advancements and ideas in machine learning (ML) and artificial intelligence (AI), including model architectures, efficiency improvements, and novel applications. It discusses developments in generative models, optimization techniques, hardware innovations, and the ethical implications of AI through both theoretical explorations and practical implementations.

Machine Learning Artificial Intelligence Generative Models Model Optimization AI Hardware AI Ethics Large Language Models Neural Networks Computational Efficiency AI Applications

The hottest Substack posts of Gonzo ML

And their main takeaways

Beyond Chinchilla-Optimal: Accounting for Inference in Language Model Scaling Laws

1 HN point • 08 Jan 24
  1. Inference is a crucial phase in language model development, accounting for the majority of the model's lifespan.
  2. When considering optimal training for language models, there is a trade-off between training a larger model using traditional guidelines or training a smaller model with more tokens for improved inference efficiency.
  3. Updating scaling laws to include mass inference suggests that training smaller models for longer periods can lead to significant cost savings and resource efficiency.

Mamba: Linear-Time Sequence Modeling with Selective State Spaces

1 HN point • 13 Dec 23
  1. Selective state space models show promise in competing with transformers in modeling long sequences, especially in text-based models.
  2. The new class of selective state space models, like S6 and Mamba, introduce the concept of selectivity as a fundamental principle in building efficient sequence models.
  3. Mamba, a linear-time sequence modeling architecture, achieves quality comparable to strong transformer models in various tasks, showcasing higher efficiency in memory usage and computation throughput.

MemWalker

1 HN point • 17 Oct 23
  1. Transformers have limitations in handling long documents and various strategies have been developed to address this.
  2. MemWalker is a novel solution that uses a memory tree construction and navigation to efficiently work with long input sequences.
  3. MemWalker outperformed other baselines in processing longer documents, showing potential as a useful tool.

Chain-of-Thought → Tree-of-Thought

1 HN point • 10 Oct 23
  1. Chain-of-Thought (CoT) involves asking the model for intermediate steps before final results.
  2. Tree-of-Thoughts (ToT) represents reasoning as a tree structure, allowing backtracking for better problem-solving.
  3. ToT strategy uses LLM as a search heuristic, enabling long-range reasoning capabilities.
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Learning to Model the World with Language

0 implied HN points • 20 Aug 23
  1. Dynalang combines world dynamics and actions with language input for better understanding.
  2. The model learns mainly from imaginary action-movie scenarios, not real experiences.
  3. Dynalang enables pre-training on large text and video corpora without taking actions.

OLMo: Accelerating the Science of Language Models

0 implied HN points • 10 Mar 24
🔬 Science Language Models Research Open Source Training Evaluation
  1. OLMo is an open language model created by Allen AI, differentiating itself by being completely open-source including logs, checkpoints, and evaluation scripts under the Apache 2.0 License.
  2. OLMo comprises three models: 1B, 7B, and 65B, demonstrating improvements in classic transformer decoders similar to GPT, such as specific tokenization for PII and non-parametric layer normalization.
  3. OLMo was trained on data from their own dataset Dolma with plans to expand beyond English, showcasing their training process with PyTorch FSDP and evaluation using their benchmark Paloma and the Catwalk framework.

Uncovering mesa-optimization algorithms in Transformers

0 implied HN points • 21 Sep 23
  1. Transformers show impressive performance in in-context learning.
  2. Mesa-optimization algorithms operate within transformers to enhance learning efficiency.
  3. Architectural modifications can incorporate mesa-optimization as a core feature in transformers.

s1: Simple test-time scaling

0 implied HN points • 12 Feb 25
đź•ą Technology AI Machine Learning Data science Computing Software Development
  1. A new model called s1-32B was created by using a small dataset of 1,000 question-answer pairs focused on reasoning. This cost about $25 to train, which is quite affordable.
  2. The method of controlling how much the model thinks during tests allows for better performance. They used a strategy called budget forcing to ensure the model generates the right amount of information.
  3. This approach showed that it's possible to achieve high-quality results with less data and resources, suggesting a promising path for future AI developments.

NVIDIA's Vision for AI

0 implied HN points • 08 Jan 25
đź•ą Technology Artificial Intelligence Computing Hardware Software Gaming
  1. NVIDIA is leading the way in AI technology, and their new RTX Blackwell chip is really powerful, making gaming and other processes faster and more efficient.
  2. Project Digits is an exciting new product that allows for powerful AI processing in a compact and portable form, which could change how we use AI at home.
  3. NVIDIA's focus on world models and agents signals a shift towards more sophisticated AI systems, making it clear they are planning for a future where AI plays a bigger role in daily life.

Generative Agents 2.0

0 implied HN points • 24 Feb 25
đź•ą Technology AI Research Simulation Data science Behavioral Science
  1. Researchers successfully created AI agents that can simulate 1,052 real people with about 85% accuracy. This means the AI can closely mimic how real people would respond in various situations.
  2. The study highlights the importance of interviews over surveys, as they provide deeper insights into people’s behaviors and thoughts, allowing the AI to generate better follow-up questions and responses.
  3. These AI agents have potential uses in social science research. They could help predict public reactions to policy changes or simulate behavioral responses, leading to new methods of understanding human decision-making.

GFlowNets

0 implied HN points • 03 Jan 24
  1. GFlowNets are generative networks that use flow networks for diverse candidate generation.
  2. These networks work by representing states as nodes and transitions as edges in a directed acyclic graph.
  3. GFlowNets can be useful in tasks requiring exploration and can be applied to fields like biology for tasks such as biological sequence design.

“Human Compatible”, Stuart Russell

0 implied HN points • 01 Jan 24
  1. The book delves into the future implications of achieving advanced artificial intelligence and the importance of ensuring its alignment with human values.
  2. Stuart Russell proposes an approach to AI that prioritizes maximizing human preferences over any other objectives embedded in machines.
  3. Inverse Reinforcement Learning is highlighted as a method for machines to learn human preferences through observing human behavior.

System 2 Attention (is something you might need too)

0 implied HN points • 05 Dec 23
  1. System 2 Attention helps transformers focus on relevant information by distinguishing between fast and controlled thinking, improving answer accuracy.
  2. Implementing System 2 Attention involves rewriting prompts to remove irrelevant data and enhance model responses in tasks like question-answering and problem solving.
  3. System 2 Attention variants were tested and showed improved quality and objectivity in factual question answering, longform generation, and math problem solving tasks.

The Generative Cyberiad

0 implied HN points • 23 Mar 23
  1. Trurl creates a machine that can write poetry, sparking a series of events with both positive and negative consequences.
  2. The story serves as a cautionary tale about the dangers and potentials of technology, urging for the responsible use of powerful tools.
  3. The narrative emphasizes the impact of creativity and poetry in inspiring change, unity, and hope in the world.

Large Language Model Programs

0 implied HN points • 28 Sep 23
  1. Language Language Models (LLMs) can be fine-tuned or used in-context for different tasks.
  2. LLM Programs offer a new method where the LLM is embedded into a program for task-solving.
  3. Implementing LLM Programs can simplify task-solving with less fine-tuning, improved precision, and no interference between steps.

[DeepMind SIMA] Scaling Instructable Agents Across Many Simulated Worlds

0 implied HN points • 17 Mar 24
đź•ą Technology AI Evaluation
  1. DeepMind developed SIMA, an agent that follows language instructions and operates in diverse 3D virtual environments using only keyboard and mouse commands.
  2. SIMA is trained on behavioral cloning and predictive models, with a focus on rich language interactions and interdisciplinary learning.
  3. Evaluation of SIMA involved overcoming challenges like asynchronous environments, and the agent showed promising results and varied performance across different tasks and environments.