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

PowerInfer: Fast Large Language Model Serving with a Consumer-grade GPU

189 implied HN points • 28 Dec 23
  1. PowerInfer is a fast inference engine for Large Language Models (LLM) optimized to run on consumer-grade GPUs.
  2. The system relies on identifying and handling 'hot' and 'cold' neurons efficiently to reduce GPU memory requirements.
  3. PowerInfer achieves significant speed improvements, up to ten times faster than previous models, without compromising model quality.

Big Post About Big Context

49 HN points • 29 Feb 24
đź•ą Technology AI Machine Learning Models Programming AGI
  1. The context size in modern LLMs keeps increasing significantly, from 4k to 200k tokens, leading to improved model capabilities.
  2. The ability of models to handle 1M tokens allows for new possibilities like analyzing legal documents or generating code from videos, enhancing productivity.
  3. As AI models advance, the nature of work for entry positions may change, challenging the need for juniors and suggesting a shift towards content validation tools.

More Agents Is All You Need

63 implied HN points • 18 Feb 24
đź•ą Technology Artificial Intelligence Machine Learning Models Improvements
  1. Having more agents and aggregating their results through voting can improve outcome quality, as demonstrated by a team from Tencent
  2. The approach of generating multiple samples from the same model and conducting a majority vote shows promise for enhancing various tasks like Arithmetic Reasoning, General Reasoning, and Code Generation
  3. Ensembling methods showed quality improvement with the ensemble size but plateaued after around 10 agents, with benefits being stable across different hyperparameter values

TinyLlama: An Open-Source Small Language Model

126 implied HN points • 06 Jan 24
  1. Introducing TinyLlama, a small language model with 1.1 billion parameters trained on 3 trillion tokens.
  2. Small language models like TinyLlama are gaining importance alongside larger models, showing promising results.
  3. Technical details of TinyLlama's architecture and training process showcase advanced techniques and efficient performance.

Thermodynamic AI is getting hotter

51 HN points • 08 Feb 24
  1. Thermodynamic AI involves stochastic building blocks for hardware, uniting software and hardware.
  2. Thermodynamic AI algorithms are based on physics principles and use stochasticity.
  3. SPUs, or stochastic processing units, in thermodynamic computers show promise over classical hardware with advantages in energy consumption and performance.
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Generative Agents: Interactive Simulacra of Human Behavior

189 implied HN points • 23 Sep 23
  1. Researchers have created generative agents that simulate human behaviors like daily routines and social interactions.
  2. The agents are hosted in a sandbox environment called 'Smallville' which is a small town with houses, shops, and parks.
  3. The agents' architecture includes components like Memory Stream for storing experiences, Reflection for abstract memories, and Planning for consistent behavior.

The convolution empire strikes back

132 HN points • 27 Oct 23
  1. Convolutional networks perform well at scale, challenging the notion that transformers excel in large datasets
  2. Researchers achieved state-of-the-art results on ImageNet using the Normalizer-Free ResNets family of architectures
  3. Computational power and data quality remain crucial in model performance, highlighting the importance of inductive biases in model selection

For Distillation, Tokens Are Not All You Need

63 implied HN points • 20 Dec 23
  1. The proposed method SLIM for LLM distillation outperforms classical distillation methods like SFT and MiniLLM.
  2. SLIM utilizes sparse logits to reduce space requirements during distillation process for better efficiency.
  3. SLIM showed better results in instruction-following and downstream tasks compared to SFT and MiniLLM.

GPT-4V is coming!

126 implied HN points • 04 Oct 23
  1. GPT-4V, a new model from OpenAI, integrates vision to understand and generate text and images.
  2. The GPT-4V model card showcases safety measures and features like refusal of risky queries.
  3. Preliminary explorations with GPT-4V show capabilities like responding to textual instructions, visual pointing, and hybrid prompts combining text and visuals.

[S4] Efficiently Modeling Long Sequences with Structured State Spaces

63 implied HN points • 12 Dec 23
  1. State Space Models (SSM) and HiPPO address the challenge of modeling long sequences efficiently.
  2. Structured State Spaces (S4) is an improved version of SSM, with techniques like decomposition of matrices and Cauchy kernel applications.
  3. S4 has shown superiority in tasks like time series prediction and language modeling, beating efficient transformers in LRA benchmark.

"Building Machines That Learn and Think Like People", 7 years later

106 HN points • 13 Oct 23
  1. The paper talks about building machines that learn and think like people by going beyond current engineering trends in what and how they learn.
  2. GPT-4V has advanced capabilities in image captioning compared to previous models, providing detailed and accurate descriptions of scenes.
  3. The progress in image captioning models like GPT-4V over the years is impressive and showcases significant advancements in AI technology.

The Guardian, 2024

189 implied HN points • 03 Apr 23
  1. The article discusses what news to expect in 2024.
  2. The content includes generative AI images.
  3. There are links to various images related to The Guardian, 2024.

Borges and AI

63 implied HN points • 08 Oct 23
  1. Viewing language modeling through Borges' eyes offers a fresh perspective on AI.
  2. Perfect language model can be thought of as a powerful fiction-writing machine.
  3. Implementing verification machines may be key in using AI-generated narratives responsibly.

Textbooks Are All You Need II: phi-1.5 technical report

63 implied HN points • 20 Sep 23
  1. A new model called phi-1.5 was introduced with commendable performance in generating Python code.
  2. Investment in high-quality datasets and common sense reasoning training is crucial in AI research.
  3. The phi-1.5 model outperforms models of comparable size on various benchmarks, excelling in both math and code reasoning.

Mortal Computers

31 HN points • 29 Sep 23
  1. Forward-Forward algorithm is an alternative to backpropagation in AI, with potential for efficient training of small networks.
  2. Exploration of 'mortal computers' challenges the idea of separating hardware and software in computing, suggesting potential for efficient analog hardware but with a lifespan.
  3. Distillation training method can enhance generalization in AI models, offering advantages over traditional class label training.

Neural Network Diffusion

1 HN point • 26 Feb 24
đź•ą Technology Neural Networks Model Evaluation
  1. Hypernetworks involve one neural network generating weights for another - still a relatively unknown but promising concept worth exploring further.
  2. Diffusion models involve adding noise (forward) and removing noise (reverse) gradually to reveal hidden details - a strategy utilized effectively in the study.
  3. Neural Network Diffusion (p-diff) involves training an autoencoder on neural network parameters to convert and regenerate weights, showing promising results across various datasets and network architectures.

Toward understanding the communication in sperm whales

2 HN points • 17 Dec 23
  1. The CETI project aims to understand sperm whale communication using ML and robots.
  2. Whale communication involves articulatory blocks, composition rules, and meaning interpretation.
  3. Studying whale communication faces challenges like lack of large datasets and involves data collection, decoding, and interaction with whales.

Conway’s Game of Life is Omniperiodic

2 HN points • 09 Dec 23
  1. Conway's Game of Life has patterns with any period, making it omniperiodic.
  2. Different types of oscillators exist in Conway's Game of Life, such as blinkers, pulsars, and gliders.
  3. The Game of Life has been proven to be omniperiodic, with oscillators found for periods previously missing.

[Google] Gemini: A Family of Highly Capable Multimodal Models

2 HN points • 07 Dec 23
  1. Google Gemini is a highly capable multimodal model that competes well with GPT models
  2. Gemini is a multimodal model that can handle various inputs like text, audio, images, and videos
  3. Gemini achieved state-of-the-art performance on benchmarks and excels in tasks like speech recognition and machine translation

Sparse Universal Transformer

3 HN points • 23 Oct 23
  1. Sparse Universal Transformer integrates Sparse Mixture of Experts to enhance computational efficiency.
  2. The SUT research utilizes special loss functions like Mutual Information Maximization for training.
  3. Experiments show SUT outperformed other transformers in various tasks with improved computational efficiency.

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.

🪆Matryoshka Representation Learning

2 HN points • 03 Nov 23
  1. Challenges with fixed-size embeddings can impact computational costs and quality in machine learning models.
  2. Matryoshka Representation Learning (MRL) introduces adaptable embeddings with nested subspaces for different task demands.
  3. MRL shows effectiveness in tasks like classification, retrieval, and few-shot learning, offering improved efficiency and performance.

Mindstorms in Natural Language-Based Societies of Mind

2 HN points • 29 Oct 23
  1. The concept of Natural-language SOMs (NLSOMs) allows for communication between modules using human language instead of exchanging tensors, creating more flexible and understandable AI systems.
  2. NLSOMs present opportunities for modularity, explainability, and human-biased AI in neural communities, leading to advancements in various tasks like visual question answering, image captioning, and prompt generation for text-to-image synthesis.
  3. The Economy of Minds (EOM) concept explores credit assignment and reward mechanisms in NLSOMs, envisioning a system where AI agents interact within an economy, offering services, earning money, and evolving through transactions, potentially integrating into human economies and societies.

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.

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.

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.

[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.

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.

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.

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.

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.

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.

“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.