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

Analyze research papers with Gemini 2.0

126 implied HN points • 23 Feb 25
🕹 Technology Artificial Intelligence Machine Learning Natural Language Processing Data science Cloud Computing
  1. Gemini 2.0 models can analyze research papers quickly and accurately, supporting large amounts of text. This means they can handle complex documents like academic papers effectively.
  2. The DeepSeek-R1 model shows that strong reasoning abilities can be developed in AI without the need for extensive human guidance. This could change how future models are trained and developed.
  3. Distilling knowledge from larger models into smaller ones allows for efficient and accessible AI that can perform well on various tasks, which is useful for many applications.

DeepSeek-V3: Technical Details

252 implied HN points • 06 Feb 25
🕹 Technology Artificial Intelligence Machine Learning Computer Science Data Analysis Software Development
  1. DeepSeek-V3 uses a new technique called Multi-head Latent Attention, which helps to save memory and speed up processing by compressing data more efficiently. This means it can handle larger datasets faster.
  2. The model incorporates an innovative approach called Multi-Token Prediction, allowing it to predict multiple tokens at once. This can improve its understanding of context and boost overall performance.
  3. DeepSeek-V3 is trained using advanced hardware and new training techniques, including utilizing FP8 precision. This helps in reducing costs and increasing efficiency while still maintaining model quality.

DeepSeek moment

441 implied HN points • 27 Jan 25
🕹 Technology AI Models Machine Learning Open Source Deep Learning
  1. DeepSeek is a game-changer in AI, trained models at a much lower cost compared to its competitors like OpenAI and Meta. This makes advanced technology more accessible.
  2. They released new models called DeepSeek-V3 and DeepSeek-R1, which offer impressive performance and reasoning capabilities similar to existing top models. These require advanced setups but show promise for future development.
  3. Their multimodal model, Janus-Pro, can work with both text and images, and it reportedly outperforms popular models in generation tasks. This indicates a shift toward more versatile AI technologies.

DeepSeek-R1: Open model with Reasoning

126 implied HN points • 10 Feb 25
🕹 Technology AI Research Machine Learning Natural Language Processing Open Source Reinforcement Learning
  1. DeepSeek-R1 shows how AI models can think through problems by reasoning before giving answers. This means they can generate longer, more thoughtful responses rather than just quick answers.
  2. This model is a big step for open-source AI as it competes well with commercial versions. The community can improve it further, making powerful tools accessible for everyone.
  3. The training approach used is innovative, focusing on reinforcement learning to teach reasoning without needing a lot of examples. This could change how we train AI in the future.

DeepSeek-V3: Training

126 implied HN points • 08 Feb 25
🕹 Technology Machine Learning Artificial Intelligence Data science Software Development Computer Science
  1. DeepSeek-V3 uses a lot of training data, with 14.8 trillion tokens, which helps it learn better and understand more languages. It's been improved with more math and programming examples for better performance.
  2. The training process has two main parts: pre-training and post-training. After learning the basics, it gets fine-tuned to enhance its ability to follow instructions and improve its reasoning skills.
  3. DeepSeek-V3 has shown impressive results in benchmarks, often performing better than other models despite having fewer parameters, making it a strong competitor in the AI field.
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Quick reflection on AI in 2024

504 implied HN points • 02 Jan 25
🕹 Technology AI Development Machine Learning Data science Software Engineering Innovations
  1. In 2024, AI is focusing on test-time compute, which is helping models perform better by using new techniques. This is changing how AI works and interacts with data.
  2. State Space Models are becoming more common in AI, showing improvements in processing complex tasks. People are excited about new tools like Bamba and Falcon3-Mamba that use these models.
  3. There's a growing competition among different AI models now, with many companies like OpenAI, Anthropic, and Google joining in. This means more choices for users and developers.

BLT: Byte Latent Transformer

315 implied HN points • 23 Dec 24
🕹 Technology Artificial Intelligence Machine Learning Data processing Software Development Innovation
  1. The Byte Latent Transformer (BLT) uses patches instead of tokens, allowing it to adapt based on the complexity of the input. This means it can process simpler inputs more efficiently and allocate more resources to complex ones.
  2. BLT can accurately encode text at a byte level, overcoming issues with traditional tokenization that often lead to mistakes in understanding languages and simple tasks like counting letters.
  3. BLT architecture has shown better performance than older models, handling tasks like translation and sequence manipulation more effectively. This advancement could improve the application of language models across different languages and reduce errors.

Not All Layers Are Equal

63 implied HN points • 31 Jan 25
🕹 Technology AI Research Machine Learning Data science Neural Networks Computational Theory
  1. Not every layer in a neural network is equally important. Some layers play a bigger role in getting the right results, while others have less impact.
  2. Studying how information travels through different layers can reveal interesting patterns. It turns out layers often work together to make sense of data, rather than just acting alone.
  3. Using methods like mechanistic interpretability can help us understand neural networks better. By looking closely at what's happening inside the model, we can learn which parts are doing what.

LCM: Large Concept Model

189 implied HN points • 04 Jan 25
🕹 Technology Artificial Intelligence Machine Learning Natural Language Processing Data science Computational Models
  1. The Large Concept Model (LCM) aims to improve how we understand and process language by focusing on concepts instead of just individual words. This means thinking at a higher level about what ideas and meanings are being conveyed.
  2. LCM uses a system called SONAR to convert sentences into a stable representation that can be processed and then translated back into different languages or forms without losing the original meaning. This creates flexibility in how we communicate.
  3. This approach can handle long documents more efficiently because it represents ideas as concepts, making processing easier. This could improve applications like summarization and translation, making them more effective.

🤘ACDC (not that one)

63 implied HN points • 29 Jan 25
🕹 Technology Artificial Intelligence Machine Learning Neural Networks Data Analysis Automation
  1. The paper introduces a method called ACDC that automates the process of finding important circuits in neural networks. This can help us better understand how these networks work.
  2. Researchers follow a three-step workflow to study model behavior, and ACDC fully automates the last step which helps identify connections that matter for a specific task.
  3. While ACDC shows promise, it isn't perfect. It may miss some important connections and needs adjustments for different tasks to improve its accuracy.

Transformer^2: Self-adaptive LLMs

63 implied HN points • 27 Jan 25
🕹 Technology Artificial Intelligence Machine Learning Data science Computing Software Development
  1. Transformer^2 uses a new method for adapting language models that makes it simpler and more efficient than fine-tuning. Instead of retraining the whole model, it adjusts specific parts, which saves time and resources.
  2. The approach breaks down weight matrices through a process called Singular Value Decomposition (SVD), allowing the model to identify and enhance its existing strengths for various tasks.
  3. At test time, Transformer^2 can adapt to new tasks in two passes, first assessing the situation and then applying the best adjustments. This method shows improvements over existing techniques like LoRA in both performance and parameter efficiency.

JAX things to watch for in 2025

378 implied HN points • 26 Nov 24
🕹 Technology AI Software Programming Data science Machine Learning
  1. The new NNX API is set to replace the older Linen API for building neural networks with JAX. It simplifies the coding process and offers better performance options.
  2. The shard_map feature improves multi-device computation by allowing better handling of data. It’s a helpful evolution for developers looking for precise control over their parallel computing tasks.
  3. Pallas is a new JAX tool that lets users write custom kernels for GPUs and TPUs. This allows for more specialized and efficient computation, particularly for advanced tasks like training large models.

Diffusion Models are Evolutionary Algorithms

441 implied HN points • 09 Nov 24
🕹 Technology AI Algorithms Evolution Machine Learning Data science
  1. Diffusion models and evolutionary algorithms both involve changing data over time through processes like selection and mutation, which can lead to new and improved results.
  2. The new algorithm called Diffusion Evolution can find multiple good solutions at once, unlike traditional methods that often focus on one single best solution.
  3. There are exciting connections between learning and evolution, hinting that they may fundamentally operate in similar ways, which opens up many questions about future AI developments.

The Super Weight in Large Language Models

189 implied HN points • 29 Nov 24
🕹 Technology AI Research Machine Learning Data science Computational Models Tech Innovation
  1. There's a special weight in large language models called the 'super weight.' If you remove it, the model's performance crashes dramatically, showing just how crucial it is.
  2. Super weights are linked to what's called 'super activations,' meaning they help generate better text. Without them, the model struggles to create coherent sentences.
  3. Finally, researchers found ways to identify and protect these super weights during the model training and quantization processes. This makes the model more efficient and retains its quality.

Deep Learning Frameworks

252 implied HN points • 01 Nov 24
🕹 Technology AI Software Machine Learning Frameworks Programming
  1. Deep learning frameworks have made it easier for anyone to build and train neural networks. They simplify complex processes and allow researchers to focus on their ideas instead of technical details.
  2. Modern frameworks effectively utilize powerful hardware like GPUs, making training faster and more efficient. This means tasks that once took a lot of time can now be done much quicker.
  3. With advancements like dynamic computational graphs and automatic differentiation, frameworks have improved flexibility and reduced errors. This helps developers experiment with new ideas easily and reliably.

Star Attention: Efficient LLM Inference over Long Sequences

126 implied HN points • 09 Dec 24
🕹 Technology AI Machine Learning Computing Data processing Software Engineering
  1. Star Attention allows large language models to handle long pieces of text by splitting the context into smaller blocks. This helps the model work faster and keeps things organized without needing too much communication between different parts.
  2. The model uses what's called 'anchor blocks' to improve its focus and reduce mistakes during processing. These blocks are important because they help the model pay attention to the right information, which leads to better results.
  3. Using this new approach, researchers found improvements in speed while preserving quality in the model's performance. This means that making these changes can help LLMs work more efficiently without sacrificing how well they understand or generate text.

ModernBERT, the BERT of 2024

63 implied HN points • 19 Dec 24
🕹 Technology AI Machine Learning Natural Language Processing Computing Data science
  1. ModernBERT is a new version of BERT that improves processing speed and memory efficiency. It can handle longer contexts and makes BERT more practical for today's tasks.
  2. The architecture of ModernBERT has been updated with features that enhance performance, like better attention mechanisms and optimized computations. This means it works faster and can process more data at once.
  3. ModernBERT has shown impressive results in various natural language understanding tasks and can compete well against larger models, making it an exciting tool for developers and researchers.

Make softmax great again

126 implied HN points • 06 Nov 24
🕹 Technology Artificial Intelligence Machine Learning Data science Neural Networks Transformers
  1. Softmax is widely used in machine learning, especially in transformers, to turn numbers into probabilities. However, it struggles when dealing with new kinds of data that the model hasn't seen before.
  2. The sharpness of softmax can fade when there's a lot of input data. This means it sometimes can't make clear predictions about which option is best in bigger datasets.
  3. To improve softmax, researchers suggest using 'adaptive temperature.' This idea helps make the predictions sharper based on the data being processed, leading to better performance in some tasks.

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.

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.

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.

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

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.

More Agents Is All You Need

63 implied HN points • 18 Feb 24
🕹 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

"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.

Big Post About Big Context

49 HN points • 29 Feb 24
🕹 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.

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.

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.

[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.

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.

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.

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

🪆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.

Neural Network Diffusion

1 HN point • 26 Feb 24
🕹 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.