The hottest Training Data Substack posts right now

And their main takeaways
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Top Technology Topics

Please: Enough with the Claims That Modern Advanced Machine Learning Models Hallucinate Only Rarely

Brad DeLong's Grasping Reality • 322 implied HN points • 17 Feb 26
🕹 Technology Training Data
  1. Modern multimodal and advanced language models often fabricate detailed but false information — like nonexistent book titles and imaginary historical maps — so hallucinations are common, not rare.
  2. These systems are essentially compressed correlation engines without a true world model, meaning they stitch patterns from training data instead of genuinely understanding or verifying reality.
  3. Techniques like RLHF and prompt engineering can reduce some errors but cannot fully eliminate unpredictable hallucinations, so reliable use often requires careful prompting or external verification of answers.

Sora Can’t Handle the Truth

Marcus on AI • 3398 implied HN points • 17 Feb 24
🕹 Technology Training Data
  1. Large language models like Sora often make up information, leading to errors like hallucinations in their output.
  2. Systems like Sora, despite having immense computational power and being grounded in both text and images, still struggle with generating accurate and realistic content.
  3. Sora's errors stem from its inability to comprehend global context, leading to flawed outputs even when individual details are correct.

AI and the — em dash

Technically • 28 implied HN points • 29 Jan 26
🕹 Technology Training Data
  1. AI models overuse em dashes because their training data contained a lot of them, especially older books and popular sites that favored that punctuation.
  2. Em dashes are token-efficient for LLMs — a single token can replace several words, so models use them to reduce prediction error and save tokens.
  3. The em-dash habit can make AI output detectable, so human writers sometimes avoid em dashes to avoid being mistaken for machine-generated text.

SWIM

Cybernetic Forests • 179 implied HN points • 14 Jan 24
🎨 Art & Illustration Training Data
  1. SWIM is a piece that visualizes the relationship between archives, memory, and training data. It explores the impact of training AI models on images and the implications for memory and synthetic images.
  2. The artist behind SWIM finds creating pieces as a way to think through ideas that might not work well with words. The process often clarifies thoughts or raises questions that are hard to articulate.
  3. The deduction of memory through photography or AI analysis is highlighted in SWIM, where a swimmer dissolves into training data, shifting the remembrance process to a mechanized model and potentially losing the essence of being remembered.
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Will synthetic data help?

Yuxi’s Substack • 19 implied HN points • 24 Nov 23
🕹 Technology Training Data
  1. A perfect model can create high-quality data to build strong AI, like AlphaZero - AIZero
  2. Without a perfect model, gathering high-quality data is essential for competent AI - AI∞ or AIx
  3. It is important to start AI systems with ground truth data and work towards bridging the gap between simulation and reality

Software²

The Gradient • 29 implied HN points • 22 Apr 23
🕹 Technology Training Data
  1. AI research is shifting focus from 'learning from data' to 'learning what data to learn from'.
  2. State-of-the-art deep learning models are becoming data sponges capable of modeling immense amounts of data.
  3. Future AI research trends may emphasize data collection and generation to improve model performance.

Peak Data

East Wind • 2 HN points • 25 Oct 23
🕹 Technology Training Data
  1. The quality and percentage of human-generated data on the internet may have reached a peak, affecting the efficacy of future AI models.
  2. Models may face challenges with outdated training data and lack of relevant information for solving newer problems.
  3. Potential solutions include leveraging RAG models, proactive data contribution by platform vendors, and maintaining incentives for human contributions on user-generated content platforms.

The Four Fundamental Quantities of LLMs: Part Zero - 📜 What are Large Language Models?

Intuitive AI • 1 HN point • 21 May 23
🕹 Technology Training Data
  1. Large language models (LLMs) are neural networks with billions of parameters trained to predict the next word using large amounts of text data.
  2. LLMs use parameters learned during training to make predictions based on input data during the inference stage.
  3. Training an LLM involves optimizing the model to predict the next token in a sentence by feeding it billions of sentences to adjust its parameters.

AI Explainability Is About to Get Worse

The Grey Matter • 0 implied HN points • 21 Apr 23
🕹 Technology Training Data
  1. AI explainability for large language models like GPT models is becoming more challenging as these models advance.
  2. Examining the model, training data, and asking the model are the three main ways to understand these models' capabilities, each with its limitations.
  3. As AI capabilities advance, the urgency to develop better AI explainability techniques grows to keep pace with the evolving landscape.

LLMs for Dummies

Digital Native • 0 implied HN points • 12 Oct 23
🕹 Technology Training Data
  1. Large language models (LLMs) like GPT-3 have rapidly improved in recent years, showing exponential growth in size and capability.
  2. LLMs work by translating words into numbers using word vectors stored in multidimensional planes, helping to capture relationships between words.
  3. There are various frameworks for LLM applications, such as solving impossible problems, simplifying complex tasks, focusing on vertical AI products, and creating AI copilot tools for faster and more efficient human work.

The Freudian CLIP

Cybernetic Forests • 0 implied HN points • 16 Oct 22
🕹 Technology Training Data
  1. Describing AI models through psychoanalysis can be a metaphorical way to understand their behavior, even though AI doesn't have human-like unconscious desires.
  2. AI models like DALLE2 have strict content restrictions to avoid generating explicit or suggestive content, but there are ways to try and bypass these restrictions, leading to the concept of spurious content.
  3. Exploring the boundaries and limitations of AI-generated images using methods like psychoanalysis can help reveal hidden aspects of the training data and understand how these models interpret and generate content.