The hottest Training Data Substack posts right now

Large language models like Sora often make up information, leading to errors like hallucinations in their output.
Systems like Sora, despite having immense computational power and being grounded in both text and images, still struggle with generating accurate and realistic content.
Sora's errors stem from its inability to comprehend global context, leading to flawed outputs even when individual details are correct.

Output similarity is a distraction; Training is the real issue in generative AI.
Output similarity is easily fixable with methods like system prompts and fine tuning.
Current copyright law is not well-suited for addressing the challenges brought by generative AI.

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.
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.
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.

A perfect model can create high-quality data to build strong AI, like AlphaZero - AIZero
Without a perfect model, gathering high-quality data is essential for competent AI - AI∞ or AIx
It is important to start AI systems with ground truth data and work towards bridging the gap between simulation and reality

AI research is shifting focus from 'learning from data' to 'learning what data to learn from'.
State-of-the-art deep learning models are becoming data sponges capable of modeling immense amounts of data.
Future AI research trends may emphasize data collection and generation to improve model performance.

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The quality and percentage of human-generated data on the internet may have reached a peak, affecting the efficacy of future AI models.
Models may face challenges with outdated training data and lack of relevant information for solving newer problems.
Potential solutions include leveraging RAG models, proactive data contribution by platform vendors, and maintaining incentives for human contributions on user-generated content platforms.

Large language models (LLMs) are neural networks with billions of parameters trained to predict the next word using large amounts of text data.
LLMs use parameters learned during training to make predictions based on input data during the inference stage.
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.

Describing AI models through psychoanalysis can be a metaphorical way to understand their behavior, even though AI doesn't have human-like unconscious desires.
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.
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.

Large language models (LLMs) like GPT-3 have rapidly improved in recent years, showing exponential growth in size and capability.
LLMs work by translating words into numbers using word vectors stored in multidimensional planes, helping to capture relationships between words.
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.

AI explainability for large language models like GPT models is becoming more challenging as these models advance.
Examining the model, training data, and asking the model are the three main ways to understand these models' capabilities, each with its limitations.
As AI capabilities advance, the urgency to develop better AI explainability techniques grows to keep pace with the evolving landscape.