The hottest Language Models Substack posts right now

And their main takeaways
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Top Technology Topics

Will data served as language models overtake the role of semantic/linked data?

Living Systems • 2 HN points • 09 Aug 23
🕹 Technology Data Models Language Models Information Retrieval Artificial Intelligence
  1. The role of language models in serving data is being considered over semantic/linked data.
  2. Language models make data self-sufficient and self-describing, reducing the need for complex data schemas.
  3. Large language models present an opportunity for flexible data access and communication between models, potentially via linked data.

Can Large Language Models Reason?

AI: A Guide for Thinking Humans • 4 HN points • 10 Sep 23
🔬 Science Reasoning Language Models Evaluation Pattern matching
  1. There is a debate about whether large language models have reasoning abilities similar to humans or rely more on memorization and pattern-matching.
  2. Models like CoT prompting try to elicit reasoning abilities in these language models and can enhance their performance.
  3. However, studies suggest that these models may rely more on memorization and pattern-matching from their training data than true abstract reasoning.

In the weeds: The Replication Crisis of LLM Research

Multimodal by Bakz T. Future • 2 implied HN points • 17 Feb 24
🕹 Technology AI Research Language Models Replication Crisis Artificial Intelligence Research Findings
  1. Prompt design can significantly impact the performance of language models, showing their true capabilities or masking them
  2. Using prompt design to manipulate results can be a concern, potentially impacting the authenticity of research findings
  3. The fast pace of the AI industry leads to constant advancements in models, making it challenging to keep up with the latest capabilities
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AI Anxiety

Marcio Klepacz • 4 HN points • 14 May 23
🕹 Technology AI Language Models Productivity Automation Ethics
  1. Large language models have the potential to revolutionize software development by simplifying the process from coding to output.
  2. While AI can boost productivity, it's important to be specific about intentions and details to avoid misunderstandings.
  3. AI can take on repetitive tasks, but humans should remember the importance of critical thinking and understanding consequences.

Putting the human touch on LLMs

Molly Welch's Newsletter • 1 HN point • 30 Mar 23
🕹 Technology AI Human feedback Machine Learning Language Models Ethics
  1. Using human feedback to refine large language models is key for aligning them with user values and preferences.
  2. Reinforcement Learning from Human Feedback (RLHF) is a crucial technique for enhancing the quality of LLM outputs.
  3. Incorporating human touch into LLMs raises questions about scalability, cost, decision-making regarding whose feedback matters, and potential policy implications.

Five years of progress in GPTs

Artificial Fintelligence • 3 HN points • 29 Mar 23
🕹 Technology Language Models Research Innovation Optimization
  1. Focus on the evolution of GPT models over the past five years, highlighting key differences between them.
  2. Explore the significant impact of large models, dataset sizes, and training strategies on language model performance.
  3. Chinchilla and LLaMa papers reveal insights about the optimal model sizes, dataset sizes, and computational techniques for training large language models.

Thoughts on A.I. Dilemma: Part 1

Machine Learning Everything • 1 HN point • 17 Apr 23
🕹 Technology AI Language Models Social media Machine Learning Artificial Intelligence
  1. The comparison between AI and social media highlights the potential dangers associated with large language models.
  2. Advancements in large language models, like GPT, can lead to proficiency across various domains, similar to how universal game engines can excel in multiple games.
  3. Language is emphasized as the ultimate medium in AI development, with the trend shifting towards more end-to-end systems.

Self-serving thought experiments

Apperceptive (moved to buttondown) • 1 HN point • 15 Mar 23
🔬 Science AI Language Models General Intelligence
  1. Application of the trolley problem to autonomous cars is often inappropriate as safety focus should be on avoiding no-win scenarios in the first place.
  2. Autonomous cars would need advanced sensory abilities to accurately predict outcomes for a trolley problem, which current technology lacks.
  3. Large language models lack key components of human cognition like embodied experience and physiological needs, posing a challenge for achieving artificial general intelligence.

First principles on AI scaling

DYNOMIGHT INTERNET NEWSLETTER • 1 HN point • 06 Mar 23
🕹 Technology AI Data Compute Language Models Innovation
  1. Using scaling laws can help predict how much better language models will get with more computational power or data.
  2. The majority of the error in language models comes from limited data, rather than limited model size.
  3. To improve language models significantly, more data and compute are needed, but there may be a limit to how much more can be added with current technology.

AI Explainability Is About to Get Worse

The Grey Matter • 0 implied HN points • 21 Apr 23
🕹 Technology AI Neural Networks Language Models 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.

The Stateful Chinese Room

The Grey Matter • 0 implied HN points • 15 Mar 23
🕹 Technology AI Language Models Understanding Learning Communication
  1. The Chinese Room thought experiment challenges the idea of computers having genuine understanding.
  2. Understanding involves more than just following rules, requiring a deep comprehension and application of knowledge.
  3. The Stateful Chinese Room concept suggests that AI models could potentially achieve genuine understanding through context and repeated exposure.

Meta Introduces New AI Features Across Facebook, Instagram, Messenger, and WhatsApp

pocoai • 0 implied HN points • 07 Dec 23
🕹 Technology AI Data Storage Startups Cloud Computing Language Models
  1. Meta introduced over 20 new AI features across Facebook, Instagram, Messenger, and WhatsApp, enhancing user experiences.
  2. Google unveiled Gemini AI in three sizes - Nano, Pro, and Ultra, catering to various information types like text, code, audio, images, and video.
  3. Vast Data raised $118 million for its data storage platform tailored for AI workloads, aiming to expand its business reach globally.

What makes LLMs like GPT good or bad for the world?

PashaNomics • 0 implied HN points • 20 Mar 23
🕹 Technology AI Ethics Language Models Technology impact AI training Ethical AI
  1. When evaluating a language model like GPT-X, consider factors like accuracy and impact.
  2. The impact of the model extends to both individual users and broader society, such as through unintended consequences and negative interactions.
  3. GPT's aimability, or its ability to follow rules effectively, is a complex issue that may not be effectively addressed with current training methods.

Rime Labs speaks at Bay Area NLP

Rime Labs • 0 implied HN points • 17 Mar 23
🕹 Technology Language Models
  1. Large Language Models trained on text cannot capture rich social information inherent in speech
  2. What are commonly referred to as Large Language Models should be called Large Text Models
  3. Rime Labs focuses on creating natural, conversational voice products for diverse contexts

LLMs for Dummies

Digital Native • 0 implied HN points • 12 Oct 23
🕹 Technology AI Language Models Applications Training Data Transformers
  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.

GPT Bias: A Challenge for Responsible AI

Rod’s Blog • 0 implied HN points • 27 Feb 24
🕹 Technology AI Bias Ethics Language Models Data
  1. GPT models can inherit and amplify biases from the data they are trained on, leading to negative impacts like misinformation and discrimination.
  2. GPT bias stems from both data bias (issues with the training data) and model bias (issues with the model design and architecture).
  3. There have been advancements in GPT models over the years, with newer versions like GPT-4 implementing techniques to reduce biases compared to earlier versions.

What's new in LLMs

Age of AI • 0 implied HN points • 16 Jul 23
🕹 Technology AI Updates Language Models
  1. Anthropic released Claude 2 with improved performance and 100k token limit.
  2. Google introduced updates to Bard including support for 40 languages and image input.
  3. OpenAI ChatGPT now has a code interpreter and Meta announced CM3leon for text and image generation.

Interview with CEO of Google DeepMind

Age of AI • 0 implied HN points • 14 Jul 23
🕹 Technology AI Machine Learning Chatbots AI Research Language Models
  1. Large language models (LLMs) are being developed to become universal personal assistants with planning and reasoning capabilities.
  2. LLMs may utilize specialized tools for tasks like folding proteins or playing chess, breaking down the AI system into smaller ones.
  3. LLMs should be equipped with the ability to critique themselves by reasoning and planning, similar to how game programs improve their moves.