The hottest Language Models Substack posts right now

And their main takeaways
Category
Top Technology Topics

📝 Guest Post: Caching LLM Queries for Improved Performance and Cost Savings*

TheSequence • 217 implied HN points • 10 Apr 23
🕹 Technology Language Models APIs
  1. Using a semantic cache can improve LLM application performance by reducing retrieval times and API call expenses.
  2. Caching LLM responses can enhance scalability by reducing the load on the LLM service and improving user experience by reducing network latency.
  3. GPTCache is an open-source semantic cache designed for storing LLM responses efficiently and offers various customization options.

Understanding Large Language Models

Startup Pirate by Alex Alexakis • 216 implied HN points • 12 May 23
🕹 Technology AI Neural Networks Deep Learning Language Models AGI
  1. Large Language Models (LLMs) revolutionized AI by enabling computers to learn language characteristics and generate text.
  2. Neural networks, especially transformers, played a significant role in the development and success of LLMs.
  3. The rapid growth of LLMs has led to innovative applications like autonomous agents, but also raises concerns about the race towards Artificial General Intelligence (AGI).
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Personality around the world

Vectors of Mind • 216 implied HN points • 16 Mar 23
🔬 Science Psychometrics Language Models Personality Traits
  1. Personality models show consistent traits across languages, especially the Big Two: social self-regulation and dynamism.
  2. Understanding personality across languages requires bilingual cohorts or careful translations, as words may not have direct equivalents.
  3. Research suggests that analyzing language models in multiple languages could lead to a universal model of personality, potentially superior to the Big Five.

Program-Aided Language Models

Deep (Learning) Focus • 196 implied HN points • 22 May 23
🕹 Technology AI Programming Reasoning Language Models Deep Learning
  1. LLMs can struggle with tasks like arithmetic and complex reasoning, but using an external code interpreter can help them compute solutions more accurately.
  2. Program-Aided Language Models (PaL) and Program of Thoughts (PoT) techniques leverage both natural language and code components to enhance reasoning capabilities of LLMs.
  3. Decoupling reasoning from computation within LLMs through techniques like PaL and PoT can significantly improve performance on complex numerical tasks.

Inside Alpaca: The Language Model from Stanford University that can Follow Instructions and Match GPT-3.5

TheSequence • 203 implied HN points • 06 Apr 23
🕹 Technology Language Models Artificial Intelligence Academic Research
  1. Alpaca is a language model from Stanford University that can follow instructions and is smaller than GPT-3.5.
  2. Instruction-following models like GPT-3.5 have issues with false information, social stereotypes, and toxic language.
  3. Academic research on instruction-following models is challenging due to limited availability of models similar to closed-source ones like OpenAI's text-davinci-003.

LLM agents and integration dead-ends

Democratizing Automation • 146 implied HN points • 12 Jul 23
🕹 Technology AI Integration ML Generative AI Language Models
  1. The biggest immediate roadblock in generative AI unlocking economic value is the barrier of enabling direct integration of language models
  2. Many are exploring the use of large language models (LLMs) for various business tasks through LLM agents, which are facing challenges of integration and broad scope
  3. The successful commercial viability of LLM agents depends on trust, reliability, management of failure modes, and understanding of feedback dynamics

What Google's Leaked Letter tells us about the AI Landscape [Finance Fridays]

Technology Made Simple • 199 implied HN points • 06 May 23
🕹 Technology AI Finance Big Tech Open Source Language Models
  1. Open source in AI is successful due to its free nature, promoting quick scaling and diverse contributions.
  2. The rigid hiring practices and systems in Big Tech can stifle innovation by filtering out non-conformists.
  3. The leaked letter questions the value of restrictive models in a landscape where free alternatives are comparable in quality.

Links #7

Splitting Infinity • 19 implied HN points • 02 Feb 24
🕹 Technology Community AI Language Models Blockchain Scientific research
  1. In a post-scarcity society, communities of hobbyists can lead to significant innovations driven by leisure time and interest rather than necessity.
  2. Drug discovery challenges stem from a lack of understanding of diseases and biology, proposing an alternative approach focusing on experimental drug use and patient data collection.
  3. Language models are scaling down for efficient inference, suggesting that combinations of smaller models may outperform training larger ones.

Are AI systems conscious? I interviewed Rob Long, a philosopher studying digital minds, to find out

muddyclothes • 176 implied HN points • 27 Apr 23
🕹 Technology AI Ethics Language Models
  1. Rob Long is a philosopher studying digital minds, focusing on consciousness, sentience, and desires in AI systems.
  2. Consciousness and sentience are different; consciousness involves subjective experiences, while sentience often relates to pain and pleasure.
  3. Scientists study consciousness in humans to understand it; empirical testing in animals and AI systems is challenging without direct self-reports.

LangChain: PM's Guide for Building Smarter Products

Product Mindset's Newsletter • 9 implied HN points • 03 Mar 24
🕹 Technology Product Management Language Models Integration
  1. LangChain is a framework for developing applications powered by language models that are context-aware and can reason.
  2. LangChain's architecture is based on components and chains, with components representing specific tasks and chains as sequences of components to achieve broader goals.
  3. LangChain integrates with Large Language Models (LLMs) for prompt management, dynamic LLM selection, memory integration, and agent-based management to optimize building language-based applications.

Practico-inertia

Internal exile • 8 implied HN points • 01 Mar 24
🕹 Technology AI Generative models Search Engines Language Models
  1. Generative models like Google's Gemini can create controversial outputs, raising questions about the accuracy and societal impact of AI-generated content.
  2. Users of generative models sometimes mistakenly perceive the AI output as objective knowledge, when it is actually a reflection of biases and prompts.
  3. The use of generative models shifts power dynamics and raises concerns about the control of reality and information by technology companies.

AI (Automated Interpolation)

Logging the World • 139 implied HN points • 26 Apr 23
🕹 Technology AI Machine Learning Artificial Intelligence Generative models Language Models
  1. Models are good at interpolating known data but struggle with extrapolating beyond that, which can lead to significant errors.
  2. AI models excel at interpolation tasks, creating mashups of existing styles based on training data, but may struggle to generate genuinely new, groundbreaking creations.
  3. Great works of art often come from pushing boundaries and exploring new styles, something that AI models, bound by training data, may find challenging.

#16: Notes on Arithmetic in GPT-4

Loeber on Substack • 9 HN points • 20 Feb 24
🔬 Science Language Models Machine Learning
  1. GPT-4, while not inherently built for arithmetic, showed surprising accuracy in approximating addition, hinting at some degree of symbolic reasoning within its capabilities.
  2. Accuracy in arithmetic tasks with GPT-4 decreases as the complexity of the task increases, with multiplication showing the most significant drop in accuracy.
  3. A 'dumb Turing Machine' approach can enhance GPT-4's symbolic reasoning capabilities by breaking down tasks into simpler steps, showcasing promising potential for scaling up to more complex symbolic reasoning.

Stanford CRFM suggests moving to workflows rather than tasks for evaluation!

MLOps Newsletter • 58 implied HN points • 04 Sep 23
🕹 Technology Language Models Quantization AI Tools
  1. Stanford CRFM recommends shifting ML validation from task-centric to workflow-centric for better evaluation
  2. Google introduces Ro-ViT for pre-training vision transformers, improving on object detection tasks
  3. Google AI presents Retrieval-VLP for pre-training vision-language models, emphasizing retrieval to enhance performance

What Noam Chomsky gets wrong about AI

ML Powered • 98 implied HN points • 10 Mar 23
🕹 Technology AI Language Models Intelligence Machine Learning
  1. Machine learning models like ChatGPT can be as efficient or even more efficient than the human brain in certain tasks.
  2. Measuring intelligence of machine learning models based solely on the ability to apply the scientific method is unrealistic.
  3. Modern language models like ChatGPT can understand and parse phrases with ease, contradicting claims of their failure in understanding language.

GPT Store, text-to-3d in under 10 seconds, DeepSeekMoE 16B, jailbreaking advanced LLMs, LLaVA-ϕ, Microsoft's open-source agent framework, and more

AI Brews • 12 implied HN points • 12 Jan 24
🕹 Technology AI Language Models Open Source AI Tools
  1. OpenAI launched the GPT Store for finding GPT models and a revenue program for GPT builders.
  2. DeepSeek released DeepSeekMoE 16B, a large language model with 16.4B parameters trained from scratch.
  3. Microsoft Research introduced TaskWeaver, an open-source agent framework to convert natural language requests into executable code.

Will AI replace 'knowledge workers'?

Nick Merrill • 78 implied HN points • 12 May 23
🕹 Technology AI Language Models Automation Future of work
  1. AI may replicate work of 'knowledge workers' but many of these jobs may never have been necessary in the first place
  2. Uncertainty about AI replacing jobs is at the core of the discussion, and it's linked to broader societal structures
  3. There could be a possible third path towards liberation for people among the discourse around AI and knowledge work

LLMs are machines. Are you one too?

70 Years Old. WTF! • 58 implied HN points • 19 Feb 23
🕹 Technology AI Machine Learning Language Models Text generation
  1. LLMs are Large Language Models, which are computer systems trained to generate language based on patterns.
  2. LLMs can write better than most humans, but they lack the freedom of expression that humans have.
  3. The difference between how a human writes and how a machine like ChatGPT generates text is the ability to freely use explicit language.

Three key developments

Prompt Engineering • 39 implied HN points • 22 May 23
🕹 Technology AI Language Models
  1. AI is rapidly advancing, especially in the medical field.
  2. New technology like ImageBind can link different types of data with images as a common basis.
  3. Fine-tuning language models with a small number of prompts can significantly improve performance.

Solves maths Olympiad problems, doesn’t grasp place value: What kind of intelligence is GPT-4?

Augmented • 39 implied HN points • 05 Apr 23
🕹 Technology Artificial Intelligence Education Assessment Intelligence Language Models
  1. GPT-4 can solve complex problems but struggles with basic math concepts.
  2. Large language models like GPT-4 excel in certain areas but show limitations in understanding.
  3. The standards used to measure intelligence need to be reevaluated based on the capabilities of AI like GPT-4.