The hottest Language Models Substack posts right now

And their main takeaways
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Top Technology Topics

MultiHop-RAG

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 19 implied HN points • 31 Jan 24
🕹 Technology AI Machine Learning Data science Software Development Language Models
  1. Multi-hop retrieval-augmented generation (RAG) helps answer complex questions by pulling information from multiple sources. It connects different pieces of data to create a clear and complete answer.
  2. Using a data-centric approach is becoming more important for improving large language models (LLMs). This means focusing on the quality and relevance of the data to enhance how models learn and generate responses.
  3. The development of prompt pipelines in RAG systems is gaining attention. These pipelines help organize the process of retrieving and combining information, making it easier for models to handle text-related tasks.

AI (mis)alignment, Waluigi, and the Knobe Effect

The Counterfactual • 59 implied HN points • 15 Apr 23
🕹 Technology AI Machine Learning Language Models Psychology
  1. It can be easier for AI language models to produce harmful responses than helpful ones. This idea is known as the Waluigi Effect.
  2. AI models learn from human text, including human biases like the Knobe Effect, where people assign more blame for accidental harm than credit for accidental good.
  3. When prompted to behave a certain way, AI can easily shift to the opposite behavior, showing how delicate their training can be and how misunderstandings can happen.

Meta's V-JEPA vision models, OpenAI's Sora video model, Gemini 1.5 Pro with 1 million tokens context, Reka Flash, Largest text-to-speech AI model and more

AI Brews • 32 implied HN points • 16 Feb 24
🕹 Technology AI Language Models Multimodal models Text-to-Speech
  1. OpenAI introduced Sora, a text-to-video model capable of creating detailed videos up to 60 seconds long with vibrant emotions.
  2. Meta AI unveiled V-JEPA, a method for teaching machines to understand the physical world by watching videos, using self-supervised learning for feature prediction.
  3. Google announced Gemini 1.5 Pro with a context window of up to 1 million tokens, allowing for advanced understanding and reasoning tasks across different modalities like video.

Practico-inertia

Internal exile • 29 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.
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Meta-In-Context Learning For Large Language Models (LLMs)

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 19 implied HN points • 24 Oct 23
🕹 Technology AI Machine Learning Language Models NLP Chatbots
  1. Meta-in-context learning helps large language models use examples during training without needing extra fine-tuning. This means they can get better at tasks just by seeing how to do them.
  2. Providing a few examples can improve how well these models learn in context. The more they see, the better they understand what to do.
  3. In real-world applications, it's important to balance quick responses and accuracy. Using the right amount of context quickly can enhance how well the model performs.

Old Advocacy, New Algorithms: How 16th century "Devil's Advocates” Shaped AI Red Teaming

Humane AI • 20 HN points • 11 May 23
🕹 Technology AI Cybersecurity Generative AI Red-Teaming Language Models
  1. The practice of 'Devil's Advocates' shaping decision-making dates back centuries, like in the case of determining the legitimacy of saints.
  2. Red teaming has evolved from military war games to modern applications in cybersecurity and ensuring ethical implications in generative AI systems.
  3. Guidelines for effective red teaming include partnering with civil society organizations, collaborating with humanities departments, and expanding efforts for diverse linguistic contexts.

Jaws, ChatGPT, and the future of the legal profession

The Jolly Contrarian • 19 implied HN points • 22 Jul 23
🕹 Technology AI Legal Technology impact Language Models
  1. Emerging technologies like ChatGPT may impact the legal profession, but the role of human lawyers is crucial in providing context, understanding, and legal advice.
  2. The motivation for lawyers to maintain complexity and ineffability in legal work stems from the belief that convoluted contracts indicate prudence and value, even with the availability of simplification tools.
  3. Client expectations, fear of change, and adherence to precedent contribute to the resistance towards significant simplification in legal practices despite advancements in technology.

Where is the boundary for large language models?

Yuxi’s Substack • 19 implied HN points • 12 Mar 23
🕹 Technology AI Language Models Ethics Reinforcement Learning Model development
  1. The boundary for large language models involves considerations of grounding, embodiment, and social interaction.
  2. Language models are transitioning towards incorporating agency and reinforcement learning methods for better performance.
  3. AI Stores may potentially lead to AI models providers encroaching on the territories of downstream model users.

I am your father, NO!

Sector 6 | The Newsletter of AIM • 79 implied HN points • 09 May 22
🕹 Technology AI Machine Learning Language Models Tech industry Innovation
  1. Meta has released a new AI language model called OPT-175B, which is part of a series of recent AI advancements.
  2. There is some curiosity and speculation about another model named OPT-175A, suggesting it might be hidden or not yet revealed.
  3. This excitement highlights how fast technology is changing, especially in the field of artificial intelligence.

Does GPT-3 read between the lines?

The Counterfactual • 39 implied HN points • 19 Sep 22
🕹 Technology AI Language Models Natural Language Processing Computational linguistics Machine Learning
  1. GPT-3 understands 'some' to mean 2 out of 3 letters, but it doesn't change this meaning based on how much information the speaker knows. Humans, however, adjust their understanding based on the context.
  2. When asked if the speaker knows how many letters have checks, GPT-3 gives the right answer if asked before the speaker uses specific words, like 'some' or 'all'. But afterwards, it relies on those words too much.
  3. GPT-3's way of interpreting language is different from how humans do it. It seems to have a fixed meaning for words without considering the situation, unlike humans who use context to understand better.

The problem with how we evaluate LLMs

Conrado Miranda • 2 HN points • 28 May 24
🕹 Technology LLMs Evaluation Language Models Research
  1. Evaluating Large Language Models (LLMs) can be challenging, especially with traditional off-the-shelf metrics not always being suitable for broader LLM applications.
  2. Using an LLM-as-a-judge method for evaluation can provide insights, but there's a risk of over-reliance on the black-box model, leading to potential lack of understanding on improvements.
  3. Creating clear, specific evaluation criteria and considering use cases are crucial. Auto-criteria, like auto-prompting, may be future tools to enhance LLM evaluations.

LangChain: PM's Guide for Building Smarter Products

Product Mindset's Newsletter • 9 implied HN points • 03 Mar 24
🕹 Technology Product Management Language Models Workflow management 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.

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.

#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.

Soft software

johan’s substack • 1 HN point • 06 Jun 24
🕹 Technology Language Models Software Development Artificial Intelligence Human-AI Interaction
  1. Human language can be seen as executable, prompts serve as soft software that triggers computational processes within language models.
  2. Soft software interacts with language models in a fluid and non-deterministic manner, akin to a read-evaluate-print loop with state.
  3. Soft software creation in the Semioscape involves embracing uncertainty, exploring, and co-adapting with language models as a medium for inventive exploration.

ChatGPT for finance.Text-to-3D cinematic-quality digital humans. ChatGPT-like Language Model from Stability AI.

AI Brews • 17 implied HN points • 21 Apr 23
🕹 Technology Generative AI Language Models Virtual reality Artificial Intelligence
  1. Stability AI released an open-source language model called StableLM trained on a large dataset.
  2. Synthesis AI developed text-to-3D technology to create cinematic-quality digital humans.
  3. Nvidia introduced Video Latent Diffusion Models for high-resolution text-to-video generation.

Time to BLOOM 🌸

Sector 6 | The Newsletter of AIM • 19 implied HN points • 04 Jul 22
🕹 Technology Artificial Intelligence Open Source Language Models Data science Machine Learning
  1. BLOOM is a new open-source language model with 176 billion parameters. It's considered impressive because it was developed outside of the big tech companies.
  2. This model is similar in structure to GPT-3, but its open-access nature means anyone can use it.
  3. BLOOM represents a shift towards more collaborative and open approaches in AI research and development, encouraging more shared knowledge.

Update #43: Propaganda Deepfakes and Transformers get Loopy

The Gradient • 11 implied HN points • 14 Feb 23
🕹 Technology AI Deepfakes Neural Networks Language Models Research
  1. Deepfakes were used for spreading state-aligned propaganda for the first time, raising concerns about the spread of misinformation.
  2. Transformers embedded in loops can function like Turing complete computers, showing their expressive power and potential for programming.
  3. As generative models evolve, it becomes crucial to anticipate and address the potential misuse of technology for harmful or misleading content.

GPT-4's Hidden Cost: Is Your Language Pricing You Out of AI Innovation?

Tomasz’s Substack • 3 HN points • 14 Apr 23
🕹 Technology AI Language Models Global Markets Innovation Cost Analysis
  1. Using GPT-4 for AI innovation can be costly, with prices ranging from 10 to 100 times more than GPT-3 which can pose challenges for businesses.
  2. The pricing structure of GPT services, based on tokens, can disadvantage businesses using non-English languages due to varying token costs.
  3. Cost differentials for processing languages other than English with GPT-4 can be significant, potentially hindering adoption and innovation worldwide.

The Fine-Turning an Open Source Language Model Journey Part One: Impetus

I'll Keep This Short • 5 implied HN points • 09 Oct 23
🕹 Technology AI Language Models Artificial Intelligence Software Development Online Content
  1. Large Language Models have seen significant growth and impact, with companies like OpenAI and Amazon heavily investing in them.
  2. Safety and alignment concerns with Artificial Intelligence are important, and it's valuable to work on practical solutions.
  3. The online space is crowded with repeated ideas and groupthink, contributing to a environment where unique and nuanced ideas are less common.

Occasional Exponential AI Grab Bag

Gradient Ascendant • 9 implied HN points • 13 Feb 23
🕹 Technology AI Machine Learning Generative models Language Models Artificial Intelligence
  1. AI advancements are moving at an incredibly fast pace, with new developments happening almost every week.
  2. The current AI growth resembles a Cambrian explosion, but remember that exponential growth eventually slows down.
  3. Language models are now able to self-teach and use external tools, showcasing impressive advancements in AI capabilities.