The hottest Language Models Substack posts right now

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Update #43: Propaganda Deepfakes and Transformers get Loopy

The Gradient • 11 implied HN points • 14 Feb 23
🕹 Technology Language Models
  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.

Occasional Exponential AI Grab Bag

Gradient Ascendant • 9 implied HN points • 13 Feb 23
🕹 Technology Language Models
  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.

Putting the human touch on LLMs

Molly Welch's Newsletter • 1 HN point • 30 Mar 23
🕹 Technology Language Models
  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.

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 Language Models
  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.

Can Large Language Models Reason?

AI: A Guide for Thinking Humans • 4 HN points • 10 Sep 23
🔬 Science Language Models
  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.

AI Anxiety

Marcio Klepacz • 4 HN points • 14 May 23
🕹 Technology Language Models
  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.

In the weeds: The Replication Crisis of LLM Research

Multimodal by Bakz T. Future • 2 implied HN points • 17 Feb 24
🕹 Technology Language Models
  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

Five years of progress in GPTs

Artificial Fintelligence • 3 HN points • 29 Mar 23
🕹 Technology Language Models
  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 Language Models
  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 Language Models
  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 Language Models
  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.

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

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

pocoai • 0 implied HN points • 07 Dec 23
🕹 Technology 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.

Understanding Understanding

The Grey Matter • 0 implied HN points • 13 Aug 23
🕹 Technology Language Models
  1. The concept of understanding exists on a spectrum, not as a binary state.
  2. LLMs might have suboptimal understanding in some areas, but they are not fundamentally limited.
  3. LLMs can potentially develop a theory of mind and a world model, showing the ability to understand complex concepts.

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

PashaNomics • 0 implied HN points • 20 Mar 23
🕹 Technology Language Models
  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.

Hot Topics #22 (Apr. 3, 2023)

The Merge • 0 implied HN points • 03 Apr 23
🕹 Technology Language Models
  1. Fast Imitation of Skills from Humans (FISH) can train robots with less than a minute of demonstrations.
  2. Regularization and Lipschitz regularization are key in Optimal Transport-Based Distributionally Robust Optimization.
  3. Chain of Hindsight technique helps align language models with human preferences by training on feedback sequences.

Interview with CEO of Google DeepMind

Age of AI • 0 implied HN points • 14 Jul 23
🕹 Technology 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.

What's new in LLMs

Age of AI • 0 implied HN points • 16 Jul 23
🕹 Technology 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.

LLMs for Dummies

Digital Native • 0 implied HN points • 12 Oct 23
🕹 Technology Language Models
  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.

AI Explainability Is About to Get Worse

The Grey Matter • 0 implied HN points • 21 Apr 23
🕹 Technology Language Models
  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 AI author illusion

Skybrian’s Blog • 0 implied HN points • 19 Apr 23
🕹 Technology Language Models
  1. We chat with fictional characters now, creating imaginary worlds.
  2. Machine-made writing has tricks that mess with assumptions about authorship.
  3. Interacting with AI chatbots can be like playing turn-based games with fictional characters.

The Stateful Chinese Room

The Grey Matter • 0 implied HN points • 15 Mar 23
🕹 Technology Language Models
  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.