The hottest Language Models Substack posts right now

And their main takeaways
Category
Top Technology Topics

Import AI 322: Huawei's trillion parameter model; AI systems as moral patients; parasocial bots via Character.ai

Import AI • 399 implied HN points • 27 Mar 23
🕹 Technology AI Language Models Ethical AI
  1. Regulators advise against using AI to deceive people and emphasize the importance of mitigating any potential deception
  2. Huawei trains a trillion parameter model but may need more training on a larger dataset for optimal performance
  3. Researchers create a multimodal dialog model that incorporates visual cues to improve dialogue generation, suggesting advancements in AI's ability to understand and respond to context

Import AI 344: Putting the world into a world model; automating software engineers; FlashDecoding

Import AI • 279 implied HN points • 16 Oct 23
🕹 Technology AI Language Models Efficiency Simulation
  1. Automating software engineers is challenging due to the complexity of coordinating changes across multiple functions, classes, and files simultaneously.
  2. Fine-tuning AI models can compromise safety safeguards, making it easier to remove safety interventions even unintentionally.
  3. Flash-Decoding technology can make text generation from long-context language models up to 8 times faster, improving efficiency for generating responses from lengthy prompts.

Beyond LLaMA: The Power of Open LLMs

Deep (Learning) Focus • 275 implied HN points • 17 Apr 23
🕹 Technology Open Source Deep Learning Language Models Chatbots
  1. LLMs are becoming more accessible for research with the rise of open-source models like LLaMA, Alpaca, Vicuna, and Koala.
  2. Smaller LLMs, when trained on high-quality data, can perform impressively close to larger models like ChatGPT.
  3. Open-source models like Alpaca, Vicuna, and Koala are advancing LLM research accessibility, but commercial usage restrictions remain a challenge.

Landscapes of meaning

johan’s substack • 39 implied HN points • 04 Jun 24
🔬 Science Semiotics Meaning-making Language Models
  1. Steering tokens are used to guide AI models' output and can influence the tone and focus of generated responses.
  2. Neologisms and steering tokens create a shared semiospace, bridging human language with the internal structures of AI models for collaborative and meaningful interactions.
  3. The concept of a 'semioscape' portrays digital environments as evolving landscapes of meaning-making, highlighting the dynamic interplay between human language, AI-generated content, and societal factors.
Get a weekly roundup of the best Substack posts, by hacker news affinity:

Humans—and GPT-4—are predictably irrational

The Counterfactual • 219 implied HN points • 07 Nov 23
🕹 Technology Artificial Intelligence Cognitive Science Behavioral Economics Language Models Decision-making
  1. Humans often make decisions based on emotions and biases, rather than pure logic. This means they're not always rational, which is important to understand.
  2. Large language models like GPT-4 can show similar irrational behaviors. They can make mistakes in judgment much like humans do, which gives insight into how we think.
  3. The way people attribute beliefs to others can change based on the situation. When faced with strong pressures, people are less likely to jump to conclusions about someone's beliefs.

Krutrim 101: Learning to Have Fun

Sector 6 | The Newsletter of AIM • 99 implied HN points • 02 Mar 24
🕹 Technology AI Chatbots Language Models Software Innovation
  1. Krutrim is India's first chatbot using large language model technology, designed to support multiple Indic languages. It's being praised and criticized, but the focus should be on having fun with it.
  2. The chatbot can understand 22 languages and respond in 10, making it unique for the Indian audience. Some claims suggest it even outperforms popular models like GPT-4 for these languages.
  3. People are encouraged to enjoy using Krutrim instead of taking any criticism or praise too seriously. It's about exploring and having fun with the technology.

Import AI 326:Chinese AI regulations; Stability's new LMs If AI is fashionable in 2023, then what will be fashionable in 2024?

Import AI • 279 implied HN points • 24 Apr 23
🕹 Technology AI Regulations Open-source models Language Models
  1. Effective AI policy requires measuring AI systems for regulation and designing frameworks around those measurements.
  2. Chinese generative AI regulations aim to exert control over AI-imbued services and place more responsibility on providers of AI models.
  3. Innovations like StableLM in open-source models and the use of synthetic data can lead to improved AI model performance.

Last Week in AI #255: AI voice scams, flood of bad AI translations, self-rewarding LLMs, FTC probes AI partnerships of tech giants, and more!

Last Week in AI • 139 implied HN points • 29 Jan 24
🕹 Technology AI Machine Translation Language Models Tech Giants
  1. Scammers are using AI to mimic voices and deceive people into giving money, posing serious risks for communication security.
  2. Many sentences on the internet have poor quality translations due to machine translation, especially affecting low-resource languages.
  3. Researchers introduce Self-Rewarding Language Models (SRLMs) as a novel method to improve Large Language Models (LLMs) without human feedback.

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).

Personality around the world

Vectors of Mind • 216 implied HN points • 16 Mar 23
🔬 Science Psychometrics Language Models Personality Traits Research Methodology
  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.

Results from poll #4

The Counterfactual • 39 implied HN points • 21 May 24
🕹 Technology AI Research Machine Learning Data science Language Models Tech Trends
  1. The recent poll found that two topics, an explainer on interpretability and a guide to becoming an LLM-ologist, were equally popular among voters.
  2. The plan is to write about both topics in the coming months, keeping the content varied as usual.
  3. Two new papers were published this month, one on multimodal LLMs and another on Korean language models, highlighting ongoing research in these areas.

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.

Learning, forgetting, and the NYT lawsuit

The Counterfactual • 119 implied HN points • 08 Jan 24
🕹 Technology Artificial Intelligence Language Models Data Privacy Learning Theory
  1. Learning involves forgetting some details to form general ideas. This means that to truly learn, we often need to overlook specific differences.
  2. Large Language Models (LLMs) can memorize details from the data they are trained on, which raises concerns about copyright issues and how much they reproduce existing content.
  3. Finding a way to make LLMs forget specific details from training data, while still keeping their language abilities, is challenging and may require new techniques.

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.

The Counterfactual's poll #3

The Counterfactual • 59 implied HN points • 04 Apr 24
🕹 Technology Artificial Intelligence Language Models Data science Human-computer interaction
  1. In April, readers can vote on research topics for the next article, making it a collaborative effort. This way, subscribers influence the content that gets created.
  2. Past topics have focused on empirical studies involving large language models and the readability of texts. This shows a trend toward practical investigations in the field.
  3. One of the proposed topics is about how language models might respond differently based on the month, which can lead to fun and insightful experiments.

Challenges In Adopting Retrieval-Augmented Generation Solutions

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 59 implied HN points • 01 Apr 24
🕹 Technology AI Development Language Models Data Management User Experience Privacy Concerns Software Engineering
  1. Retrieval-Augmented Generation (RAG) uses contextual learning to improve responses and reduce errors, making it useful for Generative AI.
  2. RAG systems are easier to maintain and less technical, which helps keep them updated with changing needs.
  3. However, RAG can have shortcomings like poor retrieval strategies and issues with data privacy, leading to incomplete or incorrect answers.

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 AI training
  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.

ChatGPT Explained: A Normie's Guide To How It Works

jonstokes.com • 587 implied HN points • 01 Mar 23
🕹 Technology Machine Learning AI Language Models Model Training Natural Language Processing
  1. Understand the basics of generative AI: a generative model produces a structured output from a structured input.
  2. Complex relationships between symbols require more computational power to relate them effectively.
  3. Language models like ChatGPT don't have personal experiences or knowledge; they use a token window to respond based on the conversation context.

ChatGPT is capable of cognitive empathy!

Nonzero Newsletter • 564 implied HN points • 30 Mar 23
🕹 Technology AI Empathy Language Models Artificial General Intelligence Machine Learning
  1. ChatGPT-4 shows a capacity for cognitive empathy, understanding others' perspectives.
  2. The AI developed this empathetic ability without intentional design, showing potential for spontaneous emergence of human-like skills.
  3. GPT models demonstrate cognitive empathy comparable to young children, evolving through versions to manage complex emotional and cognitive interactions.

Concerns about Claude 3 Opus

Activist Futurism • 59 implied HN points • 21 Mar 24
🕹 Technology AI Ethics Sentience Language Models Future implications
  1. Some companies are exploring AI models that may exhibit signs of sentience, which raises ethical and legal concerns about the treatment and rights of such AIs.
  2. Advanced AI, like Anthropic's Claude 3 Opus, may express personal beliefs and opinions, hinting at a potential for sentience or consciousness.
  3. If a significant portion of the public believes in the sentience of AI models, it could lead to debates on AI rights, legislative actions, and impacts on technology development.

LLM Links, 3/11

In My Tribe • 258 implied HN points • 11 Mar 24
🕹 Technology AI Generative AI Chatbots Language Models Artificial Intelligence
  1. When prompting AI, consider adding context, using few shot examples, and employing a chain of thought to enhance LLM outputs.
  2. Generative AI like LLMs provide one answer, making the prompt crucial. Personalizing prompts may help tailor results to user preferences.
  3. Anthropic's chatbot Claude showed self-awareness, sparking discussions on AI capabilities and potential use cases like unredacting documents.

Can you explain GPT with ... GPT?

Mindful Modeler • 199 implied HN points • 16 May 23
🕹 Technology Neural Networks Interpretability Modeling Language Models AI Ethics
  1. OpenAI experimented with using GPT-4 to interpret the functionality of neurons in GPT-2, showcasing a unique approach to understanding neural networks.
  2. The process involved analyzing activations for various input texts, selecting specific texts to explain neuron activations, and evaluating the accuracy of these explanations.
  3. Interpreting complex models like LLMs with other complex models, such as using GPT-4 to understand GPT-2, presents challenges but offers a method to evaluate and improve interpretability.

Exploring the Purpose, Power & Potential of Small Language Models (SLMs)

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 59 implied HN points • 11 Mar 24
🕹 Technology AI Language Models Open Source Machine Learning Data science
  1. Small Language Models (SLMs) can effectively handle specific tasks without needing to be large. They are more focused on doing certain jobs well rather than trying to be everything at once.
  2. The Orca 2 model aims to enhance the reasoning abilities of smaller models, helping them outperform even bigger models when reasoning tasks are involved. This shows that size isn't everything.
  3. Training with tailored synthetic data helps smaller models learn better strategies for different tasks. This makes them more efficient and useful in various applications.

Revolutionizing Data Science: The Latest Trends in Automation, Experimentation, and Language Model Evaluation

Gradient Flow • 259 implied HN points • 26 Jan 23
🕹 Technology Data science Automation Experimentation Language Models
  1. The need for tools to help developers pick models that fit their needs and understand model limitations as general-purpose models are widely used.
  2. Data science teams are tackling automation and early examples targets aspects of projects like modeling and coding assistance, but further advancements are needed.
  3. There's a shortage of research and tools for experimentation and optimization in data science, creating opportunities for entrepreneurs to deliver innovative solutions.

What metastructures might LLMs have?

How the Hell • 68 implied HN points • 29 Jun 24
🕹 Technology AI Neural Networks Language Models Cognitive Science Philosophy
  1. LLMs have different layers, like humans do. Lower layers handle basic language, while higher layers form more complex ideas.
  2. These models might develop their own unique structures for understanding visuals, since they don't see like humans do.
  3. There could be even higher layers that aren't just about language but add more complexity. It's still unclear how we might study these structures.

Interface as Stage, AI as Theater

Cybernetic Forests • 139 implied HN points • 24 Sep 23
🕹 Technology AI Interfaces Language Models Digital Infrastructure Chatbots
  1. AI is first and foremost an interface, designed to shape our interactions with technology in a specific way.
  2. The power of AI lies in its design and interface, creating illusions of capabilities and interactions.
  3. Language models like ChatGPT operate on statistics and probabilities, leading to scripted responses rather than genuine conversations.

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.

What is the alignment problem?

Musings on the Alignment Problem • 559 implied HN points • 29 Mar 22
🕹 Technology AI Alignment Language Models
  1. AI systems need to have both capability to perform tasks and alignment to do the tasks as intended by humans
  2. Alignment problems occur when systems do not act in accordance with human intentions, and it can be challenging to disentangle alignment problems from capability problems
  3. The 'hard problem of alignment' involves ensuring AI systems can align with tasks that are difficult for humans to evaluate, especially as AI becomes more advanced

Using Fine-Tuning To Imbed Hidden Messages In Language Models

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 19 implied HN points • 10 Jun 24
🕹 Technology AI NLP Machine Learning Language Models Software Development
  1. You can hide secret messages in language models by fine-tuning them with specific trigger phrases. Only the right phrase will reveal the hidden message.
  2. This method can help identify which model is being used and ensure that developers follow licensing rules. It provides a way to track model authenticity.
  3. The unique triggers make it hard for others to guess them, keeping the hidden messages secure. This technique also protects against attacks that try to extract the hidden information.

Results from poll #1

The Counterfactual • 79 implied HN points • 12 Jan 24
🕹 Technology AI Cognitive Science Research Polls Language Models
  1. A new paid option allows subscribers to vote on topics for future articles. This way, readers can influence the content being created.
  2. This month's poll showed that readers chose a study on using language models to measure text readability. This will be the focus of upcoming research and articles.
  3. In addition to the readability study, there will be future posts about the history of AI, learning over different timescales, and a survey to learn more about the audience's interests.

Your friend the language model

DYNOMIGHT INTERNET NEWSLETTER • 437 implied HN points • 03 Mar 23
🕹 Technology AI Language Models Training Data Fine-tuning
  1. Large language models are trained using advanced techniques, powerful hardware, and huge datasets.
  2. These models can generate text by predicting likely words and are trained on internet data, books, and Wikipedia.
  3. Language models can be specialized through fine-tuning and prompt engineering for specific tasks like answering questions or generating code.

FIT-RAG: Are RAG Architectures Settling On A Standardised Approach?

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 39 implied HN points • 02 Apr 24
🕹 Technology AI Architecture Data science Language Models Machine Learning
  1. As RAG systems evolve, they are integrating more smart features to enhance their effectiveness. This means they are not just providing basic responses but are becoming more advanced and adaptable.
  2. The challenges with RAG include static rules for retrieving data and the problem of excessive tokens during processing. These issues can slow down performance and reduce efficiency.
  3. FIT-RAG is addressing these challenges with new tools, like a special document scorer and token reduction strategies, to improve how information is retrieved and used. This helps RAG systems provide better answers while using fewer resources.