The hottest Applications Substack posts right now

And their main takeaways
Category
Top Technology Topics

Happy 1st Birthday, GPT-4

Marcus on AI • 1380 implied HN points • 16 Mar 24
🕹 Technology AI Ethics Applications Progress Competition
  1. There seems to be a possible plateau in GPT-4's capability, with no one decisively beating it yet.
  2. Despite challenges, there has been progress in discovering applications and putting GPT-4 type models into practice.
  3. Companies are finding putting Large Language Models into real-world use challenging, with many initial expectations proving unrealistic.

Strategies for an Accelerating Future

One Useful Thing • 1033 implied HN points • 20 Feb 24
🕹 Technology AI Machine Learning Innovation Applications Future
  1. Advancements in AI, such as larger memory capacity in models like Gemini, are enhancing AI's ability for superhuman recall and performance.
  2. Improvements in speed, like Groq's hardware for quick responses from AI models, are making AI more practical and efficient for various tasks.
  3. Leaders should consider utilizing AI in their organizations by assessing what tasks can be automated, exploring new possibilities made possible by AI, democratizing services, and personalizing offerings for customers.

AI #50: The Most Dangerous Thing

thezvi • 937 implied HN points • 08 Feb 24
🕹 Technology AI Privacy Ethics Security Applications
  1. Gemini Ultra is Google's latest AI model, described better than GPT-4 but conservative in responses.
  2. AI language models like ChatGPT and Google are widely used and offer mundane utility, despite some limitations.
  3. AI advancements raise concerns about deepfakes, fake IDs, and a need for regulations to address security risks.

Unveiling the Revolutionary Architecture behind LLMs - "Attention is all you need"

Mindful Matrix • 219 implied HN points • 17 Mar 24
🕹 Technology AI NLP Architecture Training Applications
  1. The Transformer model, introduced in the groundbreaking paper 'Attention Is All You Need,' has revolutionized the world of language AI by enabling Large Language Models (LLMs) and facilitating advanced Natural Language Processing (NLP) tasks.
  2. Before the Transformer model, recurrent neural networks (RNNs) were commonly used for language models, but they struggled with modeling relationships between distant words due to their sequential processing nature and short-term memory limitations.
  3. The Transformer architecture leverages self-attention to analyze word relationships in a sentence simultaneously, allowing it to capture semantic, grammatical, and contextual connections effectively. Multi-headed attention and scaled dot product mechanisms enable the Transformer to learn complex relationships, making it well-suited for tasks like text summarization.

An AI Haunted World

One Useful Thing • 972 implied HN points • 19 Dec 23
🕹 Technology AI Models Applications Development Autonomy
  1. The development of open source AI models is democratizing AI usage and allowing for easier modification and widespread deployment.
  2. The efficiency and affordability of LLMs will lead to AI being incorporated into various products for troubleshooting, monitoring, and interaction, potentially creating an 'AI haunted world'.
  3. Future AI integration may involve hierarchies of various AI models working together, with smart generalist AIs delegating tasks to cheaper, specialized AIs.
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Artificial Intelligence: The Rise of AI in Earnings Call Summarization

The Fintech Blueprint • 334 implied HN points • 30 Jan 24
🕹 Technology AI Finance Analysis Tools Applications
  1. AI is revolutionizing financial analysis through earnings call summarizations by tools like Bloomberg, AlphaSense, TiredBanker, and Aviso.
  2. AI helps in quickly isolating key points from earnings calls and deriving insights that improve financial decision-making.
  3. AI-driven tools have the potential to mitigate human error in analyzing financial data and are expected to see universal adoption in the financial services sector.

What is a data product?

davidj.substack • 71 implied HN points • 15 Mar 24
🕹 Technology Data Products Interfaces Applications
  1. A data product can take various forms and be consumed in different ways, always requiring an interface for consumption.
  2. From raw data like CSV files to refined database tables, streams, JSON files, and ORM abstracted layers, all can be considered data products.
  3. BI tools, AI automation, and semantic layers play crucial roles in creating consumable data products for various industries, making data more refined and accessible.

Sora: The Explorer

Cybernetic Forests • 139 implied HN points • 18 Feb 24
🕹 Technology AI Video Generation Applications
  1. New text-to-video models like Sora by OpenAI are pushing boundaries in video generation, offering longer and more diverse outputs compared to previous models.
  2. Sora's method involves training on a variety of video formats like widescreen, vertical, and square, leading to more efficiency and comprehensive use of video data for generation.
  3. One challenging aspect of Sora is its ability to create multiple synthetic scenarios that all lead to the same outcome, posing risks of misinformation and manipulation in media content.

Generative AI: What Happened, Where It's Going 2023-2024

Rod’s Blog • 416 implied HN points • 19 Dec 23
🕹 Technology AI Predictions Applications Ethics Regulation
  1. Generative AI is rapidly advancing and has a wide range of applications from enhancing creativity to solving real-world problems.
  2. In 2023, Generative AI saw explosive growth, with a significant number of organizations implementing it in various business functions.
  3. Expected trends in 2024 for Generative AI include more advanced language models, more creative applications, and increased focus on ethical and responsible considerations.

Embracing weirdness: What it means to use AI as a (writing) tool

One Useful Thing • 887 implied HN points • 05 Sep 23
🕹 Technology AI Writing Applications Innovation
  1. AI is weird and different from traditional software, so we need to embrace its uniqueness to fully understand its capabilities.
  2. AI can do much more than just act as a thesaurus or grammar checker; it has the potential to help in creative idea generation and simulate individual readers for market feedback.
  3. To unlock the true value of AI, we should experiment with unconventional uses of AI tools while being mindful of ethical concerns and technical limitations.

Synthetic data: Anthropic’s CAI, scaling, OpenAI’s Superalignment, tips, and open-source examples

Democratizing Automation • 332 implied HN points • 29 Nov 23
🕹 Technology AI Data Models Methods Applications
  1. Synthetic data is becoming more important in AI, with a focus on removing human involvement.
  2. Proponents believe that using vast amounts of synthetic data can lead to breakthroughs in AI models.
  3. Open and closed communities are both utilizing synthetic data for different end goals.

Graphene: New Comprehensive Study, Degradation, and Industry Trends

Tessa Fights Robots • 43 implied HN points • 06 Mar 24
🔬 Science Research Toxicity Applications Biology Industry
  1. A recent study highlighted the toxicity and degradation of graphene, raising concerns about its broad applications and potential hazards on the environment and human health.
  2. Further research is needed on the degradation of graphene-based nanomaterials to understand their environmental impact and health risks. Some compounds like humic acid and specific enzymes play a role in the biodegradation process.
  3. Graphene is a trending material in various industries, but there are growing concerns about its safety, likening it to the new asbestos or Glyphosate 2.0. Monitoring industry trends and applications of graphene is important for understanding its impact.

Edge 379: A Summary Of Our Series About LLM Reasoning

TheSequence • 14 implied HN points • 19 Mar 24
🕹 Technology AI Research Tech Models Applications
  1. The series explored different methods and technologies related to reasoning in Large Language Models (LLMs).
  2. Reasoning in LLMs involves working through problems logically to reach conclusions, emerging at a certain scale and not applicable to small models.
  3. The series covered topics like Chain-of-Thought (CoT), System 2 Attention (S2A), tree-of-thoughts, and graph-of-thoughts as techniques for LLM reasoning.

March Newsletter

RSS DS+AI Section • 11 implied HN points • 01 Mar 24
🕹 Technology Data science AI Ethics Research Applications
  1. The newsletter discussed various updates and activities in the field of data science and AI, including committee activities, advancements in research, and real-world applications.
  2. Ethical considerations, bias, diversity, regulation, and safety in AI and data science were highlighted as hot topics in the newsletter, with examples of AI-related consequences and efforts to improve safety.
  3. The newsletter also featured practical tips, how-to guides, and bigger picture ideas in the field, providing a broad range of information for data science practitioners.

When Does Science Self-Correct? Lessons from a Replication Crisis in Early 20th Century Chemistry

The Good Science Project • 63 implied HN points • 14 Nov 23
🔬 Science Chemistry Psychology Replication Applications
  1. Science can struggle to correct errors from the scientific record, even with healthy reforms in place.
  2. Non-replicable findings can still hold influence and get cited as much as replicable ones.
  3. Natural sciences can swiftly correct mistakes with practical consequences, while social sciences face challenges in self-correction due to less tangible applications and high acceptance of contradictory findings.

How to train ChatGPT on your own text (train a text AI to generate content about your docs, book, website, etc)

Mythical AI • 235 implied HN points • 19 Feb 23
🕹 Technology AI Chatbots Models Training Applications
  1. Large language models like ChatGPT can summarize articles, write stories, and engage in conversations.
  2. To train ChatGPT on your own text, you can use methods like giving the AI data in the prompt, fine-tuning a GPT3 model, using a paid service, or using an embedding database.
  3. Interesting use cases for training GPT3 on your own data include personalized email generators, chatting in the style of famous authors, creating blog posts, chatting with an author or book, and customer service applications.

The Wide and Wondrous World of Centaurs and Agents

Future History • 170 implied HN points • 23 Jun 23
🕹 Technology AI Automation Software Applications Machine Learning
  1. Centaurs and Agents are a new type of software that blend human input with autonomous decision-making capabilities.
  2. Individuals benefit more from Centaurs than companies due to easier adoption and productivity gains.
  3. Small, specialized AI applications will be in high demand, bridging the gap between different software systems and reducing tedious tasks.

Building LLM application using RAG

Mindful Matrix • 1 HN point • 07 Apr 24
🕹 Technology AI Data processing Cloud Computing Machine Learning Applications
  1. LLMs have limitations like not being able to update with new information and struggling with domain-specific queries.
  2. RAG (Retrieval Augmented Generation) architecture helps ground LLMs by using custom knowledge bases for generating responses to queries.
  3. Building a simple LLM application using RAG involves steps like loading documents, splitting data, embedding/indexing, defining LLM models, and retrieval/augmentation/generation.

How to make history with LLMs & other generative models

Leigh Marie’s Newsletter • 74 HN points • 21 Sep 23
🕹 Technology Machine Learning Infrastructure Applications Observability Privacy
  1. LLMs like Github Copilot can augment developer productivity and provide new opportunities for AI-enabled developer tools startups
  2. Generative models can significantly enhance efficiency for knowledge workers in fields like consulting, legal, medical, and finance, offering potential for startups in these areas
  3. New infrastructure opportunities exist around running large models locally, providing compute resources for model training, and challenging incumbents in ML frameworks and chips

Must Learn AI Security Compendium 3: Exploring the Different Types of AI Technology

Rod’s Blog • 59 implied HN points • 12 Sep 23
🕹 Technology AI Applications Challenges Types Future
  1. AI can be categorized into Narrow AI, General AI, and Super AI based on capabilities, each with different levels of human-like intelligence.
  2. AI can also be classified based on functionality into Reactive Machines, Limited Memory, Theory of Mind, and Self-awareness, each with unique ways of processing information and interacting with the environment.
  3. Applications of AI in various industries like healthcare, finance, transportation, and retail are transforming decision-making, efficiency, and innovation, but ethical considerations and challenges like data quality and interpretability must be addressed for responsible AI development.

Must Learn AI Security Compendium 2: Generative AI vs. Machine Learning

Rod’s Blog • 59 implied HN points • 11 Sep 23
🕹 Technology AI Machine Learning Generative AI Applications Ethics
  1. Machine learning empowers computers to learn from data without explicit programming, helping them make predictions and decisions.
  2. Generative AI focuses on creating new data based on training data, emphasizing creativity and innovation.
  3. Both machine learning and generative AI have unique applications - from fraud detection and image recognition in machine learning to image generation and music composition in generative AI.

SociaLLM: proposal for a language model design for personalised apps, social science, and AI safety research

Engineering Ideas • 19 implied HN points • 19 Dec 23
🕹 Technology AI Social Science Research Applications Training
  1. SociaLLM is a foundation language model trained on chat, dialogue, and forum data with stable message authors and timestamps.
  2. Industrial applications of SociaLLM include personalized content recommendations, customer service, education, and mental health support.
  3. SociaLLM has research and AI safety applications in social science, collective intelligence, and studying mechanisms to prevent deception and collusion in AI.

AI Explained: Retrieval-Augmented Generation (RAG)

Year 2049 • 8 implied HN points • 26 Jan 24
🕹 Technology AI Machine Learning Data science Applications Education
  1. RAG solves problems with AI like hallucinations, outdated knowledge, being too general, and privacy concerns
  2. RAG allows for retrieving specific knowledge, adding new updated documents easily, and not training the AI on your data
  3. RAG can be used to create assistants for tasks like onboarding new employees, customer service, coding, and design, improving productivity through knowledge access

November Newsletter

RSS DS+AI Section • 23 implied HN points • 04 Nov 23
🕹 Technology Data science AI Research Generative AI Applications
  1. The newsletter covers various topics in Data Science and AI including ethics, research, and practical applications.
  2. Committee activities include calls for new members, updates on AI Safety Summit, and announcements for events like the Christmas social.
  3. The newsletter also highlights significant developments in AI research, such as GenAI, robotics, and Large Language Models.