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TheSequence Substack focuses on the latest trends and innovations in AI, covering open source LLM models, generative AI advancements, and multimodal generative AI. It discusses new research, frameworks, and tools, highlighting their impact on software development and AI applications' efficiency and capabilities.

Artificial Intelligence Generative AI Open Source AI Models Language Models Machine Learning Frameworks AI Research AI Applications in Software Development Multimodal Generative AI

The hottest Substack posts of TheSequence

And their main takeaways

Edge 364: About COSP and USP: Two New LLM Reasoning Methods Built by Google Research

133 implied HN points • 25 Jan 24
🕹 Technology AI Language Models Research Machine Learning Data science
  1. Two new LLM reasoning methods, COSP and USP, have been developed by Google Research to enhance common sense reasoning capabilities in language models.
  2. Prompt generation is crucial for LLM-based applications, and techniques like few-shot setup have reduced the need for large amounts of data to fine-tune models.
  3. Models with robust zero-shot performance can eliminate the need for manual prompt generation, but may have less potent results due to operating without specific guidance.

More Super Models is All We Need

77 implied HN points • 18 Feb 24
🕹 Technology AI/ML Tech Releases ML Research Real World ML AI Radar
  1. Last week saw the release of five major foundation models in the generative AI space, each from a different tech giant, showcasing innovative advancements in various areas like text-to-video generation and multilingual support.
  2. These new models are not only significant for the future of generative AI applications but also highlight the unique innovations and contributions made by different companies in the AI field.
  3. The continuous evolution and release of these super models are driving progress and setting new standards in the field of generative AI, pushing boundaries and inspiring further advancements.

📌 ML Engineering Event: Lineup for apply() 2024 is Now Live!

42 implied HN points • 08 Mar 24
🕹 Technology AI ML Events
  1. The lineup for the apply() 2024 ML Engineering Event, featuring industry leaders from LangChain, Meta, and Visa, is now live.
  2. The agenda includes keynote sessions on LangChain, semi-supervised learning, and uplift modeling by experts from the respective fields.
  3. Attendees can look forward to gaining insights and actionable tips for mastering AI and ML at the event.
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📌 Exciting news! The speaker lineup for apply() 2024 is now live

21 implied HN points • 15 Mar 24
🕹 Technology AI ML Data science Events
  1. The speaker lineup for apply() 2024 event is now live, featuring industry leaders from companies like LangChain, Meta, Visa, and more.
  2. The event offers actionable insights to master AI and ML in production, with sessions on topics like LangChain Keynote, Semi-Supervised Learning, and Uplift Modeling.
  3. Attendees can register for free to join the event live on April 3rd, with the option to receive on-demand videos as well.

Edge 379: A Summary Of Our Series About LLM Reasoning

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.

The Sequence Chat: Microsoft's Evan Chaki on Semantic Kernel and Combining LLMs with Conventional Programming Languages

294 implied HN points • 26 Apr 23
🕹 Technology AI Machine Learning Programming Open Source Development Tools
  1. Semantic Kernel enables developers to create AI applications using large language models without writing complex code or training custom models.
  2. Memory systems and data connectors play a crucial role in enhancing productivity and efficiency in LLM-based applications.
  3. Hybrid programming with natural language and traditional programming languages can automate tasks like creating educational content and contract Q&A, leading to faster, error-free results.

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

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.

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

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.

📝 Guest Post: An introduction to Similarity Search*

182 implied HN points • 03 Apr 23
🕹 Technology Embeddings Models Algorithms
  1. Vector similarity search is essential for recommendation systems, image search, and natural language processing.
  2. Vector search involves finding similar vectors to a query vector using distance metrics like L1, L2, and cosine similarity.
  3. Common vector search strategies include linear search, space partitioning, quantization, and hierarchical navigable small worlds.

Edge 273: Horizontal Federated Learning

133 implied HN points • 14 Mar 23
🕹 Technology ML Research
  1. Horizontal Federated Learning involves datasets across nodes sharing the feature space but differing in the sample space.
  2. Google's research on Personalized Federated Learning addresses privacy challenges by allowing custom modifications to the global model at node level.
  3. Syft is a framework combining federated learning, secure multi-party computations, and differential privacy to enable private computations in deep learning models.