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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

The Sequence Chat #475: Ed Sim, Forbes Top Tech Investor, on AI Investing, Security, Agents and More

21 implied HN points • 23 Jan 25
🕹 Technology AI Investing Cybersecurity Startups Venture Capital
  1. Investing early in AI involves backing technical founders before they even start their company. It's about helping them develop their ideas and getting them the right support as they launch.
  2. Building a startup in the AI space should always begin with creating a great product, no matter how much money you have. It's important to focus on getting user feedback and refining your offering rather than spending excessively.
  3. AI security is becoming crucial as tech evolves. Companies need to be proactive in protecting against AI-driven cyber threats, and there are opportunities for startups to innovate in this space by securing AI implementations in various industries.

The Sequence Chat: Yohei Nakajima on Creating BabyAGI, Autonomous Agents and Investing in Generative AI

140 implied HN points • 06 Mar 24
🕹 Technology AI Generative AI Venture Capital Autonomous Agents Open Source
  1. BabyAGI project focuses on autonomous agents and AI enhancements for task execution, planning, and reasoning over time.
  2. Challenges in adopting autonomous agents include human behavior changes and enabling AI access to tools for task execution.
  3. Future generative AI trends include AI integration across various industries, increased passive AI usage, and automation of workflows with AI workers.
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Edge 374: Some Technical Details we Learned About OpenAI's Sora

140 implied HN points • 29 Feb 24
🕹 Technology AI Generative models Video Engineering
  1. OpenAI's Sora is a groundbreaking text-to-video model that can create high-quality videos up to a minute long.
  2. The release of Sora has caused a lot of excitement and discussion in the generative AI community and media outlets.
  3. While OpenAI has not revealed extensive technical details about Sora, the model includes some clever engineering optimizations.

Edge 453: Distillation Across Different Modalities

28 implied HN points • 03 Dec 24
🕹 Technology AI Data science Machine Learning Web Development Research
  1. Cross-modal distillation allows one model to teach another model that works with a different type of data. This means you can share knowledge even if the models are processing images, text, or something else entirely.
  2. This method can be really helpful when there's not much paired data available. It helps improve the learning process in situations where gathering data might be difficult.
  3. Hugging Face’s Gradio lets developers create AI applications for the web easily. It's a neat tool that helps bring AI to everyday use in a user-friendly way.

Edge 445: A New Series About Knowledge Distillation

35 implied HN points • 05 Nov 24
🕹 Technology AI Data science Machine Learning Computing Engineering
  1. Knowledge distillation helps make large AI models smaller and cheaper. This is important for using AI on devices like smartphones.
  2. A key goal of this process is to keep the accuracy of the original model while reducing its size.
  3. The series will include reviews of research papers and discussions on frameworks like Google's Data Commons that support factual knowledge in AI.

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.

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.

Edge 376: The Creators of Vicuna and Chatbot Arena Built SGLang for Super Fast LLM Inference

98 implied HN points • 07 Mar 24
🕹 Technology Artificial Intelligence Programming Open Source Machine Learning
  1. SGLang is a new open source project from Berkeley University designed to enhance interactions with Large Language Models (LLMs), making them faster and more manageable.
  2. SGLang integrates backend runtime systems with frontend languages to provide better control over LLMs, aiming to optimize the processes involved in working with these models.
  3. The framework created by LMSys offers significant optimizations that can boost the inference times in LLMs by up to 5 times, showcasing advancements in processing vast amounts of data at incredible speeds.

Edge 372: Learn About CALM, Google DeepMind's Method to Augment LLMs with Other LLMs

98 implied HN points • 22 Feb 24
🕹 Technology Artificial Intelligence Machine Learning Data science Research
  1. Knowledge augmentation is crucial in LLM-based applications with new techniques constantly evolving to enhance LLMs by providing access to external tools or data.
  2. Exploring the concept of augmenting LLMs with other LLMs involves merging general-purpose anchor models with specialized ones to unlock new capabilities, such as combining code understanding with language generation.
  3. The process of combining different LLMs might require additional training or fine-tuning of the models, but can be hindered by computational costs and data privacy concerns.

📝 Guest Post: Evaluating LLM Applications*

91 implied HN points • 11 Mar 24
🕹 Technology AI Machine Learning Evaluation Development Monitoring
  1. Traditional software development practices like automation and testing suites are valuable when evaluating Large Language Models (LLMs) for AI applications.
  2. Different types of evaluations, including judgment return types and sources, are important for assessing LLMs effectively.
  3. A robust evaluation process for LLM applications involves interactive, batch offline, and monitoring online stages to support rapid iteration cycles and performance improvements.

📝 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.

Google Goes Small and Open Source with Gemma

84 implied HN points • 25 Feb 24
🕹 Technology AI Open Source Generative AI ML Research AI Tech Releases
  1. Google released Gemma, a family of small open-source language models based on the architecture of its Gemini model. Gemma is designed to be more accessible and easier to work with than larger models.
  2. Open-source efforts in generative AI, like Gemma, are gaining traction with companies like Google and Microsoft investing in smaller, more manageable models. This shift aims to make advanced AI models more widely usable and customizable.
  3. The rise of small language models (SLMs) like Gemma showcases a growing movement towards more efficient and specialized AI solutions. Companies are exploring ways to make AI technology more practical and adaptable for various applications.

📌 ML Engineering Event: Mastering AI and ML at Production Scale at apply()

84 implied HN points • 19 Feb 24
🕹 Technology AI ML
  1. The event offers real-world insights from engineering leaders on ML model deployment and best practices.
  2. Participants can engage in sponsor-free knowledge sharing sessions with peers, focusing on in-depth discussions.
  3. Attendees have the opportunity to network with a diverse group of AI and ML engineers, including industry veterans and emerging leaders.

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.

Text-to-Video Games and 1-Bit Models: Two Monumental Generative AI Research Milestones in One Week

77 implied HN points • 03 Mar 24
🕹 Technology AI Generative AI ML Research AI Tech Releases Real World ML
  1. Genie by Google DeepMind can create 2D video games from text, opening doors to interactive environments in simulations, gaming, and robotics.
  2. BitNet b1.58, a 1-bit model by Microsoft and University of Chinese Academy of Sciences, offers cost-efficient and high-performance training for Large Language Models (LLMs).
  3. The pace of research in generative AI is rapid, leading to groundbreaking advancements like Genie and BitNet b1.58.

Edge 378: Meet TimesFM: Google's New Foundation Model for Time-Series Forecasting

70 implied HN points • 14 Mar 24
🕹 Technology Machine Learning AI
  1. Time series forecasting is crucial in various fields like retail, finance, manufacturing, healthcare, and more, despite lagging behind other areas in AI development.
  2. Google has introduced TimeFM, a pretrain model with 200M parameters trained on over 100 billion time series data points, aiming to advance forecasting accuracy.
  3. The new TimeFM model from Google will soon be accessible in Vertex AI, showcasing a shift towards leveraging pretrained models for time series forecasting.

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.

📝 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.

SmallCon: Free virtual conference for GenAI builders ft. Meta, DoorDash, Mistral

14 implied HN points • 29 Nov 24
🕹 Technology Artificial Intelligence Software Development Tech Events Innovation Startups
  1. SmallCon is a free online conference for people interested in Generative AI. It's a great opportunity to learn from experts in the field.
  2. The conference will feature talks and discussions from big companies like Meta and DoorDash. Attendees will get insights on the latest trends and technologies in AI.
  3. You can register now to save your spot and gain knowledge on building effective AI models and applications. It's a chance to learn how to make the most out of small AI models.

Explore the Global Generative AI Landscape 2024 by AIport

56 implied HN points • 18 Mar 24
🕹 Technology AI
  1. The Global Generative AI Landscape 2024 report by AIport offers insights into 107 international companies developing 128 generative models, expanding beyond typical American and European focus.
  2. The study covers six continents and more countries than previous similar projects, providing a comprehensive analysis of the global GenAI landscape.
  3. The report is reader-friendly and showcases how international companies are driving GenAI development, highlighting the widespread impact across various regions.

One AI for Navigating Any 3D Environment

56 implied HN points • 17 Mar 24
🕹 Technology AI ML Research Real World ML AI Radar
  1. Google DeepMind created a new model, SIMA, that can navigate any 3D environment by following language instructions.
  2. SIMA can translate abstract instructions into mouse and keyboard actions for navigating different 3D worlds.
  3. This AI breakthrough has implications for embodied AI environments, simulations, and other areas requiring physical tasks.