AI can now send Bitcoins!
Plus: New AI research from Google & Stanford. RLTF improves LLMs for Code. generation
Hello Engineering Leaders and AI Enthusiasts!
Welcome to the 60th edition of The AI Edge newsletter. This edition brings you “Major breakthrough: AI can now send Bitcoins!”
And a huge shoutout to our incredible readers. We appreciate you! 😊
In today’s edition:
🤑 AI can now send Bitcoins!
🤖 Google & Stanford researchers use LLMs to solve Robotics challenges
💻 RLTF improves LLMs for Code generation
📚 Knowledge Nugget: Vector Database Design Patterns by
Let’s go!
AI can now send Bitcoins!
The recent introduction of AI tools by Lightning Labs allows AI applications to hold, send, and receive Bitcoin. The tools leverage Lightning Network, a second-layer payment network for faster and cheaper Bitcoin transactions. By integrating high-volume Bitcoin micropayments with popular AI software libraries like LangChain, Lightning Labs addresses the lack of a native Internet-based payment mechanism for AI platforms.
Why does this matter?
This development eliminates the need for outdated payment methods, reducing costs for software deployment and expanding the range of possible AI use cases. The integration of Lightning into AI models has the potential to enable new applications that were previously not feasible.
Google & Stanford researchers use LLMs to solve Robotics challenges
Recent research has found that pre-trained LLMs can complete complex token sequences, including those generated by probabilistic context-free grammars (PCFG) and ASCII art prompts. The study explores how these zero-shot capabilities can be applied to robotics problems, such as extrapolating sequences of numbers to complete simple motions and prompting reward-conditioned trajectories to discover and represent closed-loop policies.
Although deploying LLMs for real systems is currently challenging due to latency, context size limitations, and compute costs, the study suggests that using LLMs to drive low-level control could provide insight into how patterns among words could be transferred to actions.
Why does this matter?
Potential applications for this approach beyond robotics are that it could be used to model and predict sequential data like stock market prices, weather data, traffic patterns, etc. Also, it could learn game strategies by observing sequences of moves and positions, then use that to play against opponents or generate new strategies.
RLTF improves LLMs for Code generation
Researchers have proposed a novel online reinforcement learning framework called RLTF for refining LLMs for code generation. The framework uses unit test feedback of multi-granularity to generate data in real time during training and guide the model toward producing high-quality code. The approach achieves SotA performance on the APPS and the MBPP benchmarks for their scale.
Why does this matter?
RLTF can potentially improve LLMs' performance on code generation tasks. Current RL methods for code generation use offline frameworks and simple unit test signals, which limits their exploration of new sample spaces and does not account for specific error locations within the code.
Knowledge Nugget: Vector Database Design Patterns by
In this enlightening article, the author
delves into the challenges of incorporating ML models into traditional databases and highlights the importance of vector databases for working efficiently with vector data.
They shed light on the unique properties of ML model outputs, represented as vectors, and how these vectors can be used to quantify similarity. The author explores the advantages of vector databases and provides valuable patterns for building applications that can effectively leverage the power of vector data.
Why does this matter?
Traditional databases are not optimized for handling vector data efficiently, which can hinder the performance and effectiveness of ML-powered applications. By understanding the significance of vector databases and leveraging them appropriately, developers and data scientists can improve vector data processing, retrieval, and analysis, leading to more accurate and powerful AI applications.
What Else Is Happening❗
🌿 Wow! AI-based laser pesticide & herbicide without chemicals! (Link)
🔥 Wildfire Detection Startup Pano AI Secures Additional $17M. (Link)
💡 Stable Foundation’s SDXL Bot is now LIVE! (Link)
📚 Groundbreaking survey papers on LLMs with 600 references and much more! (Link)
✈️ Air Force demonstrates AI's role in airspace dominance. (Link)
🛠️ Trending Tools
Trimmr: AI app that shortens YouTube videos into shareable clips, helping creators produce viral content.
MyMod AI: Twitch chatbot that uses AI to moderate chat and create dynamic interactions with custom commands.
Comicify AI: AI tool that converts boring text into cool comic strips in just 2 steps, making it fun and free.
GREMI: AI tool that finds search trends and creates content to rank for them, all in a single click.
Ayfie Personal Assistant: AI-powered tool simplifying document analysis, summarization, and content creation.
Beamcast: AI tool that allows running commands on selected text or web pages, harnessing the power of ChatGPT.
AI Collective: Front-end for AIs, including ChatGPT, generating text and images with advanced features.
Mersei: AI assistant for teams, trained with content from various sources to improve productivity.
That's all for now!
By subscribing to The AI Edge, you join the company of readers from Moody’s, Vonage, Voya, WEHI, Cox, INSEAD, and other reputable organizations.
Thanks for reading, and see you tomorrow. 😊
Looks like we have competition now in the Twitch chat bot realm! We have a site offering an A.I. chat bot as well: https://aitwitch.chat