AI Driven Software Architect @ai_driven_arch

A big part of my job is extracting technical information from subject matter experts. While Juno seems tailored to user research, it seems like a similar #GenAI approach would work for technical fact gathering to support #softwarearchitecture

Using a model tuned for task routing will make agents more reliable, but what about tuning a model to break the problem down into individual tool steps to make agents more powerful?

Ehsan Shareghi @EhsanShareghi

2 years ago

Using a model tuned for task routing will make agents more reliable, but what about tuning a model to break the problem down into individual tool steps to make agents more powerful?

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It's these types of improvements that will allow agents capable of doing real work.

Ehsan Shareghi @EhsanShareghi

2 years ago

It's these types of improvements that will allow agents capable of doing real work.

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Designing language agents: (1) Why should you care about uncertainty? (2) Given a small set of data, is it better to fine-tune your agent, or to calibrate its uncertainty? (3) Could you just rely on LLM's verbal uncertainty? We answer these questions: uala-agent.github.io

Ehsan Shareghi @EhsanShareghi

2 years ago

Designing language agents: (1) Why should you care about uncertainty? (2) Given a small set of data, is it better to fine-tune your agent, or to calibrate its uncertainty? (3) Could you just rely on LLM's verbal uncertainty? We answer these questions: uala-agent.github.io https://t.co/7BamRUK95W

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AI just won't stop 🤯 Just this week, 160+ AI models were released from companies like Google, META, Alibaba, more. Here are the 9 incredible AI models you need to know about: 1/ FMA-Net, a new AI model for video deblurring.

Introducing ''AgentBoard: An Analytical Evaluation Board of Multi-turn LLM Agents'' 📈Fine-grained Metrics: 🔍 Spotting incremental progress 📊Systematic Board: 🤔 What makes a LLM ace the agent game? Leaderboard: hkust-nlp.github.io/agentboard/ Repo: github.com/hkust-nlp/Agen… 🧵[1/4]

The Self-Operating Computer Framework now supports the OCR-based approach for click decisions, by far surpassing previous methods attempted in the project. It's now the default - just type `operate`. Discovering the capability shown in the demo is a bit stunning to me. This…

Josh @josh_bickett

2 years ago

The Self-Operating Computer Framework now supports the OCR-based approach for click decisions, by far surpassing previous methods attempted in the project. It's now the default - just type `operate`. Discovering the capability shown in the demo is a bit stunning to me. This… https://t.co/qTOcV8FOY4

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Seems like endless possibilities for an agent with memory, tools, and a planning layer. RAG (or a conversation with another agent) is just another tool. #softwarearchitecture with #GenAI, becomes an agent that can do Google searches, RAG, planning, and summarized reasoning.

Jerry Liu @jerryjliu0

2 years ago

Seems like endless possibilities for an agent with memory, tools, and a planning layer. RAG (or a conversation with another agent) is just another tool. #softwarearchitecture with #GenAI, becomes an agent that can do Google searches, RAG, planning, and summarized reasoning.

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🪜 4 Levels of Agents for RAG 🤖 There’s an entire spectrum of agentic capabilities offering simple-to-advanced reasoning on top of your data. Here’s a mini-🧵 on the 4 different levels of agents and how they can augment your RAG pipeline 👇 1️⃣ Tool Use: A linear pipeline that…

Introducing 'Prompt Engineering with Llama 2' — an interactive guide covering prompt engineering & best practices for developers, researchers & enthusiasts working with large language models. Access the notebook in the llama-recipes repo ➡️ bit.ly/3Hu266D

Imagine "untraining" a Large Language Model on specific content. The New York Times doesn't want its content in ChatGPT. OpenAI would have to retrain its models from scratch. Removing any content from their models would cost them millions of dollars. I just read a new paper…

📢Tasks with > 10k classes (e.g. information extraction) are hard for in-context learning: typically a tuned retriever or many in-context calls per input are used ($$$) Infer-Retrieve-Rank (IReRa) is a SotA program using 1 frozen retriever with a query predictor and reranker.

📰GPT-Newspaper This project combines six agents to autonomously build a newspaper - content, outline, everything! By the same team (@tavilyai) that created GPT-Researcher Code: github.com/assafelovic/gp…

Merging LLMs Or Better Still - Knowledge Fusion of LLMs! A couple of weeks ago, Abacus AI open-sourced a merged and stacked LLM. Fundamentally, you are combining multiple pre-trained LLMs to come up with a new model that is more performant! FuseLLM takes this approach further…

An approximation of this is GitHub repo commit history. Don’t just train on the final code, train on all the trails and traces.

Andrej Karpathy @karpathy

2 years ago

An approximation of this is GitHub repo commit history. Don’t just train on the final code, train on all the trails and traces.

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Custom LLM and AI Agents (RAG) On Structured + Unstructured Data - AI Brain For Your Organization Imagine a ChatGPT-like interface over all your structured (database) and unstructured data. Ideally, you want to ask a question to an AI bot, and it should be able to run multiple…