LLMs
- Traditional AI - The most secure, understandable, and performant. However, Good implementations of traditional AI require that we define the rules behind the system, which makes it unfeasible for many of the use cases that the other 2 techniques thrive on.
- Supervised Machine Learning- Middle of the road b/w AI and Deep Learning. Good when we have
Devansh • How to Pick between Traditional AI, Supervised Machine Learning, and Deep Learning [Thoughts]
Where would I add generative AI? Generative AI has the ease of accessibility of traditional AI, where people think it is understandable, but it does not have that feature in itself. It also has the opaque and costly nature of DL. Many companies are at the moment rushing into developing things with generative AI without having any prior foundation in AI and any processes set up to manage it: data ops, devops, …
Traditional AI forces you to think about how something works, understand the system, and then define the rules for it. ML lets you use features and feature importance to shortcut some. Deep Learning allows you to brute force it. Generative AI allows you to brute force without any background in DL.
🤖 Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks.
- Why CrewAI
- Getting Started
- Key Features
- Examples
- Local Open Source Models
- CrewAI x AutoGen x ChatDev
- Contribution
- 💬 CrewAI Discord Community
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joaomdmoura • GitHub - joaomdmoura/crewAI: Framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks.
The need for better AI or LLM-specific infrastructure, along with the host of problems that come with non-deterministic of LLMs, means that there’s more software work ahead of us, not less. Abstraction layers like LLMs create more possibilities and thus, more work.
Is this a good thing or a bad thing? I’m not sure.
A great example of this is frontend... See more
Is this a good thing or a bad thing? I’m not sure.
A great example of this is frontend... See more
Shortwave — rajhesh.panchanadhan@gmail.com [Gmail alternative]
However, a key risk with several of these startups is the potential lack of a long-term moat. It is difficult to read too much into it given the stage of these startups and the limited public information available but it’s not difficult to poke holes at their long term defensibility. For example:
- If a startup is built on the premise of taking base
AI Startup Trends: Insights from Y Combinator’s Latest Batch
Pipeline RobustQA Avg. score Avg. response time (secs) Azure Cognitive Search Retriever + GPT4 + Ada 72.36 >1.0s Canopy (Pinecone) 59.61 >1.0s Langchain + Pinecone + OpenAI 61.42 <0.8s Langchain + Pinecone + Cohere 69.02 <0.6s LlamaIndex + Weaviate Vector Store - Hybrid Search 75.89 <1.0s RAG Google Cloud VertexAI-Search + Bison... See more
arXiv:2405.02048v1 [cs.IR] 3 May 2024
OpenAI is treating its new marketplace seriously now: The brand new GPT store will come with REVENUE SHARING.... (missing in the Plugins launch)
and launching a Stateful Assistants API:
- Persistent Threads (/api/openai/threads)
- Built in Retrieval (chunking etc done for you)
- Code Interpreter (RIP Adv Data Analysis?)
- Speech to Text and Text to... See more
and launching a Stateful Assistants API:
- Persistent Threads (/api/openai/threads)
- Built in Retrieval (chunking etc done for you)
- Code Interpreter (RIP Adv Data Analysis?)
- Speech to Text and Text to... See more
swyx • Tweet
memary: Open-Source Longterm Memory for Autonomous Agents
memary demo
Why use memary?
Agents use LLMs that are currently constrained to finite context windows. memary overcomes this limitation by allowing your agents to store a large corpus of information in knowledge graphs, infer user knowledge through our memory modules, and only retrieve... See more
memary demo
Why use memary?
Agents use LLMs that are currently constrained to finite context windows. memary overcomes this limitation by allowing your agents to store a large corpus of information in knowledge graphs, infer user knowledge through our memory modules, and only retrieve... See more
GitHub - kingjulio8238/memary: Longterm Memory for Autonomous Agents.
Data
- Right now, GPTs are the easiest way of sharing structured prompts, which are programs, written in plain English (or another language), that can get the AI to do useful things. I discussed creating structured prompts last week, and all the same techniques apply, but the GPT system makes structured prompts more powerful and much easier to create,
Ethan Mollick • Almost an Agent: What GPTs can do
My $0.02 is that a lot of the future research/work there will be figuring out how to identify effective sub-graphs to provide additional context, to avoid having to pass in the entire graph. As well as trying to identify ontology-less structures in real-time, which includes NER and RE, as well as named entity/relationship... See more