Chroma aims to bridge the gap between demo and production systems by offering a seamless user experience from development to deployment through vector retrieval and search.
The concept of multiple smaller agents working together holds great potential for solving problems and improving productivity in AI applications.
Deep dives
Chroma: A Simple Solution to Complex Problems
Chroma is an open-source vector database that offers a simple yet powerful solution for robust production systems in machine learning. By focusing on the geometry and topology of the latent space, Chroma aims to turn sexy demos into reliable applications. With a distributed version and a hosted offering in the pipeline, Chroma plans to make its developer-friendly API accessible for small and large production use cases. The future of embeddings also looks promising, with the potential for multimodal embeddings and fine-tuned embedding spaces. Despite concerns about AI regulation, Jeff Huber emphasizes that industry self-governance is crucial, while being mindful of the appropriate timing for regulations.
The Exciting Potential of Agents in AI
The concept of agents in AI is both overhyped and underrated. While there has been a recent hype cycle around agents, we are still in the early stages of exploring their capabilities. Jeff Huber finds the idea of multiple smaller agents working together to solve problems more feasible and practical than one encompassing agent. Use cases for agents, such as a bot managing email inboxes, hold great potential for improving productivity. The key lies in providing agents with context and ensuring they are highly specialized for specific tasks.
The Power of Embeddings and the Future of AI
Embeddings continue to play a significant role in AI, with opportunities for both text-based and multimodal embeddings. Chroma, along with other embedding providers, is exploring fine-tuning over embedding spaces to tailor them to specific datasets and use cases. However, Jeff Huber highlights that large context windows are overhyped, emphasizing the need for thoughtful context rather than increasing context size. In terms of future AI capabilities, embedding models are expected to become more powerful, enabling advancements in reasoning machines and the widespread adoption of agents throughout various software applications.
The Journey from Demo to Production with Chroma
Chroma aims to bridge the gap between demo and production systems by offering a seamless user experience from development to deployment. With the upcoming distributed version and a cloud-hosted offering, Chroma plans to provide developers with easy on-ramps to build robust applications using vector retrieval and search. Jeff Huber acknowledges the talent aspect of AI, assuring that it is still day zero and there are ample opportunities for entrepreneurs, researchers, and professionals to make an impact in the field.
On today’s episode we talk with Jeff Huber, the CEO and Co-founder of Chroma. We talk about what sets Chroma apart from its competitors, new developments in AI technology, and advice for listeners who want to get started in AI.
0:00 intro
1:02 starting chroma
6:08 vector databases
10:03 interesting use cases for vector databases
13:14 what sets chroma apart?
23:00 unresolved questions in LLMs
32:45 multiple agents vs. one agent to rule them all
34:50 chroma’s future
38:00 embedding models
43:00 over-hyped/under-hyped
44:30 AI regulation
48:42 is it too late to get into AI?
With your co-hosts:
@jasoncwarner
- Former CTO GitHub, VP Eng Heroku & Canonical
@ericabrescia
- Former COO Github, Founder Bitnami (acq’d by VMWare)
@patrickachase
- Partner at Redpoint, Former ML Engineer LinkedIn
@jacobeffron
- Partner at Redpoint, Former PM Flatiron Health
@jordan_segall
- Partner at Redpoint
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