Farsight | enabling reliable LLM applications @farsight_ai
Applying LLM tech to difficult use cases in finance and insurance where reliability and accuracy key. Open-sourcing useful pieces and libraries along the way farsight-ai.com Manhattan, NY Joined August 2022-
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Today I attended @GoogleStartups and @flybridge's AI Conference at Google’s NYC office 🚀. With so many insightful speakers and attendees, it was highly valuable for uncovering how to build an AI company in today’s landscape. Thread below with key takeaways for all AI founders🧵
💀Prompt engineering is a painful necessity - we decided to automate it 🚀 👉Methodology Analysis Article:medium.com/p/45a7bea3d895 👉GitHub:github.com/farsight-ai/fa… 👉Docs: api.farsight-ai.com/farsight-opro 👉Original Paper: arxiv.org/pdf/2309.03409… "Large Language Models as Optimizers"…
🚨Open-source models are now beating GPT-3.5-turbo in the financial domain. Full post here: medium.com/@kunal_79217/a… At Farsight, we created an automated benchmark to evaluate LLMs’ question-answering abilities on company financials. We find: 🤗 Open-source model SOLAR-10.7B…
A Stanford Lab released a 32k context length embedding model last week. Is it the real deal? ⚠️TLDR; No - embeddings saturate their information capacity at around 300-500 tokens. Break down on why below. Model: huggingface.co/togethercomput… Disclaimer: All credits for these insights…
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85 Followers 251 Following… plus many conversations about where the space is going, what emerging tech and applications excite innovators and VCs alike, and how we can all contribute to making AI as safe and responsible as possible. Thanks @GoogleStartups and @flybridge for a great event!
4.) The Steak vs Sizzle method (by @thoropass’s Sam Li) for deciding which product directions & features should be at the top of the priority list: The Steak is the substance, something that drives a lot of value and may contribute to differentiation. The Sizzle is something…
3.) AI today (esp GenAI) has the potential to be wrong, at times with drastic consequences. To create ethical & innovative AI solutions that both get attention and build trust with your end customer, builders should follow this thought process as @GoogleAI’s Alexander Reese said:…
2.) @flybridge's @chazard shared tangential thoughts on some specific areas application-layer AI startups could focus on to find long term success: - Building ever-increasing data moats that improve used model performance - Integration directly into existing customer workflows -…
1.) “When building a company, center it around something that is possible with today’s technology and defensible / improved with the technology of the future, not replaced.” @dpr_648 from @flybridge (my favorite quote from today) This ideology should be top of mind to make the…
Today I attended @GoogleStartups and @flybridge's AI Conference at Google’s NYC office 🚀. With so many insightful speakers and attendees, it was highly valuable for uncovering how to build an AI company in today’s landscape. Thread below with key takeaways for all AI founders🧵
I know we've spent too much time on this. Hopefully others won't have to
💀Prompt engineering is a painful necessity - we decided to automate it 🚀 👉Methodology Analysis Article:medium.com/p/45a7bea3d895 👉GitHub:github.com/farsight-ai/fa… 👉Docs: api.farsight-ai.com/farsight-opro 👉Original Paper: arxiv.org/pdf/2309.03409… "Large Language Models as Optimizers"…
Great work by @KatiePelton3 implementing and making sure this prompt automation extends to GPT-3.5-Turbo! Excited for upcoming extensions
💀Prompt engineering is a painful necessity - we decided to automate it 🚀 👉Methodology Analysis Article:medium.com/p/45a7bea3d895 👉GitHub:github.com/farsight-ai/fa… 👉Docs: api.farsight-ai.com/farsight-opro 👉Original Paper: arxiv.org/pdf/2309.03409… "Large Language Models as Optimizers"…
Not only did @KatiePelton3 extend an extremely important paper to ChatGPT, she also created an entire package (try it out‼️) dedicated to making this technology accessible to those building complex use cases w/ LLMs. Love our team!!
💀Prompt engineering is a painful necessity - we decided to automate it 🚀 👉Methodology Analysis Article:medium.com/p/45a7bea3d895 👉GitHub:github.com/farsight-ai/fa… 👉Docs: api.farsight-ai.com/farsight-opro 👉Original Paper: arxiv.org/pdf/2309.03409… "Large Language Models as Optimizers"…
Too many use cases in industry for genAI are held back by: 1. Models not understanding domain knowledge 2. Not knowing how to gauge baseline model performance in use case & how augmentations to their systems are improving performance A benchmark like this gives some clarity.
🚨Open-source models are now beating GPT-3.5-turbo in the financial domain. Full post here: medium.com/@kunal_79217/a… At Farsight, we created an automated benchmark to evaluate LLMs’ question-answering abilities on company financials. We find: 🤗 Open-source model SOLAR-10.7B…
A Stanford Lab released a 32k context length embedding model last week. Is it the real deal? ⚠️TLDR; No - embeddings saturate their information capacity at around 300-500 tokens. Break down on why below. Model: huggingface.co/togethercomput… Disclaimer: All credits for these insights…
Always good practice to double-click into a paper
A Stanford Lab released a 32k context length embedding model last week. Is it the real deal? ⚠️TLDR; No - embeddings saturate their information capacity at around 300-500 tokens. Break down on why below. Model: huggingface.co/togethercomput… Disclaimer: All credits for these insights…
@jxmnop Honestly, explaining simple FNNs as a knitted blanket may be a cool analogy. The knots are individual neurons, where different types of knots serve different functions. Different knots may need different threads (weights/density), and when you put it together you get a blanket
@rowancheung absolutely agree, great tutorial. We @farsight_ai put together a developer-focused one on how to deploy your own API (more flexibility) with EC2 / Heroku and connect it to your GPT - check it out here! bit.ly/custom-api
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