Rishi Reddy @rishkoreddy
Founder Supermat AI - Ensuring rad data transformations for Gen AI Joined August 2011-
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One of my first issues for Supervised (almost a year ago!!) was covering the hype around vector databases following the release of ChatGPT and, perhaps more importantly, the leak of Llama 1. I felt this was as good a time as any to check back in on how things have developed.
@MenloVentures 4. Unlock proprietary data Over 80% of enterprise data is inaccessible because it’s trapped in unstructured file formats or document stores like your email inbox. By leveraging LLMs to make this data usable, AI-native apps can build proprietary data moats over time.
@MenloVentures 1. Displace services with software Nearly 80% of the economy is service-based, untouched by tech due to unstructured data & complex reasoning requirements. AI is now making inroads, turning SaaS on its head with “Services-as-Software.”
In 2023, enterprises spent $1.1B+ on the LLM infra stack—making it the largest new market in generative AI & a massive opportunity for startups The total funding raised is $10B+ as of March 2024 (excluding $1B+ raises) Comment for link to the deep dive of this sector
@gregisenberg “People with TASTE in an AI world who know how to build awareness.” This is spot on. The combination of excellent design and distribution will dominate.
@benlandautaylor @pmarca 1880's energy with 2024 technology -- unstoppable.
I think people don't appreciate the "model" part of "language models" enough. Deep learning is cool, but everything we have now is downstream of work done in the 1980s by George Armitage Miller and Christiane Fellbaum - pyschology and linguistics professors, not computer…
One thing they don’t teach you in business school is that product pricing is often determined through live negotiations with Stephen King.
One thing they don’t teach you in business school is that product pricing is often determined through live negotiations with Stephen King.
text has magical properties. it has the built in modularity, reflexivity, compositionality of the natural languages. it is the best latent space evolution ever came up with for inter creature communication
text is the universal interface text is how we communicate the vision in our mind to a computer and the angel trapped inside reifies imagination into pixels
'Consonance' is my project that explores how AI interprets the spoken word. This is an excerpt from a James Joyce novel. I use his words exactly for the prompt, in the style of artist John Lavery. Funded by @futurescreensni A collaboration with @HeaneyCentre + Armchair & Rocket
Wild that writing itself is a fairly recent innovation. I’m seeing a shift in how incoming tech is slowly course-correcting the rampant commodification of language by SEO.
Wild that writing itself is a fairly recent innovation. I’m seeing a shift in how incoming tech is slowly course-correcting the rampant commodification of language by SEO.
if you’re 10-15 years old, aren’t working 80-hour weeks, and the founder of a VC backed startup, get it together
Design language + language language PMF = *magic* Time to stop treating that second part as an afterthought
Big ideas = ideas that can transcend campaigns, channels, countries via @alexjmurrell
The content-to-hashtag ratio was always the writing on the wall
My vocal fry makes said font bold italic
AdTechGod 🍪 @AdtechGod
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@simonw Thank the goodnesses this pattern is emerging and there aren’t API wars.
Context is I want to add more features relating to this to llm.datasette.io so I'd like to use language that's widely understood but also as concise as possible llm openai-compatible-endpoints add perplexity https://... is a bit much!
What's a good name for that pattern where LLM API providers have endpoints that speak the same JSON protocol as OpenAI, and can be called using the OpenAI client libraries? I've been calling them "OpenAI compatible endpoints" but that's a bit of a mouthful
there's are all the connections based off a single block
first steps into a spatial @AREdotNA
@OpenAIDevs I wonder if this uses open source vector db like @milvusio or in-house solution? And if the 10,000 files limit is due to cost consideration or scalability? It's exciting to see that OpenAI is all in retrieval approach. I'm also personally believing in the critical role of…
@ArYoMo Given a document and a schema, return the information in the document as the schema.
I am once again reminding you that structured extraction using LLMs is going to transform every single industry in the next 10 years
@AravSrinivas kudos to the perplexity designers for being so amazing they made a new industry template 💪
@perplexity_ai And here is elmo chat: Structured, clean output of the summary, abstract and key bullet points of the paper. The model behind it is mixtral 8x7b (from @MistralAI who I respect immensively). Mixtral 8x22b is being tested.
Anyone have extensive experience taking PDF's and extracting data and moving to Excel document? What's best tool for this? All data is standardized so should be "easy" for the tool to do, even if I have to "train" it, right?
It's not an unreasonable mental model to form to be honest - enormous weird blobs of vector floating point matrices and token embeddings are hardly an obvious way that this technology might work
This could also explain why so many people instantly assume that "fine-tuning" a model is the obvious right way to teach it new information, as opposed to using more effective but less obvious techniques like RAG (not helped by that being a pretty terrible acronym)
Yet another example illustrating an inconvenient truth: If you build a RAG system that can be used to process data from untrusted sources, and you include the ability to embed images from or links to external domains, then you're going to have prompt injection exfiltration holes
👉 Meet Bobby Tables, but for LLMs! 🔥 An exploit case study with Google NotebookML. 🧐 embracethered.com/blog/posts/202… #llm #infosec #ml
@simonw This is something I've gone back and forth a lot on from working on the AI news pipelines. It's nice to sometimes get the data from the urls, but have found more often than not it's better to scrape them out, or otherwise substitute that information. A very weird set of…