John M.

If you're connected with me, you know I scream about how necessary integrations are. Check out this piece where I explain more for insideBIGDATA!

21

New Mindset Needed for Large Language Models - https://buff.ly/3XbN4vp #AI #ML #LLMs #genAI

Check out our latest post on Clappy.ai, "Leverage Layout-Aware RAG for More Accurate Retrieval of Knowledge from Documents." Discover how to harness the power of Retrieval-Augmented Generation (RAG) for documents in PDF, Doc, and Markdown formats. Dive into the specifics of implementation and explore our reference repository to get started with advanced RAG for your knowledge base. Don't miss out on this essential read! Continue reading here for all the details: The Power of Hierarchical Indexing with RAG. (https://lnkd.in/gbiVHfcH)

2

Need accuracy & speed in your GenAI stack? Superlinked + Redis = smarter and faster apps. Tyler Hutcherson Ben Gutkovich Daniel Svonava Ash Vijayakanthan Jim Allen Wallace Albin Bergh Yael Fainsilber Adrienne Cohen #RAG #genai #vectordb

20

3 Comments

Hey there! I'm diving into RAG, Semantic Search, and Vector Search for structured data and would love to get some insights. If you're knowledgeable in these areas and open to a friendly chat, I’d really appreciate picking your brain 😁 EDITS: For a bit of context, I have a mix of data, both structured and unstructured, and am looking to use semantic search to query the data. Let's say a singer has a name and a location. These are structured data in the DB. The singer also has songs with the lyrics. All song lyrics are stored in the DB. I am looking to combine both structured and unstructured data to perform semantic search. "Who is the singer from Seoul?" or "Who is singing a song about ducks?". Querying unstructured data is easy and a common use case, but I rarely saw a combination of the two.

11

10 Comments

This is a very interesting article on sparse vectors, which can help overcome limitations of vector search related to domain specific keywords. And of course, the code in the example is clear and clean! Sparse vectors have a few values and many zeros. Dense vectors have limited or no zeroes. But really, what we're talking about semantic search on terms where the context is not present or harder to infer. The author presents this as specialized keyword search such as medical terms or other domain specific terminology. "These scores reflect the significance of each term in the context of the document or query, guiding the model to allocate more weight to terms that are likely to be more meaningful for retrieval purposes." Highly recommend reading. Nice work Paul Iusztin. And a nod to Qdrant and their vector search technology that supports sparse vectors. https://lnkd.in/gcBWfTg8

3

When looking for a vector database, choosing the right vector search algorithms to establish the proper trade-off between accuracy and speed of vector retrieval is challenging. This article demystifies the foundational concepts of vector search so you can set up your vector database successfully. https://lnkd.in/e-9tfSfh #VectorDB

5

KV-cache quantization is super useful when you have a large batch-size and/or longer context-windows. This requires on-the-fly quantization. Happy to see HQQ used in this context: https://lnkd.in/eyxyPnhP

70

I just posted about Ploze to Product Hunt: https://lnkd.in/ebtktneU - I'd appreciate it if you'd take a look. (Unlock and integrate your digital life using Ploze: Load your data on-device from exports from a myriad of services, and view it all in an integrated searchable timeline. There is no cloud component to Ploze - it works on your computer.)

8

Interesting summary from The Wall Street Journal on which AI copilot to use. 🤔 What is your AI of choice? Personally, I use a mix of Perplexity and ChatGPT.

21

2 Comments

Heard about Conviva time-state analytics but not sure what this novel technology to compute stateful metrics (which we use to measure and improve users QoE in realtime) is? Here are two quick examples that show how to build a stateful metric without writing a complex and very expensive SQL query. Video Streaming use case: https://lnkd.in/dHHpqetq Retail use case: https://lnkd.in/dHHpqetq

41

If LLM’s replicate human preferences and biases, it’s interesting to consider whether we can run psychological experiments on those models more quickly and ethically than we can with humans and whether those results would transfer.

2

1 Comment

AI is innovating a variety of industries across the US and Agriculture is no exception. 🐄 My new friend, Catie McVey, has created a new "Agtech" startup that uses patented 3D Modeling and Machine Learning algorithms to evaluate the health of cows by looking at specific characteristics such as hoof morphology and facial characteristics. Working with her to architect how to get source data from "The Stockyards" to "The Cloud" has been a lot of fun. I am constantly discovering new ways AI can make an impact in industries from legal, to healthcare and now agriculture! Read more about Catie's journey on RIoT - Internet of Things' blog #AI #software #agtech

9

A new research paper by our incredible teams at Google Research and Google DeepMind shows the strong performance of Med-Gemini across a variety of multimodal tasks, including  2D and 3D imaging in radiology, histopathology, ophthalmology, and dermatology, and genomics. Med-Gemini ranked best in class on chest X-ray and CT scan report generation, and is able to perform genomics-based disease prediction and mortality risk more accurately than a standard linear polygenic risk score (PRS) based approach. This work couldn't have been possible without close collaboration with our partners, including a panel of specialists who assessed and rated our models as part of this research. These promising results showcase the potential of LLMs to transform medicine as we know it. https://lnkd.in/dHx8_DYh

278

4 Comments

I've been playing around with creating an opinionated AI Deep Tech Scale to help non-experts think about their strategy. Exploring this scale: Understanding how companies strategize their identity, team structures, costs, and activities is important. Success isn't confined to creating "God Models"; companies can achieve significant success by "connecting power to the people" and maintaining model agnosticism. For example, some might argue that SaaS companies are merely wrappers around database technologies, yet it's undeniable that they have generated significant value. #AI #DeepTech #Strategy #NonExperts #SaaS 

8

Got It AI's Amol Kelkar explains how an opinionated RAG platform can deliver much Faster Time to Value, Better User Experience, Reliable/Trusted results compared to open source components a team has to string together and work for a while to get an optimized solution. And yet you can have a Choice of LLMs. This is one of the best blogs in #generativeai on RAG. Chandra Khatri Charles H. Martin, PhD Gokhan Tur Dilek Hakkani-Tur Rohan Relan Rohit Kumar Amit Zavery Ashwin Ram Marco Murgia Krishna Hegde Michelle Yee Debanjan Saha Ashish Motivala Saleem Haque Shanmugam Chinnasamy Dipender Saluja Bill Brougher Eros Resmini Gautam Mundewadi Hung Tran Raghav Rastogi Lynne Wander Navin Chaddha Sean Grogan

32

5 Comments