William Vaughan

How do the costs compare of implementing a low-latency write-ahead-log (WAL) on S3 Express One Zone and one implemented as a State-Machine-Replication (SMR) system (such as Paxos/Raft/Kafka)? I built a cost model for both to find out. https://lnkd.in/dwczVutW

43

5 Comments

I am excited to share the latest blog, co-authored by myself, Junhwi Kim and Vyas Sekar, detailing how we managed to successfully localized and improved LLM by combining Fine-Tuning and RAG. https://lnkd.in/gKDkgGPq

69

3 Comments

Exciting work from Meta and World Resources Institute who have used AI to create the most detailed map ever made of trees across the planet. This allows anyone to query the height of forest canopies or even individual trees (when they stand isolated enough). The data is now freely available. As a fun test, I used the provided browser (https://lnkd.in/ghxQFmxt) to find one of my favorite neighborhood trees - the giant sequoia in Seattle's Leschi Park. The data accurately shows this tree towering above the others in the park (screenshots in comments below). The team used AI at Meta's DINOv2 visual transformer architecture in order to infer the height of trees given just a satellite or aerial color image as input. This is a very similar technique to 3D photos that you may have seen in your Facebook feed (https://lnkd.in/gXCygk5H), where the depth of an image is computed via AI in order give a 3D effect. I'm looking forward to this work continuing and becoming an important way in which the world measures, reports, and verifies the carbon stored in trees. As industries (from big tech to airlines) purchase biosphere carbon credits to offset emissions, it's critical that they have the best data possible. Improved imagery and AI offer a path to getting there. More info in the project video below.

59

9 Comments

In this article we are providing the major architectural building blocks and blueprint for building end-to-end GenAI-based applications that are ready for production. I’d like to call out several key considerations as you go about implementing and designing these llm-based applications. #llms #llmops #chatgpt4 #aiml #gemini #mlops #data https://lnkd.in/dMev5ncq

8

Character.ai let’s you create characters and chart with them. From Harry Potter, Elon Musk, Beyoncé, Super Mario, Psychologist, Alan Turing etc, maybe even Taylor Swift. You get at your disposal millions of AI personas you can engage. Watch the CEO of character.ai, Noam Shazeer, explains the why behind this technology. #characterAI #AI

4

3 Comments

Quick tip - DDL for vector indexes https://lnkd.in/gwtNtE_r

38

What people leaders can learn from Large Language Models (LLMs) Because LLMs are trained from human data, it's unsurprising that their behavior mirrors that of their human counterparts. It's worth taking a closer look at human teams through the lens of what we've learned about LLM performance. While it wasn't the authors' goal, this paper highlights many LLM issues and recommendations that parallel what is needed for human business teams. Here are the key takeaways from section 5. - Assigning tasks to individuals based on their specialties improves performance. - A clearly identified leader and a comprehensive feedback system will keep the team on track. - Clear role definition ensures that individuals stay focused and reduces mistakes. - Planning and task decomposition improve decision-making and execution. As a final note, the paper mentions the use of a rotating leader to improve the overall performance of the multi-agent response. Applied to human teams, this is a novel approach with great potential. Leaders who stay connected to technological developments would inject that fresh knowledge into their teams.  What team wouldn't benefit from periodically cycling through people leaders who have kept their skills sharp and relevant? https://lnkd.in/gXUp88th

21

It was exciting, challenging, and rewarding to write this O'Reilly book Graph-Powered Analytics and Machine Learning with TigerGraph with my coauthors Phuc Kien Nguyen and Alexander Thomas. You can still get it for free from TigerGraph, if you act soon. We're appreciate your feedback and maybe a review on Amazon. #graphanalytics #machinelearning #graphalgorithms

45

3 Comments

Forbes just posted my new piece about the symbiotic relationship between AI and NLP. In a nutshell, the piece explains why the consistency and control of NLP provides a great foundation to build cutting edge AI powered applications. NLP may not be the sexy new toy, but it's the cornerstone to successfully working with unstructured content and getting all you can out of LLM's and SLM's. https://lnkd.in/et8hd5jz

23

1 Comment

Thats the exact explanation I gave to a group of investors last week. (… it’s just Precision/Recall, attributes vectorization as “embedding”, K-Nearest Neighbor, User Search Intent, DAG, ML algorithms, etc all over again …). LOL and they asked what’s new this time around. And I was like not much, ML is still the backbone, and AI is the inference and actions based on the model. It works better when there is a feedback loop in a closed system. But the current state of the art like ChatGPT is based on scraping the Internet (garbage in, garbage out). Kinda like early search engines. What is a better analogy is that NVIDIA/Snowflake etc are selling tools, aka pickaxes to prospectors. The gold is up to you to find. And the opportunity right now is to monetize industry specific (healthcare, travel, financial services) clean data to build models with (aka a treasure map for the prospectors). From an enterprise perspective, what’s out there today aka generative AI isn’t very useful. Too much hype. To build useful engines will require an effort similar to search revolution 2 decades ago. From

14

3 Comments

🍜Anybody up for some ramen? I am excited to share that I am back in the garage, building again (it’s actually a house in the Berkeley Hills). We are taking the wraps off of Supercog AI, a new startup focused on solving Application Integration using LLM powered agents. My co-founder in this new venture is a great friend of mine (and a former co-founder partner), Alex Osborne. Alex and I have worked around the application integration space for a long time, and we are unrealistically excited about applying the GenAI stack to this problem. Not many people relish the task to get some bits to move from system A to system B. But it’s a critical job to be done, and that’s led to a lot of brittle scripts, a myriad of inflexible “no code” tools, and a lot of hand crafted SQL. But the power of the Large Language Model, trained on a huge corpus of information, offers a remarkable tool to solve this problem. The LLM you use today already knows the APIs of hundreds of popular systems. It knows the SQL dialects of every major database. It understands the semantics and data schemas of many popular SaaS systems. It understands much of the specific domain in which your business operates. I like to call the AI revolution “the last platform shift”. That may turn out to be hyperbole, but I definitely believe we are in the very early stages of seeing what this new stack can enable. I know we are hardly the first folks to claim that “amazing stuff is coming!”. But this is why Alex and I have decided to focus on a real and hard problem. If we can prove that LLMs can power a new way to solve this problem, it will be the first successful *new* approach in 15 years.. We will have much more to share about this new platform soon. In the meantime, if you’re interested in getting a peek - or even better if you have application or data integration tasks that you would like some help with - please reach out because we want to help. #genai #startups #backinthegarage

163

37 Comments

It’s been fascinating learning how to build products with LLMs. This is a really good collection of lessons that resonated. https://lnkd.in/eivFPPAF

11

2 Comments

According to Iliya, data compression laid the foundation for generative AI and gave us the big bang moment in deep learning. Here is a breakdown of how data compression led to generative AI, like large language models (LLMs): 1. Data Compression and Statistical Patterns: Imagine trying to compress a text file. You look for repeating patterns and store them efficiently. Data compression algorithms do this mathematically. In the process, they uncover the underlying statistical patterns in the data. This includes things like how often words appear together, the sequence of letters in words, etc. 2. Next Token Prediction: With this statistical knowledge, we can start to predict the next item in a sequence. For text, this could be predicting the next letter in a word, the next word in a sentence, and so on. 3. From Prediction to Generation: Once we can predict the next item, we can keep going. We predict the next word based on the previous ones, then the next one based on that, and so on. This allows us to generate entirely new sequences that resemble the original data. In the case of text, this is where generative AI comes in. We can generate new text that follows the same statistical patterns. #AI #DataCompression #DeepLearning #MachineLearning

5

One of the biggest challenges with operationalizing safe and responsible AI is on measurement and benchmarks. This release from MLCommons comes after more than six months of cross-industry and academia collaboration to define and implement a POC for standardized AI Safety benchmarks. It has been great to work with this diverse group of experts. Read more about it here and stay tuned for more in the coming months! Eric Horvitz Dean Carignan Besmira Nushi Kurt Bollacker

10

Seismic’s AI enablement assistant, Aura Copilot, is now available for early access. Teams can now get faster access to answers and content, automate tedious tasks, and gain insights for better business decisions. Aura also now integrates with Microsoft Teams and Microsoft 365 Copilot to give teams in-the-moment answers and content recommendation. https://lnkd.in/d_mSbDXK

44

1 Comment

Interesting paper showing that LoRA doesn't learn your data that well which raises questions on how effective it is at fine tuning your Gen AI model. LoRA Learns Less and Forgets Less - https://lnkd.in/eG4tEuSe

19

There is so much research going into bringing the power of LLMs into the Enterprise context. The main techniques are k-shot learning or prompt engineering, RAG (Retrieval Augmented Generation) & Model Fine tuning (not to mention many variations of these e.g. Agentic RAG, GraphRAG etc.) However, I came across an interesting take on this from IBM and they are calling it "InstructLab" - where the claim is that they are able to teach the LLM "new skills" grounded in an Enterprise context. On the surface it looks like a k-shot learning + fine turning on steroids. Approach seems to be to utilize a small "skills training dataset" in order to generate a large synthetic dataset (perhaps utilizing a specialized LLM) that is then incorporated into the LLM weights natively. This could be a very interesting concept as long as the cost of adding new skills is not very high (when compared to fine tuning). Exciting times! #genAI #enterpriseAI

40

2 Comments

The new voice input for ChatGPT has much improved latency, real-time interactions, you can interrupt it and it does ton of voice styles. (Like it or not, the "interactive xfriend" apps that are going to spring from this will be insane, esp. if there's memory.)

1