Ciera Jaspan

Check out the latest in our "Debuggability" blog series! This blog is authored by Kevin Downey Jason Johl and me and talks about how we built a Debuggability Paved Road at Intuit using Ephemeral Containers and Argo Workflows. We'd love to hear your thoughts on the topic. Also, what tools do you build as-a-platform to help debug service, compute and network issues in Kubernetes at scale? #intuit #kubernetes #debug

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You don’t normally see LLM researchers and engineers talking about their day-to-day work. Big shoutout to this YouTube video from the Anthropic LLM Interpretability team, the team behind the Golden Gate Claude. It’s amazing to see that most of the team members have worked for the team for only one year, and yet they have made such a big breakthrough in interpretability research. Another thing I want to highlight is the unique challenges facing the team and how they approach these challenges, that is, through fast trial-and-error iterations and constant evaluation of what may work and what to prioritize next. #artificialintelligence #machinelearning #deeplearning https://lnkd.in/eUaPAYch

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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

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When you need to play with your data in Gen Ai, you need RAG and Chunking is one of the main component in the RAG framework while consuming or processing your data. Excellent post below describing how in different ways chunking in RAG can be implemented..

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Ever wonder how exactly your knowledge sources are being used during RAG? We did too. I'm excited to share our early work in explaining LLMs, "RAGE Against the Machine: Retrieval-Augmented LLM Explanations"! 🎸✨ 📄 Paper: https://lnkd.in/eV4qF8YV 📺 Video: https://lnkd.in/e6mNV8tt Last week at ICDE 2024, I introduced RAGE, an interactive tool designed to explain the outputs of LLMs augmented with retrieval capabilities (RAG). RAG needs explaining since it is unclear how the presence and order of knowledge sources (among other things) affects the LLM's answer! 🤖💡 RAGE helps users understand how LLMs generate answers by identifying parts of the input context that, when moved or removed, change the LLM's response. This counterfactual approach makes AI decision-making more transparent, and allows us to derive a form of citation. 🔄📊 Key features include: - Combination Tests: Which sources lead to which answers? 🔍 - Permutation Tests: How does the answer change if sources are reordered? 🔀 - Interactive Demo: Explore behaviors of real LLMs using real data. 📚 Whether it's determining the greatest tennis player or answering your own unique questions, RAGE supports various use cases. Our code (coming soon) is designed with interfaces, making it highly adaptable to any LLM, retriever, or text data source! 🎾🏆 For technical details and implications of our findings, please check out our paper. Along with my co-authors at University of Waterloo, York University and AT&T, we are working on several extensions to explain many unexplained aspects of LLMs, RAG, and beyond. Stay tuned for more updates and an open-source RAGE code release in the near future! 🚀💡 #ExplainableAI #LLM #RAG #Research

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Its about solving the problem, not 'having AI' in the system. This is from last March, but the Wave 1 vs. Wave 2 distinction does seem to be playing out. Less 'generate me content', more 'synthesize vast amounts of relevant data and aid in decisonmaking or signal extraction'. https://lnkd.in/ehFBFC32 They don't talk about multi-agent systems here, but looking forward to more startups with a focus on leveraging agentic design patterns (https://lnkd.in/ed5rMmFe) to streamline real world workflows.

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It was great to hear Robert Wong Julie Willem Angie McAllister, Ph.D. and Markus Kunesch on the recent episode of the @Ed-Technical podcast -- the first of a three-part series about how we're applying Google AI to our work in education and learning: https://lnkd.in/e7hQi6a2. This episode delves deep into LearnLM, how we're making Gemini tuned for pedagogy and how we're integrating this technology into our mainstream products to improve learning outcomes, and evolve how people learn on Google products. It explains inspiring new product ideas, and the steps we take across the company to understand and enhance the journey of learning in a responsible and innovative way. #LearnLM #AI #Education #Google

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New TLA+ related blog post, inspired by a recent post by Murat Demirbas (Distributolog) about the classic linearizability paper. This post is longer than my typical fare. https://lnkd.in/gKGFemUH

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Early this year I started to educate myself in quantum computing. Today I presented my progress on the field at my company eBay. This is a summary of what I have learned so far https://lnkd.in/ekc43Fa4

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Last weekend, at the AI tinkerers' human in the loop hackathon (https://lnkd.in/gyRwwztb), my team and I built MetabolixAI, an AI-driven nutrition platform using glucose monitor readings. We designed AI agents that can adapt to real-time glucose data and process user-uploaded meal images, thereby continuously improving its recommendations. These agents, acted based on dietary patterns and glucose responses, and delivered tailored nutrition plans with minimal human input. This got me thinking—how else can agents be used to analyze complex, ever-changing datasets in healthcare, especially when it comes to precision nutrition? And what are the risk/compliance frameworks to ensure correctness? https://lnkd.in/gVuzsGEe

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I first joined Amazon in December of 2005. Even though I left for two years, my "Amaversary" still coincides with the end of the year and a natural moment for reflection. As I reached my 17 year mark this December, I also had the privilege of working with Amazon Careers to share some of my journey and thoughts about individual contributor technical leader and people manager career paths. In my ruminating, I put together far more content than could be used for a single article. So, I've decided to post here as a series of Q&As - in case others might find my observations, experiences, and perspectives interesting or useful. Below is the first of 6. 1. 𝘽𝙧𝙞𝙚𝙛𝙡𝙮 𝙨𝙝𝙖𝙧𝙚 𝙮𝙤𝙪𝙧 𝙘𝙖𝙧𝙚𝙚𝙧 𝙟𝙤𝙪𝙧𝙣𝙚𝙮; 𝙬𝙝𝙖𝙩 𝙢𝙤𝙩𝙞𝙫𝙖𝙩𝙚𝙙 𝙮𝙤𝙪 𝙩𝙤 𝙩𝙖𝙠𝙚 𝙤𝙣 𝙙𝙞𝙛𝙛𝙚𝙧𝙞𝙣𝙜 𝙘𝙖𝙧𝙚𝙚𝙧 𝙥𝙖𝙩𝙝𝙨, 𝙄𝘾 𝙫𝙨 𝙢𝙖𝙣𝙖𝙜𝙚𝙧? I started my career as a software engineer 25 years ago. I've worked for startups, enterprise companies, and 7 different organizations over 17 years and two tours at Amazon. I've written firmware for robotic camera mounts; built highly scalable distributed systems; designed restaurant marketing, ordering, logistics, and operations software; created automated test harnesses for mobile games; tested HD DVD encoding; helped launch grocery delivery to customers around the globe - just to name a few of the many cool things I've had the privilege to work on. I've been an entry-level software engineer, a technical lead of teams, a software manager on a couple of occasions, chief software architect reporting to a CTO, and (for the last few years) Amazon Principal Engineer directly supporting a VP with about 200 engineers within my remit. I first became a manager because I mistakenly thought it was the only way to continue growing as a leader, and in many ways I failed. I learned some very humbling lessons about leadership through that experience, which I used to be a better individual contributor technical leader on my path to Senior Engineer. My second time as manager was because my organization had a need that I could fill. I had grown enough as a leader that this time I was much more successful. I said yes to an opportunity that started with managing a small team through delivery of a complex and critical project. Once I grew a high performing team that delivered that initial project, I took on more scope and expanded into multiple teams. After a couple of years of success growing and investing in my teams so that we could deliver greater value to our customers and business, I decided to prioritize being close to my family. For me, at that time, it meant giving up my people management role and returning to the role of an individual contributor technical leader that supports a globally distributed organization. Even though I really enjoyed my time as a people manager, there is no better job for me than that of a Principal Engineer. #amazon #careerdevelopment #technicalleadership #peoplemanagement

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Semantic-IDs has become our stepping stone connecting LLMs and recsys. Check out this paper to learn how it is also used to transform id representations in large embedding models.

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According to Eric Schmidt, in the next year or so, the union of the large context window, agents, and text-to-action, when delivered at scale, is going to have an impact on the world that no one understands yet. Listen to the fascinating discussion in the "The Age of AI, Eric Schmidt" youtube video - https://lnkd.in/dFTKgBVY

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Project update! My application tracking extension is now doing fuzzier matching and injecting new information into LinkedIn (and other sites) to keep me from doubling up on applications. Next up: local storage and bulk import/export operations...

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While there are certainly individual variations in skill and experience when discussing modern software development the story is much more nuanced than pure individual effort.  * software development is a team sport -- individual performance is heavily impacted by the system in which they are asked to operate in.  * we pair skill and experience to this model of understanding performance by expecting people to be able to influence and adapt in direct proportion to their seniority, aka the more senior you are the more responsible you are for the outcomes of your work, the less senior you are the more your manager and management chain are responsible.  * humans want to succeed, we're social animals who derive purpose from demonstrating skill, and working collectively. All of which is to say there is no way you have over 1000 people underperforming at your company without senior leadership deeply implicated in.  If nearly 10% of your company is failing, versus just having the market move against you, you need to fire the CEO.

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Interesting comparison of human-based ride hailing services (UberX, Lyft) vs autonomous-driving one from Waymo. TLDR: Waymo is more expensive and a lot slower to get you to your destination. And this is in the city where Waymo vehicles and software are probably most optimized for. This isn't to bash on Waymo and other self-driving car platforms, which have made incredible progress. That being said, the problem is not solved and it will take many more billions and many more years to get autonomous driving services like this to be price and performance competitive to current ride hailing services. https://lnkd.in/eb4uZ2Um #autonomouscars #AI

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A data-driven approach to driving wound healing outcomes Marcella Gomez, University of California, Santa Cruz https://lnkd.in/gAkWZYJT Meet: https://lnkd.in/g4AAWkcK YouTube Stream: https://lnkd.in/gwtqaTmf Talk details: https://lnkd.in/gWn3B3cU Subscribe for talk announcements: https://lnkd.in/g5ciuNuX Abstract: Precision medicine requires an ability to predict the response of an individual to a prescribed treatment regimen a priori. Thus, advancement in the field is challenged by a lack of predictive models and, arguably, a lack of time-series information for a highly dynamic system. Here, we discuss work in wound healing, for accelerating wound closure. We argue that timing of treatments is as critical to consider as the choice of drug or therapy. Due to system size and complexity, data-driven methods need to be explored to develop multi-dimensional quantifiable indicators tracking systemic changes. In this work I discuss how bioelectronic devices enhanced with deep learning can help facilitate real-time sensing and actuation for automated decisions in treatment for wound healing and preliminary work in transcriptomic based classification of wound states. Bio: Marcella M. Gomez is an associate professor at UC Santa Cruz in the department of Applied Mathematics and Associate Dean for Diversity, Equity, and Inclusion for Baskin Engineering. She received her PhD from Caltech in 2015 and a B.S. from UC Berkeley in 2009; both degrees in Mechanical Engineering. Her research interests are in the broad field of bio-control leveraging methods in machine learning and control theory. Applications range from controlling single-cell response to driving complex systems such as wound healing. #modeling #simulation #medicine #healing #trauma #predictivemodeling #datascience #data #woundhealing #biology #bioengineering #systemanalysis #sensing #omics

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An open letter from Meta employees to Mark Zuckerberg and Meta's leadership. Very well articulated, I couldn't have done it myself better! I left Meta end of 2023 because of many things similar to what's mentioned in the letter, so I can definitely relate.

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Another contribution to the research community from our team! We’ve just released our work on Large Concept Models, a novel training paradigm for language modeling. Instead of predicting the next token like usual LLMs, Large Concept Models are trained to reason at a higher level over what we call “concepts”, materialized by sentence representations in a multilingual / multimodal embedding space. We believe this to be closer to how humans think when communicating. For example, when giving a presentation multiple times, we keep in mind the same high-level plan of ideas to convey, but the exact words we use can vary from one time to another. Check out our paper and GitHub repository to learn more https://lnkd.in/eUmMUuuC

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