Hema Raghavan

Checkout my recent Forbes article on counting the cost of an AI deployment.

22

1 Comment

We are not slowing down on Gen AI, come see why...and how it changes things, the nature of work. https://lnkd.in/gW3ettg3

2

At a Generative AI thought leadership dinner with other CTOs in NYC today and some interesting trends are emerging: * a consensus that LLMs are converging on a general set of features and basically comparable performance * a consensus that an org's data pipelines will matter more than the LLM chosen * and a group of people who read the TOS that are really scares about what these LLMs will do with access to all your data. Lastly, my prediction: the work of tech is moving from the keyboard to the whiteboard. Ai as microservice means structuring your data, thinking with ai, and chaining ai agents will supercharge your career and your cause -- but only if social change orgs platform their data. In my study of polymaths, Charles Babbage designed the basic idea for a unit of computing being something that takes an input, does something to it and gives an output -- the so called Babbage difference engine. In a real way, GenAI is approaching a world where chaining inputs ala a babbage difference engine is accessible to all. This can be an awesome opportunity if we get Non profits to work together!

11

3 Comments

I just read an insightful piece by Eric Broda (published in Towards Data Science) that outlines the Agentic Mesh—a new ecosystem designed for autonomous agents to interact, collaborate, and transact. His work introduces a structured approach for managing the coming surge in autonomous agents, showing how they can function independently yet collectively. Agentic Mesh serves as a networked environment where autonomous agents operate in a safe, transparent, and efficient manner. It allows agents to find each other and securely interact under a framework of policies. Each agent has a specific purpose and autonomy, making it a valuable contributor to the ecosystem without sacrificing security or control. Core Components: 1. Marketplace: A centralized space where users discover agents by tasks and capabilities, offering a trusted environment for engagement. 2. Registry: A metadata repository organizing each agent’s purpose, owner, and policies, allowing agents and users to securely find and connect with each other. 3. DNS Integration: Each agent has a recognizable digital identity, enabling seamless discovery and interaction, just like an internet service. 4. Agent Stack: This layered structure of communication protocols, control services, and interfaces helps agents operate effectively and responsibly. It ensures that agents act independently within controlled parameters. Agentic Mesh’s effectiveness depends on agents sharing consistent attributes, including: • Purpose: A clear objective drives each agent’s actions. • Ownership: Transparency around ownership fosters responsibility and trust. • Autonomy: Agents act independently within set parameters to complete tasks. • Discoverability: Easily locatable, ensuring efficient interaction within the network. • Intelligence: Agents utilize language models to plan and execute tasks, adapting to complexity as needed. Operational Flow: Registration, Discovery, and Execution Agent interactions in the Mesh follow three core flows: 1. Registration: Agents set up purpose, policies, and registration data to gain visibility in the mesh. 2. Discovery: Through the registry, agents and users locate potential collaborators based on defined needs. 3. Execution: Engaged agents use AI models to plan and perform tasks, enlisting collaborators when necessary to complete workflows. Read the full article: https://lnkd.in/eVmq5BFC

16

1 Comment

🆕📨 Newsletter 🚀 Vector Search Unpacked (with Prashanth Rao of Kùzu Inc.): - Comprehensive guide on choosing the right vector search system - Importance of scalability, including horizontal scalability and real-time index updates - Prioritize performance features like real-time index updates, robust vector index support, and hybrid search - Consider deployment options, scalability requirements, and integration with existing infrastructure https://lnkd.in/gchQKFU3

25

The cost of training top models is increasing 2-3X per year, and at this rate will reach $1 billion per model by 2027. The findings are from Epoch AI and follow their recent analysis of increases in compute for foundation models running at 4-5X per year. They've also provided some interesting data on the relative breakdown of cost components like staff, hardware, and energy. A few thoughts after reading this: - I would assume staff costs won't increase as much as hardware and energy costs, so the relative contribution of human labor to future AI models will decrease, in yet another sign of capital's increasing dominance over labor in the industry (just look at NVIDIA's ever-rising stock price). - If compute is increasing 4-5X per year but cost is increasing 2-3X, is it safe to assume that training runs are getting more efficient? - Some people, like Anthropic CEO Dario Amodei, have said we'll see $1 billion training runs in the next year. Does this mean the 2-3X pace is about to increase? That Epoch is wrong in its estimate? Or that Amodei is overstating the cost, possibly to put fear into competitors who can't keep pace? - Only a few companies are going to be able to train future models. We're likely going to see further consolidation. Perhaps we get to a future with only two large general purpose models, like the Windows/MacOS and Android/iOS duopolies we've seen in the past. Article here: https://lnkd.in/g5SejXen

7

Faster, more accurate, less data needed = BaseModel.AI

5

I am deeply honored to receive the *AI Strategist* award from Signals AI Summit. This recognition reaffirms the importance of inviting diverse voices into the AI conversation and building platforms that amplify those perspectives. 🎉 Celebrating today alongside the incredible leaders who were recognized alongside me: Pallavi Sharma 💖, Sean Sweeney, Tom Keefe, and Vivek Bharathi Muruganandam. Your achievements inspire me — and so many others! Kudos to the winners across all the categories: 🌟 AI Innovators: Apryl Beverly, MBA in Marketing, Ashley Gross, Awa K. Penn, Anders Thorup-Jensen, and Erin R. 🌟 AI Thought Leaders: Edward Frank Morris, Jenny Kay Pollock, Madhu Vohra, Wendy Turner-Williams, and Nathan Burke 🌟 AI Trailblazers: Aprelle Duany, Claire Farwell, Eugina Jordan, Heather Murray, and Reut Lazo Together, we have the power to shape the future of AI. Let’s continue to push boundaries and redefine what’s possible. 💫 #ai #aileadership #aistrategy

295

141 Comments

Model Explainability means different outcomes for different users. We can't have one size fits all when explaining model prediction. At the same time, unless the explainability is true to the model, it has credibility issues! All regulators expect to have explainable ML models, which is not standardised! How do you scale ML explainability in high-risk and high-compliance use cases? Continuing the article in Forbes, I've written about various XAI outcomes and how to approach setting up such explainability. #AryaXAI #ExplainableAI #EnterpriseAI #AIRegulations https://lnkd.in/gp7YgzB3

13

1 Comment

A really practical guide to LLMs is now available! Learn what to do, what to avoid, from prompting to RAG, to fine-tuning, and evaluation. Definitively worth reading. https://lnkd.in/dCC6uKjs

34

Today's article on MCMC - read to learn why there are better and more efficient techniques than chasing LLMs and other #AI buzz. https://lnkd.in/eaw6Paan

8

Large language models-- or #LLMs --are a type of generative pretrained transformer (GPT) that can create human-like text and code. There's a lot of talk about GPTs and LLMs lately, but they've actually been around for years! In this video, Martin Keen briefly explains what a LLM is, how they relate to foundation models, and then covers how they work and how they can be used to address various business problems.

1

This is a good article on using LLMs in the real world. It covers many aspects that we learned at Nurish over the last few months and more. Using LLMs is an inexact science, but has its own needs in processes and tools. While most of the attention has been on the LLMS, their capabilities, and RAG, there are mundane issues that need to be taken care of, issues where the solutions for pre-LLM development are not that well-suited. These include version management, integration, testing, data management, quality assessment, and more. This article covers many of those practical aspects. Eugene Yan, Bryan Bischof, Charles Frye & more https://applied-llms.org/

9

Something that stuck with me during my interview with Naveen Rao, VP of Generative AI at Databricks: "I take the hard path." He built an AI-focused chip company Nervana in 2014 when machine learning was still academic theory and the chip industry was deeply out of favor. Naveen saw computing architecture would need to fundamentally change for ML workloads. And then in 2019, observing the power of LLMs like BERT, Naveen foresaw the need for enterprise-grade services to manage their training, deployment, and customization. That vision led to a $1.3B acquisition by Databricks in 2023. What I love about Naveen's approach is his willingness to build for markets that don't exist yet. "There isn't a market, employees don't even know about it. So how the hell do you build a company?" You educate. You convince. You get people to see what you see. It's brutal work, but it's important work. You're reshaping how people think about problems.

176

8 Comments

Predictions for the future of LLMOps are varied.   But what is clear from speaking to Clemens Rawert, Co-Founder at Langfuse;   Is that users have become a lot more thoughtful and systematic as LLMs have matured.   And building golden datasets are going to be even more important for quality benchmarks.   Just 2 points of importance at Langfuse (YC W23), Take a look at the video to find out what else.... 💡

6

I wrote a blog post on a thorny but underappreciated problem in ML-based marketing personalization: event attribution. It's how you go from the actions you take and customer behavior you observe to creating training experiences for your model. Good attribution makes a huge difference in driving uplift, there are a lot of degrees of freedom in configuring it, and at the same time, it's rarely discussed in ML/bandits/RL literature.

47

4 Comments

Great article exploring the impact of GenAI on creative industries. As mentioned in my framework for evaluating GenAI use cases (https://lnkd.in/gADr5vUP), use cases that require fluency (human quality output such as creative content generation), will experience the most disruptive impact of LLMs on the short to medium term. #artificialintelligence #largelanguagemodels #GenerativeAI #airevolution

22