Hema Raghavan

Exciting news for customers: Fine-tuning for Anthropic’s Claude 3 Haiku model is now available in Amazon Bedrock—currently the only managed service to offer this capability. This means our customers can now customize Claude 3 Haiku with their own data to build GenAI applications tailored to their specific needs, while keeping their data secure. Since we launched the preview a few months ago, we've already seen some great use cases. One of our customers, Robin AI, a legal AI company, is using this feature to optimize legal workflows and help lawyers speed up contract processes. With a fine-tuned Claude 3 Haiku in Bedrock, they are helping lawyers finish contract review processes 90% faster. Will be great to see how other customers use this new capability. https://lnkd.in/g2jU8Kfv

324

5 Comments

🚀 Transform AI Conversations: Join Our Exclusive vCon Use Cases Zoominar on May 14th! 🗓️ Attention trailblazers in the world of AI and communication! 🌟 Following the resounding success of our "Introduction to vCon" Zoominar, Thomas McCarthy-Howe and I are thrilled to invite you to Part II of our groundbreaking series: "vCon Use Cases: Revolutionizing AI Conversations". 🎉 As a pending Internet Engineering Task Force (IETF) RFC, virtualized conversations (vCons) are set to become a critical building block for AI task managers, revolutionizing the way we store, analyze, and leverage customer dialogues in AI applications. 📈 Join us on May 14th at 1 PM Eastern US for an interactive online Zoominar, where we'll explore the transformative use cases of vCons across various domains: 🌟 Customer Experience: Conversational CRM Listening to Customers Mass Customization 🌟 Efficiency: Compliance and Consent Generative AI 🌟 Observability and Transparency: Robot Food Enterprise Search Agent Governance 🌟 Business Transformation: New Revenue Business Improvement Throughout the Zoominar, you'll have ample opportunities to ask questions, share your insights, and engage in thought-provoking discussions with our expert speakers and fellow attendees. 💬 🔥 Limited spots available! Secure your place now by registering here: https://lnkd.in/ec5q5ErV 🔥 If you missed our previous Zoominar, "Introduction to vCon," don't worry! Simply send an email to [email protected], and we'll provide you with exclusive access to the recording. 📩 Seize this opportunity to explore the transformative potential of vCons and stay at the forefront of AI and communication innovation. Let's unlock new possibilities together! 🔓 #vCon #ArtificialIntelligence #CX

22

Fantastic panel on GTM for Enterprise AI. Thanks to fellow panelists for a sharp discussion and to Emergent Ventures for hosting. Anupam Rastogi Ankur Jain Preethy Padmanabhan Chandini M Ammineni Jason Griggs Deb Bardhan #GTM #EnterpriseAI

64

Interested in #GenAI and its impact on #softwaredevelopment? Read EQengineered's Principal Technical Consultant/Architect Ed Lyons' recent essay. #techinsights #softwareengineering #digitaltransformation #GenAI #LLMs #EQengineered

7

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

Lin Guan and Yifan Zhou have led a very interesting study investigating the effectiveness of Vision Language Models in critiquing robot behaviors in terms of the desirability of their "style." If you are wondering how our work on LLM's inability to verify correctness squares with this study on using them as style critics, see my earlier post https://lnkd.in/gBYAr5qq ! This is the reason why being a style critic is one of the roles LLMs can play in LLM-Modulo framework: https://lnkd.in/gyAyKx4E Check out the details of the work @ https://lnkd.in/gfVZ4EYW ( Lin Guan also has a nice twitter thread on the work here: https://lnkd.in/gnieU-qM )

89

2 Comments

Just read Swathi Young’s latest post on AI readiness, and it’s spot on. Her breakdown of building a strong AI foundation is essential reading for any organization. 🌍 She discusses how the rise of generative AI is revolutionizing the way we work - and putting the spotlight on data like never before. If your team isn’t already thinking about scalable infrastructure, AI governance, and data management, take a look at her insights STAT. A bit of what caught my eye: 📊 Data is king – Strong data pipelines are crucial for leveraging AI effectively. 🛠 Infrastructure matters – Invest in scalable cloud platforms for AI to thrive. ⚖️ Governance is key – Establish ethical AI guidelines and frameworks early. 🚀 Start small, think big – Pilot AI projects with high potential, minimal disruption. If you already follow me, you know my mantra when it comes to all things AI… 🌟 Identify the business problem you’re trying to solve 🌟 Start small 🌟 Test and learn, test and learn, test and learn 🌟 Stay courageously curious Highly recommend reading Swathi’s full post and following her for ongoing AI insights (link found in comments, below ⬇️). #ai #innovation #leadership #tech

28

17 Comments

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

Kudos to Kshitiz Parashar for his pioneering work on Agentic Chunking! His latest primer has quickly become the must-read guide for AI Application Engineers looking to optimize LLMs for smarter chunking, better retrieval, and sharper accuracy. This Speaks volumes about the calibre of the team which I am super proud of.  Even a lean, dedicated group can now harness the power of LLMs to design state-of-the-art architectures—proof that AI is leveling the playing field like never before. Ready to dive in? Explore the full article here: https://lnkd.in/eJ8cem3x #aiagents #chunking

37

1 Comment

Excited for OSACON (the open source analytics virtual conference) in JUST TWO WEEKs (register -> https://lnkd.in/g4-GvjS6)! This year I'm speaking about - guess what? AI (!) My angle is about how it has completely changed my daily workflows as a data practitioner and startup founder, exploring the good, the bad and the overhyped, more about my talk here -> https://lnkd.in/gTmQd6PU

20

1 Comment

Last month I had the privilege of participating in a panel discussion on Generative AI at Enterprise, alongside Sayan Maity, PhD and Arpita V., hosted by Roger M. & Data Science Salon at Google HQ in San Francisco.  We tackled critical topics, such as why rapid prototyping is commonplace across companies, yet few successfully transition AI projects into production. Arpita pointed out that for recommendation and ranking problems, inference speed is crucial. Companies often utilize LLMs for offline evaluations while relying on high-performing, traditional models for online consumer interactions to mitigate the risk of unpredictable outputs. It's essential to recognize that many problems can be effectively addressed using smaller models like ROBERTA or BERT, without overhauling existing machine learning pipelines with #LLMs. Our discussion also covered #copilots , the #agentic framework, model size, #inference time, #cost , and #scalability . Ultimately, the pivotal question remains: Is the ROI substantial enough? Does deploying generative AI into production pay off? Furthermore, can we quantify the productivity gains for individuals using copilots? For insights and answers to these discussions, I invite you to listen to our conversation. #engineeringexcellence, #AI, #evaluations , #bestpractices, #GenAI, #DSSSF https://lnkd.in/gefHHDhb

7

1 Comment

Look how strong #Phi-3.5-MoE is! On a completely unseen (the dataset didn't ever exist during the training time), low resource multilingual, and a new healthcare domain dataset, it strongly holds the ground against all those larger or private models. A new paper from MSR India. An extensive evaluation of various language models for multilingual healthcare scenarios. https://lnkd.in/gNSX3gj8

42

1 Comment

I wrote an article with David Yuan and Diego Z. from Tidemark, where we dove into how AI is shaking up the data game. For years, the big players—those with massive datasets—had an edge because their data acted like “gravity,” pulling customers in and keeping them there. But AI is changing that. Now, with tools to integrate and make sense of data across systems, startups can compete without needing to play by the big guys’ rules. It’s a new era of agility where data orchestration may just be the next big advantage. Exciting times for those rethinking their data strategy! https://lnkd.in/eqGDACYW #AI #DataStrategy #Innovation #Partnership

24

2 Comments

Unapologetically Technical's newest episode is now live! In this episode of Unapologetically Technical, I interview Hubert Dulay the author of Streaming Data Mesh and Developer Advocate at StarTree. We talked about his early experience with web backends like CORBA and SOAP and how those prepared him for data work. He shares his advice for those with web development skills to transition into data and what it's like for a person leaving a company after a long tenure there. We discuss his time at Cloudera and Confluent and how the data industry has changed over time. We talk about his recent experience with Flink and StarTree. We go in-depth into streaming data mesh, covering what it is and how to create real-time data products. We discuss the ways teams can create joined or enriched data products across domains. We round out the discussion with some tips on how to get data mesh adoption. Watch the full episode here:

10

1 Comment

Short version of my argument for human-centered AI.

156

10 Comments

"Productization is an affirmation," writes Adrian Bridgwater, "when any good or service evolves to a point where it can be productized and boxed, it denotes a certain solidity and branded consumability." In our conversation, I was delighted to share how #SAS equips organizations with readily deployable AI technology to productionize real-world use cases. https://lnkd.in/enUGCgAi #AIML #sasusers

9

Exciting announcement today - Introducing Moveworks Enterprise Search The initial promise of RAG, leveraging LLMs for a superior search experience, was quickly overshadowed by its intrinsic challenges—accuracy and hallucinations. These issues, compounded by the vast scale of data, diverse formats, and the complex web of permissions within organizations, have limited the widespread adoption of RAG for enterprise search. To address these shortcomings, Moveworks introduces Agentic RAG, which combines the latest in LLM advancements with the unique demands of enterprises - data accuracy, scale, various integrations, and business context. Moveworks Enterprise Search leverages Agentic RAG to deliver not just search results, but accurate, reliable, and contextually relevant insights personalized to the user's intent, all within our existing Agentic AI assistant as well as a deeper search mode. Want to contribute building Agentic RAG? My team is hiring an experienced PM to build out the platform that brings content for search - Job: https://lnkd.in/gctjfmBH - Learn more: https://lnkd.in/g_ZBVH_N

58

A/B Testing myth: “Longer tests = more sample size = more power” The shared post gives a great formal intuition why “longer tests = more sample size = more power” sometimes is NOT true. Must read for every experimenter! For more information on this topic also check out section 3.4 in this paper (https://lnkd.in/evBypYtu) by Ron Kohavi & collegues or this post (https://lnkd.in/euE33Th7)

101

26 Comments