Jaime Teevan

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

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Why ‘open’ #AI systems are actually closed, and why this matters "We highlight three main affordances of ‘open’ AI, namely transparency, reusability, and extensibility." https://lnkd.in/gVjHDq4X

Why ‘open’ #AI systems are actually closed, and why this matters "We highlight three main affordances of ‘open’ AI, namely transparency, reusability, and extensibility." https://lnkd.in/gVjHDq4X

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State, local and education customers are getting an AI boost! 🧠 Google Public Sector's Amanda Stange discussed transformative AI applications in a State Scoop podcast at Google Cloud Next. 🚀 From climate insights to disaster response systems, Google is addressing real-world challenges. The Pacific West stands out with its innovative use of AI. Plus, Google's Gemini AI is streamlining government workplaces. ⏱️ #GoogleCloud #AI #googleforgov #GoogleCloudNext

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For our next Center for Public Sector AI demo, I give a quick overview of Anthropic's Claude (3.5 Sonnet Model) generative AI solution. I have found Claude's use of 'artifacts' makes it easier to maintain longer, more complex prompts that require maintaining and updating separate components (i.e., coding, prototyping, policies, etc.). #govtech #AI #genAI

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4 Comments

Short version of my argument for human-centered AI.

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Check out the Artificial Intelligence (AI) News Roundup for the Week of December 23 presented by the Mississippi Artificial Intelligence Network (MAIN)—the nation's first and only statewide AI initiative! One of the objectives of MAIN is to raise awareness about AI. MAIN helps facilitate this awareness by staying up-to-date on AI news. As part of this effort, MAIN shares daily AI news and compiles a weekly roundup featuring a variety of AI-related updates. Stay connected with MAIN and learn about FREE AI resources available to anyone in Mississippi: https://mainms.org #MAIN #Mississippi #MississippiAI #MississippiArtificialIntelligenceNetwork #AI #News #LatestNews #NewsUpdate #ArtificialIntelligence #AINews #Workforce #WorkforceDevelopment #GenerativeAI #GenAI

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OFAI aims to enhance our understanding of AI and its potential to augment human capabilities while promoting responsible development practices.

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This recent study on Whisper's hallucination problems is super interesting (and somewhat concerning): https://lnkd.in/gAqRy95U > "in one sound bite, Whisper correctly transcribed a single, simple sentence, but then hallucinated five additional sentences that contained the words “terror,” “knife” and “killed,” none of which were in the original audio" At Rev, accuracy and reliability is non-negotiable. Unlike other transcription services that might ‘hallucinate’ extra sentences, we ensure every word transcribed is exactly what was spoken. #rev #ai #asr #speechtotext #stt #languagetechnologies #speech #whisper #openai

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8 Comments

WHAT HAPPENS WHEN AI AGENTS START TALKING TO EACH OTHER? This question was dissected by Joanne Chen in Forbes. She spoke with Chi Wang, a principal researcher at Microsoft who’s behind the opensource multi-agent framework, AutoGen, to find out. With caveats for balancing autonomy and control, and designing for human-agent interaction which can take place in one or more interfaces, multi-agent systems provide 3 top benefits: 1. “Modularity: Distributing complex tasks across specialized agents makes the overall system more modular. This modularity simplifies development, testing, and maintenance, as capabilities can be added or tweaked without revamping the entire system. Troubleshooting is also streamlined, as issues can often be isolated to individual agents. 2. Specialization: Think of multi-agent systems as teams of experts, each contributing unique knowledge and abilities to collectively tackle difficult problems. Tasks are broken down into components and assigned to the agent best equipped to handle them. As each agent processes its part of the task and passes information to the next, the output is progressively refined and improved. Through such specialization, the resulting systems can achieve results that generalist agents struggle to match. This approach is conceptually similar to techniques like prompt chaining, where a human user breaks down an intricate task into a series of subtasks and iterates toward a desired outcome through conversation with the model. Chi offers the example of a multi-agent system tasked with analyzing data and providing insights and recommendations. In this scenario, each agent focuses on a different aspect of the task: some specialize in data retrieval and presentation, others in deep analysis and insight generation, and others in planning and decision-making. This division of labor allows each agent to work on what it does best, leading to faster, more accurate outcomes. 3. Collaborative learning: In multi-agent systems, the interactions among individual agents can give rise to solutions that exceed what any single agent could achieve in isolation. By allowing agents to work together, critique one another, and share their insights, the system can develop a more comprehensive understanding of the problem at hand. This is especially valuable when dealing with complex, multifaceted issues,” - Joanne Chen, General Partner, Foundation Capital, as reported in Forbes. As I ponder the real-world handoffs between AI and humans - a field that’s called Human-Computer Interaction (HCI) - the idea of multiple agents collaborating to solve my multi-step tasks leaves little margin for error. And the potential for more time in my day. What can a multi-agent system do to improve your day? Join the conversation in comments- What’s an AI agent? https://lnkd.in/eUGx9zx4 #ai #llm #agenticai #aiagent #hci Image credit: Forbes

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22 Comments

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

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141 Comments

Here's an article I wrote about the amazing Fem.AI event last month with insights from Dr. Anirudh Devgan (Cadence Design Systems), Reshma Saujani (Girls Who Code), and Dr. Paula Goldman (Salesforce) on how diverse teams reduce bias, foster innovation, and build trust in AI systems. #FemAI #WomenInTech #AIEthics #DiversityInAI #TechInclusion #GirlsWhoCode #ArtificialIntelligence #TechLeadership #AIInnovation #WomenInSTEM #TechDiversity #ResponsibleAI #AIBias #TechEquity #FutureOfTech

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1 Comment

Check out a paper being presented at the GenAI workshop at @chi2024 tomorrow where Larry Birnbaum and I explore how GPT can help with analytical narrative structure. https://lnkd.in/gxiF6MU7

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2 Comments

A quick reminder that we're sponsoring another CFP on research into generative AI in the workplace! Topics of interest include technologies that augment human capabilities (in addition to substituting for them), over/under-reliance interventions, and creative or unexpected uses of language models in productivity scenarios.   We tried to keep the proposing process quite lightweight and fast: Just 500 words, due June 3, with decisions by June 12!   See the full call for more info: https://lnkd.in/g__3jpj3

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In our opening salvo for our book on the AI-Centered Enterprise, co-authored with Natarajan Balasubramanian and Amit Joshi, our editorial opinion article on "What Companies are Getting Wrong about AI" was published in TechTarget News. In this article, based on our research, we argue for a nuanced three phase approach to AI implementation in organizations. Arun Rao, my co-founder at samvid.ai, joins us as collaborator for this article. #enterpriseai #responsibleai https://lnkd.in/g3E4dx64

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3 Comments

This week I went to the first user group of Cube, thanks to Artyom Keydunov. It was a great event with many new cool announcements from Cube that I'll separately post about. After half a day spent talking with users, and experts such as Josh Klahr (Snowflake), Francesco Puppini (Prof and Author of The Unified Star Schema), Katie Hindson and Hamzah Chaudhary (founders of LightDash), Galen Marchetti etc, and reading more after the event from the best minds like Sanjeev Mohan and Professor Christopher Manning, a question popped in my mind: Are Semantic Layers the treasure map for LLMs? Semantic layers are not new, they started in 1991, when BusinessObjects patented the semantic layer. It’s a great concept but it hasn’t really taken hold in the enterprise. Now LLMs are generating renewed interest in the semantic layer. Semantic layers really unlock the future of where things will go with AI. If you want trustworthy data, you need a semantic layer. If you let an LLM loose on a schema versus let an LLM loose on a semantic layer on top of that schema with some basic curation, you're going to get much better results. My hot takes from the blog post below: 1️⃣ Text-to-SQL startups, while hyped up, are destined to fail without a semantic layer.  2️⃣ Data Products are trending: It's becoming expected in your product to feed some data back to the person paying for it. The intersection of semantic models, plus data sharing and distribution is extremely powerful.  3️⃣ To unlock LLMs, we need first to write the literature of data modeling for data analytics. This will serve as the foundation of the dialogue that we will then have with LLMs.  4️⃣ The industry has been talking about a universal source of truth for semantic layers but it's not going to happen, because it's not how reality works. 5️⃣ We’re entering a golden age of vertical specific analytical software to be built on top of semantic layers. The treasure that semantic layers help uncover isn't just gold and jewels, but something far more valuable in the modern business world: trustworthy, actionable insights. By providing a well-defined framework for understanding data relationships and business logic, semantic layers enable LLMs to dig deeper and extract nuggets of wisdom that might otherwise remain buried. Read more below 👇

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11 Comments

This is an essential wave to capture. Looking to a "program" mindset for how we interact with LLMs is essential for efficiency and evolution of fine-tuning and structured interaction. Now, lets make the tools amazing as well. #llm #prompt #microsoftai

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