Ted Schadler

Want to learn more about how retrieval augmented generation (#RAG) leverages vector databases to overcome the limitations of off-the-shelf LLMs? This article written by Aerospike Chief Scientist Naren Narendran walks through the functions and benefits of RAG, and how it can facilitate a radical makeover of LLMs and real-time AI environments. https://lnkd.in/exxgPGVU

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Explore this four-part series of the top most viewed and highly-rated Gartner webinars that focus on four key parts to the GenAI strategic journey: vision, value, risk and adoption. Watch now. #GenerativeAI #GenAI #Leadership #Webinar

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Another demonstration of ScienceCast.org #AI enhancements of arXiv postings - free, simple interface https://lnkd.in/e_gXsF_9

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GenAI is revolutionizing industries faster than ever before. However, what happens in tightly regulated fields such as finance and healthcare? In our new article, "Gen AI for Regulated Industries," we delve into both the potential hurdles and prospects that GenAI offers these sectors. Discover more: https://lnkd.in/eWQcdsz6

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One day, Quantum Compute will just be another modality that everyone will be able to tap into. However, technological revolutions by their very nature require widespread awareness, fundamental training and societal preparedness in the lead up- just as much as they require advanced research and engineering wizardry. I am looking forward to speaking at the Quantum World Congress in DC next week on the topic of Quantum workforce development.

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This California Management Review article explores the ROI of AI ethics and governance investments through two lenses: loss aversion and value generation. While many companies focus on minimizing risks like legal penalties and reputational harm, the article argues that a value generation approach—using ethics to build trust, foster innovation, and create competitive advantages—offers greater long-term benefits. By integrating ethics into strategic planning, organizations can both mitigate risks and enhance overall growth.

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As a tech entrepreneur, I've learned that building a lasting business requires an unwavering focus on the fundamentals - strong financials, a passionate team, and innovative products. In a recent episode of M&A Science, I had the opportunity to share some insights on navigating the challenges and opportunities that come with growing a tech company, both organically and through strategic acquisitions. I'm grateful to the M&A Science team for providing a platform to discuss these important topics. Building a successful tech company is a marathon, not a sprint - I hope the experiences I’ve shared can be helpful to fellow entrepreneurs on that journey. Listen to the episode here: avpt.co/40IxOrr #MAScience #TechEntrepreneur

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Use Gartner's impact radar for generative AI to plan investments and strategy with four key themes in mind: ✅ Model-related innovations ✅ Model performance and AI safety ✅ Model build and data-related ✅ AI-enabled applications Explore all 25 technologies and trends. #GartnerIT #GenerativeAI #AI #Artificialintelligence

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The adoption of #GenerativeAI in businesses after two years is still slow, and the continuum of the kinds of impacts it delivers shows why. Read on to learn more https://okt.to/S4p0Ro

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Proposing a framework for #AIPolicy: The current technology-centered approach to AI policy is resulting in fragmented and piecemeal efforts -- often decoupled from the intended real-world results. We lack a comprehensive AI policy framework to support coherent and effective policy making anchored in meaningful outcomes aligned with our collective social priorities. I propose a Social Outcomes and Priorities centered (SOP) framework for AI policy, its core components, and recommendations on their implementation for #USA (the framework is applicable globally). The SOP framework provides a mechanism to build policy and regulatory structure encouraging outcomes that strengthen society’s core foundations and offer uniform and equitable benefits while minimizing the risks. Check out the paper for more details. I’ll soon publish an executive summary with a tldr version. #artificialintelligence #policy #aiforsociety #technologypolicy #machinelearning #datascience #genai #generativeai #llm #aisafety #aicognition #aiandhumans #aiforgood #aiautomation #AIEthics #AIBias #AISafety #AIGovernance #AIPolicy #AIRegulation #AIPrivacy #AIFairness #AIAccountability #AITransparency #AIHumanRights #AISocialImpact #AIWorkforceImpact #AIEducationImpact #AIEnvironmentalImpact #AIDigitalDivide #AIAlgorithmicJustice #AIDataSovereignty #AIInformedConsent #AITrustBuilding #AIHumanAICollaboration #AIEthicalDesign #AIResponsibleInnovation #AISustainableDevelopment #AIInclusivity https://lnkd.in/ggsfgE55

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Nature Index 2024 Research Leaders — previously known as Annual Tables — reveal the leading institutions and countries/territories in the natural and health sciences, according to their output in Nature Index journals in 2023. https://lnkd.in/gN4X2DWH

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Great insights, Rahul Kulkarni! I completely agree that cognitive AI is poised for significant growth. As a Personal Branding Expert and Digital Marketing Strategist, I've observed the top AI use case in my industry to be Content Generation and Optimization. AI-powered tools are revolutionizing: 1. Content creation: AI-driven copywriting and blog post generation. 2. SEO optimization: AI-based keyword research and meta tagging. 3. Social media management: AI-powered scheduling and engagement. However, I believe the true potential lies in Cognitive AI's ability to: 1. Analyze human emotions and behavior. 2. Provide personalized recommendations. 3. Enhance decision-making processes. My take: AI will augment human creativity, not replace it. What's your top AI use case observation? #AI #ML #DigitalMarketing #PersonalBranding #ContentGeneration"

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One of the emergent capabilities in LLMs is Theory of Mind (ToM) which is a psychological framework used to understand how humans communicate, each forming their own state machine of the other’s state of mind. The idea that AI could really exhibit ToM has been highly debated ever since Michal Kosinski wrote about it. This new study in Nature Portfolio digs in deeper, teasing apart various elements and evaluating LLMs against human baselines. Reading this study - including differences between the models’ behavior - I am wondering if LLMs may appear as “humans” from a different culture, and if that metaphor “normalizes” human machine interaction vis-à-vis human perception of the machine. #llm #AI #openai #llama

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The future-focus of the STM US Conference meant lots of great predictions and advice from people representing the research office, researchers, and research librarians! I summarize much of what I learned - check out this HighWire blog post.

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Times are shifting from Conversational LLMs to Agentic LLMs. The capabilities of Large Language Models (LLMs) are rapidly evolving. They have transitioned from passive question-answering dialog systems to active agents interacting with the digital world. However, with great power comes great responsibility—and risk. While the prospect of automation seems enticing, this capability can quickly spiral into a nightmare. Allowing LLMs to autonomously generate and invoke API calls is akin to opening Pandora's box; a single unexpected or dangerous call could unleash total havoc. Fortunately, a solution is on the horizon! Introducing GoEX, a design proposal by University of California, Berkeley researchers Shishir Patil and Tianjun Zhang, the minds behind Gorilla LLM and RAFT. It offers a path to creating safer autonomous Agentic AI applications. GoEX directly addresses the risks, ensuring that the autonomous actions of AI are both safe and reliable. 📑 Check out the detailed blog here: https://aka.ms/goex-blog Blog by: Cedric Vidal

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Today we report a significant advance in understanding the inner workings of AI models. We have identified how millions of concepts are represented inside Claude Sonnet, one of our deployed large language models. This is the first ever detailed look inside a modern, production-grade large language model. This interpretability discovery could, in future, help us make AI models safer. We mostly treat AI models as a black box: something goes in and a response comes out, and it's not clear why the model gave that particular response instead of another. This makes it hard to trust that these models are safe: if we don't know how they work, how do we know they won't give harmful, biased, untruthful, or otherwise dangerous responses? How can we trust that they’ll be safe and reliable?

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