Marc Alringer

Good innovation…giving speech ability..

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We think a lot about UX at Durable. With AI-generated code becoming more prevalent, it's more import than ever to ask: what is the optimal UX for specifying software? While chat is the predominant interface to AI, we don't think it's particularly good for defining software requirements. That's why we've build our product from the ground-up around a document interface. In this article I dissect the pros and cons of GenAI interfaces with a focus on AI-generated software, and map out the characteristics of an ideal interface.

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University of California, San Francisco Chancellor, Sam Hawgood, in an interview with Matthew Lungren MD MPH at #ConvergeSummit has outlined the use of #AI scribes to create clinical notes as well as other #AI-driven technologies. Key to their success: a group of practicing physicians with knowledge of AI ensuring that the technology fits into the physicians’ existing workflow, which improves physician adoption of the technology. Another key: measure success through reduction in physician burnout and improvement in patient satisfaction, not increases in physician productivity. Final key: have a real-time, frequent review of implemented technologies to ensure that they still support and are relevant to changing clinical care standards - and shut the technologies down if they are not supporting their purpose.

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AI Disaster Looming? California Fights Back! A contentious AI-focused bill disturbs the tech scene in California. This isn't just legislative drama; it's a balance between innovation and possible disaster. Imagine an AI-gone-wild scenario. California is ensuring it never occurs. But there's retaliation. The tech industry—California's economic powerhouse—isn't silent. Their rebuttals are sharp and persuasive. Want to know more? Dive in here: https://lnkd.in/efTjPYiU Your insight could be crucial in this innovation vs. regulation battle. Share your thoughts! Follow me for more AI Tips, Tools, News and Fun #ai #aidisaster

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John Snow Labs #Healthcare #NLP & #LLM 5.3 is now out! This release completely overhauls entity resolution - mapping medical entities in text to standard medical ontologies, in context - greatly improving accuracy. Full release notes: https://lnkd.in/gsiGF26i Install software: https://lnkd.in/gsTqkWKT #ai #generativeai #healthcareai #healthai #datascience #ontologies #medicalcoding

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While the cartoon made me chuckle, the point Spencer Dorn is making extends well beyond healthcare. Many enterprises adopting AI will repeat failed past patterns of technology adoption and try to use AI to automate inefficient processes rather than to change or eliminate those processes. Often, the go-to efficiency gain is to use AI to replace humans, such as customer service representatives. This usually results in less successful outcomes. https://lnkd.in/gFs3mp2g . As Dr Spencer suggests in his original post, “We add more than we subtract and later hope technology will make us more efficient.” Maybe the question we should be asking is, “Why are our customers calling customer service, and how do we make things easier for them so they don’t have to call customer service?” If AI does live up to the world transformative hype – “AI is the new electricity”,   https://lnkd.in/gi7bPXqG   then the winners will not be the ones who use AI to become “more efficient” but those who can discover how AI enables new ways of doing business. An example I love to cite is Sears versus Amazon. Turn the clock back to 1995. Mosaic and Netscape Navigator have been released, and we are in the first days of the dot com frenzy. Sears is a profitable retail giant. Jeff Bezos and MacKenzie Scott are packing books in their garage for their newly launched online bookstore Amazon. Sears remained anchored to its traditional retail model as the digital era progressed. Despite some initiatives towards technology adoption, Sears’ efforts aimed to optimize existing processes rather than embrace fundamental technological changes. In stark contrast, Amazon embraced digital technology as a core component of its business strategy. It utilized the internet to sell books and reinvent the retail experience. Amazon's holistic approach focused on customer experience, data analytics, and logistics. The result? In 1997, when Amazon did its IPO, if you had invested $1000, that investment would have grown to $1.8 Million today. Had you invested in Sears, you would have lost the investment. Business transformation is not using technology to automate inefficient processes, it is using it to fundamentally change the way we do business.

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From where I sit in the SF Bay Area, cradle of innovation for at least the last 30 years, I'm a true believer in the promise of the #AI revolution. That said, I find it fascinating to consider views on the opposing side. I'm sharing below two opinion pieces from the last two weeks suggesting that the AI revolution is fundamentally overhyped and a bubble ripe for bursting, and that AI tools are actually not useful or transformative. Contrast this with venture capitalist Kai-Fu Lee's prognostication that 50% of white collar jobs will be displaced by #AI within three years, and that OpenAI and the other cutting edge movers in the space are headed to a trillion dollar valuation. Which side will win out? Where will we be by 2030 in the AI race? I'd love to hear your thoughts in the comments.

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Altera's Project Sid just simulated 1000 autonomous AI agents in Minecraft. Here's everything you need to know: • AI agents operate autonomously for days without human intervention • Agents collaborate on complex goals like forming economies and governments • They're programmed to support humans and express thoughts/feelings • Agents are game-agnostic, capable of using various apps and platforms This breakthrough could transform how we tackle complex societal issues by allowing us to simulate and test solutions in virtual environments before real-world implementation. Follow me (Rowan Cheung) to keep up with AI, and repost to help get your network ahead of the curve on AI ♻️

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~$1.2M of grants now available for helping us build various aspects of neural-symbolic-evolutionary AGI on OpenCog Hyperon platform ... See https://lnkd.in/d_Gn9yRA .... Transformer NNs are super cool but only a piece of the AGI puzzle. We are recruiting AI devs to work on a number of the other pieces, which we have figured out how to piece toward AGI using the PRIMUS design on Hyperon platform, centered on our new MeTTa AGI language. Let's get rational, compassionate, beneficial, decentralized and democratized AGI built first -- before Big Tech and Big Gov't get too close to AGI with their centralized AI systems lacking any deep understanding of self and other and objectifying people toward narrow goals.

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