Dr. Lance B. Eliot is a world-renowned expert on Artificial Intelligence (AI) with over 7.8+ million amassed views of his AI columns and been featured on CBS 60 Minutes. As a CIO/CTO seasoned executive and high-tech entrepreneur, he combines practical industry experience with deep academic research.
Supreme Court Justice Gorsuch Decries The Human Toll Of Too Much Law So Let’s Put Generative AI To Work And Overrule This Problem For GoodIn today’s column, I am continuing my ongoing and extensive analysis regarding what’s trending for the latest in AI startups, especially so for founders seeking venture capital funding, see my series at.
This makes life easier. Prospective investors can see a tangible artifact. Prospective customers can kick the tires. You shift from a purely conceptual pitch to one that entails the rubber meets the road, as it were. I dare say nearly anyone pitching an AI startup must put together a viable MVP, namely, it can’t be less than a bona fide MVP else you won’t seemingly get to first base.
Anyway, let’s shift into AI mode to see why MVQ is vital for AI-based systems and startups devising AI at the core of their products or services.I will begin by setting the record straight about what today’s AI consists of. There are myths that need to be busted. The customary means of achieving modern generative AI involves using a large language model or LLM as the key underpinning.
The advent of AI hallucinations or fabrications reflects a current structural weakness in how generative AI is devised and there is a tremendous amount of AI research occurring to reduce or mitigate these vexing and serious AI issues, see my in-depth analysis at. Just to let you know, some fervently assert that no matter what is done, existing ways of building and fielding generative AI are going to inextricably lead to AI hallucinations .
Realize too that patient-sent notes are typically filled with confounding wording and require at times intense scrutiny to make sense of them. Another consideration is that such notes are typically contextually based. Without a grasp of the patient’s situation and context, the note might seem inexplicable.
Should you proceed to build the AI and then see what MVQ you perchance arrive at , or should you first determine what is the marketplace’s acceptable MVQ at the get-go and then see if you can build the AI to that target? Flipping to the other side of the coin, you can of course start by exploring the marketplace to ascertain what an acceptable MVQ would be, even if you have nothing in hand to showcase what the AI consists of. The downside is that you might get false reads on what the MVQ is. People can conceptually arrive at MVQs that aren’t based on reality. To some degree, having an MVP provides a more grounded MVQ.
You are pleased that you have the AI aimed at a viable quality level. This indicates likely marketplace acceptance. Nice. Off you go into la-la-land.Inopportunely, you’ve allowed intruders to outgun you. A competitor comes along and does something similar but is at a quality level below yours, yet also hits the viability criteria. The chances are that they might have a lower cost of entry than you because they didn’t have to build in that added quality that you have in your AI.
Large Language Models Llms Generative AI Venture Capital Greylock Partners Sarah Guo Conviction Startups Founders Entrepreneurs Minimum Viable Quality MVQ Minimum Viable Product MVP
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