OPINIONS EXPRIMÉES EN PUBLIC

Evangelos Simoudis

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1 sources · 3 points de vue · 3 sujets

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Evangelos Simoudis sur AI-assisted knowledge work evaluation, AI intellectual property protection, government data integration. Explorez 3 points de vue par thème, avec des éléments tirés de 1 source.

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AI intellectual property protection

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Corporations need to consider how much IP they expose to AI labs

Simoudis says claims about Chinese models obtaining knowledge from U.S. models have implications beyond competition among U.S. labs. He says this makes corporations consider carefully how much of their intellectual property they expose to these labs.

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When Everyone Is 10x More Productive, No One Is

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That has implications, frankly, for where my mind went: everything that is happening with Chinese models, or what is being claimed about Chinese models—that they are taking, whether through distillation or other means, knowledge from U.S. models. So this is what makes this story extremely important to me, with implications not only for what’s happening in the competition among U.S. labs, but for what’s happening more broadly. And that makes corporations, to your point, think very carefully about how much of their IP they open up to those neo-labs.
Contexte

So here’s why it’s fascinating. As you know, I take the corporate approach a lot. There has been so much noise about the quality and performance of Anthropic’s models, why they are being adopted more broadly, how that has implications for Anthropic’s revenue, and how it makes the company ready to become public before the end of the year. Yet here you have two researchers, one of whom is clearly associated with Anthropic, using OpenAI’s Codex. That’s point number one. Point number two is the fact that OpenAI is taking these prompts, as you say, and using them to improve their model. I mean, they started accepting some of that narrative.

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AI-assisted knowledge work evaluation

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The human orchestrator must explain how AI-generated work was produced

In AI-augmented knowledge work, the human acts as an orchestrator who must understand—and be able to explain—how AI-generated outputs were produced; inability to do so signals quality issues.

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When Everyone Is 10x More Productive, No One Is

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Being able to do this, again, to me, is the human as orchestrator. But as orchestrator, you need to be able to understand the connections. Even if the machine doesn’t give you the connections explicitly, you better be able to say, “Here’s how this thing was arrived at.” If you cannot, I think there are issues with the quality of your work.
Contexte

Yeah.

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government data integration

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Simoudis recalls a need for broader data connectivity after 9/11

Simoudis says analysis of the events leading to 9/11 highlighted a need for broader connectivity among the available data and analysis of the links established.

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When Everyone Is 10x More Productive, No One Is

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the big realization from the analysis of what led to the events of 9/11 was that we really needed much broader connectivity among the data we had, and subsequent analysis of the links that were established.
Contexte

Actually, I would say that, for me, what was quite central in that transition, if you will, was the realization by the government of the importance of link analysis using commercial tools. Many, many years ago, I had worked in an environment that was developing very proprietary tools to support those types of missions. As a result, there were silos galore. I think That then became a very important approach that found its way into commercial operations as well and led to big data and techniques to massively analyze that type of data that we were able to bring together. I think 9/11 did much more for data analysis than what we had previously in the 1990s with data warehousing and those kinds of technologies. It showed the importance of bringing together federated and disparate databases.

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