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Everything can be formulated as an optimization problem.

The Intelligence Tsunami ( distinctively discusses emerging autonomous intelligence, based largely on Yann LeCun's work, and commercializing solutions by solving real problems for real customers.

Yann LeCun is Meta's Chief AI scientist and a Turing Award winner. A good introduction to his thoughts is in the blog post "Yann LeCun on a vision to make AI systems learn and reason like animals and humans" (

At their core, deep neural networks (DNN) do math. When text is entered into large language model prompts, it is converted to numbers, math is done, and the result is converted back into text.

Recently, LeCun posted on X a simple but profound idea regarding the design of DNNs: "Everything can be formulated as an optimization problem."

That not only describes what DNNs do from a technical perspective, but it is also the key to the elusive product/market fit for technology solutions. The Supercharging Productivity section of the book has stories of companies that successfully commercialized products and others that crashed on the rock trying. The successful stories all focused on solving a real problem for real customers. Being very clear about defining the problem is crucial. Ambiguity about what the problem is can be a killer. Once the problem is clearly defined, the next most important question is what metric defines success. In the future, what will be measured to know that the problem is solved? Again, being very clear about this is crucial. This metric is the north star guiding the development of an autonomously intelligent agent that solves the problem. One north star is much better for navigation than a constellation of stars. This is where LeCun's insight is key to unlocking product/market fit. The key success metric is what the DNN is designed to optimize. Successfully developing and deploying requires solving both a business and a technical problem. Solving the business problem does not require an in depth understanding how DNNs work. Solving the technical problem does not require understanding why this problem and this metric were chosen. The success metric is the interface between the business and the technical professionals on the team. Very likely, the definition of the problem and the design of an intelligent agent solution that can be deployed in a reasonable amount of time and budget is an iterative process until both the business and technical members of the team agree on the first version of the agent to be deployed. The Innovation Canvas discussed in the book illustrates this iterative process.

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John Warner


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