“I’m a scientist and an engineer”

This was posted by Yann LeCun today about Science vs Engineering.

Here is a list of things I am *NOT* saying:

  • you need a PhD to do Science. You don’t. A PhD teaches you to do research, but you can learn that on your own (though it’s much easier with a mentor).
  • you need to get papers accepted by a journal or conference to publish: you don’t. You can just post it in http://ArXiv.org . Many influential papers never went through the formal peer review process, or went through it after they became influential.
  • engineering is not science: it can be, depending on your methodology. I’m a scientist *and* an engineer. These activities are complementary and need each other. – science requires formal papers: it doesn’t. A clear explanation on a website and a piece of code on a public repo will do.

What I *AM* saying is that science progresses through the collision of ideas, verification, analysis, reproduction, and improvements. If you don’t publish your research *in some way* your research will likely have no impact.”

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Neil Raden

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Yann LeCun: “Distorted view of reality”

Yann LeCun: “Don’t confuse the superhuman knowledge accumulation and retrieval abilities of current LLMs with actual intelligence.

“If you are a student interested in building the next generation of AI systems, don’t work on LLMs.”

“It seems to me that before “urgently figuring out how to control AI systems much smarter than us” we need to have the beginning of a hint of a design for a system smarter than a house cat. Such a sense of urgency reveals an extremely distorted view of reality.” Link

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Expert Panel at DecisionCAMP

On September 19, 2024 we will run online an Interactive Panel “Ask an Expert” moderated by James Taylor, Decision Management Solutions. Register for free here. You still have time until June 15 to submit an abstract of your possible presentation at this major annual gathering of Decision Intelligence practitioners.

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LLM Shines Solving a DMCommunity Challenge

ChatGPT produced an amazingly good solution for DMCommunity’s March-2024 Challenge “AnalyzeEmployees”. Of course, this challenge deals with one of the favorite LLM application areas of Q&A. Still, it generated the correct (!) Python code based on plain English questions.

I tried to do it myself and quickly generated the same solution using Copilot. It was interesting to see what LLM does when you change only the JSON file name from “Employees.json” to “XYZ.json” (it refused to work), or when you modify the names of Employee’s attributes inside the questions (it still utilizes the old names). It is also interesting to compare this automatically generated solution with other solutions produced by traditional rules-based decision intelligence tools (Corticon, OPL, and OpenRules).

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When Not to Use Generative AI

Gartner: “Generative AI is only one piece of the much broader AI landscape, and most business problems require a combination of different AI techniques. Ignore this fact, and you risk overestimating the impacts of GenAI and implementing the technology for use cases where it will not deliver the intended results.Link

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IDC’s “Decision Intelligence Framework”

According to research firm IDC, “Decision intelligence is the missing piece of an organization’s technology stack, giving users the insights they need to make the best decisions with the best possible outcomes. This technology will change how information is synthesized, insights are developed, and decisions are made at scale.Link

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Going bigger will not work indefinitely

OpenAI CEO, Sam Altman, says further progress will not come from making LLMs bigger. “I think we’re at the end of the era where it’s going to be these, like, giant, giant models,” he told an audience at an event held at MIT late last week. “We’ll make them better in other ways.” Altman’s declaration suggests an unexpected twist in the race to develop and deploy new AI algorithms. Link

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Looking for AI use-cases

Ben Evans: “We’ve had ChatGPT for 18 months, but what’s it for? What are the use-cases? Why isn’t it useful for everyone, right now? Do Large Language Models become universal tools that can do ‘any’ task, or do we wrap them in single-purpose apps, and build thousands of new companies around that?” “One upon a time every startup had SQL inside, but that wasn’t the product, and now every startup will have LLMs inside.Link

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DM Vendors adding GenAI to their Products

Sparkling Logic announced the launch of AI Assistant, that leverages a large language model (LLM) form of generative AI pre-trained on various decision management tasks. Users will be able to interact with AI Assistant using natural language to create and modify project assets such as data, decision flows, and business rules and generate easy-to-understand explanations for decision logic outcomes. Link

Sapiens Decision new copilot ModelAI leverages GenAI to automatically convert natural language to decision models allowing business users to increase the access, speed, and efficacy of decision automation. Link

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