Progress Towards the Holy Grail

In 1996 Prof. Gene Freuder wrote the paper “In Pursuit of the Holy Grail” proposed that Constraint Programming was well-positioned to pursue the Holy Grail of computer science: the user simply states the problem and the computer solves it. For the last 7 years he runs a series of workshops aims to encourage and disseminate progress towards that goal.

This year workshop will be held on Sep 2. It includes a panel discussion “Have Chatbots Reached the Holy Grail?”. The answer is presumably “no, but”. Why not? What progress do they embody? How can we get closer? Neuro-symbolic, bespoke chatbots, coping with hallucinations, beyond LLMs, … ? If you would like to participate in the panel, submit a PDF containing a one-paragraph position statement – see Link

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Combining Constraint Solvers and LLMs


David Ferrucci: “Ever wondered how AI can tackle complex enterprise problems with precision and reliability? By combining constraint solvers and LLMs, we can turn natural language into actionable knowledge that drives intelligent applications. This approach ensures that our solutions are not only mathematically precise but also interactive and collaborative.

From acquiring business knowledge to using it to drive problem-solving applications, we bridge the gap between language fluency and precise reasoning. Link

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Supply Chain => Decision Intelligence 

Niels van Hove: “Decision Intelligence is a natural evolution in Supply Chain planning & execution technology”

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Challenge June-2024 “Stable Marriage Problem”

This challenge deals with the famous stable marriage problem

Given n men and n women, where each person has ranked all members of the opposite sex in order of preference, marry the men and women together such that there are no two people of opposite sex who would both rather have each other than their current partners. When there are no such pairs of people, the set of marriages is deemed stable. Link

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Paul Haley takes AI models for a spin

On June 3 Paul Haley, a well-known expert in rule engines and natural language processing, posted “A language model is not enough“. Paul asked AI to help him to pass an exam to earn a private pilot certificate. Read Paul’s description of what happened. Here is Paul’s conclusion: “I am interested in using AI to help people learn.  With recent advances, I wondered how much closer we were to off-the-shelf tutoring using language models. In what follows, it’s clear that current language models know enough to be fantastic tutors, but it’s also clear that they are not very good at reasoning, staying on task, or otherwise pursuing purposeful dialog.”  Link

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Postgres is eating the database world

“What makes PostgreSQL so capable? Sure, it’s advanced, but so is Oracle; it’s open-source, as is MySQL. PostgreSQL’s edge comes from being both advanced and open-source, allowing it to compete with Oracle/MySQL. But its true uniqueness lies in its extreme extensibility and thriving extension ecosystem.” Link See also “Radical Simplicity

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“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

Link Profile

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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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