Miss Manners is back – Challenge June-2023

This problem used to be one of the popular benchmarks for rule engines 20 years ago. It is interesting to see how modern decision engines can represent and solve this problem today. So, Miss Manners is throwing a party, and being a good host, she wants to arrange good seating. She wants to seat the guests in a boy-girl-boy-girl arrangement so that each guest will have someone on the left or right that has a common hobby. Help Miss Manners to do it for parties with 16, 32, 64, and 128 guests described in the Excel file that you may download from here. Please submit your solutions using your favorite decision modeling tool. Link

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An active repository of all human knowledge?

Yann LeCun tweeted:

Imagine a future in which your daily interaction with the world of information is mediated by an AI assistant.

This AI assistant would be like an active repository of all human knowledge.

It will become your best rampart *against* misinformation.” Link

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OptaPlanner continues as Timefold

Geoffrey De Smet: OptaPlanner, an Open Source project for planning optimization, has entered a new chapter. “The project I worked on for seventeen years, has matured under Red Hat’s wings for the past ten years. Last year, when Red Hat’s strategy changed, it became apparent that the project needed a new, sustainable future. Therefore, we founded a company around it. A company that lives and breathes planning optimization, to further nurture the Open Source project to its fullest potential, under a new name. Timefold is the continuation of OptaPlanner.” Link

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Red Hat and IBM: Business Automation Update

Frequently Asked Questions about IBM Process Automation Manager Open Edition and IBM Decision Manager Open Edition, formerly known as Red Hat Process Automation Manager and Red Hat Decision Manager – latest update Link

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Consistency Checks of Large Language Models

Do Language Models Know When They’re Hallucinating References? Current state-of-the-art language models (LMs) are notorious for generating text with “hallucinations,” a primary example being book and paper references that lack any solid basis in their training data. However, we find that many of these fabrications can be identified using the same LM, using only black-box queries without consulting any external resources. Consistency checks done with direct queries about whether the generated reference title is real are compared to consistency checks with indirect queries which ask for ancillary details such as the authors of the work. This suggests that the hallucination may be more a result of generation techniques than the underlying representation. Link

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wAIting for Godot

Kurt Cagle’s “When the Jobs Don’t Come Back“: “The hopes that AI will result in significant staff reductions may be premature at best. What it will do is make it easier for smaller companies to become capable of producing products and services that the larger companies currently compete in. Put it another way, because investors reacted so quickly to reduce headcount at a time when human specialized skills are needed more than ever, they are putting themselves at a significant business disadvantage at a critical juncture in time.Link

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How do we define intelligence?

Dan Everett wrote in Linkedin comments: from Oxford Dictionary
intelligence: the ability to acquire and apply knowledge and skills.

LLMs have developed capabilities that programmers did not specifically program.
Language translations Theory of mind Looks like that meets the acquire skills requirement. ✅

LLMs can generate their own training data to improve their performance,
By applying knowledge of the data they have been trained on.
No external input required. Looks like that meets the apply knowledge requirement. ✅

Is that the way humans work? No. But there are many examples of intelligence outside of humans. Trees can recognize saplings from their seeds and direct resources to those saplings. Is that intelligence?

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Don Knuth takes ChatGPT for a ride

Donald Knuth: “Since one of today’s popular recreations is to play with chatGPT, I decided on 07 April 2023 to try my own little experiment, as part of a correspondence with Stephen Wolfram. The results were sufficiently interesting that I passed them on to a few friends the next day, and I’ve also been mentioning them in conversation when the topic comes up. So I was asked to post the story online, and here it is!Link

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MiniCP: A lightweight Java Constraint Programming Solver

Many decision problems (logistics, production, space, etc.) aiming at an optimal use of resources can be formulated as constraint combinatorial optimization problems. Unfortunately, these problems are difficult to solve mainly for two reasons: 1) They require complex algorithms to design and develop; 2) Finding an optimal solution can be computationally intensive. Constraint Programming (CP) solvers allow you to address these problems. Recently three well-known CP experts developed “MiniCP“, a light-weight CP Solver implemented in Java. You may freely download it and enroll in the training course. Link

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AI Decisioning Platforms

James Taylor in his new post “AI Decisioning Platforms – It’s Time To Own One” talks about the recent Wave report by Mike Gualtieri of Forrester which focuses in on a core set of vendors and compares them in detail as a follow-up to his earlier AI Decisioning Landscape report. “It started as a review of Business Rules Management Platforms, evolve to talk about Digital Decisioning Platforms and now focuses on AI Decisioning Platforms to emphasize the value of these platforms to those deploying and exploiting AI.” Link

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