LocalSolver has been renamed to Hexaly which claims to be the world’s fastest optimization solver for Routing, Scheduling, and Packing problems. Link
Check out also Timefold solver that handles the same problems and now inherited OptaPlanner after Red Hat announced its End of Life. Link
Jim Sinur: “All of the art for the first quarter resulted from experiments with Gen AI. I used Kaiber for the videos and Microsoft Image Creator. I started the videos with the following images to give the AI tool a starting point and a storyboard to tell the story of each song. Overall, I liked the experience as an artist, but it won’t keep me from doing art by hand or fractals, which also require computer assistance. Here are the art pieces for each song in the order they are on the album.” Link
This was written today by Cassie Kozyrkov, Google’s first Chief Decision Scientist. “Can we calm down, please? AI is just another approach to automation. Automation is not new and it’s not ‘solved‘… I understand the AI disillusionment that’s been creeping up recently. People have been promised magic, but LLMs are still just a tool! They can drive incredible results *and* they are imperfect tools that need a lot more work... Consumers are smart. They want things that actually solve problems. Technology is cool, but the industry wins when products are built to solve specific problems for specific people.” Link
In 1960, the famed computer scientist Don Knuth wrote a technical paper in which he considered an integer programming model for minimizing memory access latency of an IBM 650. It included 51 variables and 43 constraints. Knuth ran Gomory’s algorithm on an IBM 650 with less than 10K of memory, but was unable to find an optimal solution. In 1995 Dimitris Alevras successfully found the optimum value of 22,996 using CPLEX on a SPARCstation 5 and it took hours. These days open source MIP solvers are able to find an optimal solution in tenths of a second. Link
Gartner predicts that by 2028, 25% of chief data and analytics officer vision statements will become “decision-centric”, surpassing “data-driven” slogans, as human decision-making behaviors are modeled to improve Data &Analytics value. Link
Arash Aghlara goes further suggesting that Data & Analytics leaders should STOP thinking about “data” and “analytics” altogether. Link
Harshil Patel describes how to align Data-Driven and Decision-Centric approaches using Machine Learning. Link
Richard Susskind writes “about the use of AI to draft legislation and regulation and then to generate legal rules in the form of executable code.” He states that AI will draft complex legislations within five years and new tools will make breaking the law harder.” Link
Business decision models frequently use lookup tables that may contain thousands of data rows. There are at least two issues to be addressed when representing lookup tables: 1) users need to be able to modify the table data without modifications in the decision model; 2) the tables may contain hundreds of thousands of rows and still should be highly efficient when used by decision services. So, this month’s challenge provides a concrete example of lookup tables from the claim processing domain. Each claim includes lists of procedures and diagnoses with their types, codes, and other information. Your decision model needs to validate each claim against the standard lists of compatible and incompatible pairs of procedures and diagnoses represented as ICD-10 codes. Link
From an expert working on cutting edge neuro symbolic AI solutions, integrating LLM and decision management systems to bring real AI value to the enterprises: “Enterprise should not consider Gen AI as the solution to implement their interactive, customer facing, conversation agents. This is too risky business as of today. Rule-based systems have been deployed in the industries for more than 25 years, and are taking millions of decisions per day. Symbolic AI based on deep learning models, such as Generative AI, should be combined with knowledge graph, inference rules and ontologies as they make precise, reasoned, and explainable decisions.” Link Related articles: “Neuro-Symbolic AI” “How does neuro-symbolic AI actually work?“
Daniel Kahneman, a psychologist who won a Nobel Prize in economics for his insights into how ingrained neurological biases influence decision making, died Wednesday at the age of 90. Link Read “Thinking, Fast and Slow” for free.
Decision models (similarly to databases) frequently deal with analysis of collections of objects. This month’s Challenge deals with such a situation: you need to help an HR office create a rules-based service to analyze its employees. Each employee has a unique name, age, gender, marital status, locations (places of residence), number of children, salary, and more attributes. This information is coming to the service as a JSON request such as in this file. Your service should find answers to questions such as:
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