“DI is AI for Decisions”

Posted on August 22, 2024 by decisionmanagementcommunity
Posted in Artificial Intelligence, Decision Intelligence | Leave a comment

James Gosling about GenAI

Posted on August 18, 2024 by decisionmanagementcommunity

James Gosling, the Father of Java, offers a thought-provoking critique on the GenAI hype, highlighting both its potential and the risks of overestimating its capabilities. Link

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IBM AI Roadmap for the next few years

Posted on August 18, 2024 by decisionmanagementcommunity

Armand Ruiz, VP of Product – AI Platform @IBM, describes what is coming:

  • 2024: Build Modular and Multimodal Transformers for New Enterprise Applications
  • 2025: Alter the Scaling of Generative AI with Neural Architectures Beyond Transformers
  • 2026: Bring Robust Strategic Reasoning and Commonsense Knowledge to AI
  • 2028: Develop Broadly Intelligent Agents That Learn Autonomously
  • 2030+: Build Adaptable and Generalist AI for Effective Human-Machine Collaboration

“Beyond 2030, we aim to create adaptable and generalist AI that can collaborate effectively with humans. These AI models will be composed of modules with different cognitive abilities—such as perception, memory, emotion, reasoning, and action—allowing them to exhibit behavioral norms for social interactions and mutual theory of mind.” Link

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Gartner’s Definition of “Open Systems”

Posted on August 17, 2024 by decisionmanagementcommunity

David Pidsley, The Decision Strategist at Gartner wrote: By “open” we’re referring to an open systems computing design pattern that allows for easy integration with other tools and platforms, often through well-documented APIs and standard protocols. Open systems provide some combination of interoperability, portability, and open software standards. This may but isn’t necessarily open source (with permission to use, copy and distribute it, either as is or with modifications, and that may be offered either free or with a charge – i.e. where the source code must be made available). Link

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Decision Model as Approximation for Business Problem

Posted on August 16, 2024 by decisionmanagementcommunity

Carlos Armando Zetina: “The model is an approximation for a business’s decision problem because it inevitably needs to make simplifying assumptions such as limited scope, finite horizons, deterministic parameters, and cost approximations to be amenable to solving. Since the optimality gap is a measure based on the model, using it to measure a solution’s success is misleading.

What matters to the business is its Key Performance Indicators (KPIs) e.g. out of stock, actual transportation costs, lead times, revenue, profit margins and other KPIs the solution can directly impact. These KPIs should be tracked continuously before and after the optimization solution is in production, guiding model adjustments to make decisions that align better with the ultimate business goals. Optimization solutions in industry are living entities that require continuous improvement and KPI monitoring. Solving the model is only the beginning of the decision science process.” Link

Posted in Decision Models, Decision Monitoring, Decision Optimization | Leave a comment

Solutions for July-2024 Challenge “Smart Investment”

Posted on August 11, 2024 by decisionmanagementcommunity

We received 9 solutions for our July-2024 Challenge “Smart Investment”.
Used decision optimization tools:
– IBM CPLEX
– Llama3/CPLEX/watsonx
– Corticon
– Prolog
– ChatGPT/Zimpl
– IPython/ortools
– cDMN
– Excel Solver
– OpenRules Rule Solver.

Posted in Challenges, ChatGPT, Decision Optimization, LLM, Products | Leave a comment

Problem-solve with your peers

Posted on August 6, 2024 by decisionmanagementcommunity

Gartner distributed a nicely formulated email: “Where is your next great idea going to come from? With an industry and role that is constantly evolving, some of the greatest insights you can get are from your peers.” It is true for several upcoming conferences such as:

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When will anyone start making actual money from AI?

Posted on August 5, 2024 by decisionmanagementcommunity

Wall Streeters have been asking this question for weeks and some for months: “When will anyone start making actual money from artificial intelligence?  “In the 18 months since ChatGPT kicked off an AI arms race, tech giants have promised that the technology is poised to revolutionize every industry and used it as justification for spending tens of billions of dollars on data centers and semiconductors needed to run large AI models. Compared to that vision, the products they’ve rolled out so far feel somewhat trivial — chatbots with no clear path to monetization, cost saving measures like AI coding and customer service, and AI-enabled search that sometimes makes things up.” Link

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About LLM Hallucinations

Posted on August 1, 2024 by decisionmanagementcommunity

Andriy Burkov: “I often hear that LLMs hallucinate because they weren’t trained on quality data. This is not what a hallucination is. A hallucination is a situation when the model generates information that was close to the fringe of its training domain. So, you cannot fix hallucinations by providing better data because the fringe of the training set will not go anywhere; it can only change the shape.”

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Dantzig’s Story

Posted on July 27, 2024 by decisionmanagementcommunity

“If I had known that the problems were not homework but were in fact two famous unsolved problems in statistics, I probably would not have thought positively, would have become discouraged, and would never have solved them.” George Dantzig

The movie “Good Will Hunting” has a scene that is inspired by the life of George Dantzig, the inventor of the simplex algorithm who introduced the world to the power of optimization. In the movie, Matt Damon plays an MIT janitor who anonymously solves a difficult math problem that a math professor posted on a hallway blackboard. In real life, George Dantzig was studying statistics under professor Jerzy Neyman at UC Berkeley. Here is how Dantzig described it: “I arrived late one day at one of Neyman’s classes. On the blackboard there were two problems that I assumed had been assigned for homework. I copied them down. A few days later I apologized to Neyman for taking so long to do the homework — the problems seemed to be a little harder than usual.” Read more here.

Posted in Human Intelligence, Innovation, Most Influential, Optimization | Leave a comment