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

Posted in Artificial Intelligence, Trends | Leave a comment

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

 AI Colors Past Videos

Posted on July 27, 2024 by decisionmanagementcommunity

“This is mind-blowing AI-restored footage (by HistoryColored) – from 1896 of the cities Paris and Lyon in France. You can see pedestrians and vehicles of the past in various locations across the two cities. This footage was primarily filmed and produced by pioneers of motion pictures, the Lumière brothers. Brothers Auguste and Louis Lumière created one of the first motion picture cameras, the Cinématographe Lumière. Locations featured in this video are: The Eiffel Tower, Place des Cordeliers, Place de la Concorde, and Place du Pont.” Link

Posted in Art, Artificial Intelligence | Leave a comment

More about Decision Fairness

Posted on July 25, 2024 by decisionmanagementcommunity

The question “Could we achieve fairness in our automatic decision-making?” continues to be on minds of many decision intelligence partitioners. Jacob Feldman in his new post “How decision models deal with fairness” looks at 7 real-world decision-making applications in development of which he was involved. Majority of these applications dealt with complex business problems in which “fairness” was presented (or not) in clearly defined business objectives. Geoffrey De Smet considers “What is fair?” in the context of employee scheduling. From cashiers to nurses to police officers: everyone demands a fair shift schedule. He refers to the new article “Load balancing and fairness in constraints” from Timefold in which they define constraints that penalize possible solutions based on their unfairness. The overwhelming interest in the above publications shows that decision fairness will continue to be a hot topic.

Posted in Decision Intelligence, Decision Making, Fairness | Tagged , | Leave a comment

Making decisions within an uncertain environment

Posted on July 23, 2024 by decisionmanagementcommunity

Warren Powell provided interesting comments the the latest NY Times article “When it comes to math, AI is dumb” that states: “Early computers followed rules. AI follows probabilities. But in mathematics, there is no probable answer, only the right one”.

Probabilists will argue (correctly) that we have very elegant mathematics for describing probabilities. After all, the world is stochastic, and we need to model this uncertainty. Industries like airlines and hotels have mastered the art of modeling an uncertainty as part of their revenue management problems. @optimaldynamics models the uncertainty of shipper demands when planning fleets.

It is true that when making decisions, we ultimately have to pick a single decision despite having to consider its impact on an uncertain future. And in theory there is a single optimal decision, not just in terms of maximizing performance now, but which maximizes performance into an uncertain future. However, finding what we think (and hope) is the best decision, we absolutely have to model the uncertainties that affect how well this decision performs. Link

Posted in Decision Making, Decision Modeling, Uncertainty | Leave a comment

Does software engineering get worse?

Posted on July 20, 2024 by decisionmanagementcommunity

Ulrich Junker: “Imho, software engineering gets worse in each decade. Initially, computers were programmed by punch cards and programmers needed to execute their programs on paper to anticipate issues. This was tedious, but very instructive.

Next came PCs with compilers. People could quickly try out their programs. Execution on paper was replaced by the debugger. Interestingly, bug databases started to pop up in this phase. Software companies openly admitted that they shipped buggy software.

Then came open software, IDEs, frequent releases. Many programmers are simply stitching code fragments together and then walk through a debugger to understand the behavior. Bugs were considered inevitable and many bug reports simply constituted requests for feature refinements as most software lacks a clear specification of its behavior.

Now we are getting LLMs for code generation … let us see which kind of software we are getting thanks to them …” Link

Posted in Software Development | Leave a comment

Gartner Says AI Ready For Decision Intelligence Market

Posted on July 19, 2024 by decisionmanagementcommunity

David Pidsley from Gartner wrote today: “Decision intelligence (DI) is not about driving decisions with data; it’s about deriving data from decisions to achieve better business outcomes. Decision intelligence platforms (DIPs) are software used to create solutions that support, automate and augment decision making of humans or machines, powered by the composition of data, analytics, knowledge and artificial intelligence techniques. Gartner predicts that by 2026, 75% of global enterprises will apply decision intelligence practices for logging decisions for subsequent analysis.” David listed the following DIPs: 4Paradigm 第四范式, ACTICO, Aera Technology, Airin, Inc., Cogility Software, Corridor Platforms, CRIF, Decisions, Diwo, Cloverpop, Elemental Cognition, Faculty, FICO, FlexRule, IBM, InRule, Merlynn Intelligence Technologies, o9 Solutions, Inc., OpenRules, Inc., Palantir Technologies, ParetoSC, Quantexa, Rainbird Technologies, Rulex, SAS, Sparkling Logic, Inc, Spindox, Trisotech, Verteego, XpertRule Software. Link

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Knowledge Representation and Reasoning

Posted on July 17, 2024 by decisionmanagementcommunity

Dan Selman wrote: “The key to Knowledge Representation and Reasoning is building your ontology. It doesn’t matter if you are reasoning using a Knowledge Graph, a rules engine or a relational database — you need to do the work of understanding the concepts in your domain, and their properties and relationships.

That is fundamentally different to “find me chunks of text similar to this chunk of text”.

I think folks are getting confused because an LLM can be a useful aid in understand the concepts in a domain, extracting instances of known domain concepts from text, and searching for text based on vector embedding, and they are a Swiss Army knife for data format transformation and text generation.

Let’s use the right tool for the right problem. LLMs are great at many things, but reasoning is not one of them.” Link

Posted in Knowledge Representation, LLM, Reasoning | Leave a comment

5 Submissions for July’s Challenge “Smart Investment”

Posted on July 16, 2024 by decisionmanagementcommunity

We’ve already received 5 submissions for Challenge July-2024 “Smart Investment“:

Posted in Decision Modeling | Leave a comment