Andrew NG‘s answer is negative. He posted: “As coding becomes easier, more people should code, not fewer! One question I’m asked most often is what someone should do who is worried about job displacement by AI. My answer is: Learn about AI and take control of it, because one of the most important skills in the future will be the ability to tell a computer exactly what you want, so it can do that for you. Coding (or getting AI to code for you) is a great way to do that.” Link
Meinolf Sellman: “The key to efficiency and smooth, low-cost, and low-surprise operations is to make better decisions. And that has always been the objective of AI. Not the silly chatbot AI that you see being hyped. Not the expensive AI that asks you to spend millions on GPUs and data initiatives either. The AI that companies need to oil their operations is Optimization.
All of the above asks for decision-making under uncertainty can be done with optimization solvers.” Link Constraint Solvers Linear and MIP Solvers
The Post Office in DS City has come up with a new measure to limit packages that can be sent under a new flat rate, regardless of weight or volume. The “strap measure” of a box is the length of the longest side plus two times the sum of the two shorter sides. For the new flat rate for sending a packet, the strap measure may not exceed 100 inches.
Your e-commerce client Mr. Bates sells large comforters of 9,000 cubic inches in volume and wants to make use of this new flat rate if possible. Can you design a box that will be large enough to hold these comforters and that can be sent at a flat rate price? Link
Fermat’s Library shares interesting facts on LinkedIn. Here is one of them. In the 1780s a German schoolteacher gave his 8-year-olds a problem to keep them busy. He asked them to add up all the numbers from 1 to 100: 1 + 2 + 3 + … + 98 + 99 + 100 = ?
One student came up with the answer in just 2 minutes. The boy told the teacher that he had just “folded” the numbers so that 1 joins with 100, 2 joins with 99… Each pair of numbers added up to 101. He counted 50 pairs of numbers and so he multiplied 50 by 101 which gave him the answer 5050.
That boy’s name was Carl Friedrich Gauss.
Stéphane Dalbera posts brief biographies of the most influential figures in the development of AI and computer science in general. Here are quotes from his latest post about Marvin Minsky with comments by Philippe Kahn: “Minsky helped develop early AI systems for solving mathematical problems, playing chess, and simulating visual perception. He focused on creating machines that could replicate human thought but remained skeptical of the idea that machines could quickly achieve true intelligence… In The Society of Mind (1986), Minsky expanded his theory that intelligence emerges from the interactions of simple processes, or “agents,” each responsible for a different aspect of thought.
In the 1980s and 1990s, Minsky remained a leading figure in AI, advocating for interdisciplinary approaches to understanding intelligence.
Despite early successes, Minsky was critical of AI’s progress and skeptical of claims that machines were close to true intelligence. He recognized the challenges of creating machines capable of understanding and reasoning like humans. ” Link
Mike Gualtieri wrote: “All of AI Agents need a decisioning capability. Having said that, the breadth and depth of decision intelligence technologies are so broad that specialized AI Decisioning Agents can be much more sophisticated and subject to more direct human governance. Why? 1) Take a decisioning technique like mixed integer programming. That is super valuable. Sure an Agent can call a “tool” to do that but it also requires deep integration and configuration. 2) AI Decisioning platforms also enable human decision-logic also known as rules. They provide pretty nice tools that business experts can use to combine these rules. And, there is also decision optimization, champion challenger, a/b testing, simulation and more.
It is true that general AI agents could gain these capabilities, but I think there is a strong case for AI Decision Agents that in turn would be used as a “tool” by other AI Agents because of the human tooling to configure, change, and monitor those decisions.” Link
David Pidsley from Gartner announced today that Gartner is ready to publish a new Magic Quadrant for DI Platforms. Here is a list of featured vendors who offer a decision intelligence platform:
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, paretos, Quantexa, Rainbird Technologies, Rulex, SAS, Sparkling Logic, Inc, Spindox, Trisotech, Bamboo Rose, XpertRule Software. Link
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