Stéphane Dalbera posted today: “This quote elegantly describes the true art of software development. It’s not enough to dream up bold strategies or to architect utopias on whiteboards. The challenge lies in translating those strategic desires into tactical realities – code that runs, systems that scale, domains that make sense.
Continue readingCyc was a “child” of Doug Lenat, who devoted 40 years to trying to make AI more human. David Reed just blogged about it: “Is Cyc dead, as the obituary here claims? Did Deep Neural Nets kill it? I think they both have been part of a process that is killing AI by focusing only on ‘Representation of knowledge’. My own view is that the neural net training approach used in all LLMs today is not the way to get ‘intelligence’. Nor is the pure Cyc-style approach. Both focus only on manipulating Representation, and in particular representations amenable to simple computation machines (neural networks or symbolic logic). Intelligence, such as what we see in humans, which I call Cognition, isn’t primarily contained within the skull.” Link
P.S. Doug Lenat was our keynote speaker at DecisionCAMP in 2012 – see https://dmcommunity.org/2023/09/06/in-memory-of-doug-lenat/.
Stephane Dalbera: “The fantasy of programming in natural language didn’t emerge with Large Language Models; it’s been a topic of discussion for decades. It’s interesting to note that Edsger W. Dijkstra’s 1978 critique remains, in many aspects, highly relevant today.
In his paper titled “On the foolishness of ‘natural language programming’,” Dijkstra argued that the inherent ambiguities and imprecision of natural languages make them unsuitable for programming purposes. He emphasized the value of formal symbolisms in programming, stating that they are an effective tool for ruling out various forms of nonsense that are almost impossible to avoid when using natural language.
Dijkstra’s insights continue to resonate in contemporary discussions about the feasibility and desirability of natural language programming, especially in the context of advancements in AI and machine learning.
At a glance: The future will be unambiguous, or it will not be.” Link
An analytical firm assigns different cases to its analysts using the following rules:
Given a list of analysts with their current workload and a list of new cases, you need to help the firm to decide which analysts should be assigned to these cases. If there are multiple options, the firm prefers to minimize the overqualification when analysts are assigned to the cases below their levels. Can you create a decision service capable of addressing this and similar problems? Link
A team of researchers from UC Berkeley and UC San Francisco has unlocked a way to restore naturalistic speech for people with severe paralysis. This work solves the long-standing challenge of latency in speech neuroprostheses, the time lag between when a subject attempts to speak and when sound is produced. Using recent advances in artificial intelligence-based modeling, the researchers developed a streaming method that synthesizes brain signals into audible speech in near-real time. Link
SolverMax attempted to use an LLM tool to create an entire, non-trivial optimization model, with the AI doing all the programming. They asked Copilot to “Design a crop rotation optimization model“. Copilot responds with a comprehensive specification for this situation, but the entire process was not straightforward. It is described at https://www.solvermax.com/blog/can-ai-code-an-entire-optimization-model. Here is the conclusion:
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Kurt Cagle: “I’ve been interviewed hundreds of times over my career for work-related positions, consultations, and podcasts. An interview is a conversation. It is a chance to get to know the people you might be working with, to assess the company, to find out more about the kind of personalities that would be around me. It’s a chance to network, make new contacts, and explore alternative ways of working together, for profit or otherwise, in the future.
An AI interview tells me that the people involved were unwilling to take the time even to have a conversation. It means that their primary driving motivation is financially driven rather than driven by a real problem to be solved, that they view people as being interchangeable as long as they find the necessary minimal skillset, and that they fear exposure to the public. There is no way that the business culture at such a company would be anything but toxic.” Link
Jacob Feldman just published a new book “Business Decision Modeling with OpenRules” available from Amazon. “This book is oriented to subject matter experts who want to build operational decision models for their business environments. No programming skills are required. The objective is to help readers learn quickly how to apply the decision modeling approach to building real-world decision models. This book will guide readers through practical examples, starting with simple business problems and moving to complex ones. Reading the book, you will end up with a deep understanding of practical decision-modeling techniques. You will learn how to apply Business Rules, Machine Learning, and Optimization tools to build your domain-specific decision models.” Link
Despite the predictions of doomsayers, Java is still a very relevant and clever choice for startups of any size in 2025. Especially with all the goodies just released with Java 24. Java is entirely responsible for Netflix’s success story. It still does all the heavy lifting in the backend—successfully. Link
Stanford Institute for Human-Centered Artificial Intelligence (HAI): “The mission of the AI Index is to provide unbiased, rigorously vetted, and globally sourced data for policymakers, researchers, journalists, executives, and the general public to develop a deeper understanding of the complex field of AI. To achieve this, we track, collate, distill, and visualize data relating to artificial intelligence.” The 2025 AI Index Report is coming soon on April 7. Here are the key takeaways from the 2024 AI Index Report:
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