Feb 28th 2025 by Benito Gonzalez
AI-created, human-edited.
In a recent episode of Intelligent Machines, tech luminary Stephen Wolfram joined hosts Leo Laporte, Paris Martineau, and Jeff Jarvis for a fascinating deep dive into artificial intelligence, computational paradigms, and the fundamental nature of machine learning.*
The Distinction Between AI and Machine Learning
The conversation kicked off with Laporte noting that Wolfram referred to the field as "machine learning" rather than "AI." Wolfram explained that AI has always been a moving target—what was once considered AI (like computers doing math) is now commonplace. Machine learning, on the other hand, refers to a more specific computational approach using neural networks and similar tools, which has seen tremendous progress in recent years.
"AI has sometimes been the thing which is just a bit out of reach to computers, which makes it a little hard to define. And now we've got AGI, which is again just out of reach to computers type thing. I don't think it's a well-defined concept," Wolfram noted.
The Rock Wall Analogy
One of the most illuminating moments came when Wolfram described his understanding of machine learning through what might be called his "rock wall" analogy:
"Imagine that your task is to build a wall. One way you can do that is you make these very precisely engineered bricks and you set them up... Plan B is you see a bunch of rocks lying around on the ground and as you build your wall you find a rock that's roughly the right shape. You stick that one in."
According to Wolfram, machine learning is like the second approach—finding computational "rocks" that fit into training patterns rather than precisely engineered solutions. This approach works for getting things "roughly right" but isn't suitable for tasks requiring 100% precision.
Wolfram Alpha vs. LLMs: Precision vs. Approximation
The hosts explored the contrast between deterministic systems like Wolfram Alpha and probabilistic large language models. Wolfram explained that Wolfram Alpha uses "definite algorithms that we explicitly write and taking data from the world that we've kind of gone through the trouble of explicitly curating," whereas machine learning systems are trained on examples and learn to approximate solutions.
"If you want to get it 80% right, then using machine learning is a great thing. If you want to get it 100% right, then using machine learning is usually not the right thing," Wolfram explained.
The Computational Paradigm
When asked about the intersection between his work on computational paradigms and modern AI, Wolfram presented a compelling vision of the future:
"My vision of the future is that there is this layer of kind of linguistic interface and then there is this kind of computational bedrock, this kind of computational knowledge bedrock, and that the thing that we don't yet know really that well is we only have sort of the coarsest ways to interface between that sort of linguistic layer and the kind of computational bedrock."
This suggests a future where AI serves as a natural language interface to more precise computational systems—combining the best of both worlds.
The Future of Programming and "Computational X"
Wolfram shared his excitement about how AI is democratizing programming and computational thinking. The Wolfram Notebook Assistant, which he described as different from Wolfram Alpha, helps users set up "bricks of computation" that they can build upon rather than simply providing answers.
"The way I see sort of the future of things like software development is the first thing you have to do is to imagine what you're trying to do computationally," Wolfram said. "Once you've gotten some distance towards that, then you can ask our notebook assistant. It'll write a piece of computational language code, that'll do a piece of what you want."
Wolfram predicted a future of "computational X for all X"—where fields from archaeology to zoology would have computational versions accessible to practitioners without programming expertise.
Beyond Human Intelligence
When asked about AGI, Wolfram offered a nuanced perspective. He pointed out that superhuman computation already exists in both nature and our computers, but what matters is whether these computations align with human interests or values.
"Having original things is easy," Wolfram noted. "If you have a sequence of random numbers, that sequence of random numbers will be original at all stages. The question is does it align with anything you care about?"
He envisions a future where AI civilizations exist alongside human civilization—similar to how we coexist with the natural world, which is "off doing all kinds of complicated things" independent of human understanding or control.
The ChatGPT Moment
Reflecting on the public reaction to ChatGPT, Wolfram compared it to the personal computer revolution—a moment of "consumerization" where a technology previously confined to specialized domains suddenly became accessible and relevant to everyday people.
"Nobody knew ChatGPT was going to work, including the people who built it," he observed, noting that we crossed a threshold in capability that made AI suddenly impressive to humans. However, he cautioned against expecting an endless series of similar breakthroughs, noting that technological surprises often represent singular thresholds rather than continuous rapid advancement.
Conclusion
Throughout the conversation, Wolfram brought philosophical depth and technical precision to topics that often generate more heat than light. His perspective—bridging deterministic computation and machine learning—suggests a future where these approaches complement rather than compete with each other, with human guidance remaining essential to direct computational power toward meaningful goals.
As AI continues to evolve, Wolfram's insights remind us that the most promising path forward may not be replicating human intelligence but augmenting it with computational capabilities that extend our reach in ways we're only beginning to imagine.
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Feb 26 2025 - Stephen Wolfram
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