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Her talk was particularly relevant to me because she emphasized how she is studying business students, not computerscience students her research is about how non-CS students interact with computing and programming. Most data science programs cover these topics, but most computerscience programs dont.
In NaturalSciences, students often struggle to understand and apply science topics, and have difficulties imagining or realizing their importance and application in real life. As John Doerr, successful venture capitalist and early investor in Google, recently phrased it: “Sustainability is the next computerscience”.
I was fascinated by the physical processes that underlie information processing, so studied physics and computerscience initially. Meet Dirk When I started out, the field of neuroscience did not exist as it does today.
But it really wasn’t physics, or computerscience, or math, or biology, or economics, or any known field. They’re about the core basic science of the computational universe, and the principles it involves—in a sense the foundational science of computation. What is that science?
But it’s a fundamental claim that we’re making—that can be thought of as a matter of naturalscience—that in our universe only computation can occur, not hypercomputation. And this is where our pieces of “falsifiable naturalscience” come in. Some correspond to theoretical computerscience.
And if we’re going to make a “general theory of mathematics” a first step is to do something like we’d typically do in naturalscience, and try to “drill down” to find a uniform underlying model—or at least representation—for all of them. From a computerscience perspective, we can think of it as being like a type hierarchy.
And a key “natural-science-like” observation is that the transformer architecture of neural nets like the one in ChatGPT seems to successfully be able to learn the kind of nested-tree-like syntactic structure that seems to exist (at least in some approximation) in all human languages.
As a result, a new discipline, known as research computing, has emerged to apply computers, not just software, to research including to help scientists capture images, construct models, which are turned into simulations, and analyse results. Research computing is a sub-discipline of computerscience.
But what about something more like a theory in naturalscience? In which we might treat our automatically generated proof as an object for empirical studyexploring its characteristics, trying to get intuition about it, and ultimately trying to deduce the analog of natural laws that give us a human-level way of understanding it.
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