Most of my research involves coding algorithms and running computational experiments with them. It also involves lots of trial-and-error, both with the algorithms themselves and with assorted parameters that govern their functioning. Back in the Dark Ages, I did all this with programs that ran at a command prompt (or, in Linux terms, in a …
I just finished reading a somewhat provocative article on the CIO website, titled “10 reasons to ignore computer science degrees” (when hiring programmers). While I’m not in the business of hiring coders (although I recent was hired as a “student programmer” on a grant — the Universe has a sense of humor), I find myself …
I thought I would follow up on my June 29 post, “Does Computer Science Help with OR?“, by giving a quick example of how exposure to fundamentals of computer science recently helped me. A current research project involves optimization models containing large numbers of what are basically set covering constraints, constraints of the form \(\displaystyle …
Fair warning: tl/dr. After reading a blog post yesterday by John D. Cook, “Does computer science help you program?“, I decided to throw in my two cents (convert to euros at your own risk) on a related topic: does computer science (which I will extend to including programming) help you as an OR/IE/management science/analytics professional? …
In a previous post Half of all jobs (> $60k/y) coding related? I wrote In the future there will be two kinds of jobs. Workers will either Tell computers what to do or Be told by computers what to do I’ve been pushing Michigan State University to offer a coding bootcamp experience to all undergraduates who want …
These predictions are from a recent survey of AI/ML researchers. See SSC and also here for more discussion of the results. When Will AI Exceed Human Performance? Evidence from AI Experts Katja Grace, John Salvatier, Allan Dafoe, Baobao Zhang, Owain Evans Advances in artificial intelligence (AI) will transform modern life by reshaping transportation, health, science, …
I have not, until recently, invested significant time in trying to understand climate modeling. These notes are primarily for my own use, however I welcome comments from readers who have studied this issue in more depth. I take a dim view of people who express strong opinions about complex phenomena without having understood the underlying …
This talk gives the history of neural networks in the framework of Bayesian inference. Deep learning is (so far) quite empirical in nature: things work, but we lack a good theoretical framework for understanding why or even how. The Bayesian approach offers some progress in these directions, and also toward quantifying prediction uncertainty. I was …
I recently came across this anecdote in Von Neumann, Morgenstern, and the Creation of Game Theory: From Chess to Social Science, 1900-1960. One night in early 1945, just back from Los Alamos, vN woke in a state of alarm in the middle of the night and told his wife Klari: “… we are creating … …
It is a common belief in genomics that nonlinear interactions (epistasis) in complex traits make the task of reconstructing genetic models extremely difficult, if not impossible. In fact, it is often suggested that overcoming nonlinearity will require much larger data sets and significantly more computing power. Our results show that in broad classes of plausibly …
Inspired by the topics discussed in this earlier post, I’ve been reading Michael Nielsen’s online book on neural nets and deep learning. I particularly liked the subsection quoted below. For people who think deep learning is anything close to a solved problem, or anticipate a near term, quick take-off to the Singularity, I suggest they …
One of the SCI FOO sessions I enjoyed the most this year was a discussion of deep learning by AI researcher Juergen Schmidhuber. For an overview of recent progress, see this recent paper. Also of interest: Michael Nielsen’s pedagogical book project. An application which especially caught my attention is described by Schmidhuber here: Many traditional methods …
We all know that biology (along with other sciences) is becoming ever more data intensive. Biologists (among other scientists) are not terribly well prepared for this, because of a lack of computational culture, lack of computational training, and a lack of tools. What do they need to know? This question drove us in organizing a …