Computational statistics

Mathematical methods and human thought in the age of AI

Artificial intelligence (AI) is the name popularly given to a broad spectrum of computer tools designed to perform increasingly complex cognitive tasks, including many that used to solely be the...

About Us - Siegel Family Endowment

About Our Chairman “Computational thinking is more than just a way to approach problem solving. It’s a way of processing and understanding the world through […]

Modular: The Claude C Compiler: What It Reveals About the Future of Software

Compilers occupy a special place in computer science. They're a canonical course in computer science education. Building one is a rite of passage. It forces you to confront how software actually works, by examining languages, abstractions, hardware, and the boundary between human intent and machine execution.

zach lieberman on Twitter / X

Today I was able to weave a line from Myron Krueger in a talk I gave : Computation is the medium of our lifetimes - it’s not just a technical medium but a cultural medium as well. We should explore the culture of computation.— zach lieberman (@zachlieberman) April 10, 2024

An Introduction To Robust-First Computation

Did you know there's an entire field of computer science barely yet explored? Join me at the entrance to a deep rabbit hole as we take a look at Robust-First Computation.

When did Computer Science Theory Get so Hard?

I posted on  When did Math get so hard?  a commenter pointed out that one can also ask  When did Computer Science Theory Get so Hard? For t...

computational product market fit

‘computer science is no more about computers than astronomy is about telescopes.’ — edsger dijkstra

Drinking from the Firehose: Learning Computer Graphics Techniques and Programming

Graphics researchers waiting in the water fountain line at SIGGRAPH Yellowstone. Computer graphics as a field is broad, deep, complex, and intimidating. More than half a century of rapid progress driven by academic research and industry practices underly it. Parts delve pretty far into physics, math, signal processing, systems programming (and more). And performance is always at a premium—so simple, straightforward techniques are often not possible, and high-level programming abstractions not usable.

Category theory for computing science

Category theory for computing science by Michael Barr, 1990, Prentice Hall edition, in English

An Introduction to Statistical Learning

As the scale and scope of data collection continue to increase across virtually all fields, statistical learning has become a critical toolkit for anyone who wishes to understand data. An Introduction to Statistical Learning provides a broad and less technical treatment of key topics in statistical learning. This book is appropriate for anyone who wishes to use contemporary tools for data analysis.

What Are Machine Learning Algorithms? | IBM

A machine learning algorithm is the procedure and mathematical logic through which an AI model learns patterns in training data and applies to them to new data.

Content – Michael Clark

Here you’ll find documents of varying technical degree covering things of interest to me, or which I think will be interesting to those I engage with. Generally you’ll find a mix of demonstrations on statistical and machine learning topics, programming, and data processing and visualization. Most focus on application in R as that’s what I used to primarily program with, but you’ll find plenty of Python demonstrations as well. Be aware that some of the content is a bit dated, but even if the programming aspects are a bit off, the concepts should still be relevant.

The brain is a computer is a brain: neuroscience's internal debate and the social significance of the Computational Metaphor

The Computational Metaphor, comparing the brain to the computer and vice versa, is the most prominent metaphor in neuroscience and artificial intelligence (AI). Its appropriateness is highly debated in both fields, particularly with regards to whether it is useful for the advancement of science and technology. Considerably less attention, however, has been devoted to how the Computational Metaphor is used outside of the lab, and particularly how it may shape society's interactions with AI. As such, recently publicized concerns over AI's role in perpetuating racism, genderism, and ableism suggest that the term "artificial intelligence" is misplaced, and that a new lexicon is needed to describe these computational systems. Thus, there is an essential question about the Computational Metaphor that is rarely asked by neuroscientists: whom does it help and whom does it harm? This essay invites the neuroscience community to consider the social implications of the field's most controversial metaphor.