cs

This is the first guest lecture in a course about interactive theorem proving. In this lecture, Jeremy Siek gives a gentle introduction to Agda, discusses th...

Our setting is logic programming, a field which attempts to design programming languages whose semantics have a close relationship to formal logic. The reason we might want to do this is that it suits our application domain more precisely than an implementation in a traditional programming language. Thus, using a logic programming language allows us to write more obviously-correct code, and perhaps even code that can be extracted cleanly from a certified implementation. Alternatively, if we did it ourselves, we’d have to do what our compiler (interpreter, …) would do anyway, so there’s no sense in doing it manually. Unfortunately, when we see a powerful tool, we are tempted to use it for everything: if our application is not ultimately-suited to the operationalization strategy of the logic programming engine we’re using, we simply obfuscate the issue in a veneer of formalism and end up with leaky abstractions. This is, I speculate, why logic programming languages have never caught on broadly for general-purpose programming. In this blog, I will detail the various trade-offs and implementation paradigms for modern logic programming engines, starting from Datalog and with a focus on program analysis.

Understanding Flat, Annoy, IVF, HNSW and Vamana vector indexes in vector databases

Binary Vector Search: The 30x Memory Reduction Revolution with Preserved Accuracy

Suppose that you want to quickly determine a sequence of eight characters are made of digits (e.g., ‘9434324134’). How fast can you go? In software, characters are mapped to integer values called the code points. The ASCII and UTF-8 code points for the digits 0, 1,…, 9 are the consecutive integers 0x30, 0x31, …, 0x39 … Continue reading Quickly identifying a sequence of digits in a string of characters

Integer values are typically stored using 32 bits. Yet if you are given an array of integers between 0 and 131 072, you could store these numbers using as little as 17 bits each—a net saving of almost 50%. Programmers nearly never store integers in this manner despite the obvious compression benefits. Indeed, bit packing and … Continue reading How fast is bit packing?

Leslie Lamport: "How to Write a 21st Century Proof”Mathematicians have made a lot of progress in the last 350 years, but not in writing proofs. The proofs th...

_posted by Shriram Krishnamurthi_ [Edit on 2012-08-27, 12:31EDT: added code and pictures below. 2012-08-27, 13:10EDT: also incorporated some comments.] I wrote this on the Racket educators' mailing list, and Eli Barzilay suggested I post it here as well...

I was inspired, after reading the excellent blog post Let Futures Be Futures, by the author's thought experiment of a language in which all functions are coroutines and this is used to express asynchr…

This is a 1 hour general-audience introduction to Large Language Models: the core technical component behind systems like ChatGPT, Claude, and Bard. What the...

This paper appeared in OSDI'22. There is a great summary of the paper by Aleksey (one of the authors and my former PhD student, go Aleksey...

Extension for Visual Studio Code - C0 IDE features for VSCode

Let’s face it: teaching is hard. There’s the obvious time commitmentrequired for classes and lectures, but striving for excellence in educationnecessarily re...

A curated collection of HCI demo videos produced during the golden age from 1983-2002.

Stephen Wolfram explores the broader picture of what's going on inside ChatGPT and why it produces meaningful text. Discusses models, training neural nets, embeddings, tokens, transformers, language syntax.