Higher Logics

I typically keep this blog about computer science, but I also dabble in a bit of philosophy. I was initially struck by Bostrom's simulation argument when I first read it years ago. Over the years, I've cycled a few times between disputing what I believed were some of its assumptions, and cautious acceptance after realizing my mistake. The simulation argument in its simplest form is that one of the following must be true: simulations of humans can't be built, or simulations of humans won't be built, or we are almost certainly living in a simulation I think this argument is absolutely valid, so one of those outcomes is true. Claiming #3 is most likely is what's known as the simulation hypothesis , and has such proponents as Elon Musk. Sabine Hossenfelder recently argued against the simulation hypothesis by basically asserting that #1 above is plausible, but I actually think #2 is the most likely case. For reference, Bostrom calls future civilizations capable of r

In my last  two posts  on automatic differentiation (AD), I described some basic primitives that implement the standard approach to forward mode AD using dual numbers, and then a dual representation of dual numbers that can compute in reverse mode. I'm calling these "co-dual " numbers, as they are the categorical dual of dual numbers. It didn't click at the time that this reverse mode representation seems to be novel. If it's not, please let me know! I haven't seen any equivalent of dual numbers capable of computing in reverse mode. When reverse mode AD is needed, most introductions to AD go straight to building a graph/DAG representation of the computation in order to improve the sharing properties and run the computation backwards, but that isn't strictly necessary. I aim to show that there's a middle ground between dual numbers and the graph approach, even if it's only suitable for pedagogical purposes. Review: Dual Numbers Dual numbers augment

Continuing from my last post on implementing forward-mode automatic differentiation (AD) using C# operator overloading , this is just a quick follow-up showing how easy reverse mode is to achieve, and why it's important. Why Reverse Mode Automatic Differentiation? As explained in the last post, the vector representation of forward-mode AD can compute the derivatives of all parameter simultaneously, but it does so with considerable space cost: each operation creates a vector computing the derivative of each parameter. So N parameters with M operations would allocation O(N*M) space. It turns out, this is unnecessary! Reverse mode AD allocates only O(N+M) space to compute the derivatives of N parameters across M operations. In general, forward mode AD is best suited to differentiating functions of type: R → R N That is, functions of 1 parameter that compute multiple outputs. Reverse mode AD is suited to the dual scenario: R N → R That is, functions of many parameters that r

I've recently been researching optimization and automatic differentiation (AD) , and decided to take a crack at distilling its essence in C#. Note that automatic differentiation (AD) is different than numerical differentiation . Math.NET already provides excellent support for numerical differentiation . C# doesn't seem to have many options for automatic differentiation, consisting mainly of an F# library with an interop layer, or paid libraries . Neither of these are suitable for learning how AD works. So here's a simple C# implementation of AD that relies on only two things: C#'s operator overloading, and arrays to represent the derivatives, which I think makes it pretty easy to understand. It's not particularly efficient, but it's simple! See the "Optimizations" section at the end if you want a very efficient specialization of this technique. What is Automatic Differentiation? Simply put, automatic differentiation is a technique for calcu

More adventures in C, I revisited my exception handler implementation libex . I realized there's an even simpler version that's more efficient if we give up the requirement that exceptions can be propagated to callers: #define TRY #define THROW(E) goto E #define CATCH(E) goto FINALLY_H; E: #define FINALLY FINALLY_H: This implementation requires only a single exception handling block per function, which is probably good idea as a general rule. Usage looks like: static void foo(size_t sz) { char* x; TRY { x = (char*)malloc(sz); if (x == NULL) THROW(ENoMem); // do something with x } CATCH(ENoMem) { // handle out of memory } FINALLY { if (x != NULL) free(x); } } Unlike libex, this version is actually compatible with break and continue, although using them runs the risk of skipping the FINALLY handler. An almost equally simple version of exception handlers which ensures that break/continue cannot skip the FINALLY block wraps everything in a loop:

The async/await idiom is becoming increasingly popular. The first widely used language to include it was C#, and it has now spread into JavaScript and Rust. Now C/C++ programmers don't have to feel left out, because async.h is a header-only library that brings async/await to C! Features: It's 100% portable C. It requires very little state (2 bytes). It's not dependent on an OS. It's a bit simpler to understand than protothreads because the async state is caller-saved rather than callee-saved. #include "async.h" struct async pt; struct timer timer; async example(struct async *pt) { async_begin(pt); while(1) { if(initiate_io()) { timer_start(&timer); await(io_completed() || timer_expired(&timer)); read_data(); } } async_end; } This library is basically a modified version of the idioms found in the Protothreads library by Adam Dunkels, so it's not truly ground bre