Re: unboxed structure fields (or class slots)

As I recall, from first encounters with Harlequin Common Lisp, the early AI used “reasoning systems” - forward chaining and backward chaining. HCL offered their add-on Prolog system as well as a forward chaining system. Perceptrons didn’t participate in that realm.

We used Perceptrons starting in the early 90’s for building classifiers and recognizers. And then Kolmogorov showed that a simple feedback PN could be used to model chaotic attractor basins of dimensions equal to the number of input legs. Atmospheric turbulence has a fractal dimension around 7 and that led us to train a multilayer PN’s one long weekend on speckle images, using a DG Eagle, to train a “rubber mirror” for removal of atmospheric scintillation of star images.

We also used Probabilistic Neural Networks to build target trackers for search and destroy missions - LIDAR underwater mine detection systems, and EKV Kinetic Kill Vehicles for taking out incoming nuclear warheads.

Early Kohonen (highly interconnected mesh) networks were used for storing knowledge domains. 

LLMs would be another 20-30 years later…



> On Jan 13, 2026, at 10:28, Bradford Miller (as bradford dot w dot miller at gmail dot com) <[email protected]> wrote:
> 
> Lisp (LISt Processing) wan’t supposed to be FORTRAN. Perceptrons inability to deal with XOR set back neural networks by decades, thanks mainly to Minsky’s arguments. Regardless, one of the main reason Lisp caught on for AI, IMHO was its ability to modify itself at runtime, the reflection of some degree of natural language (objects have properties, etc.). There wasn’t much interest in numerical processing outside of the basics. Even MACSYMA did things symbolically, not so much numerically. That was one the big wins. You wanted numerics, there was always APL. And later Lisps did get heavily influenced by ALGOL if you haven’t noticed (not always in a positive way, but that’s possibly just me).
> 
> A lot of binary stuff also got improved in ZetaLisp so the LispMs could use Lisp down to the device driver level.
> 
>> On Jan 13, 2026, at 9:45 AM, Yuri Davidovsky (as work at disclosure dot ie) <[email protected]> wrote:
>> 
>> While we are on the topic of perceptrons, they are the actual reason why I am being very critical of the Common Lisp specs as incomplete due to its lack of focus on numerical type handling and binary datatypes IO (really this is the main gripe I am having with lisp, all else is secondary).
>> 
>> The reason why it appears to be lacking in the specs (in my opinion), is that lisps were the current tool of AI research of the time and in that period expert systems were in the vogue and assumed to be THE AI. However, in the last decade (or even a decade and a half, depending how you count) neural networks became the architecture of choice for artificial intelligence study, becoming the second wing of research in the area. So the wings we have are:
>> 
>> 1. Expert systems, which rely on precise rule matching and symbolic processing. The spec expected to rule here.
>> 2. Neural networks — statistical feature extraction tools that rely on numerical processing and high throughput.
>> 
>> So essentially by skimming over numeric processing at the time when the specs were written, Common Lisp neutered itself as a contender in its own game, and when it suddenly turned out that neural networks are THE AI, it ended up as audience, rather than a participant. The spec never took requirements for neural network processing into account, despite that they were well known at the time and only limited performance of the hardware was constraining their usefulness.
>> 
> 
> 
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