
From:  Tim Daly 
Subject:  Re: Axiom musings... 
Date:  Mon, 18 Oct 2021 02:00:50 0400 
Re: writing a paper... I'm not connected to Academia
so anything I'd write would never make it into print.
"Language level parsing" is still a long way off. The talk
by Guy Steele [2] highlights some of the problems we
currently face using mathematical metanotation.
For example, a professor I know at CCNY (City College
of New York) didn't understand Platzer's "funny
fraction notation" (proof judgements) despite being
an expert in Platzer's differential equations area.
Notation matters and is not widely common.
I spoke to Professor Black (in LTI) about using natural
language in the limited task of a humanrobot cooperation
in changing a car tire. I looked at the current machine
learning efforts. They are no where near anything but
toy systems, taking too long to train and are too fragile.
Instead I ended up using a combination of AIML [3]
(Artificial Intelligence Markup Language), the ALICE
Chatbot [4], Forgy's OPS5 rule based program [5],
and Fahlman's SCONE [6] knowledge base. It was
much less fragile in my limited domain problem.
I have no idea how to extend any system to deal with
even undergraduate mathematics parsing.
Nor do I have any idea how I would embed LEAN
knowledge into a SCONE database, although I
think the combination would be useful and interesting.
I do believe that, in the limited area of computational
mathematics, we are capable of building robust, proven
systems that are quite general and extensible. As you
might have guessed I've given it a lot of thought over
the years :)
A mathematical language seems to need >6 components
1) We need some sort of a specification language, possibly
somewhat 'propositional' that introduces the assumptions
you mentioned (ref. your discussion of numbers being
abstract and ref. your discussion of relevant choice of
assumptions related to a problem).
This is starting to show up in the hardware area (e.g.
Lamport's TLC[0])
Of course, specifications relate to proving programs
and, as you recall, I got a cold reception from the
LEAN community about using LEAN for program proofs.
2) We need "scaffolding". That is, we need a theory
that can be reduced to some implementable form
that provides conceptlevel structure.
Axiom uses group theory for this. Axiom's "category"
structure has "Category" things like Ring. Claiming
to be a Ring brings in a lot of "Signatures" of functions
you have to implement to properly be a Ring.
Scaffolding provides a firm mathematical basis for
design. It provides a link between the concept of a
Ring and the expectations you can assume when
you claim your "Domain" "is a Ring". Category
theory might provide similar structural scaffolding
(eventually... I'm still working on that thought garden)
LEAN ought to have a textbook(s?) that structures
the world around some form of mathematics. It isn't
sufficient to say "undergraduate math" is the goal.
There needs to be some coherent organization so
people can bring ideas like Group Theory to the
organization. Which brings me to ...
3) We need "spreading". That is, we need to take
the various definitions and theorems in LEAN and
place them in their proper place in the scaffold.
For example, the Ring category needs the definitions
and theorems for a Ring included in the code for the
Ring category. Similarly, the Commutative category
needs the definitions and theorems that underlie
"commutative" included in the code.
That way, when you claim to be a "Commutative Ring"
you get both sets of definitions and theorems. That is,
the inheritance mechanism will collect up all of the
definitions and theorems and make them available
for proofs.
I am looking at LEAN's definitions and theorems with
an eye to "spreading" them into the group scaffold of
Axiom.
4) We need "carriers" (Axiom calls them representations,
aka "REP"). REPs allow data structures to be defined
independent of the implementation.
For example, Axiom can construct Polynomials that
have their coefficients in various forms of representation.
You can define "dense" (all coefficients in a list),
"sparse" (only nonzero coefficients), "recursive", etc.
A "dense polynomial" and a "sparse polynomial" work
exactly the same way as far as the user is concerned.
They both implement the same set of functions. There
is only a difference of representation for efficiency and
this only affects the implementation of the functions,
not their use.
Axiom "got this wrong" because it didn't sufficiently
separate the REP from the "Domain". I plan to fix this.
LEAN ought to have a "data structures" subtree that
has all of the definitions and axioms for all of the
existing data structures (e.g. RedBlack trees). This
would be a good undergraduate project.
5) We need "Domains" (in Axiom speak). That is, we
need a box that holds all of the functions that implement
a "Domain". For example, a "Polynomial Domain" would
hold all of the functions for manipulating polynomials
(e.g polynomial multiplication). The "Domain" box
is a dependent type that:
A) has an argument list of "Categories" that this "Domain"
box inherits. Thus, the "Integer Domain" inherits
the definitions and axioms from "Commutative"
Functions in the "Domain" box can now assume
and use the properties of being commutative. Proofs
of functions in this domain can use the definitions
and proofs about being commutative.
B) contains an argument that specifies the "REP"
(aka, the carrier). That way you get all of the
functions associated with the data structure
available for use in the implementation.
Functions in the Domain box can use all of
the definitions and axioms about the representation
(e.g. NonNegativeIntegers are always positive)
C) contains local "spread" definitions and axioms
that can be used in function proofs.
For example, a "Square Matrix" domain would
have local axioms that state that the matrix is
always square. Thus, functions in that box could
use these additional definitions and axioms in
function proofs.
D) contains local state. A "Square Matrix" domain
would be constructed as a dependent type that
specified the size of the square (e.g. a 2x2
matrix would have '2' as a dependent parameter.
E) contains implementations of inherited functions.
A "Category" could have a signature for a GCD
function and the "Category" could have a default
implementation. However, the "Domain" could
have a locally more efficient implementation which
overrides the inherited implementation.
Axiom has about 20 GCD implementations that
differ locally from the default in the category. They
use properties known locally to be more efficient.
F) contains local function signatures.
A "Domain" gives the user more and more unique
functions. The signature have associated
"pre and post conditions" that can be used
as assumptions in the function proofs.
Some of the useravailable functions are only
visible if the dependent type would allow them
to exist. For example, a general Matrix domain
would have fewer user functions that a Square
Matrix domain.
In addition, local "helper" functions need their
own signatures that are not user visible.
G) the function implementation for each signature.
This is obviously where all the magic happens
H) the proof of each function.
This is where I'm using LEAN.
Every function has a proof. That proof can use
all of the definitions and axioms inherited from
the "Category", "Representation", the "Domain
Local", and the signature pre and post
conditions.
I) literature links. Algorithms must contain a link
to at least one literature reference. Of course,
since everything I do is a Literate Program
this is obviously required. Knuth said so :)
LEAN ought to have "books" or "pamphlets" that
bring together all of this information for a domain
such as Square Matrices. That way a user can
find all of the related ideas, available functions,
and their corresponding proofs in one place.
6) User level presentation.
This is where the systems can differ significantly.
Axiom and LEAN both have GCD but they use
that for different purposes.
I'm trying to connect LEAN's GCD and Axiom's GCD
so there is a "computational mathematics" idea that
allows the user to connect proofs and implementations.
7) Trust
Unlike everything else, computational mathematics
can have proven code that gives various guarantees.
I have been working on this aspect for a while.
I refer to it as trust "down to the metal" The idea is
that a proof of the GCD function and the implementation
of the GCD function get packaged into the ELF format.
(proof carrying code). When the GCD algorithm executes
on the CPU, the GCD proof is run through the LEAN
proof checker on an FPGA in parallel.
(I just recently got a PYNQ Xilinx board [1] with a CPU
and FPGA together. I'm trying to implement the LEAN
proof checker on the FPGA).
We are on the cusp of a revolution in computational
mathematics. But the two pillars (proof and computer
algebra) need to get know each other.
Tim
[0] Lamport, Leslie "Chapter on TLA+"
in "Software Specification Methods"
https://www.springer.com/gp/book/9781852333539
(I no longer have CMU library access or I'd send you
the book PDF)
[1] https://www.tul.com.tw/productspynqz2.html
[2] https://www.youtube.com/watch?v=dCuZkaaou0Q
[3] "ARTIFICIAL INTELLIGENCE MARKUP LANGUAGE"
https://arxiv.org/pdf/1307.3091.pdf
[4] ALICE Chatbot
http://www.scielo.org.mx/pdf/cys/v19n4/14055546cys190400625.pdf
[5] OPS5 User Manual
https://kilthub.cmu.edu/articles/journal_contribution/OPS5_user_s_manual/6608090/1
[6] Scott Fahlman "SCONE"
http://www.cs.cmu.edu/~sef/scone/
On 9/27/21, Tim Daly <axiomcas@gmail.com> wrote:
> I have tried to maintain a list of names of people who have
> helped Axiom, going all the way back to the preScratchpad
> days. The names are listed at the beginning of each book.
> I also maintain a bibliography of publications I've read or
> that have had an indirect influence on Axiom.
>
> Credit is "the coin of the realm". It is easy to share and wrong
> to ignore. It is especially damaging to those in Academia who
> are affected by credit and citations in publications.
>
> Apparently I'm not the only person who feels that way. The ACM
> Turing award seems to have ignored a lot of work:
>
> Scientific Integrity, the 2021 Turing Lecture, and the 2018 Turing
> Award for Deep Learning
> https://people.idsia.ch/~juergen/scientificintegrityturingawarddeeplearning.html
>
> I worked on an AI problem at IBM Research called Ketazolam.
> (https://en.wikipedia.org/wiki/Ketazolam). The idea was to recognize
> and associated 3D chemical drawings with their drug counterparts.
> I used Rumelhart, and McClelland's books. These books contained
> quite a few ideas that seem to be "new and innovative" among the
> machine learning crowd... but the books are from 1987. I don't believe
> I've seen these books mentioned in any recent bibliography.
> https://mitpress.mit.edu/books/paralleldistributedprocessingvolume1
>
>
>
>
> On 9/27/21, Tim Daly <axiomcas@gmail.com> wrote:
>> Greg Wilson asked "How Reliable is Scientific Software?"
>> https://neverworkintheory.org/2021/09/25/howreliableisscientificsoftware.html
>>
>> which is a really interesting read. For example"
>>
>> [Hatton1994], is now a quarter of a century old, but its conclusions
>> are still fresh. The authors fed the same data into nine commercial
>> geophysical software packages and compared the results; they found
>> that, "numerical disagreement grows at around the rate of 1% in
>> average absolute difference per 4000 fines of implemented code, and,
>> even worse, the nature of the disagreement is nonrandom" (i.e., the
>> authors of different packages make similar mistakes).
>>
>>
>> On 9/26/21, Tim Daly <axiomcas@gmail.com> wrote:
>>> I should note that the lastest board I've just unboxed
>>> (a PYNQZ2) is a Zynq Z7020 chip from Xilinx (AMD).
>>>
>>> What makes it interesting is that it contains 2 hard
>>> core processors and an FPGA, connected by 9 paths
>>> for communication. The processors can be run
>>> independently so there is the possibility of a parallel
>>> version of some Axiom algorithms (assuming I had
>>> the time, which I don't).
>>>
>>> Previously either the hard (physical) processor was
>>> separate from the FPGA with minimal communication
>>> or the soft core processor had to be created in the FPGA
>>> and was much slower.
>>>
>>> Now the two have been combined in a single chip.
>>> That means that my effort to run a proof checker on
>>> the FPGA and the algorithm on the CPU just got to
>>> the point where coordination is much easier.
>>>
>>> Now all I have to do is figure out how to program this
>>> beast.
>>>
>>> There is no such thing as a simple job.
>>>
>>> Tim
>>>
>>>
>>> On 9/26/21, Tim Daly <axiomcas@gmail.com> wrote:
>>>> I'm familiar with most of the traditional approaches
>>>> like Theorema. The bibliography contains most of the
>>>> more interesting sources. [0]
>>>>
>>>> There is a difference between traditional approaches to
>>>> connecting computer algebra and proofs and my approach.
>>>>
>>>> Proving an algorithm, like the GCD, in Axiom is hard.
>>>> There are many GCDs (e.g. NNI vs POLY) and there
>>>> are theorems and proofs passed at runtime in the
>>>> arguments of the newly constructed domains. This
>>>> involves a lot of dependent type theory and issues of
>>>> compile time / runtime argument evaluation. The issues
>>>> that arise are difficult and still being debated in the type
>>>> theory community.
>>>>
>>>> I am putting the definitions, theorems, and proofs (DTP)
>>>> directly into the category/domain hierarchy. Each category
>>>> will have the DTP specific to it. That way a commutative
>>>> domain will inherit a commutative theorem and a
>>>> noncommutative domain will not.
>>>>
>>>> Each domain will have additional DTPs associated with
>>>> the domain (e.g. NNI vs Integer) as well as any DTPs
>>>> it inherits from the category hierarchy. Functions in the
>>>> domain will have associated DTPs.
>>>>
>>>> A function to be proven will then inherit all of the relevant
>>>> DTPs. The proof will be attached to the function and
>>>> both will be sent to the hardware (proofcarrying code).
>>>>
>>>> The proof checker, running on a field programmable
>>>> gate array (FPGA), will be checked at runtime in
>>>> parallel with the algorithm running on the CPU
>>>> (aka "trust down to the metal"). (Note that Intel
>>>> and AMD have built CPU/FPGA combined chips,
>>>> currently only available in the cloud.)
>>>>
>>>>
>>>>
>>>> I am (slowly) making progress on the research.
>>>>
>>>> I have the hardware and nearly have the proof
>>>> checker from LEAN running on my FPGA.
>>>>
>>>> I'm in the process of spreading the DTPs from
>>>> LEAN across the category/domain hierarchy.
>>>>
>>>> The current Axiom build extracts all of the functions
>>>> but does not yet have the DTPs.
>>>>
>>>> I have to restructure the system, including the compiler
>>>> and interpreter to parse and inherit the DTPs. I
>>>> have some of that code but only some of the code
>>>> has been pushed to the repository (volume 15) but
>>>> that is rather trivial, out of date, and incomplete.
>>>>
>>>> I'm clearly not smart enough to prove the Risch
>>>> algorithm and its associated machinery but the needed
>>>> definitions and theorems will be available to someone
>>>> who wants to try.
>>>>
>>>> [0] https://github.com/daly/PDFS/blob/master/bookvolbib.pdf
>>>>
>>>>
>>>> On 8/19/21, Tim Daly <axiomcas@gmail.com> wrote:
>>>>> =========================================
>>>>>
>>>>> REVIEW (Axiom on WSL2 Windows)
>>>>>
>>>>>
>>>>> So the steps to run Axiom from a Windows desktop
>>>>>
>>>>> 1 Windows) install XMing on Windows for X11 server
>>>>>
>>>>> http://www.straightrunning.com/XmingNotes/
>>>>>
>>>>> 2 WSL2) Install Axiom in WSL2
>>>>>
>>>>> sudo apt install axiom
>>>>>
>>>>> 3 WSL2) modify /usr/bin/axiom to fix the bug:
>>>>> (someone changed the axiom startup script.
>>>>> It won't work on WSL2. I don't know who or
>>>>> how to get it fixed).
>>>>>
>>>>> sudo emacs /usr/bin/axiom
>>>>>
>>>>> (split the line into 3 and add quote marks)
>>>>>
>>>>> export SPADDEFAULT=/usr/local/axiom/mnt/linux
>>>>> export AXIOM=/usr/lib/axiom20170501
>>>>> export "PATH=/usr/lib/axiom20170501/bin:$PATH"
>>>>>
>>>>> 4 WSL2) create a .axiom.input file to include startup cmds:
>>>>>
>>>>> emacs .axiom.input
>>>>>
>>>>> )cd "/mnt/c/yourpath"
>>>>> )sys pwd
>>>>>
>>>>> 5 WSL2) create a "myaxiom" command that sets the
>>>>> DISPLAY variable and starts axiom
>>>>>
>>>>> emacs myaxiom
>>>>>
>>>>> #! /bin/bash
>>>>> export DISPLAY=:0.0
>>>>> axiom
>>>>>
>>>>> 6 WSL2) put it in the /usr/bin directory
>>>>>
>>>>> chmod +x myaxiom
>>>>> sudo cp myaxiom /usr/bin/myaxiom
>>>>>
>>>>> 7 WINDOWS) start the X11 server
>>>>>
>>>>> (XMing XLaunch Icon on your desktop)
>>>>>
>>>>> 8 WINDOWS) run myaxiom from PowerShell
>>>>> (this should start axiom with graphics available)
>>>>>
>>>>> wsl myaxiom
>>>>>
>>>>> 8 WINDOWS) make a PowerShell desktop
>>>>>
>>>>> https://superuser.com/questions/886951/runpowershellscriptwhenyouopenpowershell
>>>>>
>>>>> Tim
>>>>>
>>>>> On 8/13/21, Tim Daly <axiomcas@gmail.com> wrote:
>>>>>> A great deal of thought is directed toward making the SANE version
>>>>>> of Axiom as flexible as possible, decoupling mechanism from theory.
>>>>>>
>>>>>> An interesting publication by Brian Cantwell Smith [0], "Reflection
>>>>>> and Semantics in LISP" seems to contain interesting ideas related
>>>>>> to our goal. Of particular interest is the ability to reason about
>>>>>> and
>>>>>> perform selfreferential manipulations. In a dependentlytyped
>>>>>> system it seems interesting to be able "adapt" code to handle
>>>>>> runtime computed arguments to dependent functions. The abstract:
>>>>>>
>>>>>> "We show how a computational system can be constructed to
>>>>>> "reason",
>>>>>> effectively
>>>>>> and consequentially, about its own inferential processes. The
>>>>>> analysis proceeds in two
>>>>>> parts. First, we consider the general question of computational
>>>>>> semantics, rejecting
>>>>>> traditional approaches, and arguing that the declarative and
>>>>>> procedural aspects of
>>>>>> computational symbols (what they stand for, and what behaviour
>>>>>> they
>>>>>> engender) should be
>>>>>> analysed independently, in order that they may be coherently
>>>>>> related. Second, we
>>>>>> investigate selfreferential behavior in computational processes,
>>>>>> and show how to embed an
>>>>>> effective procedural model of a computational calculus within that
>>>>>> calculus (a model not
>>>>>> unlike a metacircular interpreter, but connected to the
>>>>>> fundamental operations of the
>>>>>> machine in such a way as to provide, at any point in a
>>>>>> computation,
>>>>>> fully articulated
>>>>>> descriptions of the state of that computation, for inspection and
>>>>>> possible modification). In
>>>>>> terms of the theories that result from these investigations, we
>>>>>> present a general architecture
>>>>>> for procedurally reflective processes, able to shift smoothly
>>>>>> between dealing with a given
>>>>>> subject domain, and dealing with their own reasoning processes
>>>>>> over
>>>>>> that domain.
>>>>>>
>>>>>> An instance of the general solution is worked out in the context
>>>>>> of
>>>>>> an applicative
>>>>>> language. Specifically, we present three successive dialects of
>>>>>> LISP: 1LISP, a distillation of
>>>>>> current practice, for comparison purposes; 2LISP, a dialect
>>>>>> constructed in terms of our
>>>>>> rationalised semantics, in which the concept of evaluation is
>>>>>> rejected in favour of
>>>>>> independent notions of simplification and reference, and in which
>>>>>> the respective categories
>>>>>> of notation, structure, semantics, and behaviour are strictly
>>>>>> aligned; and 3LISP, an
>>>>>> extension of 2LISP endowed with reflective powers."
>>>>>>
>>>>>> Axiom SANE builds dependent types on the fly. The ability to access
>>>>>> both the refection
>>>>>> of the tower of algebra and the reflection of the tower of proofs at
>>>>>> the time of construction
>>>>>> makes the construction of a new domain or specific algorithm easier
>>>>>> and more general.
>>>>>>
>>>>>> This is of particular interest because one of the efforts is to build
>>>>>> "all the way down to the
>>>>>> metal". If each layer is constructed on top of previous proven layers
>>>>>> and the new layer
>>>>>> can "reach below" to lower layers then the tower of layers can be
>>>>>> built without duplication.
>>>>>>
>>>>>> Tim
>>>>>>
>>>>>> [0], Smith, Brian Cantwell "Reflection and Semantics in LISP"
>>>>>> POPL '84: Proceedings of the 11th ACM SIGACTSIGPLAN
>>>>>> ymposium on Principles of programming languagesJanuary 1
>>>>>> 984 Pages 23–35https://doi.org/10.1145/800017.800513
>>>>>>
>>>>>> On 6/29/21, Tim Daly <axiomcas@gmail.com> wrote:
>>>>>>> Having spent time playing with hardware it is perfectly clear that
>>>>>>> future computational mathematics efforts need to adapt to using
>>>>>>> parallel processing.
>>>>>>>
>>>>>>> I've spent a fair bit of time thinking about structuring Axiom to
>>>>>>> be parallel. Most past efforts have tried to focus on making a
>>>>>>> particular algorithm parallel, such as a matrix multiply.
>>>>>>>
>>>>>>> But I think that it might be more effective to make each domain
>>>>>>> run in parallel. A computation crosses multiple domains so a
>>>>>>> particular computation could involve multiple parallel copies.
>>>>>>>
>>>>>>> For example, computing the Cylindrical Algebraic Decomposition
>>>>>>> could recursively decompose the plane. Indeed, any treerecursive
>>>>>>> algorithm could be run in parallel "in the large" by creating new
>>>>>>> running copies of the domain for each subproblem.
>>>>>>>
>>>>>>> So the question becomes, how does one manage this?
>>>>>>>
>>>>>>> A similar problem occurs in robotics where one could have multiple
>>>>>>> wheels, arms, propellers, etc. that need to act independently but
>>>>>>> in coordination.
>>>>>>>
>>>>>>> The robot solution uses ROS2. The three ideas are ROSCORE,
>>>>>>> TOPICS with publish/subscribe, and SERVICES with request/response.
>>>>>>> These are communication paths defined between processes.
>>>>>>>
>>>>>>> ROS2 has a "roscore" which is basically a phonebook of "topics".
>>>>>>> Any process can create or look up the current active topics. eq:
>>>>>>>
>>>>>>> rosnode list
>>>>>>>
>>>>>>> TOPICS:
>>>>>>>
>>>>>>> Any process can PUBLISH a topic (which is basically a typed data
>>>>>>> structure), e.g the topic /hw with the String data "Hello World".
>>>>>>> eg:
>>>>>>>
>>>>>>> rostopic pub /hw std_msgs/String "Hello, World"
>>>>>>>
>>>>>>> Any process can SUBSCRIBE to a topic, such as /hw, and get a
>>>>>>> copy of the data. eg:
>>>>>>>
>>>>>>> rostopic echo /hw ==> "Hello, World"
>>>>>>>
>>>>>>> Publishers talk, subscribers listen.
>>>>>>>
>>>>>>>
>>>>>>> SERVICES:
>>>>>>>
>>>>>>> Any process can make a REQUEST of a SERVICE and get a RESPONSE.
>>>>>>> This is basically a remote function call.
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>> Axiom in parallel?
>>>>>>>
>>>>>>> So domains could run, each in its own process. It could provide
>>>>>>> services, one for each function. Any other process could request
>>>>>>> a computation and get the result as a response. Domains could
>>>>>>> request services from other domains, either waiting for responses
>>>>>>> or continuing while the response is being computed.
>>>>>>>
>>>>>>> The output could be sent anywhere, to a terminal, to a browser,
>>>>>>> to a network, or to another process using the publish/subscribe
>>>>>>> protocol, potentially all at the same time since there can be many
>>>>>>> subscribers to a topic.
>>>>>>>
>>>>>>> Available domains could be dynamically added by announcing
>>>>>>> themselves as new "topics" and could be dynamically lookedup
>>>>>>> at runtime.
>>>>>>>
>>>>>>> This structure allows functionlevel / domainlevel parallelism.
>>>>>>> It is very effective in the robot world and I think it might be a
>>>>>>> good structuring mechanism to allow computational mathematics
>>>>>>> to take advantage of multiple processors in a disciplined fashion.
>>>>>>>
>>>>>>> Axiom has a thousand domains and each could run on its own core.
>>>>>>>
>>>>>>> In addition. notice that each domain is independent of the others.
>>>>>>> So if we want to use BLAS Fortran code, it could just be another
>>>>>>> service node. In fact, any "foreign function" could transparently
>>>>>>> cooperate in a distributed Axiom.
>>>>>>>
>>>>>>> Another key feature is that proofs can be "by node".
>>>>>>>
>>>>>>> Tim
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>> On 6/5/21, Tim Daly <axiomcas@gmail.com> wrote:
>>>>>>>> Axiom is based on firstclass dependent types. Deciding when
>>>>>>>> two types are equivalent may involve computation. See
>>>>>>>> Christiansen, David Thrane "Checking Dependent Types with
>>>>>>>> Normalization by Evaluation" (2019)
>>>>>>>>
>>>>>>>> This puts an interesting constraint on building types. The
>>>>>>>> constructed types has to export a function to decide if a
>>>>>>>> given type is "equivalent" to itself.
>>>>>>>>
>>>>>>>> The notion of "equivalence" might involve category ideas
>>>>>>>> of natural transformation and univalence. Sigh.
>>>>>>>>
>>>>>>>> That's an interesting design point.
>>>>>>>>
>>>>>>>> Tim
>>>>>>>>
>>>>>>>>
>>>>>>>> On 5/5/21, Tim Daly <axiomcas@gmail.com> wrote:
>>>>>>>>> It is interesting that programmer's eyes and expectations adapt
>>>>>>>>> to the tools they use. For instance, I use emacs and expect to
>>>>>>>>> work directly in files and multiple buffers. When I try to use one
>>>>>>>>> of the many IDE tools I find they tend to "get in the way". I
>>>>>>>>> already
>>>>>>>>> know or can quickly find whatever they try to tell me. If you use
>>>>>>>>> an
>>>>>>>>> IDE you probably find emacs "too sparse" for programming.
>>>>>>>>>
>>>>>>>>> Recently I've been working in a sparse programming environment.
>>>>>>>>> I'm exploring the question of running a proof checker in an FPGA.
>>>>>>>>> The FPGA development tools are painful at best and not intuitive
>>>>>>>>> since you SEEM to be programming but you're actually describing
>>>>>>>>> hardware gates, connections, and timing. This is an environment
>>>>>>>>> where everything happens allatonce and allthetime (like the
>>>>>>>>> circuits in your computer). It is the "assembly language of
>>>>>>>>> circuits".
>>>>>>>>> Naturally, my eyes have adapted to this rather raw level.
>>>>>>>>>
>>>>>>>>> That said, I'm normally doing literate programming all the time.
>>>>>>>>> My typical file is a document which is a mixture of latex and
>>>>>>>>> lisp.
>>>>>>>>> It is something of a shock to return to that world. It is clear
>>>>>>>>> why
>>>>>>>>> people who program in Python find lisp to be a "sea of parens".
>>>>>>>>> Yet as a lisp programmer, I don't even see the parens, just code.
>>>>>>>>>
>>>>>>>>> It takes a few minutes in a literate document to adapt vision to
>>>>>>>>> see the latex / lisp combination as natural. The latex markup,
>>>>>>>>> like the lisp parens, eventually just disappears. What remains
>>>>>>>>> is just lisp and natural language text.
>>>>>>>>>
>>>>>>>>> This seems painful at first but eyes quickly adapt. The upside
>>>>>>>>> is that there is always a "finished" document that describes the
>>>>>>>>> state of the code. The overhead of writing a paragraph to
>>>>>>>>> describe a new function or change a paragraph to describe the
>>>>>>>>> changed function is very small.
>>>>>>>>>
>>>>>>>>> Using a Makefile I latex the document to generate a current PDF
>>>>>>>>> and then I extract, load, and execute the code. This loop catches
>>>>>>>>> errors in both the latex and the source code. Keeping an open file
>>>>>>>>> in
>>>>>>>>> my pdf viewer shows all of the changes in the document after every
>>>>>>>>> run of make. That way I can edit the book as easily as the code.
>>>>>>>>>
>>>>>>>>> Ultimately I find that writing the book while writing the code is
>>>>>>>>> more productive. I don't have to remember why I wrote something
>>>>>>>>> since the explanation is already there.
>>>>>>>>>
>>>>>>>>> We all have our own way of programming and our own tools.
>>>>>>>>> But I find literate programming to be a real advance over IDE
>>>>>>>>> style programming and "raw code" programming.
>>>>>>>>>
>>>>>>>>> Tim
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> On 2/27/21, Tim Daly <axiomcas@gmail.com> wrote:
>>>>>>>>>> The systems I use have the interesting property of
>>>>>>>>>> "Living within the compiler".
>>>>>>>>>>
>>>>>>>>>> Lisp, Forth, Emacs, and other systems that present themselves
>>>>>>>>>> through the ReadEvalPrintLoop (REPL) allow the
>>>>>>>>>> ability to deeply interact with the system, shaping it to your
>>>>>>>>>> need.
>>>>>>>>>>
>>>>>>>>>> My current thread of study is software architecture. See
>>>>>>>>>> https://www.youtube.com/watch?v=W2hagw1VhhI&feature=youtu.be
>>>>>>>>>> and https://www.georgefairbanks.com/videos/
>>>>>>>>>>
>>>>>>>>>> My current thinking on SANE involves the ability to
>>>>>>>>>> dynamically define categories, representations, and functions
>>>>>>>>>> along with "composition functions" that permits choosing a
>>>>>>>>>> combination at the time of use.
>>>>>>>>>>
>>>>>>>>>> You might want a domain for handling polynomials. There are
>>>>>>>>>> a lot of choices, depending on your use case. You might want
>>>>>>>>>> different representations. For example, you might want dense,
>>>>>>>>>> sparse, recursive, or "machine compatible fixnums" (e.g. to
>>>>>>>>>> interface with C code). If these don't exist it ought to be
>>>>>>>>>> possible
>>>>>>>>>> to create them. Such "legolike" building blocks require careful
>>>>>>>>>> thought about creating "fully factored" objects.
>>>>>>>>>>
>>>>>>>>>> Given that goal, the traditional barrier of "compiler" vs
>>>>>>>>>> "interpreter"
>>>>>>>>>> does not seem useful. It is better to "live within the compiler"
>>>>>>>>>> which
>>>>>>>>>> gives the ability to define new things "on the fly".
>>>>>>>>>>
>>>>>>>>>> Of course, the SANE compiler is going to want an associated
>>>>>>>>>> proof of the functions you create along with the other parts
>>>>>>>>>> such as its category hierarchy and representation properties.
>>>>>>>>>>
>>>>>>>>>> There is no such thing as a simple job. :)
>>>>>>>>>>
>>>>>>>>>> Tim
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>> On 2/18/21, Tim Daly <axiomcas@gmail.com> wrote:
>>>>>>>>>>> The Axiom SANE compiler / interpreter has a few design points.
>>>>>>>>>>>
>>>>>>>>>>> 1) It needs to mix interpreted and compiled code in the same
>>>>>>>>>>> function.
>>>>>>>>>>> SANE allows dynamic construction of code as well as dynamic type
>>>>>>>>>>> construction at runtime. Both of these can occur in a runtime
>>>>>>>>>>> object.
>>>>>>>>>>> So there is potentially a mixture of interpreted and compiled
>>>>>>>>>>> code.
>>>>>>>>>>>
>>>>>>>>>>> 2) It needs to perform type resolution at compile time without
>>>>>>>>>>> overhead
>>>>>>>>>>> where possible. Since this is not always possible there needs to
>>>>>>>>>>> be
>>>>>>>>>>> a "prefix thunk" that will perform the resolution. Trivially,
>>>>>>>>>>> for
>>>>>>>>>>> example,
>>>>>>>>>>> if we have a + function we need to typeresolve the arguments.
>>>>>>>>>>>
>>>>>>>>>>> However, if we can prove at compile time that the types are both
>>>>>>>>>>> boundedNNI and the result is boundedNNI (i.e. fixnum in lisp)
>>>>>>>>>>> then we can inline a call to + at runtime. If not, we might have
>>>>>>>>>>> + applied to NNI and POLY(FLOAT), which requires a thunk to
>>>>>>>>>>> resolve types. The thunk could even "specialize and compile"
>>>>>>>>>>> the code before executing it.
>>>>>>>>>>>
>>>>>>>>>>> It turns out that the Forth implementation of
>>>>>>>>>>> "threadedinterpreted"
>>>>>>>>>>> languages model provides an efficient and effective way to do
>>>>>>>>>>> this.[0]
>>>>>>>>>>> Type resolution can be "inserted" in intermediate thunks.
>>>>>>>>>>> The model also supports dynamic overloading and tail recursion.
>>>>>>>>>>>
>>>>>>>>>>> Combining highlevel CLOS code with lowlevel threading gives an
>>>>>>>>>>> easy to understand and robust design.
>>>>>>>>>>>
>>>>>>>>>>> Tim
>>>>>>>>>>>
>>>>>>>>>>> [0] Loeliger, R.G. "Threaded Interpretive Languages" (1981)
>>>>>>>>>>> ISBN 007038360X
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>> On 2/5/21, Tim Daly <axiomcas@gmail.com> wrote:
>>>>>>>>>>>> I've worked hard to make Axiom depend on almost no other
>>>>>>>>>>>> tools so that it would not get caught by "code rot" of
>>>>>>>>>>>> libraries.
>>>>>>>>>>>>
>>>>>>>>>>>> However, I'm also trying to make the new SANE version much
>>>>>>>>>>>> easier to understand and debug.To that end I've been
>>>>>>>>>>>> experimenting
>>>>>>>>>>>> with some ideas.
>>>>>>>>>>>>
>>>>>>>>>>>> It should be possible to view source code, of course. But the
>>>>>>>>>>>> source
>>>>>>>>>>>> code is not the only, nor possibly the best, representation of
>>>>>>>>>>>> the
>>>>>>>>>>>> ideas.
>>>>>>>>>>>> In particular, source code gets compiled into data structures.
>>>>>>>>>>>> In
>>>>>>>>>>>> Axiom
>>>>>>>>>>>> these data structures really are a graph of related structures.
>>>>>>>>>>>>
>>>>>>>>>>>> For example, looking at the gcd function from NNI, there is the
>>>>>>>>>>>> representation of the gcd function itself. But there is also a
>>>>>>>>>>>> structure
>>>>>>>>>>>> that is the REP (and, in the new system, is separate from the
>>>>>>>>>>>> domain).
>>>>>>>>>>>>
>>>>>>>>>>>> Further, there are associated specification and proof
>>>>>>>>>>>> structures.
>>>>>>>>>>>> Even
>>>>>>>>>>>> further, the domain inherits the category structures, and from
>>>>>>>>>>>> those
>>>>>>>>>>>> it
>>>>>>>>>>>> inherits logical axioms and definitions through the proof
>>>>>>>>>>>> structure.
>>>>>>>>>>>>
>>>>>>>>>>>> Clearly the gcd function is a node in a much larger graph
>>>>>>>>>>>> structure.
>>>>>>>>>>>>
>>>>>>>>>>>> When trying to decide why code won't compile it would be useful
>>>>>>>>>>>> to
>>>>>>>>>>>> be able to see and walk these structures. I've thought about
>>>>>>>>>>>> using
>>>>>>>>>>>> the
>>>>>>>>>>>> browser but browsers are too weak. Either everything has to be
>>>>>>>>>>>> "in
>>>>>>>>>>>> a
>>>>>>>>>>>> single tab to show the graph" or "the nodes of the graph are in
>>>>>>>>>>>> different
>>>>>>>>>>>> tabs". Plus, constructing dynamic graphs that change as the
>>>>>>>>>>>> software
>>>>>>>>>>>> changes (e.g. by loading a new spad file or creating a new
>>>>>>>>>>>> function)
>>>>>>>>>>>> represents the huge problem of keeping the browser "in sync
>>>>>>>>>>>> with
>>>>>>>>>>>> the
>>>>>>>>>>>> Axiom workspace". So something more dynamic and embedded is
>>>>>>>>>>>> needed.
>>>>>>>>>>>>
>>>>>>>>>>>> Axiom source gets compiled into CLOS data structures. Each of
>>>>>>>>>>>> these
>>>>>>>>>>>> new SANE structures has an associated surface representation,
>>>>>>>>>>>> so
>>>>>>>>>>>> they
>>>>>>>>>>>> can be presented in userfriendly form.
>>>>>>>>>>>>
>>>>>>>>>>>> Also, since Axiom is literate software, it should be possible
>>>>>>>>>>>> to
>>>>>>>>>>>> look
>>>>>>>>>>>> at
>>>>>>>>>>>> the code in its literate form with the surrounding explanation.
>>>>>>>>>>>>
>>>>>>>>>>>> Essentially we'd like to have the ability to "deep dive" into
>>>>>>>>>>>> the
>>>>>>>>>>>> Axiom
>>>>>>>>>>>> workspace, not only for debugging, but also for understanding
>>>>>>>>>>>> what
>>>>>>>>>>>> functions are used, where they come from, what they inherit,
>>>>>>>>>>>> and
>>>>>>>>>>>> how they are used in a computation.
>>>>>>>>>>>>
>>>>>>>>>>>> To that end I'm looking at using McClim, a lisp windowing
>>>>>>>>>>>> system.
>>>>>>>>>>>> Since the McClim windows would be part of the lisp image, they
>>>>>>>>>>>> have
>>>>>>>>>>>> access to display (and modify) the Axiom workspace at all
>>>>>>>>>>>> times.
>>>>>>>>>>>>
>>>>>>>>>>>> The only hesitation is that McClim uses quicklisp and drags in
>>>>>>>>>>>> a
>>>>>>>>>>>> lot
>>>>>>>>>>>> of other subsystems. It's all lisp, of course.
>>>>>>>>>>>>
>>>>>>>>>>>> These ideas aren't new. They were available on Symbolics
>>>>>>>>>>>> machines,
>>>>>>>>>>>> a truly productive platform and one I sorely miss.
>>>>>>>>>>>>
>>>>>>>>>>>> Tim
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>> On 1/19/21, Tim Daly <axiomcas@gmail.com> wrote:
>>>>>>>>>>>>> Also of interest is the talk
>>>>>>>>>>>>> "The Unreasonable Effectiveness of Dynamic Typing for
>>>>>>>>>>>>> Practical
>>>>>>>>>>>>> Programs"
>>>>>>>>>>>>> https://vimeo.com/74354480
>>>>>>>>>>>>> which questions whether static typing really has any benefit.
>>>>>>>>>>>>>
>>>>>>>>>>>>> Tim
>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>> On 1/19/21, Tim Daly <axiomcas@gmail.com> wrote:
>>>>>>>>>>>>>> Peter Naur wrote an article of interest:
>>>>>>>>>>>>>> http://pages.cs.wisc.edu/~remzi/Naur.pdf
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> In particular, it mirrors my notion that Axiom needs
>>>>>>>>>>>>>> to embrace literate programming so that the "theory
>>>>>>>>>>>>>> of the problem" is presented as well as the "theory
>>>>>>>>>>>>>> of the solution". I quote the introduction:
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> This article is, to my mind, the most accurate account
>>>>>>>>>>>>>> of what goes on in designing and coding a program.
>>>>>>>>>>>>>> I refer to it regularly when discussing how much
>>>>>>>>>>>>>> documentation to create, how to pass along tacit
>>>>>>>>>>>>>> knowledge, and the value of the XP's metaphorsetting
>>>>>>>>>>>>>> exercise. It also provides a way to examine a methodolgy's
>>>>>>>>>>>>>> economic structure.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> In the article, which follows, note that the quality of the
>>>>>>>>>>>>>> designing programmer's work is related to the quality of
>>>>>>>>>>>>>> the match between his theory of the problem and his theory
>>>>>>>>>>>>>> of the solution. Note that the quality of a later
>>>>>>>>>>>>>> programmer's
>>>>>>>>>>>>>> work is related to the match between his theories and the
>>>>>>>>>>>>>> previous programmer's theories.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> Using Naur's ideas, the designer's job is not to pass along
>>>>>>>>>>>>>> "the design" but to pass along "the theories" driving the
>>>>>>>>>>>>>> design.
>>>>>>>>>>>>>> The latter goal is more useful and more appropriate. It also
>>>>>>>>>>>>>> highlights that knowledge of the theory is tacit in the
>>>>>>>>>>>>>> owning,
>>>>>>>>>>>>>> and
>>>>>>>>>>>>>> so passing along the thoery requires passing along both
>>>>>>>>>>>>>> explicit
>>>>>>>>>>>>>> and tacit knowledge.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> Tim
>>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>
>>>>>>>>
>>>>>>>
>>>>>>
>>>>>
>>>>
>>>
>>
>
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