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Paradigms of Artificial Intelligence Programming (1992)

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Re: Paradigms of Artificial Intelligence Programming (1992)

#41

Post by trenchgun »

andrejlamov wrote: post #5
zelphirkalt wrote: post #4

Earlier quoted context omitted.

Emacs would be a fine choice, but is not mandatory, if you can live with less support for parens found in other editors. Emacs traditionally excels at working with lispy languages and REPLs. I started working through the book, but did not feel like using Common Lisp. Instead I used GNU Guile. I am not very far in the book yet, but so far I was able to translate between Common Lisp and Scheme easily. So for me the way…

do you think elisp will work for this book? or would one need to implement unique common lisp features in elisp? or will elisp just be too slow

You should use Common Lisp for this book, unless specifically want a challenge.

Re: Paradigms of Artificial Intelligence Programming (1992)

#42

Post by taeric »

MontyCarloHall wrote: post #27

It is interesting how almost none of these paradigms/principles are relevant to what we think of as AI today. Lisp, a language specifically formulated for AI applications, is now mostly relevant in the context of programming language theory (and essentially irrelevant to the statistical programming/linear algebra toolkits that underpin modern AI applications). Instead, Fortran and its descendants are actually what po…

I'm always surprised that the symbolic math paradigms aren't used more. I suppose most cost functions just aren't continuous?

That said, seems most of what is important in today's code is the ability to drive a gpu. Don't know why that couldn't be done from a lisp.

Re: Paradigms of Artificial Intelligence Programming (1992)

#43

Post by mark_l_watson »

I wrote a small Common Lisp book for Springer Verlag about the same time that Peter wrote this fantastic book and I then met him shortly thereafter at a Lisp Users Vendors conference in San Diego. After 30 years of following his writing, Python notebooks, etc., I think that he just has a higher level view of software and algorithms.

There is some talk on this thread about good old fashioned symbolic AI. I have mixed feelings about this. I am currently working for an all-in Common Lisp + GOFAI company, but most of my best successes in my career involved neural networks, and later deep learning.

I think we need a new hybrid approach but it is above my skill level to know what that would be. I keep hoping to see some new research and new paradigms.

Re: Paradigms of Artificial Intelligence Programming (1992)

#44

Post by mark_l_watson »

ameasure wrote: post #33
hmcamp wrote: post #21

Earlier quoted context omitted.

Hi @Paul, I’m a newbie in this field. Are you saying that this branch of AI isn’t relevant when compared to machine learning that’s based on neural networks?

Norvig addresses this at the end of f http://norvig.com/Lisp-retro.html . Basically these old AI techniques are now considered regular programming and modern AI is focused on ML. Both are useful but for different tasks.

That is a great retrospective! Still very relevant 20 years after it was updated.

Re: Paradigms of Artificial Intelligence Programming (1992)

#45

Post by pgorczak »

taeric wrote: post #42
MontyCarloHall wrote: post #27

It is interesting how almost none of these paradigms/principles are relevant to what we think of as AI today. Lisp, a language specifically formulated for AI applications, is now mostly relevant in the context of programming language theory (and essentially irrelevant to the statistical programming/linear algebra toolkits that underpin modern AI applications). Instead, Fortran and its descendants are actually what po…

I'm always surprised that the symbolic math paradigms aren't used more. I suppose most cost functions just aren't continuous? That said, seems most of what is important in today's code is the ability to drive a gpu. Don't know why that couldn't be done from a lisp.

Neural nets do use extremely long compositions of nonlinear functions whose gradients need to be computed over and over. I guess because chain rule (automatic differentiation) works so well, most of the libraries in use are only “a little” symbolic in that they can automatically figure out first derivatives for symbolic input and output variables.

Re: Paradigms of Artificial Intelligence Programming (1992)

#46

Post by KineticLensman »

hmcamp wrote: post #21
PaulHoule wrote: post #19

Earlier quoted context omitted.

When people talk about AI today they are usually talking about machine learning systems based in neural networks. The kind of symbolic AI described in this book went through several cycles of hype and disappointment to the point where many think it is obsolete. People often do it connect recent breakthroughs in SAT and SMT solvers with this history and for that matter production rules engines are dramatically better…

Hi @Paul, I’m a newbie in this field. Are you saying that this branch of AI isn’t relevant when compared to machine learning that’s based on neural networks?

Symbolic AI never managed to scale to significant and general real world problems. Neither did Machine Learning, not until the advent of fast processors and massive datasets for training which broke the scale-barrier.

Rule-based expert systems (as opposed to SAT, etc) based on Symbolic AI also have the issue that for non-trivial problems, coding the rules themselves usually requires programming expertise in addition to the domain knowledge required to capture the business logic. Things have improved a lot since the 80s, but applications remain fairly niche.

Re: Paradigms of Artificial Intelligence Programming (1992)

#47

Post by larve »

davidhs wrote: post #40
MAXPOOL wrote: post #32

One of the top 5 programming books. Old AI is today's bleeding edge computer engineering. There is an enourmous amount of free lunches for computer engineers and software startups in the old school artificial intelligence. * modern SAT solver performance is impressive. They can solve huge problems. * Writing a complex systems configurator with Prolog or Datalog can be like magic. * Expert systems. There has never bee…

What are the other 4 top programming books?

Since I also put PAIP in my top 5 books, here are my 4 other ones:

- The Pragmatic Programmer -

This changed my life when I started programming 20 years ago. Most of the practices are now common, but it was almost radical back then.

- Designing Data Intensive Applications

This is so well written, so elegantly fundamental. It's an absolute pleasure, even if I don't really refer to it in practice.

- Structure and Interpretation of Computer Programs

In many ways, the MIT version of PAIP. Its elegance complements the pragmatism of PAIP well.

- The unicorn project

My number 5 book varies often, because I haven't found many books that reach the writing quality of the other 4. This is a business novel, but it really solidified a lot of concepts for me: lean, queues, theory of constraints, what devops is about, working as a programmer in a business.

Re: Paradigms of Artificial Intelligence Programming (1992)

#48

Post by taeric »

pgorczak wrote: post #45
taeric wrote: post #42

Earlier quoted context omitted.

I'm always surprised that the symbolic math paradigms aren't used more. I suppose most cost functions just aren't continuous? That said, seems most of what is important in today's code is the ability to drive a gpu. Don't know why that couldn't be done from a lisp.

Neural nets do use extremely long compositions of nonlinear functions whose gradients need to be computed over and over. I guess because chain rule (automatic differentiation) works so well, most of the libraries in use are only “a little” symbolic in that they can automatically figure out first derivatives for symbolic input and output variables.

I thought automatic differentiation is a little different? Will have to look again. I thought the norm for a while was to manually enter the gradient, if possible, or to use a calculated one at each step.

Re: Paradigms of Artificial Intelligence Programming (1992)

#49

Post by lukashrb »

MAXPOOL wrote: post #32

One of the top 5 programming books. Old AI is today's bleeding edge computer engineering. There is an enourmous amount of free lunches for computer engineers and software startups in the old school artificial intelligence. * modern SAT solver performance is impressive. They can solve huge problems. * Writing a complex systems configurator with Prolog or Datalog can be like magic. * Expert systems. There has never bee…

Are there any examples for 2 and 3 openly accessible? While I grasped the basic idea I lack the imagination how it would look in a real world system.

Re: Paradigms of Artificial Intelligence Programming (1992)

#50

Post by lispm »

sicp-enjoyer wrote: post #37
Geee wrote: post #10

I guess this is more of a historical curiosity rather than something that is relevant today?

No this is one of the best programming books you can buy. It's just not about ML.

> No this is one of the best programming books you can buy.

It's even no-cost. The book is available for free in digital forms.

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