Taking baby steps in Lisp

Lisp can be both fascinating and frustrating. Fascinating, because you can write compact code to solve really complex problems. Frustrating, because you can easily get lost in its maze of parentheses. I, for one, have been truly smitten by Lisp. My initial encounter with Lisp did not yield much success as I tried to come to terms with its strange syntax. The books I read on the Lisp language typically gloss over the exotic features of Lisp like writing Lisp code to solve the Towers of Hanoi or the Eight Queens problem. They talk about functions returning functions, back quotes and macros that can make your head spin.

I found this approach extremely difficult to digest the language. So I decided to view Lisp through the eyes of any other regular programming language like C, C++,, Java, Perl, Python or Ruby. I was keen on being able to do regular things with Lisp before I try out its unique features. So I decided to investigate Lisp from this view point and learn how to make Lisp do mundane things like an assignment, conditional, loop, array, input and output etc.

This post is centered on this fact.

Assignment statement

The most fundamental requirement for any language is to perform an assignment. For e.g. these are assignment statements in Lisp and its equivalent in C for e.g.

$ (setf x 5)                                                         -> $ x = 5
$ (setf x (+  (* y 2) (* z 8))                               -> $x = 2y + 8z

Conditional statement
There are a couple of forms of conditional statement in Lisp. The most basic is the ‘if’ statement which is special case. You can do if-then-else without the possibility of if-then-else if-else if – else

if (condition) statement else-statement

In Lisp this is written as
$(setf x 5)
$ (if (= x 5)
(setf x  (+ x 5))
(setf  (- x 6)))

In C this equivalent to
$ x = 5
$ if (x == 5)
x = x + 5;
x = x -6;

However Lisp allows the if-then-else if – else if –else through the use of the COND statement

So we could write

$ (setf x 10)
$ (cond ((< x 5) (setf x (+ x 8)) (setf y (* 2 y)))
((= x 10) (setf x (* x 2)))
(t (setf x 8)))

The above statement in C would be
$ x = 2
$ y = 10
$ if (x < 5)
x = x + 8;
y = 2 * y;
else if (x == 10)
x = x * 2;
x = 8;

Lisp has many forms of loops dotimes, dolist, do , loop for etc. I found the following most intuitive and best to get started with
$  (setf x 5)
$ (let ((i 0))
(setf y (* x i))
(when (> i 10) (return))
(print i) (prin1 y)
(incf i

In C this could be written as
$ x = 5
(for i = 0; i < 10; i++)
y = x * i
printf(“%d %d\n”,i,y);

Another easy looping construct in C is
(loop for x from 2 to 10 by 3
do (print x))
In C this would be
(for x=2; x < 10; x = x+3)
print x;

To create an array of 10 elements with initial value of 20
(setf numarray (make-array 10 :initial-element 20))
#(20 20 20 20 20 20 20 20 20 20)
To read an array element it is
$ (aref  numarray 3)                    – – – > numarray[3]
For e.g.
(setf x (* 2 (aref numarray 4)))     – – – – > x = numarray[4] * 2

(defun square (x)
(* x x))
This is the same as

int square (x)
return (x * x)

While in C you would invoke the function as
y = square (8)

In Lisp you would write as
(setf y (square 8))

Note: In Lisp the function is invoked as (function arg1 arg2… argn) instead of (function (arg1 arg2  … argn))

a) Create a global variable *db*
(defvar *db* nil)

b) Make a function to add an employee
$(defun make-emp (name age title)
(list :name name :age age :title title))
$(add-emp (make-emp “ganesh” 49 “manager”))
$(add-emp (make-emp “manish” 50 “gm”))
$(add-emp (make-emp “ram” 46 “vp”))
$ (dump-db)

For a more complete and excellent post on managing a simple DB looks at Practical Common Lisp by Peter Siebel

Reading and writing to standard output
To write to standard output you can use
(print “This is a test”) or
(print ‘(This is a test))
To read from standard input use
(let ((temp 0))
(print ‘(Enter temp))
(setf temp (read))
(print (append ‘(the temp is) (list temp))))

Reading and writing to a file
The typical way to do this is to use

a) Read
(with-open-file (stream “C:\\acl82express\\lisp\\count.cl”)
(do ((line (read-line stream nil)
(read-line stream nil)))
((null line))
(print line)))

b) Write
(with-open-file (stream “C:\\acl82express\\lisp\\test.txt”
:direction :output
:if-exists :supersede)
(write-line “test” stream)

I found the following construct a lot easier
(let ((in (open “C:\\acl82express\\lisp\\count.cl” :if-does-not-exist nil)))
(when in
(loop for line = (read-line in nil)
while line do (format t “~a~%” line))
(close in)))

With the above you can get started on Lisp. However with just the above constructs the code one writes will be very “non-Lispy”. Anyway this is definitely a start.

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The Future of Programming Languages

How will the computing landscape evolve as we move towards the next millennium? Clearly the computer architecture will evolve towards a more parallel architecture with multiple CPUs each handling a part of the problem in parallel. However, programming parallel architectures is no simple task and will challenge the greatest minds.

In the future where the problems and the architectures will be extremely complex, the programming language will itself evolve towards simplicity. The programming language will be based on natural language that we use to define problems. Behinds the scenes of the natural language interface will be complex algorithms of Artificial Intelligence which will perform the difficult task of specifying the definition of problem into a high level programming language like C++, Java etc.

The Artificial Intelligence interface will handle the task of creating variables, forming loops and actually defining classes and handling errors. The code generated by the machine will have far less syntactical errors than those created by human beings. However while a large set of problems can be solved by the AI interface there will be a certain class of problems which will still require human intervention and the need to work in the languages of today.

One of the drivers for this natural language of programming of the future, besides the complexity of the computer architecture is the need to bring a larger section of domain experts to solve the problems in their fields without the need to learn all the complex syntax, and semantics of the current programming languages.

This will allow everybody from astrophysicists, geneticists, historians and statisticians to be able to utilize the computer to solve the difficult problems in their domain.

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Ramblings on Lisp

In the world of programming languages Lisp can be considered truly ancient along with its companion FORTRAN. However it has survived till this day. Lisp had its origins as early as 1958 when John McCarthy of MIT published the design of the language in an ACM paper titled “Recursive Functions of Symbolic Expressions and Their Computation by Machine”. Lisp was invented by McCarthy as a mathematical notation of computer programs and is based on Lambda Calculus described by Alonzo Church in 1930.

Lisp has not had the popularity of other more recent languages like C, C++ and Java partly because it has an unfamiliar syntax and also has a very steep learning curve. The Lisp syntax can be particularly intimidating to beginners with its series of parentheses. However it is one of predominant language that is used in AI domain.

Some of the key characteristics of Lisp are

Lisp derives its name from LISt Processing. Hence while most programming languages try to compute on data, Lisp computes on a data structure namely the list. A list can be visualized as a collection of elements which can be either data or functions or lists themselves. Its power comes from the fact that the language includes in its syntax some of the operations that can be performed on a lists and lists of lists. In fact many key features of the Lisp Language have found their way into more current languages like Python, Ruby and Perl.

Second Lisp is a symbolic processing language. This ability to manipulate symbols gives Lisp a powerful edge over other Programming Languages in AI domains like theorem proving or natural language processing.

Thirdly Lisp uses a recursive style of programming. This makes the code much shorter than other languages. Recursion enables the expression of the problem as a combination of a terminating condition and a self describing sub problem.  However the singular advantage that Lisp has over other programming languages is that it uses a technique called “tail recursion” . The beauty of tail recursion is that computing  space is of the order of O(1) and not O(n) that is common in languages like C,C++,Java where the size of the stack grows with each subsequent recursive call.

Lisp blurs the distinction between functions and data. Functions use other functions as arguments during computations. Lisp lists are functions that operate on other functions and data in a self repeating fashion.

The closest analogy to this is to think of machine code which is sequence of 32 bit binary words. Both the logic and the data on which they operate are 32 bit binary words and cannot be distinguished unless one knows where the program is supposed to start executing. If one were to take the snapshot of consecutive memory locations we will encounter 32 bit binary words which represent either a logical or arithmetic operation on data which are also 32 bit binary words.

Lisp is a malleable language and allows the programmer to tailor the language for his own convenience. It allows the programmer to manipulate the language so that it suits the programming style of the programmer. Lisp programs evolve from the bottom-up rather from the top-down style adopted in other languages. The design methodology of Lisp programs takes an inside-out approach rather than an outside-in method.

Lisp has many eminent die-hard adherents who swear by the elegance and beauty of being able to solve difficult problems concisely. On the other hand there are those to whom Lisp represents an ancient Pharaoh’s curse that is difficult to get rid of.

However with Lisp, “Once smitten, you remain smitten”.

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Pete Mettle felt drowsy. He had been working for days on his new inference algorithm. Pete had been in the field of Artificial Intelligence (AI) for close to 3 decades and had established himself as the father of “semantics”. He was particularly renowned for his 3 principles of Artificial Intelligence. He had postulated the Principles of Learning as

The Principle of Knowledge Acquisition: This principle laid out the guidelines for knowledge acquisition by an algorithm. It clearly laid out the rules of what was knowledge and what was not. It could clearly delineate between the wheat and chaff from any textbook or research article.

The Principle of Knowledge Assimilation: This law gave the process for organizing the acquired knowledge in facts, rules and underlying principles. Knowledge assimilation involved storing the individual rules, the relation between the rules and provided the basis for drawing conclusions from them

The Principle of Knowledge Application: This principle according to Pete was the most important. It showed how all knowledge acquired and assimilated could be used to draw inferences andconclusions. In fact it also showed how knowledge could be extrapolated to make safe conclusions.

Zengine The above 3 principles of Pete were hailed as a major landmark in AI. Pete started to work on an inference engine known as “Zengine” based on his above 3 principles. Pete was almost finished fine tuning his algorithm. Pete wanted to test his Zengine on the World Wide Web. The World Wide Web had grown into gigantic proportions. A report in May 2025 issue of Wall Street Journal mentioned that the total data that was held in the internet had crossed 400 zettabytes and that the daily data stored on the web was close to 20 terabytes. It was a well known fact that there an enormous amount of information on the web on a wide variety of topics. Wikis, blogs, articles, ideas, social networks and so on there was a lot of information on almost every conceivable topic under the sun.

Pete was given special permission by the governments of the world to run his Zengine on the internet. It was Pete’s theory that it would take the Zengine close to at least a year to process the information on the web and make any reasonable inferences from them. Accompanied by world wide publicity Zengine started its work of trying to assimilate the information on the World Wide Web. The Zengine was programmed to periodically give a status update of its progress to Pete.

A few months passed. Zengine kept giving updates on the number of sites, periodicals, blogs it had condensed into its knowledge database. After about 10 months Pete received a mail. It read “Markets will crash on March 2026. Petrol prices will sky rocket – Zengine. Pete was surprised at the forecast. So he invoked the API to check on what basis the claim had been made. To his surprise and amazement he found that a lot events happening in the world had been used to make that claim which clearly seemed to point in that direction. A couple of months down the line there was another terse statement “Rebellion very likely in Mogadishu in Dec 2027″. – Zengine.The Zengine also came with corollaries to Fermat’s last theorem. It was becoming clear to Pete and everybody that the Zengine was indeed becoming smarter by the day..It became apparent to everybody when Zengine would become more powerful than human beings.

Celestial events: Around this time peculiar events were observed all over the world. There were a lot of celestial events that were happening. Phenomenon like the aurora borealis became common place. On Dec 12, 2026 there was an unusual amount of electrical activity in the sky. Everywhere there were streaks of lightning. By evening time slivers of lightning hit the earth in several parts of the world. In fact if anybody had viewed the earth from outer space then it would have a resembled a “nebula sphere” with lightning streaks racing towards the earth in all directions. This seemed to happen for many days. Simultaneously the Zengine was getting more and more powerful. In fact it had learnt to spawn of multiple processes to get information and return to it.

Time-space discontinuity: People everywhere were petrified of this strange phenomenon. On the one hand there was the fear of the takeover of the web by the Zengine and on the other was this increased celestial activity. Finally on the morning of Jan 2028 there was a powerful crack followed by a sonic boom and everywhere people had a moment of discontinuity. In the briefest of moments there was a natural time-space discontinuity and mankind had progressed to the next stage in evolution.

The unconscious, sub conscious and the conscious all became a single faculty of super consciousness. It has always been known from the time of Plato that man knows everything there is to know. According to Platonic doctrine of Recollection, human beings are born with a soul possessing all knowledge, and learning is just discovering or recollecting what the soul already knows. Similarly according to Hindu philosophy, behind the individual consciousness of the Atman, is the reality known as the Brahman which is universal consciousness attained in a deep state of mysticism through self-inquiry.

However this evolution by some strange quirk of coincidence seemed to coincide with the development of the world’s first truly learning machine. In this super conscious state a learning machine was not something to be feared but something which could be used to benefit mankind. Just like cranes can lift and earthmovers perform tasks that are beyond our physical capacity so also a learning machine was a useful invention that could be used to harness the knowledge from mankind’s storehouse – the World Wide Web.

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