“AI”: what’s in a name?

In spite of my near-total isolation from news media, I have noticed that references to “AI” are ubiquitous. A perusal of such material suggests that “AI” means Machine Learning, which apparently means the newly discovered technique of multilevel neural networks. While this technique is spectacularly effective, it should be realized that it still is still basically a curve-fitting exercise (albeit with a novel family of curves). The result is a tool that lacks in the transparency that is taken for granted in older methods. In this essay I explore the question whether there is any connection between “AI”, as recently featured in the non-technical media, and the Artificial Intelligence envisaged by John McCarthy, who invented the term in 1955.

History of “Structured Programming”

At its introduction around 1970, structured programming was controversial. It was soon universally accepted. In this essay I trace the history of structured programming and argue that it is worthwhile to re-open the controversy.

A Bridge too Far: E.W. Dijkstra and Logic

1. Introduction

Shortly after the death of E.W. Dijkstra (1930-2002), K.R. Apt wrote an obituary under the title Portrait of a Genius [1]. At the time it seemed to me a bit over the top. Now, with the added wisdom of the intervening years, I consider the title accurate. The most striking feature of EWD’s career was that his remarkable string of successes was followed by a debacle: the book on logic that he co-authored. In this article I list EWD’s achievements, sketch their significance, and try to penetrate into the reasons why he attached such value to logic.

Conceptual Integrity: why it matters and how to get it

In “No Silver Bullet” [1] Frederick Brooks addresses the intriguing question of why some programming languages garner fanatical adherents while others are merely tolerated by their users. Brooks’s answer is that the critical criterion is whether a language has in his words “conceptual integrity”. In this article I try to nail down this nebulous concept and see how it can be used as a guide in language design.

“The unreasonable effectiveness of mathematics in the design and implementation of software systems”

Alas! If only the current state of affairs were such that a future scholar would feel impelled to write a paper with this title, just like in 1960, when Eugene Wigner wrote his widely quoted “The unreasonable effectiveness of mathematics in the natural sciences”. In the hope that at some future time it will improve the sorry state of software, let us consider how mathematics came to be “unreasonably” (i.e. surprisingly, mysteriously) effective in the natural sciences.

Explosive Knowledge: Cryptology in the 20th Century

In August 1960 the Pentagon announced that William Martin and Bernon Mitchell had not returned from vacation and said “there is a likelihood that they have gone behind the Iron Curtain”. On September 6 they appeared at a joint news conference at the House of Journalists in Moscow and announced they had requested asylum and Soviet citizenship. They revealed that they had worked for the National Security Agency (NSA). In this way the mission and activities of the NSA were made public for the first time [1]. Although these activities are much more wide-ranging than cryptology, this post will only be concerned with that small part.

All branches of knowledge had vigorously developed in the first half of the 20th century. All of it had been sustained by what I like to call a conversation: an open exchange of knowledge in books and journals. Before World War I this was also true for cryptology; afterwards, traffic on that channel fell silent. By the end of the 20th century the cryptology conversation was intense, wide-ranging, and immensely productive of innovations, of which bitcoin technology is but one example. In this post I trace the chain of events that led cryptology from its dark age, which lasted from 1918 to 1967, to its renaissance. My material is obtained, unless otherwise noted, from Crypto, a book by Steven Levy, published in 2001 [2].

Alan Robinson

On August 5, 2016 John Alan Robinson died. In him, a great scientist departed, and I mourn a dear friend. His great discovery was the resolution principle in mathematical logic, a discovery that capped two decades of development. In its turn, it spawned a plethora of new developments in computer programming. It became important enough in Artificial Intelligence to become controversial.

Wildcat Crypto

With his “Pretty Good Privacy” (PGP), Phil Zimmermann launched the era of what I call Wildcat Crypto. Up to that point discussions on cryptology had focussed on whether DES, the US government standard, was secure and whether government involvement in its development might have jeopardized its security. To avoid government involvement, PGP used IDEA as alternative to DES for its bulk encryption. Yet IDEA is a close relative of DES and shares its weakness: small, constant-size blocks. True Wildcat Crypto calls for a radical departure in the form of blocks that are much larger, and moreover, vary in length under control of the key. In this article I trace the relevant history and outline my implementation of such a radical alternative.

IA or AI?

I’ve been reading a paper [1] written two decades ago, which itself is an account of events two decades before that. It is Alan Kay nominally writing about the history of a programming language called Smalltalk. I say “nominally” because already by Smalltalk 72, Kay was losing interest in language matters and wanted concentrate on what the language, as part of the computer-as-medium, could do for thought. Along with various other sources of inspiration that led to Smalltalk (Sutherland’s Sketchpad, the Burroughs B220, the Burroughs B5000, …) Kay mentions attending in 1968 the conference presentation by Douglas Engelbart [2] that has since become known as the “Mother of All Demos”.

In 1968, when timesharing by users behind teletype terminals was regarded as avant-garde, Engelbart gave a demo that featured a number of firsts: a screen display for both and text and graphics, interactive text-editing, a mouse. All that was integrated into a fluidly handled medium. Kay gives testimony to the huge impression this made on him: “Engelbart was a prophet of Biblical proportions”.

The H-bomb and birth of the computer, part III: triumph of brains and slide-rules

The question that led to von Neumann’s involvement with Edvac in 1944-1945 was whether Teller’s design (the “Super”) for a hydrogen bomb was feasible. The question that von Neumann wanted to settle was whether in Teller’s design a fission explosion causes a self-sustained propagation of fusion in fusible material.

When Eniac was ready for the first trials in December 1945, von Neumann had convinced its owner, the Ballistics Research Laboratory, to give the ultra-secret computation from Los Alamos priority. Only the researchers from Los Alamos, Metropolis and Frankel, had the requisite security clearance to know the subject of the computation. The necessary personnel to help operating the Eniac did not. The problem was finessed by ruling that the equations and the data only were demoted from their lofty top-secret classification. Eniac performed splendidly, all 18,000 tubes working in unison for a sufficient proportion of time to get the computation completed in six weeks.