Getting bored with history lessons

Last post’s investigation into the post-Babbage history of computers took us up to around the end of the Second World War, before the computer age could really be said to have kicked off. However, with the coming of Alan Turing the biggest stumbling block for the intellectual development of computing as a science had been overcome, since it now clearly understood what it was and where it was going. From then on, therefore, the history of computing is basically one long series of hardware improvements and business successes, and the only thing of real scholarly interest was Moore’s law. This law is an unofficial, yet surprisingly accurate, model of the exponential growth in the capabilities of computer hardware, stating that every 18 months computing hardware gets either twice as powerful, half the size, or half the price for the same other specifications. This law was based on a 1965 paper by Gordon E Moore, who noted that the number of transistors on integrated circuits had been doubling every two years since their invention 7 years earlier. The modern day figure of an 18-monthly doubling in performance comes from an Intel executive’s estimate based on both the increasing number of transistors and their getting faster & more efficient… but I’m getting sidetracked. The point I meant to make was that there is no point me continuing with a potted history of the last 70 years of computing, so in this post I wish to get on with the business of exactly how (roughly fundamentally speaking) computers work.

A modern computer is, basically, a huge bundle of switches- literally billions of the things. Normal switches are obviously not up to the job, being both too large and requiring an electromechanical rather than purely electrical interface to function, so computer designers have had to come up with electrically-activated switches instead. In Colossus’ day they used vacuum tubes, but these were large and prone to breaking so, in the late 1940s, the transistor was invented. This is a marvellous semiconductor-based device, but to explain how it works I’m going to have to go on a bit of a tangent.

Semiconductors are materials that do not conduct electricity freely and every which way like a metal, but do not insulate like a wood or plastic either- sometimes they conduct, sometimes they don’t. In modern computing and electronics, silicon is the substance most readily used for this purpose. For use in a transistor, silicon (an element with four electrons in its outer atomic ‘shell’) must be ‘doped’ with other elements, meaning that they are ‘mixed’ into the chemical, crystalline structure of the silicon. Doping with a substance such as boron, with three electrons in its outer shell, creates an area with a ‘missing’ electron, known as a hole. Holes have, effectively, a positive charge compared a ‘normal’ area of silicon (since electrons are negatively charged), so this kind of doping produces what is known as p-type silicon. Similarly, doping with something like phosphorus, with five outer shell electrons, produces an excess of negatively-charged electrons and n-type silicon. Thus electrons, and therefore electricity (made up entirely of the net movement of electrons from one area to another) finds it easy to flow from n- to p-type silicon, but not very well going the other way- it conducts in one direction and insulates in the other, hence a semiconductor. However, it is vital to remember that the p-type silicon is not an insulator and does allow for free passage of electrons, unlike pure, undoped silicon. A transistor generally consists of three layers of silicon sandwiched together, in order NPN or PNP depending on the practicality of the situation, with each layer of the sandwich having a metal contact or ‘leg’ attached to it- the leg in the middle is called the base, and the ones at either side are called the emitter and collector.

Now, when the three layers of silicon are stuck next to one another, some of the free electrons in the n-type layer(s) jump to fill the holes in the adjacent p-type, creating areas of neutral, or zero, charge. These are called ‘depletion zones’ and are good insulators, meaning that there is a high electrical resistance across the transistor and that a current cannot flow between the emitter and collector despite usually having a voltage ‘drop’ between them that is trying to get a current flowing. However, when a voltage is applied across the collector and base a current can flow between these two different types of silicon without a problem, and as such it does. This pulls electrons across the border between layers, and decreases the size of the depletion zones, decreasing the amount of electrical resistance across the transistor and allowing an electrical current to flow between the collector and emitter. In short, one current can be used to ‘turn on’ another.

Transistor radios use this principle to amplify the signal they receive into a loud, clear sound, and if you crack one open you should be able to see some (well, if you know what you’re looking for). However, computer and manufacturing technology has got so advanced over the last 50 years that it is now possible to fit over ten million of these transistor switches onto a silicon chip the size of your thumbnail- and bear in mind that the entire Colossus machine, the machine that cracked the Lorenz cipher, contained only ten thousand or so vacuum tube switches all told. Modern technology is a wonderful thing, and the sheer achievement behind it is worth bearing in mind next time you get shocked over the price of a new computer (unless you’re buying an Apple- that’s just business elitism).

…and dammit, I’ve filled up a whole post again without getting onto what I really wanted to talk about. Ah well, there’s always next time…

(In which I promise to actually get on with talking about computers)


A Continued History

This post looks set to at least begin by following on directly from my last one- that dealt with the story of computers up to Charles Babbage’s difference and analytical engines, whilst this one will try to follow the history along from there until as close to today as I can manage, hopefully getting in a few of the basics of the workings of these strange and wonderful machines.

After Babbage’s death as a relatively unknown and unloved mathematician in 1871, the progress of the science of computing continued to tick over. A Dublin accountant named Percy Ludgate, independently of Babbage’s work, did develop his own programmable, mechanical computer at the turn of the century, but his design fell into a similar degree of obscurity and hardly added anything new to the field. Mechanical calculators had become viable commercial enterprises, getting steadily cheaper and cheaper, and as technological exercises were becoming ever more sophisticated with the invention of the analogue computer. These were, basically a less programmable version of the difference engine- mechanical devices whose various cogs and wheels were so connected up that they would perform one specific mathematical function to a set of data. James Thomson in 1876 built the first, which could solve differential equations by integration (a fairly simple but undoubtedly tedious mathematical task), and later developments were widely used to collect military data and for solving problems concerning numbers too large to solve by human numerical methods. For a long time, analogue computers were considered the future of modern computing, but since they solved and modelled problems using physical phenomena rather than data they were restricted in capability to their original setup.

A perhaps more significant development came in the late 1880s, when an American named Herman Hollerith invented a method of machine-readable data storage in the form of cards punched with holes. These had been around for a while to act rather like programs, such as the holed-paper reels of a pianola or the punched cards used to automate the workings of a loom, but this was the first example of such devices being used to store data (although Babbage had theorised such an idea for the memory systems of his analytical engine). They were cheap, simple, could be both produced and read easily by a machine, and were even simple to dispose of. Hollerith’s team later went on to process the data of the 1890 US census, and would eventually become most of IBM. The pattern of holes on these cards could be ‘read’ by a mechanical device with a set of levers that would go through a hole if there was one present, turning the appropriate cogs to tell the machine to count up one. This system carried on being used right up until the 1980s on IBM systems, and could be argued to be the first programming language.

However, to see the story of the modern computer truly progress we must fast forward to the 1930s. Three interesting people and acheivements came to the fore here: in 1937 George Stibitz, and American working in Bell Labs, built an electromechanical calculator that was the first to process data digitally using on/off binary electrical signals, making it the first digital. In 1936, a bored German engineering student called Konrad Zuse dreamt up a method for processing his tedious design calculations automatically rather than by hand- to this end he devised the Z1, a table-sized calculator that could be programmed to a degree via perforated film and also operated in binary. His parts couldn’t be engineered well enough for it to ever work properly, but he kept at it to eventually build 3 more models and devise the first programming language. However, perhaps the most significant figure of 1930s computing was a young, homosexual, English maths genius called Alan Turing.

Turing’s first contribution to the computing world came in 1936, when he published a revolutionary paper showing that certain computing problems cannot be solved by one general algorithm. A key feature of this paper was his description of a ‘universal computer’, a machine capable of executing programs based on reading and manipulating a set of symbols on a strip of tape. The symbol on the tape currently being read would determine whether the machine would move up or down the strip, how far, and what it would change the symbol to, and Turing proved that one of these machines could replicate the behaviour of any computer algorithm- and since computers are just devices running algorithms, they can replicate any modern computer too. Thus, if a Turing machine (as they are now known) could theoretically solve a problem, then so could a general algorithm, and vice versa if it couldn’t. Not only that, but since modern computers cannot multi-task on the. These machines not only lay the foundations for computability and computation theory, on which nearly all of modern computing is built, but were also revolutionary as they were the first theorised to use the same medium for both data storage and programs, as nearly all modern computers do. This concept is known as a von Neumann architecture, after the man who first pointed out and explained this idea in response to Turing’s work.

Turing machines contributed one further, vital concept to modern computing- that of Turing-completeness. A Turing-complete system was defined as a single Turing machine (known as a Universal Turing machine) capable of replicating the behaviour of any other theoretically possible Turing machine, and thus any possible algorithm or computable sequence. Charles Babbage’s analytical engine would have fallen into that class had it ever been built, in part because it was capable of the ‘if X then do Y’ logical reasoning that characterises a computer rather than a calculator. Ensuring the Turing-completeness of a system is a key part of designing a computer system or programming language to ensure its versatility and that it is capable of performing all the tasks that could be required of it.

Turing’s work had laid the foundations for nearly all the theoretical science of modern computing- now all the world needed was machines capable of performing the practical side of things. However, in 1942 there was a war on, and Turing was being employed by the government’s code breaking unit at Bletchley Park, Buckinghamshire. They had already cracked the German’s Enigma code, but that had been a comparatively simple task since they knew the structure and internal layout of the Enigma machine. However, they were then faced by a new and more daunting prospect: the Lorenz cipher, encoded by an even more complex machine for which they had no blueprints. Turing’s genius, however, apparently knew no bounds, and his team eventually worked out its logical functioning. From this a method for deciphering it was formulated, but it required an iterative process that took hours of mind-numbing calculation to get a result out. A faster method of processing these messages was needed, and to this end an engineer named Tommy Flowers designed and built Colossus.

Colossus was a landmark of the computing world- the first electronic, digital, and partially programmable computer ever to exist. It’s mathematical operation was not highly sophisticated- it used vacuum tubes containing light emission and sensitive detection systems, all of which were state-of-the-art electronics at the time, to read the pattern of holes on a paper tape containing the encoded messages, and then compared these to another pattern of holes generated internally from a simulation of the Lorenz machine in different configurations. If there were enough similarities (the machine could obviously not get a precise matching since it didn’t know the original message content) it flagged up that setup as a potential one for the message’s encryption, which could then be tested, saving many hundreds of man-hours. But despite its inherent simplicity, its legacy is simply one of proving a point to the world- that electronic, programmable computers were both possible and viable bits of hardware, and paved the way for modern-day computing to develop.

Artificial… what, exactly?

OK, time for part 3 of what I’m pretty sure will finish off as 4 posts on the subject of artificial intelligence. This time, I’m going to branch off-topic very slightly- rather than just focusing on AI itself, I am going to look at a fundamental question that the hunt for it raises: the nature of intelligence itself.

We all know that we are intelligent beings, and thus the search for AI has always been focused on attempting to emulate (or possibly better) the human mind and our human understanding of intelligence. Indeed, when Alan Turing first proposed the Turing test (see Monday’s post for what this entails), he was specifically trying to emulate human conversational and interaction skills. However, as mentioned in my last post, the modern-day approach to creating intelligence is to try and let robots learn for themselves, in order to minimise the amount of programming we have to give them ourselves and thus to come close to artificial, rather than programmed, intelligence. However, this learning process has raised an intriguing question- if we let robots learn for themselves entirely from base principles, could they begin to create entirely new forms of intelligence?

It’s an interesting idea, and one that leads us to question what, on a base level, intelligence is. When one thinks about it, we begin to realise the vast scope of ideas that ‘intelligence’ covers, and this is speaking merely from the human perspective. From emotional intelligence to sporting intelligence, from creative genius to pure mathematical ability (where computers themselves excel far beyond the scope of any human), intelligence is an almost pointlessly broad term.

And then, of course, we can question exactly what we mean by a form of intelligence. Take bees for example- on its own, a bee is a fairly useless creature that is most likely to just buzz around a little. Not only is it useless, but it is also very, very dumb. However, a hive, where bees are not individuals but a collective, is a very different matter- the coordinated movements of hundreds and thousands of bees can not only form huge nests and turn sugar into the liquid deliciousness that is honey, but can also defend the nest from attack, ensure the survival of the queen at all costs, and ensure that there is always someone to deal with the newborns despite the constant activity of the environment surround it. Many corporate or otherwise collective structures can claim to work similarly, but few are as efficient or versatile as a beehive- and more astonishingly, bees can exhibit an extraordinary range of intelligent behaviour as a collective beyond what an individual could even comprehend. Bees are the archetype of a collective, rather than individual, mind, and nobody is entirely sure how such a structure is able to function as it does.

Clearly, then, we cannot hope to pigeonhole or quantify intelligence as a single measurement- people may boast of their IQ scores, but this cannot hope to represent their intelligence across the full spectrum. Now, consider all these different aspects of intelligence, all the myriad of ways that we can be intelligent (or not). And ask yourself- now, have we covered all of them?

It’s another compelling idea- that there are some forms of intelligence out there that our human forms and brains simply can’t envisage, let alone experience. What these may be like… well how the hell should I know, I just said we can’t envisage them. This idea that we simply won’t be able to understand what they could be like if we ever experience can be a tricky one to get past (a similar problem is found in quantum physics, whose violation of common logic takes some getting used to), and it is a real issue that if we do ever encounter these ‘alien’ forms of intelligence, we won’t be able to recognise them for this very reason. However, if we are able to do so, it could fundamentally change our understanding of the world around us.

And, to drag this post kicking and screaming back on topic, our current development of AI could be a mine of potential to do this in (albeit a mine in which we don’t know what we’re going to find, or if there is anything to find at all). We all know that computers are fundamentally different from us in a lot of ways, and in fact it is very easy to argue that trying to force a computer to be intelligent beyond its typical, logical parameters is rather a stupid task, akin to trying to use a hatchback to tow a lorry. In fact, quite a good way to think of computers or robots is like animals, only adapted to a different environment to us- one in which their food comes via a plug and information comes to them via raw data and numbers… but I am wandering off-topic once again. The point is that computers have, for as long as the hunt for AI has gone on, been our vehicle for attempting to reach it- and only now are we beginning to fully understand that they have the potential to do so much more than just copy our minds. By pushing them onward and onward to the point they have currently reached, we are starting to turn them not into an artificial version of ourselves, but into an entirely new concept, an entirely new, man-made being.

To me, this is an example of true ingenuity and skill on behalf of the human race. Copying ourselves is no more inventive, on a base level, than making iPod clones or the like. Inventing a new, artificial species… like it or loath it, that’s amazing.

The Chinese Room

Today marks the start of another attempt at a multi-part set of posts- the last lot were about economics (a subject I know nothing about), and this one will be about computers (a subject I know none of the details about). Specifically, over the next… however long it takes, I will be taking a look at the subject of artificial intelligence- AI.

There have been a long series of documentaries on the subject of robots, supercomputers and artificial intelligence in recent years, because it is a subject which seems to be in the paradoxical state of continually advancing at a frenetic rate, and simultaneously finding itself getting further and further away from the dream of ‘true’ artificial intelligence which, as we begin to understand more and more about psychology, neuroscience and robotics, becomes steadily more complicated and difficult to obtain. I could spend a thousand posts on the subject of all the details if I so wished, because it is also one of the fastest-developing regions of engineering on the planet, but that would just bore me and be increasingly repetitive for anyone who ends up reading this blog.

I want to begin, therefore, by asking a few questions about the very nature of artificial intelligence, and indeed the subject of intelligence itself, beginning with a philosophical problem that, when I heard about it on TV a few nights ago, was very intriguing to me- the Chinese Room.

Imagine a room containing only a table, a chair, a pen, a heap of paper slips, and a large book. The door to the room has a small opening in it, rather like a letterbox, allowing messages to be passed in or out. The book contains a long list of phrases written in Chinese, and (below them) the appropriate responses (also in Chinese characters). Imagine we take a non-Chinese speaker, and place him inside the room, and then take a fluent Chinese speaker and put them outside. They write a phrase or question (in Chinese) on some paper, and pass it through the letterbox to the other person inside the room. They have no idea what this message means, but by using the book they can identify the phrase, write the appropriate response to it, and pass it back through the letterbox. This process can be repeated multiple times, until a conversation begins to flow- the difference being that only one of the participants in the conversation actually knows what it’s about.

This experiment is a direct challenge to the somewhat crude test first proposed by mathematical genius and codebreaker Alan Turing in the 1940’s, to test whether a computer could be considered a truly intelligent being. The Turing test postulates that if a computer were ever able to conduct a conversation with a human so well that the human in question would have no idea that they were not talking to another human, but rather to a machine, then it could be considered to be intelligent.

The Chinese Room problem questions this idea, and as it does so, raises a fundamental question about whether a machine such as a computer can ever truly be called intelligent, or to possess intelligence. The point of the idea is to demonstrate that it is perfectly possible to appear to be intelligent, by conducting a normal conversation with someone, whilst simultaneously having no understanding whatsoever of the situation at hand. Thus, while a machine programmed with the correct response to any eventuality could converse completely naturally, and appear perfectly human, it would have no real conciousness. It would not be truly intelligent, it would merely be just running an algorithm, obeying the orders of the instructions in its electronic brain, working simply from the intelligence of the person who programmed in its orders. So, does this constitute intelligence, or is a conciousness necessary for something to be deemed intelligent?

This really boils down to a question of opinion- if something acts like it’s intelligent and is intelligent for all functional purposes, does that make it intelligent? Does it matter that it can’t really comprehend it’s own intelligence? John Searle, who first thought of the Chinese Room in the 1980’s, called the philosophical positions on this ‘strong AI’ and ‘weak AI’. Strong AI basically suggest that functional intelligence is intelligence to all intents and purposes- weak AI argues that the lack of true intelligence renders even the most advanced and realistic computer nothing more than a dumb machine.

However, Searle also proposes a very interesting idea that is prone to yet more philosophical debate- that our brains are mere machines in exactly the same way as computers are- the mechanics of the brain, deep in the unexplored depths of the fundamentals of neuroscience, are just machines that tick over and perform tasks in the same way as AI does- and that there is some completely different and non-computational mechanism that gives rise to our mind and conciousness.

But what if there is no such mechanism? What if the rise of a conciousness is merely the result of all the computational processes going on in our brain- what if conciousness is nothing more than a computational process itself, designed to give our brains a way of joining the dots and processing more efficiently. This is a quite frightening thought- that we could, in theory, be only restrained into not giving a computer a conciousness because we haven’t written the proper code yet. This is one of the biggest unanswered questions of modern science- what exactly is our mind, and what causes it.

To fully expand upon this particular argument would take time and knowledge that I don’t have in equal measure, so instead I will just leave that last question for you to ponder over- what is the difference between the box displaying these words for you right now, and the fleshy lump that’s telling you what they mean.