Listen

As the scandal over Rupert Murdoch’s News Corporation’s illegal phone hacking activities broke to television audiences around the world, I could not help but wonder why?’And I am sure many others asked themselves the same question. What prompted Murdoch’s executives to condone illegal activities aimed at listening into private conversations? Obvious, you might say: getting the latest scoop on a murder investigation, or the most salacious titbit about the royal family. But let us delve deeper and ask again, as a child might, why? So that more readers would read the News of the World, of course! Stupid question? What drove so many people, estimated at over 4 million, a significant fraction of Britain’s population, to follow the tabloid press so avidly? The daily newspaper remains a primary source of news for the vast majority of the world’s population. Of course, most people also read more serious papers than the News of the World. Still, what is it that drives some news items to become headlines rather than be relegated to the corner of an inside page? The scientific answer is Information; capitalized here because there is more to the term than as understood in its colloquial usage. You may call it voyeurism in the case of News of the World, or the hunger to know what is happening around the world for, say, the New York Times. Both forms of enquiry suffer from the need to filter the vast numbers of everyday events that take place every second, so as to determine those that would most likely be of interest to readers. The concept of Information is best illustrated by comparing the possible headlines ‘Dog Bites Man’ and ‘Man Bites Dog’. Clearly the latter, being a far rarer event, is more likely to prompt you to read the story than the former, more commonplace occurrence. In 1948, Claude E. Shannon published a now classic paper entitled ‘A Mathematical Theory of Communication’. By then the telegraph, telephone, and radio had spawned a whole new communications industry with the AT&T company at its locus. Shannon, working at AT&T Bell Laboratories, was concerned with how fast one could communicate meaning, or information in its colloquial sense, over wires or even the wireless.

Correct

Last summer I took my family on a driving holiday in the American south-western desert covering many national parks. While driving along some of the long tracts of razor-straight highways, such as between Las Vegas and St George, Utah, I often fought drowsiness, not because of lack of sleep, but from the sheer monotony. A familiar experience for many, no doubt. Hardly any conscious thought is needed during such drives. It must be one’s ‘System’, as per Kahneman, which is most certainly doing what ever work is needed. Nevertheless, sleep is not an option. In spite of all the marvellous features embedded in the modern car, the ability to drive itself is, sadly, still missing. The cruise control button helps a bit, allowing one’s feet torelax as the car’s speed remains on an even keel. But the eyes and mind must remain awake and alert. When, if ever, one wonders, will cars with a ‘drive’ button become as common as those with an automatic transmission? Is driving along a perfectly straight stretch of highway really that difficult? After all, we all know that a modern jetliner can fly on autopilot, allowing even a single pilot to read a novel while ‘flying’ the aircraft on a long transcontinental flight. In fact, the jetliner would fly itself perfectly even if the pilot dozed off for many minutes or even hours. We insist that at least one pilot be awake and alert only for our own peace of mind, so as to be able to adequately respond to any emergency situation that might arise. First of all, the ubiquitous autopilot is itself quite a complex piece of equipment. Even to get a plane to fly perfectly straight along a desired heading at a fixed altitude takes a lot of work. The reason, as you must have guessed, is that nature, in the guise of the air on which our jetliner rides, can be quite unpredictable. Wind speeds and directions change continuously, even ever so slightly, requiring constant adjustments to the plane’s engine power, ailerons, flaps, and rudder. In the absence of such adjustments, our jetliner would most certainly veer off course, or lose or gain speed, even dangerously enough to trigger a powered dive or a stall.

Predict

‘Predicting the future’—the stuff of dreams one might imagine; the province of astrologers and soothsayers, surely. Perhaps not, the scientific mind might retort: after all, is it not the job of science to discover laws of nature, and thereby make precise, verifiable predictions about the future? But what if we were to claim that prediction is neither fanciful nor difficult, and not even rare. Rather, it is commonplace; something that we all accomplish each and every moment of our lives. Some readers may recall the popular video game, pong, where the goal is to ‘keep the puck in play’ using an electronic paddle. Figure 2 shows images of two different pong games in progress. In addition to the paddle and puck, the players’ eye gaze is also being tracked. The image on the left shows the player’s eyes tracking the puck itself. On the other hand, in the right-hand image, the player is already looking at a point where she expects the puck to travel to. The player on the left is reactive; she simply tracks the puck, and as the game gets faster, she eventually misses. The right player, in contrast, is able to predict where the puck will be, and most of the time she gets it right. Further, we often see her eyes dart faster than the puck to multiple regions of the field as she appears to recalculate her prediction continuously. What kind of player do you think you are? As it happens, almost all of us are predictive players. Even if we have never played pong before, we rapidly begin predicting the puck’s trajectory after even a few minutes of playing. The ‘reactive player’ in this experiment was in fact autistic, which apparently affected the person’s ability to make predictions about the puck’s trajectory. (The neurological causes of autism are still not well known or agreed upon; the recent research from which the images in Figure 2 are taken represent new results that might shed some more lightonthisdebilitatingcondition.) So it appears that prediction, as exhibited by most pong players, is far from being a rare and unusual ability. It is in fact a part and parcel of our everyday lives, and is present, to varying degrees, in all conscious life.

Connect

On 14 October 2011, the Apple Computer Corporation launched the latest generation of the iPhone 4S mobile phone. The iPhone 4S included Siri, a speech interface that allows users to ‘talk to their phone’. As we look closer though, we begin to suspect that Siri is possibly more than ‘merely’ a great speech-to-text conversion tool. Apart from being able to use one’s phone via voice commands instead of one’s fingers, we are also able to interact with other web-based services. We can search the web, for instance, and if we are looking for a restaurant, those nearest our current location are retrieved, unless, of course, we indicated otherwise. Last but not least, Siri talks back, and that too in a surprisingly human fashion. ‘Voice-enabled location-based search—Google has it already, so what?’, we might say. But there is more. Every voice interaction is processed by Apple’s web-based servers; thus Siri runs on the ‘cloud’ rather than directly on one’s phone. So, as Siri interacts with us, it is also continuously storing data about each interaction on the cloud; whether we repeated words while conversing with it, which words, from which country we were speaking, and whether it ‘understands’ us or not in that interaction. As a result, we are told, Siri will, over time, learn from all this data, improve its speech-recognition abilities, and adapt itself to each individual’s needs. We have seen the power of machine learning in Chapter 3. So, regardless of what Siri does or does not do today, let us for the moment imagine what is possible. After all, Siri’s cloud-based back-end will very soon have millions of voice conversations to learn from. Thus, if we ask Siri to ‘call my wife Jane’ often enough, it should soon learn to ‘call my wife’, and fill in her name automatically. Further, since storage is cheap, Siri can remember all our actions, for every one of us: ‘call the same restaurant I used last week’, should figure out where I ate last week, and in case I eat out often, it might choose the one I used on the same day last week.

Look

In ‘A Scandal in Bohemia’ the legendary fictional detective Sherlock Holmes deduces that his companion Watson had got very wet lately, as well as that he had ‘a most clumsy and careless servant girl’. When Watson, in amazement, asks how Holmes knows this, Holmes answers: . . . ‘It is simplicity itself . . . My eyes tell me that on the inside of your left shoe, just where the firelight strikes it, the leather is scored by six almost parallel cuts. Obviously they have been caused by someonewho has very carelessly scraped round the edges of the sole in order to remove crusted mud from it. Hence, you see, my double deduction that you had been out in vile weather, and that you had a particularly malignant boot-slitting specimen of the London slavery.’ Most of us do not share the inductive prowess of the legendary detective. Nevertheless, we all continuously look at the the world around us and, in our small way, draw inferences so as to make sense of what is going on. Even the simplest of observations, such as whether Watson’s shoe is in fact dirty, requires us to first look at his shoe. Our skill and intent drive what we look at, and look for. Those of us that may share some of Holmes’s skill look for far greater detail than the rest of us. Further, more information is better: ‘Data! Data! Data! I can’t make bricks without clay’, says Holmes in another episode. No inference is possible in the absence of input data, and, more importantly, the right data for the task at hand. How does Holmes connect the observation of ‘leather . . . scored by six almost parallel cuts’ to the cause of ‘someone . . . very carelessly scraped round the edges of the sole in order to remove crusted mud from it’? Perhaps, somewhere deep in the Holmesian brain lies a memory of a similar boot having been so damaged by another ‘specimen of the London slavery’?Or, more likely,many different ‘facts’, such as the potential causes of damage to boots, including clumsy scraping; that scraping is often prompted by boots having been dirtied by mud; that cleaning boots is usually the job of a servant; as well as the knowledge that bad weather results in mud.

Learn

In February 2011, IBM’s Watson computer entered the championship round of the popular TV quiz show Jeopardy!, going on to beat Brad Rutter and Ken Jennings, each long-time champions of the game. Fourteen years earlier, in 1997, IBM’s Deep Blue computer had beaten world chess champion Garry Kasparov. At that time no one ascribed any aspects of human ‘intelligence’ to Deep Blue, even though playing chess well is often considered an indicator of human intelligence. Deep Blue’s feat, while remarkable, relied on using vast amounts of computing power to look ahead and search through many millions of possible move sequences. ‘Brute force, not “intelligence”,’ we all said. Watson’s success certainly appeared similar. Looking at Watson one saw dozens of servers and many terabytes of memory, packed into ‘the equivalent of eight refrigerators’, to quote Dave Ferrucci, the architect of Watson. Why should Watson be a surprise? Consider one of the easier questions that Watson answered during Jeopardy!: ‘Which New Yorker who fought at the Battle of Gettysburg was once considered the inventor of baseball?’ A quick Google search might reveal that Alexander Cartwright wrote the rules of the game; further, he also lived in Manhattan. But what about having fought at Gettysburg? Adding ‘civil war’ or even ‘Gettysburg’ to the query brings us to a Wikipedia page for Abner Doubleday where we find that he ‘is often mistakenly credited with having invented baseball’. ‘Abner Doubleday ’ is indeed the right answer, which Watson guessed correctly. However, if Watson was following these sequence of steps, just as you or I might, how advanced would its abilities to understand natural language have to be? Notice that it would have had to parse the sentence ‘is often mistakenly credited with . . .’ and ‘understand’ it to a sufficient degree and recognize it as providing sufficient evidence to conclude that Abner Doubleday was ‘once considered the inventor of baseball’. Of course, the questions can be tougher: ‘B.I.D. means you take and Rx this many times a day’—what’s your guess? How is Watson supposed to ‘know’ that ‘B.I.D.’ stands for the Latin bis in die, meaning twice a day, and not for ‘B.I.D. Canada Ltd.’, a manufacturer and installer of bulk handling equipment, or even Bid Rx, an internet website? How does it decide that Rx is also a medical abbreviation? If it had to figure all this out from Wikipedia and other public resources it would certainly need farmore sophisticated techniques for processing language than we have seen in Chapter 2.