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Patrick Fog
So, because I'm chicken, I'm going to ignore current events and instead take this opportunity to remind you that I can't predict the future. No science fiction writer can. Predicting the future isn't what science fiction is about. As the late Edsger Djikstra observed, "computer science is no more about computers than astronomy is about telescopes." He might well have added, or science fiction is about predicting the future. What I try to do is examine the human implications of possible developments, and imagine what consequences they might have. (Hopefully entertainingly enough to convince the general public to buy my books.)
Ten years in the future, we will be living in a world recognizable as having emerged from the current one by a process of continuous change. About 85% of everyone alive in 2029 is already alive in 2019. (Similarly, most of the people who're alive now will still be alive a decade hence, barring disasters on a historic scale.)
Here in the UK the average home is 75 years old, so we can reasonably expect most of the urban landscape of 2029 to be familiar. I moved to Edinburgh in 1995: while the Informatics Forum is new, as a side-effect of the disastrous 2002 old town fire, many of the university premises are historic. Similarly, half the cars on the road today will still be on the roads in 2029, although I expect most of the diesel fleet will have been retired due to exhaust emissions, and there will be far more electric vehicles around.
You don't need a science fiction writer to tell you this stuff: 90% of the world of tomorrow plus ten years is obvious to anyone with a weekly subscription to New Scientist and more imagination than a doorknob.
Obsolescence is also largely predictable. The long-drawn-out death of the pocket camera was clearly visible on the horizon back in 2009, as cameras in smartphones were becoming ubiquitous: ditto the death of the pocket GPS system, the compass, the camcorder, the PDA, the mp3 player, the ebook reader, the pocket games console, and the pager. Smartphones are technological cannibals, swallowing up every available portable electronic device that can be crammed inside its form factor.
However, this stuff ignores what Donald Rumsfeld named "the unknown unknowns". About 1% of the world of ten years hence always seems to have sprung fully-formed from the who-ordered-THAT dimension: we always get landed with stuff nobody foresaw or could possibly have anticipated, unless they were spectacularly lucky guessers or had access to amazing hallucinogens. And this 1% fraction of unknown unknowns regularly derails near-future predictions.
In the 1950s and 1960s, futurologists were obsessed with resource depletion, the population bubble, and famine: Paul Ehrlich and the other heirs of Thomas Malthus predicted wide-scale starvation by the mid-1970s as the human population bloated past the unthinkable four billion mark. They were wrong, as it turned out, because of the unnoticed work of a quiet agronomist, Norman Borlaug, who was pioneering new high yield crop strains: what became known as the Green Revolution more than doubled global agricultural yields within the span of a couple of decades. Meanwhile, it turned out that the most effective throttle on population growth was female education and emancipation: the rate of growth has slowed drastically and even reversed in some countries, and WHO estimates of peak population have been falling continuously as long as I can remember. So the take-away I'd like you to keep is that the 1% of unknown unknowns are often the most significant influences on long-term change.
The recommendation algorithms used by YouTube, Facebook, and Twitter exploit this effect to maximize audience participation in pursuit of maximize advertising click-throughs. They promote popular related content, thereby prioritizing controversial and superficially plausible narratives. Viewer engagement is used to iteratively fine-tune the selection of content so that it is more appealing, but it tends to trap us in filter bubbles of material that reinforces our own existing beliefs. And bad actors have learned to game these systems to promote dubious content. It's not just Cambridge Analytica I'm talking about here, or allegations of Russian state meddling in the 2016 US presidential election. Consider the spread of anti-vaccination talking points and wild conspiracy theories, which are no longer fringe phenomena but mass movements with enough media traction to generate public health emergencies in Samoa and drive-by shootings in Washington DC. Or the spread of algorithmically generated knock-offs of children's TV shows proliferating on YouTube that caught the public eye last year.
... And then there's the cute cat photo thing. If I could take a time machine back to 1989 and tell an audience like yourselves that in 30 years time we'd all have pocket supercomputers that place all of human knowledge at our fingertips, but we'd mostly use them for looking at kitten videos and nattering about why vaccination is bad for your health, you'd have me sectioned under the Mental Health Act. And you'd be acting reasonably by the standards of the day: because unlike fiction, emergent human culture is under no obligation to make sense.
In the natural world, we're experiencing extreme weather events caused by anthropogenic climate change at an increasing frequency. Back in 1989, or 2009, climate change was a predictable thing that mostly lay in the future: today in 2019, or tomorrow in 2029, random-seeming extreme events (the short-term consequences of long-term climactic change) are becoming commonplace. Once-a-millennium weather outrages are already happening once a decade: by 2029 it's going to be much, much worse, and we can expect the onset of destabilization of global agriculture, resulting in seemingly random food shortages as one region or another succumbs to drought, famine, or wildfire.
Although speculation about mechanical minds goes back a lot further, the field of Artificial Intelligence was largely popularized and publicized by the groundbreaking 1956 Dartmouth Conference organized by Marvin Minsky, John McCarthy, Claude Shannon, and Nathan Rochester of IBM. The proposal for the conference asserted that, "every aspect of learning or any other feature of intelligence can be so precisely described that a machine can be made to simulate it", a proposition that I think many of us here would agree with, or at least be willing to debate. (Alan Turing sends his apologies.) Furthermore, I believe mechanisms exhibiting many of the features of human intelligence had already existed for some centuries by 1956, in the shape of corporations and other bureaucracies. A bureaucracy is a framework for automating decision processes that a human being might otherwise carry out, using human bodies (and brains) as components: a corporation adds a goal-seeking constraints and real-world i/o to the procedural rules-based element.
Searle's thought experiment begins with this hypothetical premise: suppose that artificial intelligence research has constructed a computer that behaves as if it understands Chinese. It takes Chinese characters as input and, by following the instructions of a computer program, produces other Chinese characters, which it presents as output. Suppose, says Searle, that this computer comfortably passes the Turing test, by convincing a human Chinese speaker that the program is itself a live Chinese speaker. To all of the questions that the person asks, it makes appropriate responses, such that any Chinese speaker would be convinced that they are talking to another Chinese-speaking human being.
Searle then supposes that he is in a closed room and has a book with an English version of the computer program, along with sufficient papers, pencils, erasers, and filing cabinets. Searle could receive Chinese characters through a slot in the door, process them according to the program's instructions, and produce Chinese characters as output. If the computer had passed the Turing test this way, it follows that he would do so as well, simply by running the program manually.
Searle asserts that there is no essential difference between the roles of the computer and himself in the experiment. Each simply follows a program, step-by-step, producing a behavior which is then interpreted by the user as demonstrating intelligent conversation. But Searle himself would not be able to understand the conversation.
The problem with this argument is that it is apparent that a company is nothing but a very big Chinese Room, containing a large number of John Searles, all working away at their rule sets and inputs. We many not agree that an AI "understands" Chinese, but we can agree that it performs symbolic manipulation; and a room full of bureaucrats looks awfully similar to a hypothetical Turing-test-passing procedural AI from here.
Corporations aren't the only pre-electronic artificial intelligences we've developed. Any bureaucracy is a rules-based information processing system. Governments are superorganisms that behave like very large corporations, but differ insofar as they can raise taxes (thereby creating demand for circulating money, which they issue), stimulating economic activity. They can recirculate their revenue through constructive channels such as infrastructure maintenance, or destructive ones such as military adventurism. Like corporations, governments are potentially immortal until an external threat or internal decay damages them beyond repair. By promulgating and enforcing laws, governments provide an external environment within which the much smaller rules-based corporations can exist.
(I should note that at this level, it doesn't matter whether the government's claim to legitimacy is based on the will of the people, the divine right of kings, or the Flying Spaghetti Monster: I'm talking about the mechanical working of a civil service bureaucracy, what it does rather than why it does it.)
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