By Graham Barr
AI uses human actions in the realm of decision making; making judgements. So-called machine learning is central to AI. Optimal human decision making often follows a well determinable sequence in the form of “If this happens do this, otherwise do that” etc etc Machines can learn this best sequence of actions to mimic a human with the best judgement in that situation. Of course, some humans are intelligent to the point that they are known to solve particularly hard problems and are known for their ability to think “out of the box”; those that might solve problems that have never been solved before and thus no solution is recorded as existing to the problem at hand. Notwithstanding this unusual case, typical AI applications are developed with machines that are “trained” to replicate the best solution to a potential problem available. One might surmise that we could soon reach the point where machines could out-think the most creative human thinkers and prove new mathematical theorems and push the boundaries of knowledge. Will AI be able to write brilliant novels and think of ways and (manufacturing processes) to produce faster computer chips? Or think of ways to genetically modify plants so that food production eradicates hunger using a relatively small area of land. At some point, but we aren’t there yet.
AI is sometimes mixed up, I think, with “raw” computer power. Computer power is the straight-forward speed of a computer chip and then (separately) the ever-lower price of computer storage. Computer power is about the speed at which one can do an operation; whether it takes 1 hour or 1 second. And this leads on to whether a computer can process huge amounts of data in a useful amount of time – is a problem solved in an hour worthless, whereas a problem solved in a second is very useful. The rise in computer power over the years has been phenomenal (Moore’s Law) A terabyte of data might be processed in 30 seconds now but have taken 150 years to process 30 years ago. This raw data processing power which affects the time something takes to process is often a key requirement of AI; but should not be confused with AI itself.
Turning to consider clear examples of an AI centred approach which are useful, and not dependent on any significant computer power. The replacement of tellers at supermarkets would constitute a first-stage form of AI. This constitutes replacing a task not requiring much creativity; requiring a fixed pattern of behaviour in a deterministic (reliably set) way – this would be a low-level AI application. This application works quite well through product scanning, but generally requires an overseeing assistant to sort out problems. Bank bots answering questions through text responses to typed questions, exist currently, but appear to be extremely inadequate at answering anything except the simplest of questions.
AI could be used to direct drones to fly over fields of plated crops and look for disease; then if found go and collect an appropriate spray and fly out again and spray the afflicted crop. Or those with a sickness could be checked in through an AI-driven
diagnostic process and then have medication administered. Where appropriate, they may even be operated upon by an AI driven robot.
The “box” of recorded human intelligence is still circumscribed. AI can be taught (machine learning) to write simple essays by reaping bits of seemingly required knowledge from the internet. However, It can’t go further out the box than it has been trained to do.
Forecasting.
I think economic forecasting has always challenged economists. Interestingly, It is not obvious that over the period of the last 30 years, in which computer power has improved by a factor of 1m, that economic forecasting has improved at all. This is in contrast to weather forecasting which is decidedly more scientifically deterministic. Forecasts are determined according to the (short-term, at least) predictable changes in pressure, relative humidity, temperature etc Such forecasts have improved greatly over the same period with increased computer power rather than any clear use of AI.
Economic forecasting is more difficult because it requires capturing both rational and (ex post anyway) seemingly irrational actions of humans. An important point is that humans can perform actions which are irrational (not optimal) because of a short-term emotional response. Machines, in contrast, currently can only perform rationally. Currently, that it. It is conceivable in the future that machines could be trained to capture the emotional reaction to any event at all for each individual person on the planet.
Back to economic forecasting. If one Googled “AI for economic forecasting”, one gets a list of statistical procedures that have been around for 50 years. ARIMA(Box-Jenkins), Trend-Seasonal decomposition forecasting, scenario-based econometric model forecasting etc These processes have little to do with AI as discussed. They do benefit a bit from increased computer power, mainly that they are quicker to estimate and allow more models to be considered within a period of time. But this is not material. In fact, economic-type forecasts have not improved much over 50 years. The most obvious example of this is the fact that “pollsters” got nowhere near to forecasting the results of the recent American election. If anything, one could say that they did a worst forecasting job than has ever been done.
The governor of the Reserve Bank Lesetja Kganyago may be mixing up AI with raw computer power. Increased computer power will allow his scenario-based models to be estimated with many more variables with many more assumptions, giving many more possible forecasts over many more different time periods. This will make his task rather difficult as he tries to decide which possible future set of so-called exogenous variables might pertain in the future. There is no obvious reason that he will do any better than the seemingly-unfathomly-bad forecasts of the American election pollsters.
Is AI able to tell us how to fine tune our SARB economic models so that they will produce better economic forecasts. Probably not. Could one CURRENTLY put an AI powered machine to better manage the monetary policy of the SA Reserve Bank. Unfortunately, it is quite likely.