Seven Deadly Sins Computer Game
Johna Delehanty
We are surrounded by hysteria about the future of Artificial Intelligence and Robotics. There is hysteria about how powerful they will become how quickly, and there is hysteria about what they will do to jobs.
Mistaken predictions lead to fear of things that are not going to happen. Why are people making mistakes in predictions about Artificial Intelligence and robotics, so that Oren Etzioni, I, and others, need to spend time pushing back on them?
Below I outline seven ways of thinking that lead to mistaken predictions about robotics and Artificial Intelligence. We find instances of these ways of thinking in many of the predictions about our AI future. I am going to first list the four such general topic areas of predictions that I notice, along with a brief assessment of where I think they currently stand.
A. Artificial General Intelligence. Research on AGI is an attempt to distinguish a thinking entity from current day AI technology such as Machine Learning. Here the idea is that we will build autonomous agents that operate much like beings in the world. This has always been my own motivation for working in robotics and AI, but the recent successes of AI are not at all like this.
Modern day AGI research is not doing at all well on being either general or getting to an independent entity with an ongoing existence. It mostly seems stuck on the same issues in reasoning and common sense that AI has had problems with for at least fifty years. Alternate areas such as Artificial Life, and Simulation of Adaptive Behavior did make some progress in getting full creatures in the eighties and nineties (these two areas and communities were where I spent my time during those years), but they have stalled.
Here is where we are on programs that can understand computer code. We currently have no programs that can understand a one page program as well as a new student in computer science can understand such a program after just one month of taking their very first class in programming. That is a long way from AI systems being better at writing AI systems than humans are.
C. Misaligned Values. The third case is that the Artificial Intelligence based machines get really good at execution of tasks, so much so that they are super human at getting things done in a complex world. And they do not share human values and this leads to all sorts of problems.
This same author repeatedly (including in the piece from which I took this quote, but also at the big International Joint Conference on Artificial Intelligence (IJCAI) that was held just a couple of weeks ago in Melbourne, Australia) argues that we need research to come up with ways to mathematically prove that Artificial Intelligence systems have their goals aligned with humans.
I think this case C comes from researchers seeing an intellectually interesting research problem, and then throwing their well known voices promoting it as an urgent research question. Then AI hangers-on take it, run with it, and turn it into an existential problem for mankind.
D. Really evil horrible nasty human-destroying Artificially Intelligent entities. The last category is like case C, but here the supposed Artificial Intelligence powered machines will take an active dislike to humans and decide to destroy them and get them out of the way.
Now, the seven mistakes I think people are making. All seven of them influence the assessments about timescales for and likelihood of each of scenarios A, B, C, and D, coming about. But some are more important I believe in the mis-estimations than others. I have labeled in the section headers for each of these seven errors where I think they do the most damage. The first one does some damage everywhere!
There is actually a lot wrapped up in these 21 words which can easily fit into a tweet and allow room for attribution. An optimist can read it one way, and a pessimist can read it another. It should make the optimist somewhat pessimistic, and the pessimist somewhat optimistic, for a while at least, before each reverting to their norm.
Today GPS is in the long term, and the ways it is used were unimagined when it was first placed in orbit. My Series 2 Apple Watch uses GPS while I am out running to record my location accurately enough to see which side of the street I ran along. The tiny size and tiny price of the receiver would have been incomprehensible to the early GPS engineers. GPS is now used for so many things that the designers never considered. It synchronizes physics experiments across the globe and is now an intimate component of synchronizing the US electrical grid and keeping it running, and it even allows the high frequency traders who really control the stock market to mostly not fall into disastrous timing errors. It is used by all our airplanes, large and small to navigate, it is used to track people out of jail on parole, and it determines which seed variant will be planted in which part of many fields across the globe. It tracks our fleets of trucks and reports on driver performance, and the bouncing signals on the ground are used to determine how much moisture there is in the ground, and so determine irrigation schedules.
GPS started out with one goal but it was a hard slog to get it working as well as was originally expected. Now it has seeped into so many aspects of our lives that we would not just be lost if it went away, but we would be cold, hungry, and quite possibly dead.
We see a similar pattern with other technologies over the last thirty years. A big promise up front, disappointment, and then slowly growing confidence, beyond where the original expectations were aimed. This is true of the blockchain (Bitcoin was the first application), sequencing individual human genomes, solar power, wind power, and even home delivery of groceries.
To see how the long term influence of computers has consistently been underestimated one need just go back and look at portrayals of them in old science fiction movies or TV shows about the future. The three hundred year hence space ship computer in the 1966 Star Trek (TOS) was laughable just thirty years later, let alone three centuries later. And in Star Trek The Next Generation, and Star Trek Deep Space Nine, whose production spanned 1986 to 1999, large files still needed to be carried by hand around the far future space ship or space station as they could not be sent over the network (like an AOL network of the time). And the databases available for people to search were completely anemic with their future interfaces which were pre-Web in design.
Not all technologies get underestimated in the long term, but that is most likely the case for AI. The question is how long is the long term. The next six errors that I talk about help explain how the timing for the long term is being grossly underestimated for the future of AI.
If something is magic it is hard to know the limitations it has. Suppose we further show Newton how it can illuminate the dark, how it can take photos and movies and record sound, how it can be used as a magnifying glass, and as a mirror. Then we show him how it can be used to carry out arithmetical computations at incredible speed and to many decimal places. And we show it counting his steps has he carries it.
What else might Newton conjecture that the device in front of him could do? Would he conjecture that he could use it to talk to people anywhere in the world immediately from right there in the chapel? Prisms work forever. Would he conjecture that the iPhone would work forever just as it is, neglecting to understand that it needed to be recharged (and recall that we nabbed him from a time 100 years before the birth of Michael Faraday, so the concept of electricity was not quite around)? If it can be a source of light without fire could it perhaps also transmute lead to gold?
This is a problem we all have with imagined future technology. If it is far enough away from the technology we have and understand today, then we do not know its limitations. It becomes indistinguishable from magic.
This is a problem I regularly encounter when trying to debate with people about whether we should fear just plain AGI, let alone cases C or D from above. I am told that I do not understand how powerful it will be. That is not an argument. We have no idea whether it can even exist. All the evidence that I see says that we have no real idea yet how to build one. So its properties are completely unknown, so rhetorically it quickly becomes magical and super powerful. Without limit.
When in a foreign city we ask a stranger on the street for directions and they reply in the language we spoke to them with confidence and with directions that seem to make sense, we think it worth pushing our luck and asking them about what is the local system for paying when you want to take a bus somewhere in that city.
If our teenage child is able to configure their new game machine to talk to the household wifi we suspect that if sufficiently motivated they will be able to help us get our new tablet computer on to the same network.
If we notice that someone is able to drive a manual transmission car, we will be pretty confident that they will be able to drive one with an automatic transmission too. Though if the person is North American we might not expect it to work for the converse case.
If we ask an employee in a large hardware store where to find a particular item, a home electrical fitting say, that we are looking for and they send us to an aisle of garden tools, we will probably not go back and ask that very same person where to find a particular bathroom fixture. We will estimate that not only do they not know where the electrical fittings are, but that they really do not know the layout of goods within the store, and we will look for a different person to ask with our second question.
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