summing up is a recurring series on how we can make sense of computers. drop your email in the box below to get it straight in your inbox or find previous editions here.
The Myth of a Superhuman AI, by Kevin Kelly
We don’t call Google a superhuman AI even though its memory is beyond us, because there are many things we can do better than it. These complexes of artificial intelligences will for sure be able to exceed us in many dimensions, but no one entity will do all we do better. It’s similar to the physical powers of humans. The industrial revolution is 200 years old, and while all machines as a class can beat the physical achievements of an individual human, there is no one machine that can beat an average human in everything he or she does.
I understand the beautiful attraction of a superhuman AI god. It’s like a new Superman. But like Superman, it is a mythical figure. However myths can be useful, and once invented they won’t go away. The idea of a Superman will never die. The idea of a superhuman AI Singularity, now that it has been birthed, will never go away either. But we should recognize that it is a religious idea at this moment and not a scientific one. If we inspect the evidence we have so far about intelligence, artificial and natural, we can only conclude that our speculations about a mythical superhuman AI god are just that: myths.
my probably most shared article this month and some very wise words indeed. what bugs me most however is that artificial intelligence (ai) seems to displace intelligence augmentation (ia). we try to make computers smarter, but we completely forget about making humans smarter–with the help of computers.
How to Invent the Future, by Alan Kay
As computing gets less and less interesting, its way of accepting and rejecting things gets more and more mundane. This is why you look at some of these early systems and think why aren't they doing it today? Well, because nobody even thinks about that that's important. Come on, this is bullshit, but nobody is protesting except old fogeys like me, because I know it can be better. You need to find out that it can be better. That is your job. Your job is not to agree with me. Your job is to wake up, find ways of criticizing the stuff that seems normal. That is the only way out of the soup.
it seems the more advanced our hardware and technology becomes the less we seem to innovate. i think one part of why we had so much innovation in the early days of computing was because there were people working on it who were musicians, poets, biologists, physicists or historians who were trying to make sense of this new medium to solve their problems. an argument i have proposed in my talk the lost medium last year.
The Pattern-Seeking Fallacy, by Jason Cohen
When an experiment produces a result that is highly unlikely to be due to chance alone, you conclude that something systematic is at work. But when you’re “seeking interesting results” instead of performing an experiment, highly unlikely events will necessarily happen, yet still you conclude something systematic is at work.
The fallacy is that you’re searching for a theory in a pile of data, rather than forming a theory and running an experiment to support or disprove it.
in the noise of randomness in our world we often find patterns. look at enough clouds, trees or rocks and you're predestined to find a shape like a face, animal or familiar object. the problem is this: when we look at enough random data we'll find a pattern to our liking and at the same time discarding plenty of valid results that just don't fit this pattern.