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I wanted to put all of my criticisms of AI as a potential replacement for humans into one place so people can understand how I evaluate new information about this space, and hopefully be able to critically evaluate things themselves.
# What is AI?
There is no agreed upon definition of intelligence, and thus there is no rigorous definition of the artificial variety. Artificial intelligence is a marketing term coined by Assistant Professor John McCarthy in the 1950s in an <a target="_blank" href="https://www.ie.edu/insights/articles/why-the-term-artificial-intelligence-is-misleading/#:~:text=The%20computer%20scientist%20John%20McCarthy,attract%20funding%20and%20renowned%20experts." color="blue">attempt to "zazz up" his research funding requests.</a> Vaguely, this area of research aims to develop computer systems that can learn, reason, perceive the external world and respond to it, pursue goals, etc. It essentially aims to recreate the human (which would make the creators a "god" of sorts).
I think a better way to define AI is based on what researchers are doing in the field, not what their aim happens to be. In that regard, most of the research involves developing algorithms to optimize some objective function. Imagine you are standing in a valley, surrounded by hills and mountains. Your task is to find the tallest peak. Now you may think this is simple: just look for the highest point and walk to it. Once you get there, look for yet higher points and then walk there. Keep going until you don't see any higher points. Or even better, just look at the map on your phone, locate the highest point, and walk directly to it.
This is great if you have an easy way to measure the landscape: your eyes, your phone map, etc. But this requires two things: 1) a way to acquire lots of data about the landscape (your eyes), and 2) the capacity to store and read all of the data. Further, you also need an efficient strategy to get from where you are to your objective. In practice, accomplishing these 3 things in a general sense is very hard to do.
So AI is the field of optimizing the traversal through a parameter space to reach an optimum. This optimum can be a wide variety of things: searching for websites, assembling a car, getting driving directions, etc. In this sense, the <i>applications</i> of AI are unbelievably broad, making it a hard field to talk about. AI means something different to many different people, but at the core is task optimization.
So to recap, to accomplish efficient task optimization you need:
1. The ability to acquire information about the parameter space
1. The capacity to store and read all of the data
1. Efficient and robust strategies to get to your objective
# Statistics Disguised as Intelligence
With few exceptions, most of the AI strategies to accomplish the 3 task optimization requirements are statistical (expert systems were a notable exception, using large if-then statement algorithms).
Although statistics are great at giving you uncertainty information about a prediction, they are expensive (require a lot of data) and require a strong "expected" behavior, or average.
Think of a <a target="_blank" href="https://en.wikipedia.org/wiki/Normal_distribution" color="blue">normal distribution</a>, which measures the probability that some outcome will occur. The peak is the most probable event, which in this case is both the median and the average value. Given no other information, you would expect to get that value most of the time. Given the shape of the curve, you can also predict the probability that less common outcomes will occur. The key here is that, given the model, you can <i>predict</i> a range of outcomes for a given event.
This brings me to my first criticism of statistics as AI:
## <b>Criticism 1: If the future is not normally distributed, statistics will never be able to predict it.</b>
I'm not sure how to hammer this point home harder. This is the assumption on which the entire AI field currently rests. It's the foundation of sand, or the rug waiting to be pulled. There is no universal rule that requires that the future be statistically predictable, and in fact, lots of evidence exists to suggest it is not.
A great place to look is in the financial markets. People have been trying to apply statistics to accurately predict market returns for decades. One universal truth is that it rarely works, and when it does, it doesn't work for very long.