When I started writing about Artificial Intelligence, I was convinced that AI would more disruptive to our economy and to the stock market than most people were assuming. The more I read and research the subject, the more I believe this to be true. And it is true even if there is a secret sauce that is required for computers to truly think, and we can’t discover that sauce. I don’t believe there is such a magic sauce, but no one knows for sure.
But let’s look at the effect of Artificial Narrow Intelligence (ANI), which is already being applied in many areas like robotics and driving assists. Many of the things we do as a profession have a large complement of repeatability and can be done without mechanical assistance of advanced robots or other mechanical devices. Several of those fields are teaching, accounting, law, and medical diagnostics to name a few. In the US, there are roughly 3.5 million teachers, 1.3 million accountants, 1.2 million lawyers, and 1.0 million doctors. This is a total of 7 million people in these four professions. If AI reduces the number of people required in these four fields by 50%, this would force 3.5 million people into other professions, or put them out of work. That would be disruptive! The recent Bureau of Labor Statistics shows the number of unemployed in the US at 8 million. That would jump to 11.5 million with the additional people out of work, even though there would be some trickle-down effect on who ends up actually being unemployed. And the total number would be higher when other jobs indirectly affected are included.
Can ANI really reduce needed employment in these fields by 50%? Let’s look at the teaching field in more detail, using perhaps a second grade student learning to read as an example. There is a lot of diversity in reading skills; actually more than current teachers can effectively handle in a normal sized classroom. But let’s assume each child can go into his own cubicle with its own computer. The child verbally signs on, and the computer asks what teacher he or she would like. He has his choice of a super hero, cartoon character, a male or female teacher, etc. The teacher choice will appear on the screen and talk to the student using the appropriate voice, addressing the child personally by name. The computer will have in its data base a complete history of the child’s background and reading level including any issues like difficulty in understanding reading material, speech impediment, attention deficit, and so on. In the computer’s memory would be appropriate actions for all these issues that have come from reading experts but then further refined by actual past issues with this specific student.
The child would start to read an appropriate book on something like a Kindle rather than the computer screen. The student can ask for help with any word, and the computer will monitor reading speed and periodically interrupt to “discuss” the book with the child to assure that the child understands what he is reading. At any point, the computer could choose to increase the difficulty of the book being read, or take it down a notch. In the discussions, the computer can also be monitoring speech impediments or other things that might have to be flagged for a real teacher.
Note that current computers, sensors, speech capability, and algorithms are capable of doing all the above, and work in these areas is already ongoing. It hasn’t reached the level described above, but it is just a matter of time; probably just a few years. It will then take some time to implement. But since these systems will be better than any traditional classroom, it will be financed through private schools and elite school districts. So even though it perhaps will eventually be most valuable in poorer school districts, the development will be financed by the wealthy. Once the computers and required software is fully developed, the required investment will be manageable by ALL school districts.
A similar story can be told for the fields of accounting, law and medicine. People will still be needed, but they will be used far more effectively on the exceptions.
Disruptive AI is coming!