What is the Purpose of your Job ? Why AI might not replace you.

History is our guide to introspect the idea: "Correlation (Rise of AI, loss of our job)"

I have always been curious about this idea of the rise of a new industry, artificial intelligence, and the fear of losing our job. What is the correlation between them, and whether this correlation also signals the causation? I have heard Jensen talk about the idea, and it quietly resonates with my school of thought.

Lets go back in time: So one of the predictions from uh Geoffrey Hinton(Godfather of AI) who who started the whole deep learning phenomenon. Hinton, An incredible researcher, professor at the University of Toronto, and he invented the idea of back propagation.
Backpropagation allows the neural networks to learn. Hold on to that idea and let us circle back to how people used to write software. Historically, software was written when humans applied first principles thinking. They described an algorithm, a step-by-step approach to do a functionality, and then they codified the whole algorithm or steps or procedures, and the package of the code was called software, simple like a recipe book. Now, getting back to Geoff Hinton. If we think about AI, this invention of artificial intelligence and deep learning, we have to talk about neural networks. Neural networks have a lot of neurons, also simply called math units. You can think of them like a switchboard, and we connect all of these math units together. These math units take in some input, let’s say the image of a cat, and produce or guess what the output might be. So these mathematical units that we described as neurons fire and wire together to produce or guess the output as a cat. All the other signals, let’s say, there is a signal that also guesses a dog, and there is one more signal that guesses an elephant and maybe a tiger. All of these other signals tune or switch to zero when it shows the image as a cat.

Now, this mathematical unit switchboard is very gigantic. The more information you give it, the bigger the switchboard has to be to process the information and guess the output and to be accurate.

The first time when this mathematical unit-based switchboard sees the image of a cat, it produces garbage output. You need to keep on showing it a lot of examples of cats so that there comes a stage where it produces meaningful output, which is called a cat. What Geoff Hinton discovered is the idea of back propagation. While you keep on showing all of these examples of a cat, the output transitions from garbage to guessing a cat nicely. At the back of the envelope, these mathematical units are tuning the signals up and down in order to guess the cat correctly, and they are turning down the signals for dog, for elephant, for tiger.

That is the foundation of artificial intelligence, a piece of software that can learn from given examples. That is basically what we call machine learning, a machine that has the capability to learn. Geoff Hinton discovered this process of learning, which is back propagation. Deep learning is when we move on to the complex switchboards, which is nothing but neural networks.
So one of the first big commercialized applications was image recognition, and the most important image recognition application is radiology. He predicted that about five years ago that in four to five years the world will not need any radiologist.
Because AI would have swept the whole field, but it turns out that AI has swept the whole field and every radiologist is using AI in some way. What is ironic, though, is that the number of radiologists has actually grown. Now the question is why? It’s a very interesting question. Why have the radiologists grown?

The prediction stated that the entire profession of radiologists will be wiped out, but why did we need more.


And the reason for that, if you think deeply, is because the purpose of a radiologist is to diagnose disease, not only to study the image as a task. The image studying is simply one such task in service of diagnosing the disease. Now that we can study more images quickly and more precisely without ever making a mistake, you don’t get tired easily and you can study more images. You can study it in its 3D form instead of 2D, because the AI doesn’t care whether you have given it 3D pixels or 2D pixels. You could also study it in 4D so you can study images better, which a single radiologist could not easily do.

And so the number of tests that people are able to do has increased because they are able to serve more patients. The hospital has just gotten better with more clients, with better radiologists, with more patients. As a result, the overall productivity of the system has increased and produced better economics. When the hospital has better economics, they also tend to hire more radiologists. Because the sole purpose of the job of a radiologist is not only to study images, their purpose is to diagnose a disease.

And so the overarching question that we are leading up to is: what is the purpose of a job? What is the purpose of a doctor? What is the purpose of a lawyer? What is the purpose of a radiologist? Has the purpose really changed?

There is one more question that I would like to introspect. What if my car becomes self-driving? Will all chauffeurs be out of jobs? The answer probably is no, because for some people chauffeurs are a service. For some people, chauffeurs are a part of the hospitality experience. For some people, chauffeurs are there to protect the car owner.
So the Chauffeur that would lose their jobs would be a few, and many Chauffeur would also change their jobs. Now, the ones who are losing it, their sole purpose as a chauffeur was to drive the car for the car owner.

If our job is plain, simple automation, if our job is the task, if our job is only a precise, specific task, then the possibility of losing the job is very high. But if our job is more than a task, just like the shuffle, then we might not get replaced by AI.

We are also seeing what Elon Musk is working on. He is working on machines that make machines and robots. When that happens and when everything goes into production, a whole new industry of technicians and people who have to manufacture these robots will rise. The productivity of Elon’s system will increase, and the number of jobs that never existed will rise up. We are going to have a whole new industry of people taking care of these robots with respect to manufacturing and with respect to maintenance.

Now there would also be some people who would come up with a company that manufactures Apple for robots, because people would want their robot to look different than other people’s robot. This phenomenon will give rise to a whole new industry that never existed.