Earliest Signs
In the 15th Century in the Netherlands, if you were a weaver, it was a manual skill you learned from your mother, and she from hers. It was a coarse-weave you created, but it was the only thing available. At least, that was so until the steam or water-powered textile loom came along and took away a large part of your livelihood.
That technological behemoth could turn out high quality, finely woven cloth by the yard…by the mile! That was “automation”—and its first big impact on human labor.
People have railed at automation in the past, and these weavers were said to have thrown their wooden shoes (sabot) into the machines in protest. It may not be true, but it could possibly be where we get the word “sabotage”.
There is a Difference
We need to make a distinction here. Automated Manufacture (AM) and Artificial Intelligence (AI) are two completely different concepts, and that must be perfectly clear.
Automation is not an intelligent process. It does eliminate some jobs (usually very difficult ones), but also creates many others. The machines must be maintained by someone, and usually more is produced, so it must be sold, shipped, and handled, requiring more people.
Artificial intelligence, on the other hand, is a simulation of the human thought process by a program designed to take unregulated input, interpret it in a human-centric context, and respond as a human would. For example:
A pair of empty paint cans come down twin conveyor belts, heading for two filling stations—one automated, the other controlled by AI. On the AM side, the can comes to a stop under a spigot when it hits a solenoid switch. The AM dispenses one gallon of paint, applies a lid, and presses it closed before sending it on to the labeling machine.
On the AI line, the conveyor is halted when a camera recognizes that a paint-can has arrived. It follows it protocols, fills the can, puts a lid on it, and sends it off to the labeling machine. The net effect is the same, however…
Let’s imagine that the cans fell over, or that they are upside down. Now what happens? The AM machine is going to pump a gallon of paint and make a mess. The AI machine will not.
It has been trained to react to problems. Maybe it has an air-powered piston that will kick the can into a “reject” container. If it is really sophisticated, it may have a system to rotate a can under its scanner until it recognizes that the can is oriented properly before proceeding with the operation. In either case, no paint was wasted, no mess was made, and the company saved money.
There are lots of examples of routine, middle-skilled jobs that involve relatively structured tasks, and those are the jobs that are being eliminated the fastest. Those kinds of jobs are easier for our friends in the artificial intelligence community to design robots to handle them. They could be software robots; they could be physical robots—Erik Brynjolfsson; American Academic, Professor /Director at MIT
AI Learning
As AI advances, it can be left on its own to experiment (either in reality or in simulations; it has no way to tell the difference). It has massive speed and infinite patience so it can try every possible combination within the parameters given by its human teacher.
After thousands and thousands of tries, it learns to group certain actions together that lead to success, and eliminate strategies that always fail. This is the way humans learn, too.
Advantages
A sufficiently indoctrinated AI is called an “Expert System”. Consider an AI programmed with all the knowledge, techniques, and strategies of the top 20 Thoracic Surgeons in the entire world. That would be great if the International Space Station had a remote Robotic Surgeon and an astronaut had appendicitis. And that’s only 250 miles. How about that same unit (dozens of copies) all over the world run from a central location, or each being discrete and autonomous?
That expertise can cover any field from auto mechanics to particle physics, but it can go a step further and freely combine the knowledge of one field with another in order to make new connections and extrapolations that humans might not make for years, centuries, or ever. The fact is that a hacker doesn’t know what an electrician knows, who doesn’t know what a gardener knows, who doesn’t know what an electronics engineer knows, and so on… Blending of knowledge could remove most of the impediments to our progress.
For example, we talk about esoteric materials—like Carbon Nanotubes—being necessary to build a “space elevator”. We require a material strong enough to support its own weight from Geo-stationary Orbit all the way down to the surface of the Earth so that a ride into space would cost between $1-3.00 per kilogram, instead of our current price of up to $20,000 per kilogram via rocket.
That’s all very noble, but it is difficult to accomplish. Scientists have been working on this for decades and still haven’t come up with an answer. An AI might be able to solve the problem in weeks or months. Maybe we already have the knowledge for anti-gravity, warp drive, or teleporters, but it is spread over dozens of fields and professions, and might never be discovered because there are so many barriers to understanding between disciplines.
Disadvantage—You’ll have to learn
Jobs like data entry and server maintenance have the potential for going the way of the dinosaur. These are easy tasks for early and basic artificial intelligence programs.
If you want to go into the field of artificial intelligence yourself, it’s going to be necessary to study things like statistics, robotics, and algebra. There are even courses of study arising in our educational institutions. Do your research to make sure that they are not just pandering and are actually offering something of value.
If you want to stick to the more familiar things, then Data Science is probably a very good bet. We’re still going to need human minds to figure out how to sort Big Data to get the most use out of it. So make sure you understand things like Ruby, Python, Hadoop, SQL, Java, and JavaScript, of course.
Jobs will go, but new ones will arise
AI will displace workers—there is no doubt—but people already working in Tech will have a relatively easy time of upgrading their skills to remain relevant. For people outside of tech, there are still opportunities.
The job of teaching AIs how to understand spoken English is falling to actual English teachers. By simply expanding their skills a tiny bit in the computer field, they suddenly become a much-desired commodity in the world of computer AI development.
If you step back and look at technology from every era, it has displaced jobs but also created a lot of jobs—Ginni Rometty; American Businesswoman, President/CEO of IBM
It’s only going to get better
AI will eventually be able to spot a cyberattack in mere microseconds, and end it. Right now, however, security is a great field to be working in. Some employees still make poor judgments like open unexpected e-mail attachments, or follow links to unvetted websites. And, as with anything new, IoT, or the Internet of Things, is exposing us to a brand new collection of vulnerabilities.
In the latter case, the camera which you have covering your inside front door so that you can see when the kids get home from school—it might be hackable and accessible to anyone in your neighborhood. The same is true for that nanny-cam you keep in the children’s bedroom “just in case”. If you can write code that delivers unhackable IoT devices so parents know that strangers aren’t watching their children, you’ll have a job for life.
The Takeaway
Knowing that change is coming, and is inevitable, is what will allow you to be prepared. Don’t wait until the last moment. If what you are doing now is likely to change substantially, start building your adaptation strategy immediately. This is all the warning you are likely to get so take advantage of it!
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