Things are moving at a very fast pace and people do not realize it. I understand there are many who contest this idea, claiming that the pace of technological advancement really has not increased over the last few decades.
One of the reasons I fail to buy into this idea is the power of the hivemind. When we look at the age of computing, we see things happening on a different scale as compared to our analog world.
To start, computers get faster, more powerful, and have increases in storage. This is something that I do not think anyone contests. Yes we can debate whether Moore's law is slowing down or not, but the fact that it is still showing increases. In other words, we are not going backwards.
At the same time, networks are more powerful than ever. This is essential when looking at the realm of machine learning. Artificial intelligence is going to radically change the world even if it never achieves full AGI (which is a worthy debate). The sheer power and speed is going to make it impossible for humans to keep up.
It is also going to send productivity through the roof.
Computer systems have a distinct advantage over humans. This is something that companies are beginning to leverage.
Let us look at autonomous driving technology. I will use Tesla as an example.
Each time a Tesla vehicle learns something new, that is transmitted to every vehicle across the entire fleet. Each of the near 700K Teslas on the road, upon update, instantly have the "experience" of driving that curve. A particular vehicle is not aware that it was never in Des Moines and didn't drive that road. The "brains" of the system did and that is all that is important.
The same holds true for robotics on a manufacturing floor. If one robot "learns" something, it is spread to each and every one of them. This hivemind approach makes the entire system smarter with each incremental breakthrough.
With humans, it is a different story. If one human learns a particular skill with the job, that is mostly lost if he or she departs. When someone else enters, that individual needs to be retrained. At the same time, someone further down the assembly line might not have the same knowledge.
Consider what happens if a robot needs replacing. When the new one is put in place, the software is updates and, presto, the replacement knows everything its predecessor knew.
This takes on additional meaning when we look at a field like surgery. How many surgeries does an individual surgeon performance over the course of his or her life? In a particular field, it can run into the thousands.
Obviously, it stands to reason the surgeon who did thousands of surgeries is much better than the one who performed a couple dozen. But how does one go from the later to the former? The answer is evident: by doing more surgeries.
Which surgeon do you want? I think we all would sign up for the one who is more experienced. Certainly, we would not opt to be the test case dummy for the new surgeon just learning the ropes.
With robotic surgeons this is not a problem. Each surgery performed is done by the entire system. Hence, if we have 2 surgeries a day, in 100 different hospitals, the "surgeon" is performing a couple hundred a day. Thus, there will be more surgeries performed in under a month than a 30 years veteran.
Once again, if a robotic surgeon needs replacing, the new one is immediately as skilled as the previous one. What is a 30 year veteran in a particular surgery replaced with upon retirement? Most likely someone less experienced.
This concept is spread among all different industries and categories of "learning". Hivemind is ultra powerful since it taps into the knowledge of the entire ecosystem. Humans tend to be limited to incremental knowledge by one or, perhaps, a couple people.
Computer networks do not operate in this manner. Incremental knowledge is multiplied by each node on the system. When it is utilizing machine learning, we can see the exponential nature of things.
Here we see where, while still rather "dumb" by many standards, AI systems have the ability to close the gap quickly. Even if we presume that these system will be specialized in nature, group enough of them together and it provides a virtual AGI system.
Think about a classroom. The teacher has the knowledge and is passing it on to the students. A big part of the challenge is everyone grasps the material in varying degrees. This is why test scores are all over the place. Some have to go back and review the material, hence lengthening the time required to grasp it.
With artificial hivemind systems, the material that the "teacher" has is immediately learned by the "students". The time is instant, based only upon the upload of the data, plus it is 100%. Each student learns the material perfectly and aces every test.
What makes it even crazier is that the class is never taught again. Not only does the existing students ace the material instantly, every class that comes behind automatically knows the material.
There is no need to reteach it since the knowledge is there.
This is what we are seeing taking place across the board. At present, the systems are not too advanced compared to the human knowledge base but, in many areas, it is catching up.
We are going to see massive changes in the business world as computer based systems expand their abilities. The "learning" process is ideal even if it has to come from brute force and repetition. Once the lesson is master, it is across the board and a base part of the system.
Humans simply cannot match that.
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