The picture below is an interesting summary of the general trend in computing over the last 100 years or so (MIPS = Million Instructions Per Second).
There is, however, some gap in logic here, and it frequently occurs when projections are made: an expectation that the quality will follow the quantity. But, this is not necessarily the case.
Just look at the picture above, for example: it clearly suggests that the computing power of 2015-2020 systems is greater than that of, say, a spider. But, does it mean that a modern computer can be as intelligent as a simple spider? I really doubt it.
Can we build an autonomous device today that will simulate the very basic things that a spider can do with its tiny brain and its total lack of knowledge in math, algorithms, language, gravity, genetics, etc?
Can we build a device that will know that it needs to run around and search for food, recognize its predators and hide from them, use those tiny cracks and holes to actually hide from the danger, from bright light (at night), from moving shadows, from moving water streams? Can it be also programmed to build those beautiful spider webs and wait quietly in the corner for the unsuspecting butterfly? And then wrap it in the web and put aside for later? And, ultimately, look for a mate and procreate?
And, while doing all of the above, learn new things, new dangers, new traps, new tricks, find new sources of food and water, and new places to sleep and hide?
I doubt this is possible today even if the most powerful computer is used and the latest AI/ML algorithms are applied. That’s because power doesn’t equal smarts. Power is just power.
I think that one day we will be able to do all of the above at ease. But we should stop measuring our abilities to be smart by using our abilities to be strong. This is why even the best chess program today is as dumb in the real world as the worst chess program from 1990…
Research firm Tractica forecasts that,
“The revenue generated from the direct and indirect application of AI software is estimated to grow from $643.7 million in 2016 to $36.8 billion by 2025.
This represents a significant growth curve for the 9-year period with a compound annual growth rate (CAGR) of 56.8%.“
They specifically mention, and I agree with this, that
“AI is seen as a black box that is able to process large amounts of data at scale, able to identify anomalies, predict future outcomes, or better optimize processes.”
There are some complications related to the “black box” approach, which gave the name to this blog (read here). However, that is not going to stop the growth of AI business and technology.
An interesting chart below summarizes the Top-10 cases of AI business applications, and makes the revenue projection for each case for 2025:
Notice how much money is projected for the Contract analytics ($750M or so per year) and for Algorithmic Trading (close to $2.5B!). And the Predictive maintenance field will generate about $1.4B in revenue by then.
There will be lots of business opportunities for those working in the field of AI in the future.