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We have seen that AI, which was supposed to be a failure, is coming back with gusto on the back of multi-sensor robotics and possibly androids with quantum computers.
There is some mysterious counter-intuitive behaviour in the subatomic world which seems to suggest inanimate objects do sense the external world. It would be nice to know the views of experts from different fields, such as math, science, psychology, sociology, cosmology on this intriguing subject.
One possibility that comes to mind is that probability is actually such a model, as it does incorporate randomness.
It is too early to consider an AI doctor… To be honest I will evaluate both.
A recent study indicated that 53% will choose a Human doctor and the 47% an AI one.
We will see in the future what will happen!
From recent study it seems that 61% of people see artificial intelligence making the world a better place.
The biggest benefits are seen in advances in healthcare, science and traffic control and pollution forecasting.
Today due to the hacking exploits, data security is a major concern for both consumers and companies.
The sheer potential scale of AI’s reach in consumer and IoT applications makes security even more crucial.
From recent study people now are deeply concerned about security (85%) and where their data is stored in the network, be it in edge devices or the cloud.
Some Machine Learning methods thanks to math are able to produce predictable results, enabling us to understand exactly what these AIs can do.
However, most practical system models due to their high non linearity are unpredictable by the ordinary math, because they are so complex and they may use randomness within their algorithms. An example is to forecast the industrial pollution from historical data.
So from one side we do not have mathematics to predict the capabilities of a new AI, but from another side we do have mathematics that tells us about the limits of computation.
Thanks to Alan Turing. who invented theoretical computer science, we know that there is a limit where we can never predict if any arbitrary algorithm (including an AI) will ever halt in its calculations or not (Turing, 1937).
We also can consider “No Free Lunch Theorem” which tells us there is no algorithm that will outperform all others for all problems – in other words this means that we need a new AI algorithm tailored for each new problem if we want the most effective intelligence (Wolpert, 1996; Wolpert and Macready, 1997).
We even have Rice’s Theorem which tells us that it is impossible for one algorithm to debug another algorithm perfectly – which means that, even if an AI can modify itself, it will never be able to tell if the modification works for all cases without empirical testing (Rice, 1953).