Do You Want A Peek Into Sexy Technology?
Prof. Kumar David
A friendly introduction to Artificial Intelligence and Quantum Computing: Do you want a peek into sexy technology?
Though I am pretty sure the majority of my readers are normal folk, not pain-in-the-butt science and technology buffs, I am also sure they would like a peek into new-wave technologies that are taking the world by storm. I am taking a risk today in introducing two avant grade domains without dumbing down, while at the same time trying to keep it informative. AI has taken-off; it’s a big deal in medical diagnostics, defence, self-drive cars, code-breaking, language processing, computer vision and robotics. Quantum Computing (QC) is still is in its infancy and promises a colossal increase in speed but cannot solve problems intrinsically beyond the reach of ordinary computers. There are a few QCs in existence in research laboratories but none in real world scientific, industrial or commercial application.
Artificial Intelligence
A physician suspecting jaundice palpates a patient’s liver and feels its texture, he peers into the eyes and forms an opinion about how yellow the white of the eye (sclera) is. If that’s all the information available to him he uses his experience to arrive at an opinion (diagnosis). Stick with this simple example for the moment. The weighting (relative importance) he gives the two inputs is not the same, he weighs them in his mind, mulls them over and relies on experience to arrive at a conclusion. AI tries to reproduce this in a machine. Say the two inputs in Fig.1 are the texture and the colour, the thickness of the lines on the left represent the weight given to each “measurement” – its importance. Thick lines indicate larger weight, greater importance. Maybe palpation-texture gets a big weight, colour is given less importance. The “hidden layer” is an intermediate staging point in computer processing, it’s a mixing pot; it does not denote any known feature of human thinking. Intermediate “hidden” layer data is combined again (by weights denoted again by the thickness of the lines on the right) and added to give the “output” layer, the final opinion. If by some magic we get all the weights perfect this output layer result (opinion) will be the same as the doctor’s diagnosis (“I am 70% sure this guy has hepatitis”). 
In the real world doctors work with not two, but dozens of inputs (CT scans, X-ray images, blood test reports and so on). Therefore the input layer consists of not two but numerous measurements turned into numbers by the physician (“This patent’s stomach pain is serious; in my opinion its significance is a 90 on a 0 to 100 scale of gravity). Fig. 2 shows seven data inputs to be taken into account.
The second and by far the bigger challenge is how to get the weights right (the way the inputs are combined) to arrive at the same conclusion as the expert. That is how to combine things in the same proportion as the expert. The way this is done is called learning or machine learning. Let’s start by setting all the weights at random. Obviously the output will be all wrong; it won’t be anything like what the expert opines. Ha, but now let’s modify the weights (arrow thickness) in the step by step way that a motor-mechanic tunes the timing of a motorcar engine to improve its performance.
These AI chaps don’t stop there, they take Inputs (data) and Outputs (diagnoses) from thousands of clinics and hundreds of doctors and keep “tuning” the weights till they get what the doctors (plural) say; that’s the best setting of all the weights considered together. This is not done at random, it is done by a systematic procedure called a learning algorithm which tells you which weights to adjust and by how much, in a step by step iterative procedure. You go round and round, systematically, till you get no further improvement. Then tuning the Artificial Neural Network (ANN) is said to have converged; the expert system has converged to what the doctors say. Fig.2 shows an ANN with seven inputs, three internal or hidden layers and twelve outputs (Hepatitis? What’s the cancer risk? Cirrhosis? Etc.). In some ways it’s better than a doctor because the ANN network stays consistent, always the same, it is unlikely to have gone out on a binge on Saturday night and acquired a debauched hangover. But it does not have bedside manner and needs to be reprogrammed regularly as medical expertise advances.
You can appreciate how the same concepts can be used in other fields. Hundreds of sensors and detectors feed data to the ANN of a self-driving car tuned to behave like a driver, including responding to glimpses seen through the corner of an eye. (In addition, self-drives have fast response emergency devices which are not part of the ANN). Intelligent networks can decide as soon as Kim fires a rocket whether it’s aimed at Trump’s Mar-a-Lago fleshpot and whether it’s nuclear tipped. From knowledge of a criminal’s past an ANN programme may be able to predict what he may do next. The applications are vast.
Quantum Computing
This is a bit more difficult because you must leave common sense behind and enter into a queer world of phantasmagoria. A world in which a cat can be dead and alive at the same time. No, no, not a sick cat; a sprightly lively and a dead as a doornail, at the same time, cat. Nuts! Don’t blame me, it’s the quantum chaps. This droll notion has a name, Schrodinger Wave Function – if you want to show off.
Computers (non-QC sensible ones) store information in a string of bits; each bit has a definite value of 0 or 1. Maybe 0 is a transistor off and 1 is when it is on; or maybe when a microscopic element is magnetised or not magnetised. It doesn’t matter whether it’s black-white, or male-female, as long as the states are clear and distinct. Imagine an array X consisting of two bits; so the array can read 00, 01, 10 or 11 but only one at a time. Let’s assign numerical meaning to these four states – say they stand for the three decimal numbers (ordinary numbers) 0, 1, 2 and 3. Now let’s get another two-bit array Y. Set array X to 01 and Y to 10. Now do a calculation; add X and Y and hey presto it reads 11. Recall our decimals number assignments of a moment ago; you have added 1 to 2 and got 3. Genius! You have designed and used your first digital computer. 