AI - 1.04 - history - patterns

AI - 1.04 - history - patterns

Another area of artificial intelligence research is in regard to pattern recognition.  Human beings are very good at recognizing patterns.  Human beings are also very good at seeing patterns which they have not seen before, and recognizing that they are patterns.  Computers are no good at recognizing patterns at all.  Computers will identify an exact match, but they have great difficulty in recognizing two items as being similar in any way, if they are not identical.  (I tend to tell people that computers are bad at pattern recognition because they have no natural predators.  Human beings got very good at recognizing patterns while watching for sabretooth tigers hidden in tall grass.  The human beings that didn't recognize patterns quickly, didn't survive.)

Pattern recognition is very important when we want to get computers to see something.  Computer vision is an area that we have been working on for a great many years, indeed, a number of decades, and we still haven't got it completely right.  Human children are very adept at recognizing patterns, and do it all the time.  My grandson's first word was "clock," and he was very good at recognizing all kinds of different clocks, and identifying them as clocks.  There was one clock that had numerals on the face, and was surrounded by a sunburst pattern.  There was another wall clock where a number of the numerals had fallen off.  It was mounted on a burl with irregular and ragged edges, but was still recognized as a clock.  Wrist watches were also recognized as clocks, including his mother's wrist watch, which had absolutely nothing on the face of it except the hands.  He recognized the pattern that made for a clock.  As I say, this was his first word.  He was probably about seven or eight months old when he started recognizing things as clocks.

Recognizing patterns is also important in speech recognition.  This is recognizing how to parse out the words in verbal speech, when we speak to computers.  This is definitely not the same as voice recognition, which we use in biometric authentication.  Recognizing words, despite different intonations, and possibly even dialects, is very important to being able to speak to computers and get them to recognize what we are saying.  Similar types of pattern recognition is involved in parsing out the words in the speech that we speak to computers, and then parsing the meaning of what we say, in regard to commands to the computer, or even just typing out the words so that we can dictate to our phones.

Interestingly, the same type of pattern recognition also comes into play when, having identified the words, we get the computer to do what we know as natural language processing, in terms of identifying what it is that we are requesting the computer to do and identifying meanings in what we say.

Going back to computer vision, we are trying to improve computer vision in order to implement driverless cars.  While computer vision is still imperfect, and we are constantly working to improve it, it is interesting to note, if you look at the actual statistics, that driverless cars are already better drivers than we are.  Yes, you will hear a number of bad news reports about a driverless car that has failed, or stalled, or hit someone, or created some kind of an accident.  The fact that these events make the news proves that driverless cars are better than we are.  Driverless cars have driven millions of miles, and there are a number of situations which are still very tricky for them, but the fact that any accident with the driverless car makes the news indicates how rare such accidents actually are.  We cannot retrofit all the existing cars on the road with driving software, and not all the cars on the road have the sensors necessary to process it, but if we did ban human drivers, and give over driving to driverless cars, we would, even at this point of development, be saving lives.

One of the areas relating to this is that of fuzzy logic.  As I have said, computers are good at finding an exact match, but very poor at finding something that is similar.  Fuzzy logic is an attempt to implement the idea of "similar" in computers.

An interesting point is that, at the same time that we are pursuing artificial intelligence with increasing vigor, we are also developing quantum computers.  Quantum computing is quite different from traditional computing, and one of the areas in which quantum computers will probably excel is in regard to pattern recognition, and the identification of items or situations which are similar.

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HCW - 5.04 - datacomm - physical

HCW - 5.04 - datacomm - physical

Whether you consider it either the bottom layer of the stack, or the top layer of the stack, the physical layer is the basis of all the communication.  However, we can't really say that we're doing data communication yet, since, at the physical layer, we just talk about signaling, not data.

This is because we aren't dealing with the communications as data, quite yet.  That's at the next layer up, or down, the data link layer.  What we do at the physical layer, is take the data that we want to transmit, and modulated into a signal.  At the other end, of course, we demodulate the signal that we receive, and extract data from it.  This is where the word modem comes from: it simply stands for the beginning of modulate and the beginning of demodulate.  Modem.

In order to modulate data into a signal, we have to know what medium we are using.  Are we using wires, cables, wi-fi, with no wires, free space lasers, or lasers on fiber optic cable?  We can send a signal on these various media.  When we think about wires, we are thinking about long distance wires.  We are generally thinking about the old type of telephone cables, which were twisted pair wires.  So, we don't think about just putting a voltage onto the wire, but, rather, sending a tone, a frequency of electrical waves, down the wire.  This has to do with physics, and what you can, actually, do in terms of signaling over wires over a long distance.

It's pretty much the same for the other types of media.  So, as mentioned previously, we can send a tone down the wire, and then we can change the signal, by turning it on or off, or using a high frequency or low frequency signal, or changing the amplitude or volume of the signal from high to low, or other things like that.  It is these changes, from high to low, or from on to off, that actually carry the data, not necessarily the tone itself.

We need one other concept, before we leave the physical layer, and that is the difference between simplex, half duplex, and full duplex communications.

Simplex is communications in one direction.  The easiest illustration of the concept of simplex is, in fact, what would be considered one of the more advanced communications technologies: that is, fiber optic cabling.  When we install fiber optic cable, in order to communicate, we will put a laser at one end of the cable, and a sensor at the other end.  This allows for communications only in one direction.  The laser does the sending, and the sensor does the receiving.  Even if we were to somehow fire a laser the wrong way down the cable, it wouldn't do us any good, because the laser, where the light beam ends up wouldn't be able to detect that anything is taking place.  It isn't a sensor.  So, if you want to have communication in both directions with fiber optic cabling, you have to have a *pair* of fiber optic cables.  At one end of cable A, you will have a laser sending, and, in the same location, you will attach a sensor to cable B.  At the other end of your cable pair, cable A will have a sensor, and cable B will have a laser.

Half duplex is a system where the media is capable of carrying communications in both directions, but only in one direction at a time.  The easiest illustration of this type of situation is the old World War II movies showing people communicating by a radio.  When you are speaking you are holding down a transmit button, and you cannot hear what is being said while you were holding down the transmit button.  When one person has finished speaking they ended their message with the word "over," meaning that their communication is finished, and they are now turning the communications channel over to the person on the other end.  That person, who has been listening up to this point, is then able to press their own transmit button, and send their message, but while they are transmitting they are not able to hear what is being said.

Full duplex is communication that can take place in both directions, all the time.  The easiest illustration for us is the telephone.  When we are having a telephone conversation, either party to the conversation can speak.  You can speak at any time, and you can interrupt the person who is talking, because they are able to hear what you are saying, even if they are speaking.  (That is, if you yell loud enough.)

The next step up in the ladder of data communications is at the data link layer.  Lots of really interesting stuff happens at the data link layer.  That is, it's very interesting if you are into the technology of data communications.  What happens at the data link layer tends to have to do with data modulation and demodulation, error correction, and a lot of determination about what is data, and what is not data, but is, rather, noise.  However, as I say, an awful lot of this is really technical.  Therefore, I assume that an awful lot of people are not going to care too terribly much about it.  So we are going to go on to networking.  Networking can also be very technical stuff, but there are some basic concepts involved in networking that are very important in terms of how computers, and data communication, really work.

How Computers Work [From the Ground Up]

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