Human Artificial Intelligence and Today’s Artificial Intelligence, Part 1
What People Do
Humans – people – have incredible abilities to handle information and incredible abilities to do things as a result of information. There are no real limits to what they can do.
They can create paintings, they can create stories out of whole cloth, they can decide that a choir that sings Handel should now sing Hip Hop. They can imagine and then create computers, submarines, tea kettles or street lights. They can decide to send a birthday card with roses on it to a girl they saw only once, or decide to send her a box of chocolates from another country for no better reason than that they want to do it. And so on.
The ability of people to do things (often for no reason at all, other than that they want to) with data and activities is only limited by their imaginations. Their ability to decide what people who work for them should do is just as unlimited – they might want somebody to polish the tea kettle one day and to do the balance sheet for General Motors the following morning and take their son to summer camp the day after. Who knows what they might want others to do for them? They don’t even know themselves, from one minute what they will want done the next minute! Humans are versatile – and most of what they do is very unpredictable. In fact, people don’t even like to act in what others might think is a predictable manner.
Predictive Data Handling – Just One Facet of Human Intelligence
Despite all of that, sometimes things are predictable, and then people like to handle them in a predictable way. Accordingly, one – but only one – of the many abilities that people have to handle data is their ability to use past events to (1) control or (2) predict future events or sequences:
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Controlling future activity. Humans habitually memorize an appropriate set of rules, and corresponding sequences of procedures so that, in the future, they then use these memories to control and perform tasks that are of the same type as those of their memorized rules and procedures.
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Example: A person memorizes appropriate rules and procedures and is then able to drive a car or plane, to operate an industrial machine, to add up sums or to write on different surfaces using different writing implements.
When this same type of activity is done by a computer, it is said to be done by an “artificial intelligence”:
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Example: Software (and hardware) that is created to drive a car does exactly the same thing – it memorizes the rules and procedures needed to drive a car. Such software is termed “artificially intelligent” or AI software. But, in fact, it is just straightforward application of rules to data in a fluid manner.
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Predicting future events. Humans memorize observed repeating patterns of events and use the appearance of a part of the pattern to predict the remainder in the sequence to occur in the future.
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Example: A person memorizes several examples of a blackening sky and sudden gusts of winds being followed by rain. Later, they see a blackening sky and feel gusts of winds and predict the remainder of the sequence when they say, “Looks like it is going to rain.”
When done by a computer, this kind of behavior is also termed “artificial intelligence.”
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Example: Software that is used to predict weather is of this type. It uses gigantic amounts of data, crunches them in a supercomputer and comes out with the predicted track and wind strengths for a hurricane. Such software is said to use artificial intelligence.
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Example: “Intelligent” agents such as Siri or Cortana are also this type of electronic extrapolation technology. They do two things:
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Based on what their user did in the past, they locate or do things – almost always limited to Internet, calendar or email – that the user may want to do in the future. Limiting the area of operation to just these narrowly defined areas makes it relatively straightforward to make the computer do things such as suggest a restaurant to book when it is the anniversary of the user’s spouse.
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Intelligent agents also attempt to use this kind of pattern matching to attempt to understand what the user ordered them to do and then to try and act appropriately. The results are somewhat haphazard, despite having enormous quantities of data and supercomputers to crunch it with, and despite limiting everything to narrowly defined areas where the number of possible meanings of the words in the command are limited.
The reason that results are haphazard (despite the enormous resources employed) is because the way language really works is not known and is substituted for by brute force and ignorance. The brute force is provided by the supercomputers crunching terabytes of data, and the ignorance comes from the fact that nobody in the world (except ExoTech) has yet figured out what the real rules are of how people actually use data and language. Without those rules, a supercomputer crunching terabytes is not enough and produces only semicorrect results, but with them embodied in an ExoBrain, a smartphone and no previous data is adequate.
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Peter is the main shareholder of ExoTech Ltd and the discoverer of ExoTech. He has several patents other than ExoTech/ExoBrain to his name, one of which was bought by Oakley and marketed by Motorola. Originally trained in science and medicine, Peter spent many years in various executive positions in a major non-profit organization.
Peter has a proven ability to go into situations he has never met before and walk away with the market. For example, with no prior experience in distribution, he built a national distribution system in two countries and went from 2 to 1,600 staff, with 60 wholesale installations and $320 million a year turnover, all within 18 months and with only a $50,000 investment. He changed a national tax law to his advantage in only six weeks and captured an 80 percent market share in nine months in both markets.