Why Human Intelligence is not Artificial Intelligence

Hype

The whole hype and hysteria about Artificial Intelligence (AI)  began  in 1950s . This blog is an  'aha" blog after reading an article in The MIT Reader. It gave me the clear words to express what I always knew - AI is not a replacement  for Human Intelligence

This article is adapted from Herbert Roitblat’s book
“Algorithms Are Not Enough: Creating General Artificial Intelligence.”

In the lasts seventy years, we thought it will be possible to soon create a machine that was capable of matching the full scope and level of achievement of human intelligence has been greeted with equal amounts of hype and hysteria. We’ve now succeeded in creating machines that can solve specific fairly narrow problems — “smart” machines that can diagnose disease, drive cars, understand speech, and beat us at chess — but general intelligence remains elusive.

Let’s get this out of the way: Improvements in machine intelligence will not lead to runaway machine-led revolutions. They may change the kind of jobs that people do, but they will not spell the end of human existence. There will be no robo-apocalypse.

AI is for problems that have a clear goal and a set number of possible solutions. But we humans are creative, irrational, and inconsistent.

 We humans may sometimes behave like computers, but more often, we are creative, irrational, and not always too bright.

Insight

Insight problems generally cannot be solved by a step-by-step procedure, like an algorithm, or if they can, the process is extremely tedious. Instead, insight problems are characterized by a kind of restructuring of the solver’s approach to the problem. 

One example

Here is a sequence of four numbers: 8, 5, 4, 9. Predict the next numbers in this sequence.

If you’re having trouble, try writing out the names of digits in English:

Eight five four nine

The correct answer is 1, 7, 6.

The full sequence is:

Eight five four nine one seven six three two zero.

They are listed in alphabetical order of their English names. The usual representation of the series as digits ordered numerically must be replaced by a representation in which the English names are ordered alphabetically.

The two strings problem

The two-strings problem, which was studied by the experimental psychologist Norman Raymond Frederick Maier in 1931. Imagine you are in a room with two strings hanging from the ceiling. Your task is to tie them together. In the room with you and the strings are a table, a wrench, a screwdriver, and a lighter. The strings are far enough apart that you cannot reach them both at the same time. How can these strings be tied together?

The string problem can be solved by using one of the tools as a weight at the end of one of the strings so that you can swing it and catch it while holding the other string. The insight is the recognition that the screwdriver can be used not just to turn screws but also as a pendulum weight.

People do not always behave in the systematic ways suggested by logical thought. These deviations are not glitches or bugs in human thought but essential features that enable human intelligence. 

Quirks of Human Intelligence

"Psychologists have found that people make different choices when presented with the same alternatives, depending on how the alternatives are described.  

"We have a complexity to our thinking and intellectual processes that is not always in our favor. We jump to conclusions. We are more easily persuaded by arguments that we prefer to be true or that are presented in one context or another. We do sometimes behave like computers, but more often, we are sloppy and inconsistent."

Hardly a day goes by without a call for some kind of regulation of artificial intelligence, either because it is too stupid (for example, face recognition) or imminently too intelligent to be trusted. But good policy requires a realistic view of what the actual capabilities of computers are and what they have the potential to become. If all that is necessary for a machine learning system is to engage its analytic capabilities, then the machine is likely to exceed the capabilities of humans solving similar problems. Analytic problem solving is directly applicable to systems that gain their capabilities through optimization of a set of parameters. On the other hand, if the problem requires divergent thinking, commonsense knowledge, or creativity, then computers will continue to lag behind humans for some time.

Decisions are made by humans  

Digitalization is very complex and is almost impossible to manage and finetune without an observability application like for example Splunk.

These observability apps vary significantly the results depending on (1) being a beginner, (2 an  intermediary, or (3) an experienced team of users, and C level executives

Observability shows, once again, that humans are responsible for the best decisions of a #digitaltransformationplatform. Not machines, not AI algorithms, ML, HPC. Observability depends upon the experience of the most suited executives, who have a clarity never possible before. 

Humans may make errors, but they bring sentiments and feelings machines don't have.  Humans, not machines can bring  the irrational fascination Apple computer has upon ordinary people

Telepathy

Everyone who reads this blog and feels a "click",  shows a spiritual osmosis that unites us and a telepathic relief that we are not alone. It is impossible for AI to tell us what to do.

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