A.I.

or

Asinine Intelligence

or

Artificial Ignorance

What are words? What is language? Starting with questions, I don’t like it, but it’s necessary in this case to establish a clear definition of each concept.

In basic terms, words are spoken or written units of meaning, a sign or symbol that stand for an object (cat), an action or state of being (run or feel), a notion (fast). Language is putting words together in a way that communicates something more than single units. It forms complex structures of thought, “The cat runs fast.”

I know, that’s obvious to a human who’s been listening to language since birth, and speaking it not long after. It’s not so obvious when we try to get a computer to use words and construct language.

Computers don’t think or learn the way humans do. In fact, computers neither think nor learn. Computers don’t have the necessary sensory input and feedback mechanisms to understand what any word means, and certainly no ability for stringing words together into language. Without the understanding of word meaning or how to form complete thoughts, there is no language. Dogs are smarter than the most powerful computer in the world. Rover knows the meaning of many words that he hears daily because they correspond to his experience of the world. These connections between the sound of the word and the phenomenal experience creates meaning in a dog’s brain. Computers have no sensory input to link experience with the associated words to create meaning. Think about how reading a story can stimulate in one’s brain an imaginary experience, or conversely, how we can recall a past experience and recreate it with language.

It’s a huge challenge to get computers to do anything with language. To start with, they need to be fed immense quantities of data, words and phrases, along with instructions for how to assemble sentences grammatically. They’re given massive amounts of statistical information on the likelihood of word sequence. Large Language Models (LLM) then get “trained” in part from being corrected by humans, in part by self-correcting algorithms. They’ve also been programmed to “learn” by searching the internet to collect samples for reference, for imitation, or to copy verbatim. Then with brute force processing power they crank out a semblance of language that on occasion sounds much like a real person may have spoken or written it. The major difference is that a human knows the meaning of its output, the computer doesn’t know dog shit from chocolate. There’re still more complications. To avoid being too real, certain words and phrases must be censored. Some special words, such as shit, that we all use in ordinary, everyday language are not allowed. Some phrases, like “go get fucked,” are out of line. Some ideas, insert your favorite ism, deemed wrong get nixed. We wouldn’t want our computers to be offensive.

But for all their training, learning, self-censoring and processing power, they’re totally lost with irony, satire, double meaning, with the subtleties of implication. Real humans put more into language than strictly literal meaning. We use words in ways computers can’t fathom. The way words sound, their rhyme, rhythm, assonance, alliteration—computers haven’t a clue. Consequently, computer Generative Predictive Training (the GPT in ChatGPT) tends towards bland homogenous writing. It’s writing without style, without cleverness, without conviction. It’s not intelligence. It’s barely smarter than a toaster, if the word smart actually means anything in this context.

GPT models keep growing, keep using more processing power, and yet, they cannot improve over time no matter how much power, training, or correction is pumped into them. The weaknesses of LLM and GPT are cumulative as they continue to indiscriminately scrape the internet sucking up an ever increasing amount of their own mediocre output—mediocrity squared—not a good recipe. For all the previously stated reasons, it’s impossible for these programs to improve. The spiral down is a result of the lack of understanding of words and language, by the computers, and by the programmers. There is no future for computers to write anything with insight, complexity, or competence.

Now, I’m going to spiral down with even more questions. Who really believes A.I. is smart? Why is so much time and energy being wasted on making computers do what people already do fairly well, and almost effortlessly? What’s the point of trying to make computers more like humans (emotional/irrational)? We have over eight billions of us already, ain’t that ‘nuff?

Understand more about : LLM & GPT

Too much hype makes me suspicious : AI is Overrated

More reasons for the decline : Degeneration

Here’s a little more confirmation bias : Futurism

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Right Here, Right Now

nowism

The future of humanity, a topic of continual discussion by the movers & shakers, the big thinkers and think tankers, the thinkerless and cantankerous. Scientists, sociologists and politicos all believe our problems are numerous. They believe that to solve our problems we need to look to the future, that our sights and policies are not long term enough to insure our long term survival.

Here again, a contrarian view will be presented. Our looking to the future is a bad habit. Humans have been doing it for millennia. The policy has failed. It failed in Ancient China. It failed in Egypt. It failed in Rome, and it continues to fail today on an ever grander scale. You may disagree. You may reply, “That isn’t logical. If we concern ourselves with making a better future, the future will get better.” Yes, superficially obvious and seemingly logical. The logic is based on an assumption grounded on a single condition : prediction. If one can see the future by accurately extrapolating current conditions into the next few years or decades, then it should be possible to take the necessary actions to make a better future than the one we’re blindly heading.

There’s the downfall. Our ability to predict the future is flawed. In the past the flaws were ignorance and superstition. Today the flaws are arrogance, hubris and denial. We refuse to admit that the future is impossible to predict without infinite knowledge of current conditions. There are too many variables to take into account. Our inability to make accurate predictions means we can’t make the appropriate plans for improving the future.

All of today’s big issues started decades, centuries, or millennia ago. All were ignored or assumed inevitable. All were left for some future time to find a solution. All have progressed to proportions that, now, require heroic efforts to even mediate. No matter how much technology progresses, we are so far behind, that our future, if one looks at our past performance along with current behavior, appears bleak. That’s not a detailed prediction, or much of a prediction of any kind, but it is certainly based on the human track record. Most of all, it doesn’t rely on a crystal ball, the I Ching, divining rods, clairvoyants, tarot cards, or Artificial Ignorance.

Despite the poor record, there have been a number of prescient people who had good, solid, longterm outlooks based on simple extrapolation of the trailing practices. Those prognosticators have not necessarily claimed their projections to be precise predictions, but as warnings to where we’re headed if we continued on the same path. They’ve made a stir; they’ve been naysayed; they’ve been forgotten; and just as with our immediate problems, put off. Interesting, isn’t it? Even when the outlooks are on target, or in the vicinity, they’re useless if no one listens and takes action.(1)

That doesn’t mean we can’t head in a better direction. If, instead, humans were to take on a short term focus, and ask, “What needs improvement here and now?” Ask, “What can we do to make today better?” Take action now on the immediate problems for immediate results. We would see real progress in real time, not some imaginary future. We’d have problems solved before they balloon in the future, before they escalate into problems bigger than we have the resources to solve.

A policy of fix it now, doesn’t require special people, or psychics, or multimillion dollar studies, and certainly not super computers. It only requires a little thought, and a little consideration, and a quick glance around us to see what little changes could be done to make today a little better. No reforms, no revolutions, no reinventing the wheel. Simple, little, baby-steps to improve the now.

Afterthought : I have come up against, once more, the difficulty with definitions. I’ve struggled with selfishness. I’ve struggled with individualism. I’ve found subtle and important distinctions in what these words mean and how these words are understood. After writing this post, I ooogled “short-termism” not expecting to find much, if anything. I found that it’s not something I’ve made up. No, there are dozens of articles talking about the perils of short-termism. I realized that my conception of short-termism, and its value, is quite divergent from the way those other articles speak of it. The common conception of short-termism is understood as an inconsiderate “grab all you can, devil may care” attitude. This got me to change the original subtitle of this post from “short-termism” to “nowism.” Again again, I found numerous articles about “nowism” and, of course, with a different definition from mine. Those others concentrating on mysticism, religion, and positive thinking with a splash of mindfulness. No and no. Those definitions are, as usual, shallow, trite, and off the mark. They are narrow minded. As usual, they’re not looking closely enough. They’re not examining alternate angles. By just taking a quick glance, broader views of meaning are not being considered, nor understood. Ignoring consequences of any action, near-term or far-term, self-centered or altruistic, is always missing something.

One-sided approaches never lead to good solutions. Evaluating options and analyzing multiple viewpoints takes time. It takes examined thought. Snap judgements and formulaic answers are what computers do. Real intelligence is slow, methodical, and creative.(3)

(1)Examples of note : First, The Silent Spring, Rachel Carson, 1962. Second, The Population Bomb, Paul R. & Anne H. Ehrlich, 1968. The Population Bomb is included as an example with reservation because of his fanaticism which has overshadowed the core of his argument; that overpopulation and its continued growth is the root cause of all our global issues. Third, The Limits to Growth, Club of Rome, 1972. This last one is the hardest hitting and most comprehensive combining several additional factors beyond environmental degradation and population. All three of these, among others, were published at a time when correcting course would have been far easier and more effective for securing the future.(2)

Plus a more recent example to contemplate. It also refers back to the others as well as helping bring us up-to-date. Following are two links, one to a short review of Latouche’s essay, and another to the complete work. I have just started reading the essay, and as early as the Introduction, it became clear that a review gives you a quick glance and a rough idea, but it can’t provide you the whole picture with all its connections. The complete essay is necessary to fully understand the expanse of his views on our global conditions : Farewell to Growth, Serge Latouche, original French edition, Mille et une nuits, department de la Librairie Arthème Fayard, 2007; English edition, Polity Press, 2009 — Link to a comprehensive review  : Review of Farewell to Growth 

Link to a PDF of the entire essay with parts highlighted, however every paragraph is worthy of highlight : Farewell to Growth

(2)And I keep finding more interesting related reading. This one by Isaac Asimov, written in the late 1980s, that asks the question continually going around in my mind, “Is anyone paying attention?” and his essay : Is Anyone Listening?  Does anyone care?

(3) a little stab at creativity : Ouroborus 

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