Back to analog… Looking at the forest again…

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Analog computers were used to predict tides from the early to mid-20th century, guide weapons on battleships and launch NASA’s first rockets into space. They first used gears and vacuum tubes, and later, transistors, that could be configured to solve problems with a range of variables. They perform mathematical functions directly. For instance, to add 5 and 9, analog computers add voltages that correspond to those numbers, and then instantly obtain the correct answer. However, analog computers were cumbersome and prone to “noise” – disturbances in the signals – and were difficult to re-configure to solve different problems, so they fell out of favor.

Digital computers emerged after transistors and integrated circuits were reliably mass produced, and for many tasks they are accurate and sufficiently flexible. Computer algorithms for those computers are based on the use of 0s and 1s.

Yet, 1s and 0s, pose limitations into solving some NP-hard problems. (e.g. the “Traveling Salesman” problem) The difficulty with such optimization problems, researcher Toroczkai noted, is that “while you can always come up with some answer, you cannot determine if it’s optimal. Determining that there isn’t a better solution is just as hard as the problem itself”.

[Note: NP-hardness is a theory of computational complexity, with problems that are famous for their difficulty. When the number of variables is large, problems associated with scheduling, protein folding, bioinformatics, medical imaging and many other areas are nearly unsolvable with known methods.]

That’s why researchers such as Zoltán Toroczkai, professor in the Department of Physics and concurrent professor in the Department of Computer Science and Engineering at the University of Notre Dame, are interested in reviving analog computing. After testing their new method on a variety of NP-hard problems, the researchers concluded their solver has the potential to lead to better, and possibly faster, solutions than can be computed digitally. (1)

Breaking a problem into pieces can do so many things.

But at the end you will have to look at the problem itself.

And the problem does not have any components.

But only a solution.

Visible only to those who do not see the problem.

You cannot ride the waves.

All you can do is fall into the sea and swim.

You cannot live life.

All you can do is let go and prepare to die.

Look at the big picture.

You can solve anything.

As long as you accept that you cannot…

At the end, the voltage will reach zero.

At the end, the computer will shut down.

You might see this as a sign of failure.

But it would be the first time it really solved anything…

AI jokes. Easy to tell…

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Engineer Janelle Shane took to Twitter to lay out some of the telltale giveaways that the script was written by a person pretending to be an AI algorithm for kicks.

You may recognize Shane as the person who trains neural nets to create jokes that devolve into nonsense or paint colors that almost sound real after being trained on thousands of actual examples. Yes, the AI-generated results are absurd, but they also highlight one key fact – the neural nets have no clue what the hell they’re talking about.

“I’d say the clearest giveaways are a really short memory (maybe just a couple of sentences long) and a lack of understanding of meaning and context,” Shane told Futurism. “One characteristic of neural net text is it’ll tend to mimic the surface appearance of things without really getting the meaning behind them. (1)

AI will finally manage to solve the most complex problems humanity faces. From mathematical problems to ways to deal with deadly diseases via innovative medication.

And yet, it would do that blindly.

Without ever knowing what it is doing.

Why should we care? one might argue. At the end, we will benefit from it. So what is the problem?

No problem I say!

Let AI help us! By all means!

The problem is not with AI per se. The problem is with us actually. Because it seems that it is not only AI which does not know what it does and why. It is us as well. We are wandering in the dark looking for solutions to problems without knowing the essence of what we are doing: The Why.

Why do you want to solve the mathematical problems?

Why do you want to live longer?

Why are you afraid of death?

We have forgotten to ask the simple questions. And failing to do so, providing answers to all the complex ones will mean nothing at all…

Except for an AI.