I don’t doubt that. And speed has advantages, extensive trials are useful even for the most intuitive, witness Edison and Gauss. It also seems that science, in the sense of repeatability and experimentation, was an invention that served as a corrective to the ChatGPT thinking that comes naturally to us all. How much of what we “know” is based on anything but what we have heard or read? Helen Keller’s stories comes to mind, she was accused of plagiarism. About which I excerpt this little bit from a letter to her from Mark Twain:

Oh, dear me, how unspeakably funny and owlishly idiotic and grotesque was that “plagiarism” farce! As if there was much of anything in any human utterance, oral or written, except plagiarism! The kernel, the soul–let us go farther and say the substance, the bulk, the actual and valuable material of all human utterances in plagiarism. For substantially all ideas are second hand, consciously or unconsciously drawn from a million outside sources and daily use by the garnerer with a pride and satisfaction born of the superstition that he originated them; whereas there is not a rag of originality about them any where except the little discoloration they get from his mental and moral calibre and his temperament, which is revealed in characteristics of phrasing.

The whoe Twain letter is here.

I understand your objections, but I like to think I’m looking at a bigger picture, given the curve of AI intelligence.

I’ll concede you have some valid points, but I’ve heard it before with earlier states of AI.

The concern in neo’s post is that Chat doesn’t write as good lyrics as Lerner & Loewe. This is a “the glass is 10% empty” interpretation. How in the world does a computer program come to create a mediocre version of a “My Fair Lady” song?

I say AI is only going to get better. I fear and suspect it will do so more quickly than we are ready.

To be continued….

This reminded me of an article discussing ” The Theory of Moral Sentiments” by Adam Smith. IIRC, Smith saw the development of moral sentiments as having stages, starting at what I would call the ChatGPT level, followed by a stage where the child internalized by recognizing that others had the same feelings as himself. I think that internalization is what is missing in the current state of AI development. I distinctly remember when I first had that insight about others when reading “Tom Sawyer” at about age ten. It was an aha moment.

It is curious that Adam Smith should gain new relevance in experimental economics. Maybe he will also be relevant to AI.

You just articulated the difference. You can be creative. On your own.

I dropped in this morning because the difference ‘twixt us and AI was demonstrated to me this morning by accident.

I visit my neighbor most mornings and today Joe was. Joe was rummaging around for the TV remote and Curt suddenly burst out in “What you want? Baby, I got it. …”

Until consciousness is achieved, AI will never be more than a response mechanism. It would never have had the “inspiration” to leap from the visual of Joe searching for the remote (or even knowing it was the remote being searched for as nothing was said) to Aretha singing. What experience could it have had to make those connections?

It would have to be prompted sufficiently.

And, carrying the example even further, an AI wouldn’t have noticed that the episode was an argument against it’s own intelligence.

EOT for me.

Happy New Year!

As part of the annual cycle through the Torah, Jews around the world just finished reading the story of Joseph and his brothers.

Like most of the Jewish bible the actual text is terse and especially tight-lipped about thoughts or motives.
Would AI be able to infer the motives of the players?
Would it place the interlude where Judah falls off from his brothers in its proper narrative context?
Would it parse Joseph’s own moral struggles in Egypt and how he tested his brothers?

… almost none of this is explicitly stated in the actual text. As in most great literature – there is a lot of showing and little telling – no frontal exposition.

Yet you and we understand, infer, imagine what’s missing, sympathize.

I guess I agree with Oligonicella – the word “Intelligence” is a hypey misnomer.

From the sublime to the…. well, the ephemeral: This thread started with song lyrics.

In a few seconds we could come up with a list of songs whose titles, lyrics, and humor depend on counterintuitive analogies and associations. Or that set a mood through oblique references.

Without pre-existing models to learn that tether these words and ideas, AI could not create these connections.

Yet you and we “get it”.
We understand when a hat or a cigarette in an ashtray signifies love, nostalgia, joy, loss… we can divine (!) these key words, find the mood of the lyric even from its diverse, oblique parts…. And again, the good works avoid “on the nose” telegraphing of meaning.

Would AI?
AI can create a pastiche, can analyze and resynthesize what has been previously created – and that can be very useful – but it can’t make the aha connections.

Love is like a day in June
Love is like a giraffe

AI could generate an infinite list of them, ranked by how often words appear near the word “love” – plus many other data points – but it could not evaluate the list, could not find the ones that leap off the page, or are funny. And the really “unintuitive” ones would be at the bottom of the list.

But we can.

Oligonicella:

I understand.

However, as I said to Ben David, I don’t know if deep down I’m much more than an information-manipulating machine, at least when it comes to how I think.

We may not need another approach.

om:

That’s a key question. The answer is that Deep Blue and AlphaGo were programmed in fundamentally different ways.

Deep Blue was programmed as you described — brute force look-ahead. It worked great for chess and checkers.

However, that approach won’t work for Go. In chess there are around, on average, 10-20 possible moves in each position. In Go that number is, on average, 200 moves. The Go look-ahead tree explodes past even a computer’s resources to handle.

For decades some argued that AI could never play serious Go and certainly not at the world champion level.

Then AphaGo happened and Lee Sedol went down.

The difference is that AlphaGo is based on machine learning, not the previous static brute-force method. The machine trains on data and develops a cumulative representation of what works and what doesn’t, which becomes a sort of intuition to be used later in actual play.

Ben David:

Good to hear from you.

I’m not convinced as I interact with AI that it has consciousness, creativity or soul.

My problem is that I can’t tell whether at rock bottom I’m any different from AI. I’ve got good memory and good pattern matching skills. I know how I turn those into “creativity.”

Similarly, though I don’t know Go but I do know chess, I know that the chess grandmasters who make those floating, brilliant moves which make us swoon, have spent ungodly amounts of time crunching chess data — which I understand as your argument against AI’s creativity.