In truth, no one understands how this cx

Author : samanthacaroline987
Publish Date : 2021-01-07 01:45:46


In truth, no one understands how this cx

Who sends the most messages? What are the most common words used? What are the most common Emojis used? When do people message most often? If you’ve ever wondered these things about your WhatsApp groups with your mates, then this is the article for you. Find out with some (relatively) simple Python!

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If you like, you can even build a system that “proves” that the old music is better than the new, just by selecting for those features that privilege the old evaluative criteria. Apparently today’s songs are less melodically complex, more repetitive, they embody fewer chord changes, their dynamic range is narrower, and today’s artists are irredeemably self-involved (!). Of course, the quality of the music and the selective criteria cannot be explained in terms of these descriptive features. And of course, none of these analyses admit whatever new and largely inexplicit selective criteria may actually be active in our cultural communities. But misunderstandings about what constitutes a scientific argument are as rampant as misunderstandings of creativity. (Now get off my lawn, you kids!)

Applied creativity is well within the realm of today’s AI, just as any narrow application. This is readily explained: The human creators may represent a search space of creative hypotheses for an AI to explore, incorporating the known evaluative constraints of the problem. (Here’s a recent example of a problem related to understanding human behavior during a pandemic.) An implication of this “searching for hypotheses” view is that the possible solutions exist prior to the search. Another implication is that there is a human mind hiding in the background providing a creative catalyst. Routinely, as the system designers are evaluating the results generated by their AI, they’ll notice a new evaluative basis that they didn’t think of before they started. It’ll surprise them and they may be quick to credit their system with the discovery. But while AI may alleviate some of their perspiration, humans still provide the inspiration, the creative spark.

Consider your love of old music. Have you ever wondered why we still love the music we listened to back in high school? Our evaluative criteria are generally imprinted during adolescence and young adulthood. We tend thereafter to feel that music is getting worse, generation after generation, as new music evolves beyond our tastes. The old music embodies the criteria we value. New music embodies new criteria, as well as disregarding some of the old.

It’ll happen again. Some genius working within the right community at the right time will solve the riddle of how creativity works. We’ll know because they’ll be able to explain it.

I find this cultural impediment to creativity fascinating. I wonder how many great ideas have been hatched and summarily dismissed because they weren’t aligned with the prevailing culture. Artists working in isolation internalize the creative constraints of their community, embodying their own toughest critics. The pile of crumpled paper in the wastepaper basket is a testament to this process. The challenge isn’t so much in processes of creative variation (such as propping your dog up at the piano) as in the means for selection. Incidentally, this same environment-building challenge is what limits the reach of evolutionary approaches to the problem.

It helps to consider that fundamental creativity is much harder. These are your Einsteins, your Austens, your Picassos. These are the groundbreaking discoveries that are normally associated with the promise of AGI. These are the creations that not only introduce novelty, but novelty of a sort that forces us to reconsider the creative boundaries. Fundamental creations force a shift in the evaluative creative (even when some people don’t want them to move). Vexingly, the new creative criteria are not known in advance. And this difference explains why artificial creativity is presently out of reach. (Not always and necessarily as some would claim, but presently.)

First, let’s reflect on the extent of this simple example: As the notes are combined and varied, the number of melodies rapidly increases to millions and trillions of possibilities. Often, the problem of creativity is falsely reduced to a problem of scale, which in turn suggests the idea that more data and computing power will inevitably rein in the problem. However, the scale of any given problem space isn’t the hard problem of creativity. If your goal is to create a fundamentally new type of music, how would you know, in advance, the new criteria by which a novel melody might be selected? And if your goal is to create something fundamentally new, how would you devise the new evaluative criteria that separates this AI masterpiece from my dog’s paw-banging?

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der PSMs as a factor in modeling advanced analytics, such as forecast modeling. But the behavioral differences people are exhibiting while social distancing means that a model may not be work as effectively as it would have given a standard retail year. It may be better to see if advanced modeling can be used for the next season.
Applied human creativity is so commonplace it goes largely unnoticed. It certainly doesn’t prompt soul-crushing conversations about the inevitable exhaustion of human creativity, or the perceived need for AI to liberate us from our creative limits. Again, I’m all-in on the vision of creative AI. But why are we getting so triggered about these narrow examples?

My favorite example of this musical riddle is known as the diabolus in musica (Latin for “the Devil in music”). It refers to a highly dissonant interval called a tritone. You’d recognize it immediately as Enter Sandman, Maria, or Purple Haze. But these melodies wouldn’t be selected during Medieval times. Not because they weren’t there in the problem space, but because the necessary evaluative criteria had not yet been selected. The Devil in music was steadfastly avoided.

In truth, no one understands how this complex interplay of psychological and social factors drives creativity. It’s mysterious and wonderful. When the conditions are right, at the nexus of a creative mind and the vagaries of their social circumstances, something fundamentally new and important emerges.

Not only is this applied approach readily explained, it’s readily demonstrated in AI-generated art. Admittedly, as emotive works, these examples of applied AI may be disconcerting. And if you harbor the idea that creative works are intrinsically different from other technical solutions, you may believe we’ve breached some monumental divide. But no, it’s still just applied AI.

It’ll happen again. Some genius working within the right community at the right time will solve the riddle of how creativity works. We’ll know because they’ll be able to explain it.



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