AI storytelling

Recently I read in a newspaper a list of average predictions of AI researchers on when certain achievements in AI would be reached. There were several predictions for the coming 5 to 10 years, such as an AI winning a game of StarCraft against a human champion (2022), and composing a top-40 song (2027). Only one prediction was made a considerable number of years in the future, namely writing a New York Times bestseller (2050).

I was not surprised about the short-term predictions. These were all straightforward extrapolations of today’s research. For instance, a lot of time is invested in creating StarCraft AI, and we know that a computer already has a huge advantage over humans in its speed; it just needs to get a bit better tactically to defeat human champions. Similarly, computers already write music that is indistinguishable from what humans compose, so I can see a computer write a top-40 hit today — the main problem I see for writing a song that lands in the top-40 is that the quality of the song is only a very small factor in determining whether it becomes a hit.

Why is writing a bestseller considered to be much more difficult than any of the other AI tasks?

Writing a novel is very different from composing music or creating a painting. When listening to music or watching a painting, people give their own interpretation to what they hear or see, and the computer can get pretty far by simply recombining elements of music or paintings that it has been trained with. For instance, David Cope’s first attempts at letting a computer compose music amounted to hacking Bach’s sonates into measures which were stored in a database, and then recombining these measures by making sure that the last note of each measure was the same as the last note that originally came before the measure that was chosen next. This resulted in thousands of sonates which sounded more or less like Bach sonates. The computer did not need to understand what it was doing. In contrast, when writing the text of a novel an AI needs to understand what it is writing, otherwise the text will not make sense.

You might think that no real understanding is needed to create a text, as we already see human-readable text produced by computers today. In particular, newspaper articles are often written by computers. Examples are weather reports, stock market analyses, and sports reports. However, in these cases the computer is not really producing an original text. The computer simply gets the data that need to be reported (temperatures, market fluctuations, goals scored) and translates the data according to specific rules into a text. No creativity is needed. To produce a novel, the computer must come up with an original, sensible plot that has relevance to humans, and turn that plot into a captivating text. As far as I can see, the computer cannot do that without quite deep understanding of the human condition, human emotions, human language, and the human world. And at the moment, we have no idea how we can give a computer such understanding.

Someone who has heard of “deep learning” might think that it is sufficient to train a computer with existing novels to allow it to produce a new novel. But what are you then really training the computer with? You are training it with strings of words. This might lead to a computer being able to recognize that certain strings of words are likely to be sensible sentences, but not that a string of 40,000 words is a sensible, well-readable novel. Examining words does not equate examining the plot, the meaning, or the literary quality of the novel.

You might think that we may solve the problem of making the computer create a bestseller by simply letting it produce random texts, and then assess the quality of the texts with respect to them being a bestselling novel. Using an evolutionary approach, this might reasonably quickly lead to a novel that scores high on bestselling quality. This approach might actually have merit to it, if we could give the computer an algorithm that rates a text as a bestseller. We do not have that, as even humans cannot predict whether a novel will be a bestseller.

Take, for instance, the first Harry Potter novel, which was rejected by virtually all British publishers, and only produced in a very small quantity by the last one because his 8-year-old daughter liked the book. Considering Rowling’s unskilled writing and weak plot construction, it is not surprising that the publishers did not see her combination of a mid-20th-century boarding school novel with a childish version of Lord of the Rings as likely to succeed. Expert humans did not assess Harry Potter to be the commercial success that it came to be. So expert humans cannot teach a computer to do it for them.

If expert humans cannot tell a computer how to rate a novel, you might still envision an approach by which a computer determines by itself an evaluation function for bestselling quality. If you have millions of books which are all labeled with their relative sales figures, and extra data with respect to the time when and place where the books were a success, you may be able to use them to train a computer to come up with an evaluation function that can accurately predict from the contents of a novel whether or not it will be a success. Perhaps that is possible. Perhaps not. Frankly, I think that if it was easy, then all those publishers who rejected Harry Potter would have internalized an algorithm like that and would at least have seen some value in the book, but evidently they did not.

If a computer would have a much deeper understanding of the world than any human has, it would have insights that humans cannot have. And with such insights, be able to predict bestselling quality. I believe that in principle it is possible for a computer to have much deeper understanding of the world than humans have, but we are far, far away from having such a computer since, as far as I know, nobody has any idea on how to give a computer what is needed for it to gain understanding. The conclusion that I must draw is that it probably is not impossible to get a computer to write a bestseller, but that creating a computer that can do that is not a straightforward extrapolation of the state-of-the-art in AI. Therefore, attaching any year to it is unwarranted.

So where is the year 2050 coming from in the minds of the average AI researcher? I think it represents “About 25 years in the future? Who knows what we can do by then!”

Basically, predicting an AI achievement for 2050 is equivalent to AI researchers saying “we have no idea.”

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