Creativity and Artificial Intelligence (AI)

          In 1997 IBM’s Deep Blue famously defeated chess Grand Master Garry Kasparov after a titanic battle. It had actually lost to him the previous year, though he conceded that it seemed to possess “a weird kind of intelligence.” To play Kasparov, Deep Blue had been pre-programmed with intricate software, including an extensive playbook with moves for openings, middle game and endgame.

Twenty years later, in 2017, Google unleashed AlphaGo Zero which, unlike Deep Blue, was entirely self-taught. It was given only the basic rules of the far more difficult game of Go, without any sample games to study, and worked out all its strategies from scratch by playing millions of times against itself. This freed it to think in its own way.

These are the two main sorts of AI around at present. Symbolic machines like Deep Blue are programmed to reason as humans do, working through a series of logical steps to solve specific problems. An example is a medical diagnosis system in which a machine deduces a patient’s illness from data by working through a decision tree of possibilities.

Artificial neural networks like AlphaGo Zero are loosely inspired by the wiring of the neurons in the human brain and need far less human input. Their strong point is realizing, which they do by breaking down enormous measures of information or rules, for example, the standards of chess or Go.They have had notable success in recognizing faces and patterns in data and also power driverless cars. The big problem is that scientists don’t know as yet why they work as they do.

       The reason that the two systems create that really points up the difference between them is nothing but art, literature and music. Symbolic machines can create highly interesting work, having been fed enormous amounts of material and programmed to do so. Artificial neural networks are way too exciting which actually teaches themselves and which can therefore be said to be more truly creative.

Symbolic AI produces art which that is recognizable to the human eye as art, but it’s art which that has been pre-programmed. There are no surprises.  The AARON algorithm of Harold Cohen’s Aaron produces beautiful paintings using templates which that have been programmed into it. Similarly, Simon Colton at the college of Goldsmith’s College in the University of London programs The Painting Fool to create a likeness of a sitter in a particular style. However, neither of these ever jumps past its program.

Artificial neural networks are far more experimental and unpredictable. The work springs from the machine itself without any human intervention. Alexander Mordvintsev set the ball rolling with his Deep Dream and its nightmare images spawned from convolutional neural networks (ConvNets) and that seem almost to spring from the machine’s unconscious. Then there’s Ian Goodfellow’s GAN (Generative Adversarial Network) with the machine acting as the judge of its own creations, and Ahmed Elgammal’s CAN (Creative Adversarial Network), which creates styles of art never seen before. All of them create way more challenging and difficult works—the machine’s idea of art, not ours. Rather than being a tool, the machine participates in the creation.

In AI-created music the contrast is even starker. On the one hand, we have François Pachet’s Flow Machines, loaded with software to produce sumptuous original melodies, including a well-reviewed album. On the other, researchers at Google use artificial neural networks to produce music unaided. But at the moment their music tends to lose momentum after only a minute or so.

By the two types of machines, AI created literature illustrates which are best of all the difference in what can be created. Emblematic machines are stacked with programming and rules for utilizing it and prepared to produce material of a particular sort, for example, Reuters' news reports and meteorological forecasts. An emblematic machine furnished with a database of plays on words and jokes creates business as usual, giving us, for instance, a corpus of machine-produced thump jokes. However, similarly as with craftsmanship their artistic items are in accordance with what we would anticipate.

         Artificial neural networks have no such restrictions. Ross Goodwin, now at Google, trained an artificial neural network on a corpus of scripts from science fiction films, then instructed it to create sequences of words. The result was the fairly gnomic screenplay for his film Sunspring. With such an absence of limitations, counterfeit neural systems will in general produce work that appears to be dark—or would it be a good idea for us to state "test"? This kind of machine wanders into an area past that of our current comprehension of language and can open our psyches to a domain regularly assigned as hogwash. NYU's Allison Parrish, an arranger of PC verse, investigates the line among sense and hogwash. Hence, fake neural systems can start human inventiveness. They can acquaint us with new thoughts and lift our own innovativeness.

Proponents of symbolic machines argue that the human brain too is loaded with software, accumulated from the moment we are born, which means that symbolic machines can also lay claim to emulating the brain’s structure. Symbolic machines, however, are programmed to reason from the start. 

Conversely, proponents of artificial neural networks argue that, like children, machines need first to learn before they can reason. Artificial neural networks learn from the data they’ve been trained on but are inflexible in that they can only work from the data that they have. 

To put it simply, artificial neural networks are built to learn and symbolic machines to reason but with the proper software they can each do a little of the other. An artificial neural network powering a driverless car, for example, needs to have the data for every possible contingency programmed into it so that when it sees a bright light in front of it, it can recognize whether it’s a bright sky or a white vehicle, in order to avoid a fatal accident. 

What is needed is to develop a machine that includes the best features of both symbolic machines and artificial neural networks. Some computer scientists are currently moving in that direction, looking for options that offer a broader and more flexible intelligence than neural networks by combining them with the key features of symbolic machines. 

At Deep Mind in London, scientists are developing a new sort of artificial neural network that can learn to form relationships in raw input data and represent it in logical form as a decision tree, as in a symbolic machine. In short, they’re trying to build in flexible reasoning. In a purely symbolic machine all this would have to be programmed in by hand, whereas the hybrid artificial neural network does it by itself. 

By merging the two systems in this manner could lead to more intelligent solutions and also to forms of art, literature and music which that are more accessible to human audiences while also being experimental, challenging, uncertain and fun.

    

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