How creativity can be implemented in AI?
Overview –
Artificial Intelligence (AI) takes the power of computing systems to a different level. It is amazing to even think that a computing system can emulate human beings. There are many fantastic examples of AI in various areas of our lives. That said, computing systems are still considered limited in their capabilities because they cannot think creatively like human beings. While AI can process and analyze complex data, it still does not have much prowess in areas that involve abstract, nonlinear and creative thinking. For example, it is extremely difficult to think of a computing system come up with a path-breaking scientific theory like that of relativity. Can AI overcome this limitation? AI is being enriched regularly but nothing much has been done so far to catapult AI to the next level.
What is AI?
AI is an area of computer science that studies intelligence in computing systems. Though it may sound a little strange, AI enables computers, to an extent, think, react and work like human beings. Intelligent computers can do many different things like planning, speech recognition and problem solving.
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Exploits of AI
As already stated, the exploits of AI are too many to be chronicled here. Still, the most notable achievements are briefly described below.
- AlphaGo, the AI software from Google, beat Lee Sedol, the world champion in the Chinese game of Go, an extremely complex game. Go is like chess in terms of making moves but unlike in chess, it is impossible to calculate all possible moves because there are more moves in Go then there are atoms in the universe.
- Google’s AI software wrote poetry. The software was fed with more than 11000 poems. Based on the data from the poems, the software wrote poems.
- At the Tufts University, and AI software developed a scientific theory on the regeneration of flatworms. The topic had been a mystery for 120 years.
Can AI really become creative?
Despite its exploits, it is hard to believe that AI can become creative. At least not soon. Think of the exploits described above. The common tendency in each of the above exploits has been the dependence on data, huge volumes of that. The machines first need to process and analyze data given to it before doing anything novel. All that it can do is to probably find a new pattern from many patterns already given to it. That goes against the basic principles of creativity. The human mind cannot store or process such huge volumes of data but that does not prevent it from creating something outrageously novel.
One area that is extremely difficult for AI to foray in is pure arts. According to Michael Osborne, associate professor in machine learning, University of Oxford, it is extremely difficult to teach algorithms to produce art like that human beings do. It is possible to train algorithms to churn pieces of artwork in large volumes but it is difficult to teach it the difference between quality and poor art. A survey conducted by The Guardian, a reputed newspaper in the UK, found that in the UK and the US, almost 90% of the jobs in art cannot be automated.
From the opinions received by eminent personalities, it does not seem that AI can become creative soon. Let us review a couple of these opinions.
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David Cope, Composer, author and professor emeritus of music at the University of California, Santa Cruz
Professor Cope has been trying to have computers write novels for a long time and has achieved some success. Computers are now able to write short stories but questions arise over the quality of the stories. According to Professor Cope, there may soon be a time when AI can churn out 10000 words in mere 30 minutes. But can such stories give joy and value to its readers? Probably not. The short stories written by the machines are related to one another which provides data to the computers to analyze. The basic item missing here is creativity and novelty. Computing systems rely on data even in writing short stories.
Maria Teresa Llano Rodriguez, Research associate, computational creativity group, Goldsmiths University
According to Maria, AI is constrained to become creative because of the type of data they are provided. Maria elaborates that the data quality, variety and volume are important factors in enabling AI to become creative. There has been an overall failure in providing such data to the AI systems. Though no doubts are cast on the ability of AI in this case, it still holds true that AI is dependent on data quality.
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How can AI become creative?
AI, it seems, can improve but is unlikely to match the human mind and brain. There are certain areas, however, where AI can claim to achieve total mastery such as driverless cars and vehicle manufacturing. In fact, such industries have already been going through large scale automation. To improve the capabilities of the algorithms, they need to be constantly supplied with huge, updated and varied volumes of data so that the machines can adapt and learn. Based on the learnings, it can find novel things. But areas like psychology, medicines and art will remain unconquered by AI.
Let us take the case of the horror movie Morgan where AI played a prominent role in making. Before this movie was made, as a preparation, AI was fed data from thousands of similar movies. This is where how AI works, lies. Basically, AI mimics the data it is given as input. You feed large volumes of data, AI processes and analyses the data and finds new patterns which some people call creativity. According to Jason Toy, CEO of Somatic, a start-up that develops deep learning applications, AI works on the deep learning principle. “If you feed it thousands of paintings and pictures, all of a sudden you have this mathematical system where you can tweak the parameters or the vectors and get brand new creative things similar to what it was trained on.”
The people who believe AI can be creative believe so because its achievements, unthinkable in the past, have increased in leaps and bounds. For example, no one believed that a computer could distinguish between what is and what is not cancer. Basically, such people are relying on the evolutionary trends of AI. Indeed, AI has achieved a lot in a short period. But what is ignored is that AI has done different things with a common approach – deep learning and mimicking data. But creativity demands independence.
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Implications
There are three implications of the AI becoming creative. One, it can potentially replace human beings in certain domains. It has already been causing massive disruption in such domains. Two, AI and human beings will complement each other in certain domains. For example, repetitive tasks will be left to AI while the more creative jobs are done by the humans. Last, certain domains will remain almost fully unconquered by AI.
Conclusion
AI is projected by many as something catastrophic for mankind because it is going to take away jobs. While that is partly true, AI can potentially unlock a great future for us by actually forcing greater innovations. The way forward is coordination between human and the AI.
