Finally, one of my favorite experiments is called Yossarianlives, which is a metaphorical search engine. Instead of searching for specific data, it lets you search for a concept and returns results of what its databank from internet searches say are related metaphorical concepts. Its close to a digital brainstorming session.
2. Machine Learning
In order to make machines more independent, many researchers are looking into robots building their own awareness of their surroundings over time. The aim is to reduce the requirement of humans to programme all of the information they need in advance. So now there are machines which are learning new information in the same way that toddlers do, and learning about their own body in order to learn how to move. It can even begin to imagine what is going on in the minds of people it is interacting with.
While that is interesting, the real changes will come out of letting learning computers loose on the internet’s data so that they can learn human concepts. Last year, Google created a neural network of 16,000 computers and fed it random image thumbnails from Youtube. Without any previous knowledge, it was able to form a concept of similarity between many images, and to learn what the most common object was. In case you were guessing, it was a cat. Thanks Youtube. Given more processing power and time, these machines will soon look at objects and see not only descriptions which humans have programmed, but the meaning people give to them.
3. Big data, predictions and instant experimentation
‘Big Data’ is one of the biggest trends in analytics from the past few years, already doing everything from predicting what you will search for in Google Autocomplete, which type of Toaster Amazon should recommend to you, and which Movies Netflix thinks you would like to see on a Tuesday evening. By feeding a system enough data it is able to discern the underlying trends more effectively than a person ever could and make predictions of what may work in the future. It is already predicting what music you will listen to.
Pandora’s Music Genome project gets input from music experts on thousands of songs, including how the lyrics work, aspects of the base melody, genre, style, speed, and impact. It also runs thousands of experiments with its millions of users when producing a personal track list, streamed as a radio station, and gets real-time feedback on how successful it was by how the user interacts with the suggested music. This helps it figure out how people react to and enjoy aspects of music in different settings, and so is able to produce a list of new music a customer may like.
But what about the next evolution of big data? Computers are already able to understand voice, language structure and word meanings. If big data analysed the lyrics to every song released in the last 100 years and saw how popular they fared, it is likely it could find the underlying patterns and predict new lyrics. More than that, it could instantly test them with people to see how they fared. Imagine a programme able to take a concept, find metaphors for it, use big data to predict potential lyrics which would be popular, and then produce 100 slightly different versions. It could produce a song by “singing” the lyrics using a computer voice over a synthesised track, and release each version either on Youtube or a radio streaming service. Based on user feedback, it would then amend the content and style, run the experiment again, get more feedback, until it had a song which users loved, and then release it to its iTunes account, without any human every writing a note.
Similarly, big data could be used to analyse previous links between all forms of media and internet chatter and its effect on the success of media released after that. Would there have been a way to predict the success of ‘Vampire’ based media earlier? Could it predict the rise of a music genre representing the attitudes of a demographic like Grunge did in the 90s? How far in advance could you predict what will be popular? Big Data will eventually enable all of this.
So what comes next?
While I do believe that machines will soon replace certain aspects of the creative process, I don’t think they will ever be truly creative. This is due to the distinct difference between creativity (the generation of new and valuable ideas) and craft (turning those ideas into something tangible). Machines will overtake humans in craft, and in many cases already have (manufacturing), they can produce the ‘What?’ and ‘How?’, but not the ‘Why?’. Until there is a machine which has gone beyond using inputs as data, and using data as experiences, then all of its information, no matter how much analysis went into it from however many millions of sources, is still second hand from human.
That being said, here are my predictions of creative jobs that will be at least partially replaced by machines in the next decade:
- Advertising: Programs will produce try out hundreds or thousands of designs, slogans etc, and try them out in small scale on the internet before a full campaign launch. Based on user reaction they will refine the campaign and iterate until an ideal message is found.
- Music: The first fully digitally written, sung and produced song will be released. It will likely have very generic lyrics about ‘Love’, ‘Beauty’ and use the word ‘Baby’ a lot. But the second album will show a lot more nuance and variety. And the live performances will have a lot of lighting effects but not much soul.
- Architecture & Design: By providing the exact functionality required from a building or product, a programme will produce several very different designs which all meet the underlying requirements.
- Writing, Screenwriting & TV: By finding the underlying trends in public opinion, software will be able to predict what books, films and TV shows will be popular in the 1, 2 & 3 years time. It will then compare this against previous films to suggest story arcs which the book / film / TV show should follow to enhance likelihood of success.
Do you think that machines will ever be able to produce truly creative work? Let us know in the comments below.