The European Patent Officerecently turned downan tool for a patent that described a food container.
By law, inventors need to be actual people.
This isnt the first invention by AI machines have produced innovations ranging fromscientific papersand books tonew materialsandmusic.
That said, being creative is clearly one of the most remarkable human traits.
Without it, there would be no poetry, no internet, and no space travel.
But could AI ever match or even surpass us?
Lets have a look at the research.
From a theoretical perspective, creativity and innovation is a process ofsearch and combination.
Machines come up with innovations by using generative methods.
But how does this work exactly?
There aredifferent approaches, but the state of the art is calledgenerative adversarial networks.
As an example, consider a machine that is supposed to create a new picture of a person.
Generative adversarial networks tackle this creation task by combining two sub-tasks.
The first part is the generator, which produces new images starting from a random distribution of pixels.
How does the discriminator know what a human looks like?
Well, you feed it many examples of pictures of a real person before you kick off the task.
Based on the feedback of the discriminator, the generator improves its algorithm and suggests a new picture.
These generated pictures comeextremely closeto real people.
Innovation is a problem-solving process for innovation to happen, problems are combined with solutions.
An example of the latter punch in of innovation is thePost-itnote.
An engineer developed an adhesive that was much too weak and was sitting on his desk.
Using data as an input and code as explicit problem formulation, machines can also provide solutions to problems.
Think of the Walkman.
Humans and machines also have different raw materials that they use as input for innovation.
Machines can quickly generate countless incremental innovations in forms of new versions based on the input data.
Think of theinvention of the snowboard, which connects the worlds of skiing and surfing.
Also, creativity isnt just about novelty, it is also about usefulness.
Usefulness is defined in the eye of those potentially using innovations and it is hard to judge for machines.
Humans, however, can empathize with other humans and understand their needs better.
Humans might discount ideas from AI since they feel these ideas areless authenticoreven threatening.
Or they might simply prefer ideas of their kind, an effectthat has been observedin other fields before.
As of now, many aspects of creativity remain uncontested terrain for machines and AI.
However, there are disclaimers.
For example, we can ask new questions or identify new problemsthat we solve in combinationwith machine learning.
In addition, our analysis is based on the fact that machines mostly innovate on narrow datasets.
AI could become much more creative if it could combine big, rich, and otherwise disconnected data.
