January 10, 2014

Welcome to the machine … creativity

BY :     January 10, 2014

Do you think computer can be creative? That’s a question likely to generate controversial answers.

But scientists are not afraid of controversy and IBM answers “YES” to this question. Computers can be creative, they say, and to prove it they have built a computational creativity machine that produces results that a knowledgeable human would consider novel, useful and even valuable—the characteristics of genuine creativity[1].

IBM has built a computational creativity machine that creates entirely new and useful stuff from its knowledge of existing stuff. And the secret ingredient in all this? Big data of course, and some parts of human intelligence in order to leverage these data. Again, Big data combines gathering of data from different sources, the ability to compute huge amount of data and combination of them, and finally smart algorithms to generate new ideas from existing stuff.

Computational machines have evolved a great deal since they were first used in war for code-cracking and gun-aiming and in business for storing, tabulating and processing data. But it has taken some time for these machines to match man human capabilities. In 1997, for instance, IBM’s Deep Blue machine used deductive reasoning to beat the world chess champion (G. Kasparov at this time) for the first time. It’s successor went a step further in 2011 by applying inductive reasoning to huge datasets to beat humans experts on the TV game show, Jeopardy.

IBM scientists chose cooking as the topic of their research. The company’s creativity machine produces recipes based on chosen ingredients or cooking styles. And they’ve asked professional chefs to evaluate the results and say the feedback is promising. Yes, in order to declare that a computer proposal is creative, some human must be part of the process and their decisions are final!

The team has gathered information by downloading a large corpus of recipes that include dishes from all over the world that use a wide variety ingredients, combinations of flavours, serving suggestions and so on. They also download related information such as descriptions of regional cuisines from Wikipedia, the concentration of flavour ingredients in different foodstuffs from different databases.

They then develop a method for combining ingredients in ways that have never been attempted using a “novelty algorithm” that determines how surprising the resulting recipe will appear to an expert observer. The algorithm produces thousands of millions of new ideas from the recipe design space, which leads to an impressive number of 1024 combination. Statistics science will then be applied to these intermediate (and impossible to grasp!) results. In order to find novelty in such a huge number of possibility, Bayesian probability were used to define novelty, what was called Bayesian surprise[2].

The team uses stages of human creativity to reproduce human thinking. This approach allows to define modules of thinking and some interactions between human experts and the computer can take place at each stage. So human expert can choose or enters some new parameters that will change machine behaviour.

The computer generates a number of novel dishes, explaining its reasoning for each. Of these, the expert chooses one and then makes it. The human experts seem impressed by the results. The next step is to create novel menus that could be proposed in restaurants. This is a new step in machine creativity, because composing a menu needs to compute more data than for a single recipe, taking into account interaction between recipes in a single menu.

It’ll be interesting to see where these researchers take the process next. If they’re confident that their computational creativity machine works well for designing recipes, where else could they apply it that has a similarly rich and large set of data to mine and crunch?

Combining these approach with brain-inspired chips[3] that can efficiently process real-data like a human brain, will drive these applications quickly to our preferred device: the smartphone and tablets.  That means that everybody, connected to internet (source of big-data) will have access to machine supported creativity in one single hand. Enjoy!

Creativity is defined as the generation of a product that is  judged to be novel and also to be appropriate , useful, or valuable by a suitably knowledgeable social group. See R.K. Sawyer  in Explaining Creativity: the science of human innovation. Oxford, Oxford University Press, 2012

See L. Itti and P Baldi in Bayesian surprise attracts human attention, Vis. Res. Vol. 49, n° 10, pp.1295-1306, Jun 2009; or P Baldi and L Itti in Of bits and wows: a Bayesian theory of surprise with applications to attention, Neural Netw. Vol. 23, n° 5, pp. 649-666, Jun. 2010

Synapse project; see http://www.darpa.mil/Our_Work/DSO/Programs/Systems_of_Neuromorphic_Adaptive_Plastic_Scalable_Electronics_(SYNAPSE).aspx

Philippe André


Philippe André is an expert within Business and IS architecture, Service Architecture, System modelling and Soil science. Philippe is a Certified Enterprise Architect (L4) and TOGAF9 certified. Philippe's mission is to help clients to make the best decision as far as business and IT alignment is concerned. More on Philippe.

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