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The Secret Ingredient in Computational Creativity

IBM has built a computational creativity machine that creates entirely new and useful stuff from its knowledge of existing stuff. And the secret sauce in all this? Big data, say the computer scientists behind it.

Can computers be creative? That’s a question likely to generate controversial answers. It also raises and some important issues too, like how to define creativity.

Seemingly unafraid of the controversy, IBM has darted into the fray by answering this poser with with a resounding ‘yes’. 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 hallmarks of genuine creativity.

IBM’s chosen field for this endeavour is cooking. 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.

First some background. 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 for the first time.

It’s successor, a computer called Watson, went a step further in 2011 by applying inductive reasoning to huge datasets to beat humans experts on the TV game show, Jeopardy!.

Now Lav Varshney and pals at IBM’s T J Watson Research Center in Yorktown Heights are using Watson to tackle the problem of computational creativity. They revealed some aspects of the work to the press last month and have now published more on the arXiv.

Their first problem of course is to define creativity. “Creativity is 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,” say Varshney and pals.

So a key factor in their work is that creativity is entirely subjective and so requires detailed feedback from human experts. “A computational creativity system has no meaning in a closed universe devoid of people,” they say.

What’s more, this definition implies that creativity is a process that in principle can be automated. Varshney and co discuss a well-known 8-step plan that describes the creative process. It begins with finding a problem, gathering information about it, thinking about it and then generating ideas, sometimes by combining old ones together. The final steps are to select the best ideas and put them into action.


Clearly some of these steps are easier for humans than computers and vice versa. So Varshney and co recreate this process with an innovative collaborative model in which humans perform some tasks and computers the others.

The choice of problem, to create new recipes, is clearly a human decision. The team has then 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 the “Volatile Compounds in Food” database and Fenaroli’s Handbook of Flavor Ingredients. So big data lies at the heart of this approach—you could call it the secret sauce.

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.

This relies on factors such as “flavour pleasantness”. The computer assesses this using a training set of flavours that people find pleasant as well as the molecular properties of the food that produce these flavours such as its surface area, heavy atom count, complexity, rotatable bond count, hydrogen bond acceptor count and so on.

The last stage is an interface that allows a human expert to enter some starting ingredients such as pork belly or salmon fillet and perhaps a choice of cuisine such as Thai. The computer generates a number of novel dishes, explaining its reasoning for each. Of these, the expert chooses one and then makes it.

These human experts seem impressed. “Recipes created by the computational creativity system, such as a Caymanian Plantain Dessert, have been rated as more creative than existing recipes in online repositories,” say Varshney and co.

And they have even allowed professional chef’s to test out the new system when they are designing menus. “Professional chefs at various hotels, restaurants, and culinary schools have indicated that the system helps them explore new vistas in food,” they say.

Whether the rest of us will get a chance to try it, Varshney and co do not say. Presumably it would be trivial to create a web-interface that would allow anybody to use it. And yet IBM has not offered general access.

Perhaps that’s something to look forward to. Or perhaps IBM has other ideas in mind.

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?

Suggestions in the comments section please.

Ref: : A Big Data Approach to Computational Creativity

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