The Creativity Code

The three main questions that Marcus du Sautoy addresses in his book, The Creativity Code: Art and Innovation in the Age of AI are what does it mean to be human, what makes us different from all the machines that are threatening to put us all out of jobs, and could a well-programmed machine do anything a human can?  The precision of machines to hand large volumes of data and complex algorithms are wonderful in various professions such as engineering, data mining, laboratory work, and in political and international trade.  We as humans are also creative thinkers and we all have the ability to do complex calculations too just not at the same speed as machines.  We also have the ability to have a passion for the problem solving we do in our work and to give meaning to the data and results.  In the 21st century, machines have evolved and can now learn and adapt.  One big question we need to consider with this evolution of machines is does the future of creativity also now belong to these machines?

 

Marcus du Sautoy takes the reader through this ever-changing world of artificial intelligence – a world where we find ourselves as humans running to get caught up.  Sautoy considers what machine learning means for future creativity.  The human code, the creativity code, is one that has always had an individual place in our hearts and can’t be replaced by a machine.  How can a work of art made personally by a human have the same emotional connection to its viewers as a machine reproducing the same art?  As Sautoy points out on page 3 on the text:

 

 

The goal of mathematics is to prove theorems and even though, we as mathematicians need computer algebra systems to do hard computations, there is no substitute for the self-gratification in finally proving a theorem, lemma, or corollary that ignites our spirit and passion for mathematics, problem solving, and being creative.

 

In Chapter 2, Sautoy discusses three types of creativity laid out from the theories by Margaret Boden: Exploratory, Combinational, and Transformational creativity.  With creativity comes failure and we as humans react differently to failure.  However, it is through failures that we learn from our mistakes.  Sautoy points out that we would tell machines what to do and obtain results.  However, machines have now evolved since an algorithm, which is built with code that learns from failures, learned from its mistakes.

 

Sautoy discusses Mizar, which is a library of mathematical proofs that were written in a formal language that a computer could understand and check.  Mizar was invented by the Polish mathematician Andrzej Trybulec.  However, as Sautoy points out on page 224, a professional mathematician couldn’t decipher many of the proofs.  With the example of a Mizar’s proof that there are infinitely many primes reemphasizes the point that the human touch for writing up a proof can’t be compared to a machine – the language barrier is too great.

 

Humans are unique species on Earth because we have the ability to have emotions, love, feelings, and to be conscious of the world around us.  By installing these human-like features in machines and in AI, the codes and data we give to machines will have those emotional connections to us humans.  This book reminded of the 1980's T.V. show Knight Rider and how the car, KITT, had the artificial intelligence to have a personal connection with Michael Knight to fight crime.  That show was ahead of its time with the KITT talking.  However, those times are here in 2019.  We have Amazon Alexa’s and our cars do talk to us through GPS navigation, placing calls, and voice commands to play our favorite music.  While it can be scary to think about, AI is growing at a fast pace and should machines become conscious, the art, music, writings, mathematics, and any other creative output they produce will be their own.  We can only hope to crack into their codes to get a since of what it’s like to be the machine.

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Peter Olszewski is a Mathematics Lecturer at The Pennsylvania State University, The Behrend College, an editor for Larson Texts, Inc. in Erie, PA, and is the 362nd Chapter Advisor of the Pennsylvania Alpha Beta Chapter of Pi Mu Epsilon. His research fields are in mathematics education, Cayley Color Graphs, Markov Chains, and mathematical textbooks. He can be reached at pto2@psu.edu. Webpage: www.personal.psu.edu/pto2. Outside of teaching and textbook editing, he enjoys playing golf, playing guitar and bass, reading, gardening, traveling, and painting landscapes.