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Why Complexity Science Has Relevance To Building AI Leadership Brain Trust?

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This blog is a continuation of the Building AI Leadership Brain Trust Blog Series which targets board directors and CEO’s to accelerate their duty of care to develop stronger skills and competencies in AI in order to ensure their AI programs achieve sustaining results.

My last few blogs introduced the theme and value of Science and stressed its importance to AI, and focused on the importance of AI professionals having some foundation in computing science as a cornerstone for designing and developing AI models and production processes. The Science blog series focused on three relevant disciplines to AI of 1.) computer science, 2.) complexity science and 3.) physics.

This blog introduces the importance of the field of complexity science and its relationship to AI competencies.

In the Brain Trust Series, I have identified over 50 skills required to help evolve talent in organizations committed to advancing AI literacy. The last few blogs have been discussing the technical skills relevancy. To see the full AI Brain Trust Framework introduced in the first blog, reference here.

We are currently focused on the technical skills in the AI Brain Trust Framework

Technical Skills:

1.    Research Methods Literacy

2.   Agile Methods Literacy

3.  User Centered Design Literacy

4.   Data Analytics Literacy

5.   Digital Literacy (Cloud, SaaS, Computers, etc.)

6.   Mathematics Literacy

7.   Statistics Literacy

8.  Sciences (Computing Science, Complexity Science, Physics) Literacy

9.   Artificial Intelligence (AI) and Machine Learning (ML) Literacy

10.Sustainability Literacy

What is the relevance of complexity science to AI as a discipline?

You do not see enough mention of complexity science in relationship to AI competency development. This really surprises me as complexity science is all about the traversing of disciplinary boundaries and occurs both within and between multiple systems. Complexity sciences have emerged through interdependent and overlapping influences from diverse fields, including concepts from: physics, economics, biology, sociology and computer science.

Complexity sciences strive to understand relevant “system” phenomenon that is characterized by changes, and unpredictability. A “system” is a set of connected or interdependent things or agents (such as a person, a molecule, a species, or an organization). Both systems theory and complexity science focus on the relationships between these elements rather than on each element alone within the system.

One of the important attributes of complexity science is that it produces emergence. Emergence is of particular importance in relationship to innovation where one needs to be patient and appreciate iterative inquiry and persistency. Based on everything I have learned over the past ten years in designing and developing AI models for diverse enterprise use cases from predicting forecasting outcomes on sales data sets to predicting customer call center churn rates to even reviewing AI models that predict which customers in online gaming industries will have the propensity to become a VIP customer, there is always one constant reality.

AI projects that are good never end. They are rooted in complexity sciences simply given so many variables are in play from complex data sets to continual refreshing and augmenting data sets to adding in new methods to increase the predictive accuracy or constantly ensuring the business users are applying the insights and actually advancing new outcomes versus AI models standing like soldiers in isolation of human decision making.

Emergence not only happens in innovation processes but emergence in particular occurs when random events combine to produce outcomes that have observable patterns but are unpredictable and are difficult to reproduce. Examples of unpredictable events could well be Covid-19’s mutating variants, stock market valuations, viral videos, AI deep learning models etc.

Complexity science has been a transformational element in the physical and biological sciences since the 1970’s. However, it has only been in the last decade that the relevance of complexity science to business has begun to be appreciated in full.

The applications to business and to business thinking is profound, and in many ways counterintuitive. As AI starts to revolutionize business processes and business thinking, it increases the value that the knowledge of complexity science and its implications are all the more important. 

An example of complexity in AI is that neural networks are a representation of a complex and dynamic system. AI neural networks leverage diverse disciplines from non-linear dynamics to solid state physics, and human brain physiology, and even parallel computing disciplines.

One of the increasing developments underway bringing AI and complexity sciences together is the understanding that we live in a highly creative and unpredictable world where the system (the world) we live in must adapt to unforeseeable changes that represent new possibilities or new opportunities. Appreciating more holistically the dynamics and complexities of all the factors around AI enablements is a key area for evaluating AI maturity in businesses, resulting in more holistic approaches and better risk management practices.

Hence, ensuring that companies value disciplines like complexity science and holistic system thinking operating frameworks will improve the appreciation of AI and also enable our world to adapt to a world with AI.

What key questions can Board Directors and CEOs ask to evaluate their depth of complexity skills linkages to artificial intelligence relevance?

1.) How many resources do you have that have an undergraduate degree in complexity sciences versus a masters degree or a doctoral degree?

2.) Of these total resources trained in complexity sciences disciplines, how many also have a specialization in Artificial Intelligence?

3.) How many of your most significant AI projects have expertise in complexity science and adaptive systems thinking to ensure holistic thinking in managing complex systems?

4.) How many of the Board Directors or C-Suite have expertise in complexity sciences or adaptive systems to support AI innovations and value emergence practices?

These are some starting questions above to help guide leaders to understand their talent mix in appreciating the value of complexity science disciplines to augment the specializations in artificial intelligence or data sciences in enterprise advanced analytics functions.

Summary

Board directors and CEOs need to understand their talent depth in complexity sciences to ensure that their AI programs are optimized more for success. Ensuring talent in AI has diverse disciplines is key to ensuring AI investments are successful, and continued investments are made to help them evolve and achieve the value to support humans in augmenting their decision making, or improving their operating processes.

The last blog in the three blog science literacy series will further extend the AI Brain Trust Framework, and explore some of the foundations of physics relevant to artificial intelligence

More Information:

To see the full AI Brain Trust Framework introduced in the first blog, reference here. 

To learn more about Artificial Intelligence, and the challenges, both positive and negative, refer to The AI Dilemma, to guide leaders foreward.

Note:

If you have any ideas, please do advise as I welcome your thoughts and perspectives.

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