In my earlier blog post I highlighted the key findings from Talent Diversity for Collaborative Innovation, the latest report from the Global Talent Competitiveness Index (GTCI) for 2018. In this post I will delve deeper into a finding which had particular resonance for me: cognitive diversity.
As the report highlights, teams made up of diverse people generally outperform teams of talented but similar people. Quite simply, diversity brings together different knowledge, experiences and perspectives, which pays dividends in terms of results.
A symbiotic relationship
So, what happens when you extend cognitive diversity beyond humans? With the onset of machine learning and artificial intelligence (AI), humans and machines will exist side-by-side in enterprises of all kinds.
Despite the infinite possibilities for progress the meeting of man and machine brings, one doesn’t have to look far for counter-arguments. The most common of these is that machines will enter the workplace at the expense of human job losses rather than to augment the work they do.
Some experts, however, point to a different outcome. Professor Ken Goldberg, for example, of the University of California, Berkeley, has suggested that AI opens the way to more diversity and multiplicity.
Rather than humans being led or overtaken by machines, Professor Goldberg postulates the concept of ‘Multiplicity’ that sees diverse groups of machines and humans working together harmoniously. Diversity, therefore, is part and parcel of this symbiotic working relationship.
Improving performance through diversity
Far from a sci-fi movie scene, for Goldberg, this is happening now. The combination of machine learning and data already drives many of the online services we use every day. Amazon’s ‘other people also bought’ recommendations or Netflix’s suggested movies are examples of this.
On the flipside, the more diverse the data, the better the results that AI can deliver. Goldberg highlights how research conducted at the University of California by Leo Breiman and Adele Cutler shows that multiple algorithms will outperform a single one, as long as those algorithms are diverse.
So, the more diverse the input from humans and machines, the more the potential for successful outputs and outcomes. By applying cognitive diversity to humans and machines, enterprises can make their collective thinking more diverse, ultimately leading to improved performance.
Read one of our previous blogs on transforming society in the automated vehicle age.