Leave Intuition to the Machines

However, the advent of machine learning brought forth a novel variant of machine intelligence. It demands extensive training on data, after which it operates almost instantly. While notoriously opaque, its workings are remarkably effective on average. We argue that this mirrors humans’ System 1 thinking: Human intuition is built on years of experience but operates almost instantly.

How will humans work alongside AI? The fundamental premise of most narratives is that tasks can be divided into subtasks that humans and machines undertake based on their relative strengths. This line of reasoning echoes enduring principles behind specialization, outsourcing, offshoring, and strategic alliances.

This short list could then undergo a rigorous review using systematic, logical procedures that can be thoroughly checked and explained. This is within the purview of well-trained, methodical managers using System 2 thinking, as well as traditional rule-based AI systems. But here too, the scale and computational power of AI offer advantages. Checking facts, conducting analyses, ranking candidates on multiple criteria, clustering them in higher dimensional spaces—these are all procedures that machines can and have been doing for a long time.

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To bring this idea to life, let’s consider a classic managerial dilemma: “Which project should I invest in among several options?” Some process of funneling is necessary to go from a large set of projects to a smaller set that bears closer examination. The projects could be candidates for recruitment, potential partners for a strategic alliance, or takeover targets. Conversely, creating a large enough list of initial candidates (ideation) is also important to ensure a good coverage of the possibilities. Given the vast data associated with various projects, some of which may not be easily processed, some form of intuition or judgment can be helpful, particularly under time pressure. 

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Though the ability to pivot between System 1 and System 2 has long been emphasized in decision-making research, with debate over how well humans are able to do so, it’s not generally seen as its own system. Yet if System 1 and System 2 tasks are carried out by AI, this pivoting between the two—call it System 3—is where human intelligence comes into play. 


Leave Intuition to the Machines

Is it time for System 3 thinking by humans?

So, what does this mean for managerial work? We predict that the blend of artificial intelligence (AI) and human thought will remain indispensable—at least for now—but with an unexpected twist. Far from being limited to grunt work, AI will be entrusted with some of the more creative and intuitive components of decision-making, tasks viewed as fundamentally human. It won’t replace managerial work but rather reshape it.

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This development allowed Alphabet’s AlphaGo in 2016 to triumph over Lee Sedol, the top player at Go, a game that humans were supposed to always dominate because intuitive play is crucial to success. 

Combining thinking styles across humans and AI

We propose a shift in focus from task specialization to a specialization by thinking type. If machine intelligence is capable of intuitive reasoning (System 1) on a superhuman scale, and if existing computational systems already outpace humans in logical reasoning (System 2), where does that leave room for humans? We contend that the answer is in the integration of these two systems. 

AI’s evolution has taken a different path. Its starting point is logic, akin to System 2 thinking in humans. Rapid logical computation is what allowed IBM’s Deep Blue to triumph over chess grandmaster Garry Kasparov in 1997.  

How will humans work alongside AI? The fundamental premise of most narratives is that tasks can be divided into subtasks that humans and machines undertake based on their relative strengths. Photo credit: Temple Cerulean on Unsplash.

We believe that this form of thinking is where human managers should invest their skill development efforts. It presents an exciting fusion of human cognitive flexibility in harnessing “machine precision” with “machine intuition,” maximizing the strengths of both, and mitigating their weaknesses. The metaphorical image we have is that of a human charioteer guiding the twin steeds of machine precision and machine intuition, yoked together to produce rapid progress in decision-making.

This is where System 1 thinking kicks off the process for most managers. Their years of experience in a context may have generated insights that operate subconsciously, producing what we think of as managerial intuition. But what if, rather than relying solely on their gut instinct for the initial selection, managers enlisted a large language model (LLM) to sift through the myriad initial options and generate a short list of feasible alternatives? A list generated by an LLM could be both larger and begin with a larger candidate pool. 

We generally approach tasks intuitively, only engaging System 2 if something in the environment suggests that thinking harder might be required. Though our System 1 improves naturally from the experiences and feedback we amass over time, we make conscious efforts to improve our System 2 thinking, for instance, through formal education to develop our logic. 

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منبع: https://www.qualitydigest.com/inside/innovation-article/leave-intuition-machines-020624.html

Published Jan. 15, 2024, by INSEAD.