This is a summary of the book “How to stay smart in a smart world – why human intelligence still beats algorithms” written by Gerd Gigerenzer and published by MIT Press 2022.  He is a psychologist known for his work on bounded rationality and directs the Harding Center for Risk Literacy at the University of Potsdam. He is also a partner at Simply Rational – The Decision Institute.

Recent advances in artificial intelligence have juxtaposed a different form of intelligence to ours and poses a question about the role of either intelligence. With the spectrum of reactions ranging from embracing it openly to being apprehensive about its prevalence or dominance, the author picks out a cautious approach playing on the strengths and avoiding the weaknesses. With several examples and case studies, he argues that one form of intelligence works well in stable environments with well-defined rules while the other will never lose its relevance outside that world.

The salient points from this book include assertions that AI excels in stable environments and follows rules dictated by humans, AI systems don’t perform well in dynamic environments, filled with uncertainty. Humans must try out AI to get best results. In unexplored territory, simple and transparent algorithms perform better than complex ones. Among the negative impacts, ad-based model from social media platforms can be cited. It’s possible to separate human interaction with machine supervision with clear demarcation. For example, self-driving cars could be given their own dedicated lanes where possible. Market hype and profit incentives can lead companies to overcompromise and underdeliver on digital technologies.

AI wins hands down in many games such as chess, Go etc because it learns the game rules that are fixed, it is tuned by human experts and uses brute calculation to determine the best possible move. The better defined and more stable the premise, the better the performance. The flip side is self-evident with facial recognition for instance that works 99.6% of the time. In dynamic environments, the number drops significantly. When UK police scanned the faces of 170000 soccer fans in a stadium for matches with criminal database, 93% of the matches were false.

AI is good at making correlations with huge amounts of data, even some that would have escaped humans, but it cannot recognize scenarios and deal with ambiguity. For example, Maine’s divorce rate and the United States’ per capita consumption of margarine have a significant correlation but it makes no sense. Its these false findings by AI that makes them even harder to replicate leading to a lot of waste and error in areas such as health science and biotechnology and to the tune of hundreds of billions of dollars. Assertions made today such as eat blueberries to prevent memory loss, eat bananas to get higher verbal SAT score,  eat kiwis late at night to sleep better etc may just be the opposite in due time.

Whenever the effectiveness of AI decreases, human intervention can significantly boost their performance. The human brain has a remarkable ability to adapt to constantly changing cues, contexts and situations in what is termed as vicarious functioning. Staying smart means leveraging singularity capabilities but staying in charge. AI lacks four components of common sense – a capacity to think casually, an awareness of others’ intentions and feelings, a basic understanding of space, time and objects, and a longing to join in group norms. Some tasks like recommending the nearest restaurant do not need common sense but the detection of a person crossing the road in a war zone as a threat requires it.

Complex problems do not justify complex solutions. Google Flu trends tried to predict the spread of flu with approximately 160 search terms but they still overpredicted doctors’ visits. In comparison, an algorithm from Max Planck Institute for human development simply used one data point: recent visits to the doctor from the CDC website and performed much better in predicting the flu’s spread.

Information when served subliminally or unknowingly have potential to alter our behavior. This is why ad-based model for social media can be harmful by creating distractions. With attention control technology, the user is held captive by these algorithms. Texting while driving has caused 3000 deaths per year in the United States between 2010 and 2020. In areas other than driving, smartphones have proven to be very distracting.

Finally, the business aspect of artificial intelligence must be realized in the context of historical trends with killer technologies and the commerce behind it. The author says we should be able to profit from AI but not be easily misled with expectations and predictions.

Earlier book summaries: BookSummary10.docx