14 Oct

📖In this issue

• Mohammad Hossein Jarrahi on human-AI symbiosis, intertwined automation and augmentation, the race with the machine, and tacit knowledge

• AI to predict life and death

• Varying attitudes to AI across nations will shape its futureC

• Reflections: The dawn and blossoming of AI

🎙️This week’s podcast episode

Why you should listen

Prof Jarrahi is one of the few management researchers focused on human-AI symbiosis, with some deep thinking in the space, inspiring by the concept of biological symbiosis. He has some great recent papers published including the Havard Business Review article What Will Working with AI Really Require?, and the award-winning Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making. Lots of meat in our conversation for those interested in the space!

🤖🕸️🧠Future pointers

AI to predict life and death

A paper in December 2023 in Nature Computational Science Using sequences of life-events to predict human lives used extensive data on Danes to predict lifespan and other life events. Many sites pretending to use the model and data have sprung up (I haven’t found a genuine one, here is the primary resource). The reality that these kinds of predictions will be reasonably accurate raises all sorts of questions. The best outcome is that forecasts based on current behaviors could help people change their lifestyle choices for the better. However more generally improving AI forecasts could induce a kind of fatalism in many.

Varying attitudes to AI across nations will shape its future

A survey on how nervous or excited people are about AI offerings vary widely across nations. English-speaking countries are far more nervous. Asian countries, led by China and South Korea, are far more excited. While South Africa was the only African country surveyed, most African nations would likely be dominated by excitement. Europeans are both less excited and less nervous, perhaps feeling protected by the regulatory environment. While nervousness can shape regulation and caution that avoid risks, excitement begets active, eager exploration of the possibilities. The more excited you are, as an individual or nation, the greater the likelihood you will create exciting outcomes that transcend what has come before.

Ipsos AI Monitor

💡Reflections

The dawn and blossoming of AI

This last week I have been quite unwell with a virus. One book I found to read while I was lying in bed was The Maniac by Benjamin Labatut. It was a real find, highly recommended.

Most of the book follows the life of John von Neumann, one of the few brightest minds of the last century. The author calls the book fiction, as it is written from a variety of first person accounts from people in von Neumann’s life, but all the details I checked on appeared to be correct, so it is pretty close to highly engaging biography.

von Neumann grew up in Hungary, which produced an extraordinary profusion of brilliant mathematicians and scientists. His best friend at school, Eugene Wigner, went on to win the Nobel Prize in Physics, though he himself was blown away by von Neumann’s extraordinary intellect.

Migrating to the U.S. prior to World War II, he went to Princeton’s Institute of Advanced Studies, where the world’s intelligensia, such as Albert Einstein, Kurt Godel, and Robert Oppenheimer, did their work. He worked on the Manhattan Project and other major scientific initiatives, but he also did ground-breaking work on a wide variety of domains including hydrodynamics, ballistics, meteorology, game theory, and statistics.

His real prescience was shown in his work on computing, including creating the MANIAC computer at IAS, surpassing ENIAC, the first electronic computer. While he died in 1957 of cancer, and was not at the 1956 Dartmouth conference which is usually described as the beginning on AI, much of his work was seminal for the development of AI.

He described the basic computer architecture that is still used today, he wrote about brain-inspired computing, he laid out the foundations of game theory and Monte Carlo analysis, which are relevant to a large swathe of the history of AI algorithms, and he laid the foundations for work on self-reproducing automata.

The last part of the book tells the story of Demis Hassabis, the founder of DeepMind, and the fascinating and very human story of how its AlphaGo system beat the best Go players in the world.

The link was that Hassabis, transitioning from being a chess prodigy to completely focused on AI, became fascinated by von Neumann’s last two unfinished books: The Computer and the Brain and Theory of Self-Reproducing Automata.

The fundamental ideas of AI are far from new. Seventy years of extraordinary work and insight later von Neumann’s ideas are coming to fruition.

Humans are incredible. We envision and we create, building on previous ideas and accomplishments. And there is far more to come.