We have a beautiful line of pine trees offering camouflage to our home in the heart of suburban Toronto. This sanctuary is a haven for Robins, Blue Jays, Cardinals, Finches, sparrows, occasional Magpies, and of course, energetic squirrels, rabbits, naughty raccoons, skunks, and fearless beavers who are not camera-shy and sometimes work tirelessly in broad daylight. This small sanctuary facilitates easier conversations with Ivy, my 6-year-old. A few days back, as a Robin perched under one of the snow-capped bristlecones and flew into the L-shaped large glass window frame, it was as if a high-definition film came to life. In that quiet moment, it seemed the Robin was pondering her big decision: whether to leave for her annual migration or stay. The reluctant snow and warmer winter seemed to make life both easier and more difficult for Robin at the same time. Easier because it could avoid the long flight, yet more difficult because the seasonal anomalies required adaptability and thought.
I was still reeling from the unconvincing response I gave Ivy the previous evening when we had a small discussion about my work as a Modern Scientist. She wasn’t convinced, and I was relying on that moment to teach her about how science begins with observation. She retorted, “Where is your lab?” I explained that science also involves building machines using a computer. She knows that we are building conscious machines, where she could store all my thoughts, and summon them like Alexa if she ever missed me once I was gone (we had talked about life and death). But she sought another explanation.
Me: You are conscious, self-aware. Are you?
[She nods in agreement]
Me: Self-awareness means knowing limits. You know there is a limit to crying for no reason. Do you?
Ivy: Yes!
Me: Once you understand these limits, you understand your good and not-so-good emotions. Right?
Ivy: Yes!
Me: And once you understand your emotions, you can work on managing them. Correct?
[She nods in agreement, getting bored]
Me: Just like you, conscious machines are self-aware. They have limits. Once they understand their limits, they can control themselves. But unlike your emotions, they have biases.
At this point, I lost her to a playdate. But my mind kept lingering on thinking about how humanity finds it impossible to be bias-aware, even though biases are everywhere. Confidence bias, Gambler’s fallacy, survivorship bias, regret bias, confirmation bias, hindsight bias—the list goes on. To expect intelligence from machines built by bias-unaware or bias-indifferent humans is a surefire way to increase volatility and complexity, not intelligence. With so many biases, it’s simply hard to untangle. Hence, bias-awareness seems an impossible reality for humans. No wonder humanity has failed to consistently beat a simple rule of an Index despite all the knowledge we have accumulated since then. It is not just hard to remove bias from the information of our age, it is impossible.
In such settings, to build machines that are bias-aware and still capable of working with biased information is nothing short of modern science. No information operates beyond the laws of Science. Information, like everything else, lives under the influence of gravity, even if it seems hard to conceive of an architecture for information. Modern finance, owing to its simplistic informational assumptions, is not equipped to explain informational bias. If it were, it would have explained the underperformance predicament. Modern finance lacks boundary conditions, hence it has no mechanism, and therefore, it is facing monumental challenges. The answer to bias awareness, and hence intelligence, was so simple; all we had to do was be open to history, observation, questioning, and hence Science. Thinkers have talked about mechanisms of time which rule information and determine the future of a child.
Jacob Bernoulli said probability was a chance of a chance and worked as a stochastic system.
Gregor Mendel showed how genetics was a mechanism.
Robert Brown illustrated randomness with Brownian motion.
Joseph Schumpeter said that a composite cycle of time was a phenomenon that ruled the Business Cycle.
David C. McClelland said that childhood activities predict future behavior.
Herbert Simon said complexity was simple and hierarchical.
Robert Solow said Economics was not Science with a capital S.
Kenneth Boulding said that Information flits between relevance and irrelevance.
BZ Cycles explain how dual equilibrium systems can create oscillating chemical reactions.
Ernst Pöppel said Human perception was temporal and hierarchical.
We have not seen the Robin since then; she flew away on her long journey. She was bias-aware and made a decision, not so surprisingly, before the winter storm hit Toronto. We will wait for her return and keep you posted on our conversations around simplicity, common sense, and Science.
Bernoulli, Jacob. Ars Conjectandi. Thurneysen Brothers, 1713.
Mendel, Gregor. "Experiments on Plant Hybridization." Read at the meetings of February 8th, and March 8th, 1865, of the Brünn Natural History Society.
Brown, Robert. "A Brief Account of Microscopical Observations Made in the Months of June, July and August, 1827, on the Particles Contained in the Pollen of Plants; and on the General Existence of Active Molecules in Organic and Inorganic Bodies." Philosophical Magazine, 1828.
Schumpeter, Joseph A. Business Cycles: A Theoretical, Historical, and Statistical Analysis of the Capitalist Process. McGraw-Hill, 1939.
McClelland, David C. The Achieving Society. Van Nostrand, 1961.
Simon, Herbert A. "The Architecture of Complexity." Proceedings of the American Philosophical Society, vol. 106, no. 6, 1962, pp. 467-482.
Solow, Robert M. Growth Theory: An Exposition. Oxford University Press, 1970.
Boulding, Kenneth E. The Image: Knowledge in Life and Society. University of Michigan Press, 1956.
Zhabotinsky, A.M. "A History of Chemical Oscillations and Waves." Chaos, vol. 1, no. 4, 1991, pp. 379-386.
Pöppel, Ernst. "A Hierarchical Model of Temporal Perception." Trends in Cognitive Sciences, vol. 1, no. 2, 1997, pp. 56-61.