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Artificial Intelligence Education Innovation Network Reading

Latest Read: The AI Delusion

The AI Delusion by Gary Smith. Gary holds a Ph.D. in Economics is a professor Economies at Pomona College. He was a 1967 Woodrow Wilson Fellow and a 1968 Yale University Fellow. He was awarded a Stanford Research Institute Grant in 1978 and a NSF grant for an economics computer lab beginning in 1995.

The AI Delusion by Gary Smith

Gary certainly provides a solid narrative that artificial intelligence is not perfect. On the contrary, it is quite far from perfect. As a result, we should be aware of how much blind faith is given to so many artificial intelligence services. We do this at our own peril.

IBM’s Watson is an example. Gary explains why Watson, a question-answering computer system capable of answering questions posed in natural language is a bad match for healthcare but can be an absolutely wonderful solution in other markets.

The AI Delusion certainly also reveals how many times artificial intelligence systems have simply failed. These lead to important lessons. At the same, time Gary does acknowledge that today’s machine learning has solved problems thought impossible just twenty years ago.

For example, the Obama campaigns in 2008 and his 2012 re-election deployed data analytics that were critical in his win and re-election. Yet, the Hilary Clinton campaign followed data insights from a machine learning system named Ada. This big data system advised against campaigning in Michigan and other states. This so upset former President Bill Clinton that he attempted to persuade the campaign to change strategy, however he was overruled by Ada. A powerful example of big data going off the tracks.

Gary is certainly acknowledging that machines in the future will have the ability to think, however today many are mislead by deep neural networks. Many on the surface associate brain neurons to artificial intelligence’ neural networks. Neural networks do not mimic the brain. Neural networks are indeed powerful programs that execute complex mathematical programs. However, today’s neural networks do not understand words, or images.

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Education Innovation Reading

Latest Read: The Formula

The Formula: The Universal Laws of Success by Albert-Laszlo Barabasi. He is a former physics professor at the University of Notre Dame. Today Albert is the Director of Northeastern University’s Center for Complex Network Research (CCNR) associate member of the Center of Cancer Systems Biology (CCSB) at the Dana–Farber Cancer Institute and visiting professor at the Center for Network Science at Central European University.

The Formula: The Universal Laws of Success by Albert-László Barabási

He introduced in 1999 the concept of scale-free networks and proposed the Barabási–Albert model to explain their widespread emergence in natural, technological and social systems, from the cellular telephone to the World Wide Web or online communities.

Surprising to realize 13 years ago I was reading his book Linked: How Everything Is Connected to Everything Else and What It Means. Link certainly proved very thought provoking. It has aged well since 2008. Based upon that experience I quickly read his followup Bursts: The Hidden Pattern Behind Everything We Do in 2010.

Albert addresses how you can now quantify success. This will differ obviously across markets adn professions, but the ties linking them together are quite interesting. There is a building block of his expertise in networks.

He devotes a chapter for each of his defined universal laws of success.

Performance drives success, but when performance can’t be measured, networks drive success.

In athletics networks will not help you. If you win the US Open Tennis championship it will not matter who you know. Your success will drive instant recognition.

Yet, one focus of Chapter 1 is the Red Barron who remains the most famous World War I fighter pilot. Yet, René Fonck a French pilot actually scored more kills. However grocery stores today have Red Barron pizza. There are Red Barron 3D computer games. Even Charlie Brown, the most famous children’s cartoon holds the Red Barron as a character for Snoopy. Performance truly drives success.

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Education Flat World Globalization Innovation Reading Technology

Latest Read: The Premonition

The Premonition: A Pandemic Story by Michael Lewis. A tough, certainly insightful look at men and women who understood and directly confronted the pandemic at the beginning. At the same time, they ran into bureaucratic roadblocks. Their efforts to save the country is the story of this book.

the premonition a pandemic story

For instance, as early as January 2020, Dr. Charity Dean, the assistant director of the California Department of Public Health in 2020. She certainly understood the coming pandemic and began warning California State officials. Surprisingly, Charity Dean was even prohibited from publishing the word “pandemic” in her research reports. Furthermore, as stunning as it may seem, her boss and the state locker her out of planning meetings.

Dr. Carter Mecher, senior medical advisor to the Veterans Administration initially helped craft the Bush Administration’s pandemic response plan. As a result, at the very beginning stages in January 2020, he observed similarities to the 1918 Influenza flu. Indeed, Carter was the early advocate to shut down schools to reduce spread. Tragically, he lost his own mother to COVID.

At the same time, Joe DeRisi PhD, a biochemist at UC San Francisco was involved in the development of the ViroChip. This is used to rapidly identify viruses in bodily fluids. He led a team to develop a very early COVID-19 testing facility at the outbreak of pandemic.

Dr. Richard Hatchett an epidemiologist was another who warned early on about the coming pandemic. He also contributed to the Bush era pandemic response plan. This book is a sobering reality of what could have been. These medical professionals were stopped by the same system they were trying to save. Michael certainly makes it very clear the US does not have a healthcare system.

Tipping point ignored

Surprisingly, President George W. Bush read The Great Influenza: The Story of the Deadliest Pandemic in History. As a result, he triggered a plan to confront the next pandemic with Rajeev Venkayya, Richard Hatchett and Carter Mecher. This plan continued through the Obama Administration, but stopped under Trump.

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Education Innovation Reading

Latest Read: Noise

Noise: A Flaw in Human Judgment by noted authors Daniel Kahneman, Olivier Sibony, and Cass Sunstein. Noise is simply random, unpredictable decision making that cannot be explained. At the same time, is not accountable. This is very perplexing today.

Noise: A Flaw in Human Judgment by Daniel Kahneman, Olivier Sibony, Cass R. Sunstein

At the same time, this is not easy to fully understand. Here is a good outline provided by the book:

Imagine that two doctors in the same city give different diagnoses to identical patients — or that two judges in the same courthouse give different sentences to people who have committed the same crime.
Suppose that different food inspectors give different ratings to indistinguishable restaurants — or that when a company is handling customer complaints, the resolution depends on who happens to be handling the particular complaint.
Now imagine that the same doctor, the same judge, the same inspector, or the same company official makes different decisions, depending on whether it is morning or afternoon, or Monday rather than Wednesday. These are examples of noise: variability in judgments that should be identical.

As a result, it is amazing to understand how and why people from all walks of life make really bad judgements. The fact that it can be quantified and even controlled offers us hope. However, based upon our crazy world today it only offers hope.

How many times have you found yourself impacted by Noise?

Sections that certainly stick most with organizations is within their hiring process. The authors really hit a home run here. Based upon their research and insights, this is worth the price of admission alone. Issues like prejudice are actually bias. When assessing decisions that go wrong, noise is the standard deviation of errors, while bias is the mean itself.

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Education Innovation Reading

Latest Read: The Bomber Mafia

The Bomber Mafia: A Dream, a Temptation, and the Longest Night of the Second World War by Malcolm Gladwell. His famous 2004 TED Talk about pasta sauce placed Malcolm onto the world’s new internet stage.

the bomber mafia

I also enjoy his podcast series Revisionist History. As a matter of fact The Bomber Mafia is an outcome of podcast Season 5, Episode 4. So Malcolm has delivered a rather unique book.

This is not a feel good story. Malcolm reveals the horror of war and the understanding that precision bombing dealt a harsh blow to Germany, while firebombing Japanese cities caused the deaths of tens of thousands of civilians.

The story begins with a short history of aerial bombing in World War I. Then Malcolm introduces Major General Haywood S. Hansell.

So Hansell and the mafia of Air Force leaders developed America’s high-altitude precision bombing strategy in World War II. His strategy was to limit civilian casualties as the pacific campaign was beginning to ramp up.

However, Hansell was replaced by Major General Curtis LeMay. Instead, LeMay altered the US Air Force tactic to a low altitude, fire bombing campaigns across Japan.

Did LeMay sell his soul?

Malcolm certainly structures this powerful storyline around Luke 4:2, the temptation of Christ by the devil:

And the devil took him up and showed him all the kingdoms of the world in a moment of time, and said to him, “To you I will give all this authority and their glory, for it has been delivered to me, and I give it to whom I will. If you, then, will worship me, it will all be yours.
Luke 4:2

LeMay led a devastating bombing campaign, killing hundreds of thousands of civilians. In fact, after a single firebombing of Tokyo, between 100,00 to 130,000 civilians burned to death. Yet all the firebombing did not impact Japan’s industrial capacity to wage war.

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