Leon on Everything

The challenge of epistemology “It’s Difficult to Make Predictions, Especially About the Future” — Yogi Berra A thief goes through the window in the middle of the night to make a grand jewelry heist. While tiptoeing inside the apartment, he sees the longcase clock with the time 1.05. He knows that the owners will be back at 1.15 AM. He decides that the safest thing for him is to exit and escape without getting the precious necklace. He can try again later. Unbeknownst to him, the clock had stopped working 12 hours ago, but at that moment, it just happened to be 1.05 AM. After the American philosopher Edmund Gettier, our thief experienced what has been dubbed a  Gettier  problem. These problems’ archetypes challenge the classical criteria for knowledge: justification, truth, and belief. In our example, the thief is justified in believing it’s 1.05 AM, and it is 1.05 AM, and he believes it. Yet he doesn’t have knowledge because the instrument he relies on is faulty. I recently ha...

  Neural Networks hallucinate a mistaken reality Our Cats “meanings just ain’t in the head” — Hillary Putnam You are driving your Tesla Model S down a desert route in the United States. There is no other car or pedestrian in sight. You decide to switch the autopilot as your focus shifts between the road and your partner seated next to you. At the same time, a Space X rocket is safely on the landing trajectory back to earth. All is good! Suddenly a tiny meteorite heading for the earth at just the right trajectory hits the rocket. It knocks it off course and damages its thrusters. Only the force of gravity and the wind resistance are in control of the rocket now. It crash-lands a few feet in front of your car. Tesla’s autopilot doesn’t swerve, and it doesn’t stop. It goes straight through killing you, your partner, and destroying the vehicle and its A.I. computer. What happened? The car hallucinated that the rocket was a plastic bag. Such a case or a similar one never occurred when t...

  I grew up in a highly Francophone area in East Beirut. It was customary during that time to teach French to your toddler before any other language. As such, I spoke French before I learned a single Arabic word. Also, due to my grandfather’s own name change, my parents decided to call me Léon when they registered me and chose my name. This is an old French name, and it is pronounced the way you would in French. Years later I would drop the accent when I wrote emails in English. This is not because I preferred the English pronunciation of the name but rather to make my life easier when I use the qwerty keyboard as well as not to have my English-speaking reader struggle when reading my name. But the name that my parents call me by to this day, or the name I used to introduce myself to my wife when we first met, remains Léon. And it is still pronounced the way you would in French. If you’re an Anglophone, or we are speaking in English I would be perfectly fine with you calling me Leo...

 An AI that is 99% accurate is not good enough. Think of having to digitize some handwritten document with important end-customer data. Does it make sense to use machine learning if on top of that you need human operators? Think of how many times Alexa gets you wrong? Even when it comes to playing the same song you have requested many times before. But Alexa is not a mission-critical system. You don’t care if it’s 90% accurate that alone 99%, but for critical systems, it seems that AI isn’t delivering the value it is supposed to (how far are we from the promised self-driving dream?). How much effort will be saved by a human quality controlling every document vs if a human operator digitized them from scratch? I remember a project I was tangentially involved in years ago in the pre-AI area (in fact over 20 years ago), the project manager decided to use three people to digitize the data and then use text comparison so that if two documents match then an algorithm would drop the third...