Michael Watson

Practical AI- machine learning and optimization. Operations excellence and supply chain.

[Oren Barsky @orenbarsky]

This is so good. Seriously - watch this.

He does an excellent job explaining why the Palestinian hoax works so well on "Western suckers who need to feel morally superior while staying intellectually lazy."

So many gems in this video.
I don’t know who he is, but he’s incredibly sharp.
A must-watch.

[Michael Watson @MichaelWatsonAI]

@YGamvros Congratulations. Enjoy the journey

[Michael Watson @MichaelWatsonAI]

@simoptim What do you think caused this?

[Michael Watson @MichaelWatsonAI]

@EmilyLTucker @LarrySnyder610 @EmilyLTucker . I agree, this is a great game! You or others thinking of using may find this helpful: https://t.co/iEAedeJFlU

[Michael Watson @MichaelWatsonAI]

Here are four definitions I see:
1. AI means AGI, real general intelligence.
2. AI means using Deep Learning for hard problems like self-driving cars.
3. AI means Gen AI-- this definition dominates
4. AI means Practical AI. These are business uses like regular machine learning, optimization, simulation... what we used to call Predictive and Prescriptive Analytics.

[Michael Watson @MichaelWatsonAI]

@emollick I agree, AI means different things. Here's my take: https://t.co/wDDKDpGqbj

[Michael Watson @MichaelWatsonAI]

Here's my write up of some of the operations excellence ideas in the book. https://t.co/dELksm9mvp

[Michael Watson @MichaelWatsonAI]

In the popular media, Musk is branded a “tech” guy. In reality, he knows how to make things efficiently. If you teach or are interested in operations management, I found Isaacson's book full of great stories and lessons.

[Michael Watson @MichaelWatsonAI]

Here's a link to my review of the book: https://t.co/PKglO91NEQ

[Michael Watson @MichaelWatsonAI]

Many books about successful people don't stress the role of stamina. Isaacson's Elon Musk book is an exception. It is filled with examples of his stamina. It is a good data point to back up @robinhanson's observation on its importance.

[Michael Watson @MichaelWatsonAI]

@businessbarista The Goal by Eli Goldratt. It is a novel and easy to read. It is set in a factory, but the lessons apply in many places. I have my Operations Excellence class read it, and the students love it. https://t.co/vmg7tZPv19

[Michael Watson @MichaelWatsonAI]

Building inventory buffers for a large, unexpected increase in demand is going to be expensive (things will expire). In addition to flowpile and FIFO, we need to build systems that can quickly ramp up to meet the unexpected demand. This extra capacity is also expensive, but hopefully less than stockpiling inventory.

[Michael Watson @MichaelWatsonAI]

@academic_exit This short post might be of interest to some. I wrote it for Data Scientists, but I think it applies more broadly... https://t.co/Fn8y0pddwa

[Michael Watson @MichaelWatsonAI]

@lauraalbertphd My two favorite books to mention in talks like this are "Prediction Machines" and "Competing in the Age of AI."

The examples I use usually come from the four different types of AI.

[Michael Watson @MichaelWatsonAI]

@lauraalbertphd Practical AI is what we used to call predictive and prescriptive analytics just a few years ago. When a lot of businesses talk about AI, they are talking about practical AI. AI sounds better than Analytics 😀.

[Michael Watson @MichaelWatsonAI]

@lauraalbertphd @uwisye @UWMadEngr Thanks for sharing. This is a great addition to helping explain what IE's do. I liked your points on turning data to decisions, making decisions with uncertainty, and systems and multi-objective thinking.

[Michael Watson @MichaelWatsonAI]

@hariteja1987 I agree. I had to get up to speed on sensor analytics this week, and ChatGPT was perfect

[Michael Watson @MichaelWatsonAI]

@girdley I like this idea, in general, for start ups and bootstrapped companies... here was my experience: https://t.co/oI2AoWkHcb

[Michael Watson @MichaelWatsonAI]

@MMoM12 Hi Mohsen. My big complaint with the AI movement is that they leave out optimization. The Prediction Machine book talks about judgement, but doesn't mention MIPs as a way to make that judgement. I'd love to see that change.