[xz0r]

> Logistics companies rely on huge amounts of optimization and problem-solving.

Having worked at the largest tech based Logistics company in India, I can say, we did rely on optimization and problem-solving, but none of them involved AI, they were mathematical models and not black box.

[Melting_Harps]

> Having worked at the largest tech based Logistics company in India, I can say, we did rely on optimization and problem-solving, but none of them involved AI, they were mathematical models and not black box.

I was going to say the same thing, but I worked at BMW and VW and spearheaded several initiatives with Corporate partners that relied more on optimizing via mathematical models/data sets within the warrantied parts/Takata airbag recall at BMW and the Tdi Diesel-gate buy program at VW. It entailed lots of data analysis and some trial by error on my part that eventually got us a favourable result, not AI.

AI can be useful, one day, but I'm returning back to Supply Chain analytics and Logistics and not much seems to have changed in those years. I submitted my proposal for a Supply Chain Analytics course as my final project that I drew up in 2017 at BMW and got 99.7% for my thorough, and more importunately to me, relevant analysis and execution of a scheme to optimize leadtime and overall turn over using the means and methods available back then.

A part of me wishes I could just run an algo/AI protocol with predictive modeling to do that all for me, as it almost got me fired several times trying to deploy it and I had to go over managements head and straight to the owners and corporate to get them to try it.

Luckily, I was able to negotiate a return to VW as result of my scheme, who were hemorrhaging Billions, were getting execs thrown in prison at the time so were way more receptive to ideas of cost cutting. Crazy days...

* AI initially inserted 'trail,' instead of a commonly used phrase 'trial by error' perhaps proving how autocorrect/spellcheck AI usecases are still not where they need to be to prove the point of the aforementioned post.

[a1369209993]

> commonly used phrase 'trial by error'

It's actually "trial and error", although that doesn't speak any better for autocorrupt.

[Melting_Harps]

> It's actually "trial and error", although that doesn't speak any better for autocorrupt.

Indeed. You got me there. But the point stands, as you mentioned.

[colechristensen]

There's my bet on a less hyped but very prevalent future. Really good real mathematical models of things, that is, deriving systems for your product which can predict and control from scientific laws, not just black box automation.

Of course this isn't new, but a broad focus on specializing on developing talent to create these models and methodology and tech to help would be.

[bulldoa]

what kind of mathematical models do you guys use (like convex optimizations?), I am very interested in this subject and would hope to learn more.

[_fq4v]

> they were mathematical models and not black box.

Wow... talk about changing the definition of something to fit your world view.

[bo1024]

That's still AI, it's just older-generation AI.

[mkl]

I think it's just maths. Otherwise, if not for the absence of calculation-by-machine, Isaac Newton and Archimedes would have been doing AI.

[throwawaygh]

Lots of operations research, planning, optimization, and control theory came out of funding streams that were very much in the auspices of Artificial Intelligence. In most universities, "Artificial Intelligence" is still the name of the course where Computer Science students first encounter everything related to OR, optimization, planning, etc.

It's only since 2013 or so that AI = ML = DL.

> if not for the absence of calculation-by-machine, Isaac Newton and Archimedes would have been doing AI.

From the Stanford Encyclopedia of Philosophy entry on Leibniz's Philosophy of the Mind [1]:

"He believed that such a language would perfectly mirror the processes of intelligible human reasoning. It is this plan that has led some to believe that Leibniz came close to anticipating artificial intelligence. At any rate, Leibniz's writings about this project (which, it should be noted, he never got the chance to actualize) reveal significant insights into his understanding of the nature of human reasoning. This understanding, it turns out, is not that different from contemporary conceptions of the mind, as many of his discussions bear considerable relevance to discussions in the cognitive sciences."

[1] https://plato.stanford.edu/entries/leibniz-mind/