As a retired software architect and director of software engineering, I worked with AI on and off for decades. It wasn’t until the last 5 years or so that systems have gotten fast enough to really make an impact with AI systems. But an old saying still applies - Garbage in = Garbage out. AI systems rely on knowledge from experts in the field. You don’t go survey 1,000 random people for input on how to build nuclear reactor. You get input from educated people in the field.
One AI system I had a lot of input in was when I was working as a software consultant almost 30 years ago. A team of 5 software engineers designed and built an automated warehouse system - nowhere near the sophisticated systems used today by companies like Walmart or Amazon. We gathered knowledge on how the warehouse worked from the people that worked there. It became clear early on that there were 2-3 people who had extensive knowledge on how everything worked. We used their knowledge to build the AI based inventory control system for the warehouse. The system was actually built in 5 months…and then another 5 months to workout all the kinks (flaws). As of 2 years ago the system was still in place (that’s an awful long time for a piece of software to run).
Back to cars. Ford recently laid off many of their engineers to be replaced with AI. The problem is Ford laid off too many of the WRONG engineers. Many of the engineers laid off were top engineers with decades of experience. The AI system Ford built was based on faulty knowledge from the wrong people (Garbage in = Garbage out). Ford is now hiring those engineers back to train junior engineers and for better input into the AI system.
The auto industry has made similar mistakes before that they did not learn from.
During a downturn they would offer early retirement packages to the senior engineers only to find the loss in very specific knowledge bit them in the rear after they hired new grads at a fraction of the pay. Same mistake only with AI.
I just shake my head and chuckle when I read about such things.
Strange the folks in charge never realized this. They talk about reputational issues like it’s just another line item to manage, but for some of us who are not up on all the product detail, we are simply afraid to buy a ford product. Add GM to that list now and Chrysler (a rose by any other name) has been left in the dust for years.
While I blame EPA for forcing conversion to turbo 4 cyl and unreliable transmissions, there is no reason they couldn’t beef up bearings and other engines to handle the job. So now reputations destroyed and millions of engines on recall. Duh.
Absolutely right Mike. If you work in this industry, you don’t need hindsight to see the failings happening in real time. It is obvious to the people in the trenches for example.
One issue I have with this kind of reporting and it’s a nitpick, is everything is lumped into this category of AI now when often what’s being described is a small subset of what encompasses AI. These are often describing expert systems with inference engines and not really what a modern AI system can/should be able to do. The main difference is that a true AI system can adapt its programming to new data. What they likely have is a rules based logic system that can identify a possible solution based on the outcome of the rules logic that has been programmed into it. A real AI system would eventually figure out it is wrong and develop new algorithmic logic and subsequent solutions based on those incorrect outcomes. It learns. The human experts are no longer required then.
When an AI develops faulty logic and then builds new solutions based on those incorrect outcomes, the system essentially starts walking down the wrong path and running faster. This sets off a chain reaction of compounding errors that can cause the AI to malfunction and worse case, harm the people using machinery that uses the AI instruction set.
This is called a “Feedback Loop…” An AI learns by looking at patterns in its own outputs. If the first answer is wrong, the AI treats that wrong answer as a fact. It uses this bad information to train itself for the next task. This creates a dangerous loop where every new solution becomes worse than the last one.
Then the AI suffers “The Illusion of Truth” (Hallucinations as we call them…) The AI often predicts what a correct answer should look like, rather than verifying actual facts. This can result in an AI hallucination—a completely made-up piece of information presented as a fact. When an AI uses false data to build a solution, the result will sound perfect but be completely useless or wrong.
I’d LIKE to know! About 2/3rds of my docs are using AI inputs when they see me. No effect of that… yet! One of my wife’s docs told her it had been useful with a few patients so far.
And I asked search AI about a dizziness problem I was having. It directed me to something called and “Epley Maneuver” and how to to perform it at home from the Mayo Clinic’s site. I did it, it worked.
My PC doctor uses an AI app called Medical Brain. She uses it to check in with me for blood pressure checks and other info, but it can also be used to send messages to her.
When I used it–for the first time–to ask a question, it replied, “We will submit this question to your doctor”, and apparently it did do that because she responded to my question w/in 30 minutes. So far, I have no problem with that use of AI.
Not about AI, just management in general. Last hospital I worked at, management’s answer to everything was to cut staff. Thank g*d that group was not running the place when I had appendicitis.
He’s been fairly reliable with a lot of experience.
Back a few years ago one of the Minneapolis lawyers that was from South Dakota, fjust for the fun of it, asked for a listing and short bio for South Dakota governors. The results were hilarious. Of course some correct but some just made up out of thin air or thin chips. Never heard of them. Non-existent. Seemed like when it got stuck, it couldn’t say don’t know but just made it up to fill in the blanks.
The layoffs had to have occurred several years ago:
The automaker brought back 350 veteran engineers to correct errors made by its automated design and quality systems, Bloomberg reported. Ford calls them “gray beard” engineers. The result: Ford topped JD Power’s mainstream quality ranking for the first time in 16 years.
The turnaround was enough to push Ford to the top of JD Power’s 2026 initial quality study, which measures problems reported by owners in the first 90 days of ownership. Ford scored 152 problems per 100 vehicles, ahead of Nissan and Buick. The F-150, Mustang, and Super Duty each won best in segment for the second consecutive year.
The vehicles have been built, sold and the customer’s surveys have been reviewed. For two years.
The Question; is this a real story or AI generated?
Were the Electric Vehicle engineers were laid off 18 months ago when the government changed the sales requirements for EVs? Then brought back for other positions in the company after their unemployment payments expired? That would not fit the AI narrative.
AI is the new Buzzword. Everyone is getting on the AI train - not knowing anything about it or where it will lead them. All they know (or have been told) is if you don’t get into AI, you’ll be behind everyone else.
Not strange at all. If you knew anything about the computer industry, you’d see the HUGE mistakes companies made.
. IBM allowing Microsoft to license DOS separately which cost IBM hundreds of billions.
. HP allowing Steve Wozniak (the brains behind Apple) when Steve informed them that he was working on a private side project. By all rights because Steve was an employee at the time they had first rights to anything he designed.
. Zerox invented Windows, Packet switching network (the standard used today), Object Oriented programming and design. Their thinktank thought up all these fantastic ideas, but upper management had no idea what to do with it.
. IBM and AT&T were approached by the team that invented the packet network system we all use today and offered the to be the first companies to implement it. They turned it down. They thought it wouldn’t work.
I can go on for pages and pages. Most of the problems stem from upper management not understanding the technology. The farther you go up the chain, the less technical they are.
How can we make JUNK, short lived motors? Make them put 4 cyl and put a JUNK Chinese turbo on them and KNOW that 90% of owners rarely change their oil. Did you need AI to do that? That was 1000% cost cutting short sighted management
