I sat in a room of probably 400 engineers last spring and they all laughed and jeered when the presenter asked if AI could replace them. With the right framework and dataset, ML almost certainly could replace about 2/3 of the people there; I know the work they do (I’m one of them) and the bulk of my time is spent recreating documentation using 2-3 computer programs to facilitate calculations and looking up and applying manufacturer’s data to the situation. Mine is an industry of high repeatability and the human judgement part is, at most, 10% of the job.
Here’s the real problem. The people who will be fully automatable are those with less than 10 years experience. They’re the ones doing the day to day layout and design, and their work is monitored, guided, and checked by an experienced senior engineer to catch their mistakes. Replacing all of those people with AI will save a ton of money, right up until all of the senior engineers retire. In a system which maximizes corporate/partner profit, that will come at the expense of training the future senior engineers until, at some point, there won’t be any (/enough), and yet there will still be a substantial fraction of oversight that will be needed. Unfortunately, ML is based on human learning and replacing the “learning” stage of human practitioner with machines is going to eventually create a gap in qualified human oversight. That may not matter too much for marketing art departments, but for structural engineers it’s going to result in a safety or reliability issue for society as a whole. And since failures in my profession only occur in marginal situations (high loads - wind, snow, rain, mass gatherings) my suspicion is that it will be decades before we really find out that we’ve been whistling through the graveyard.
that will come at the expense of training the future senior engineers until, at some point, there won’t be any (/enough)
Anything a human can be trained to do, a neural network can be trained to do.
Yes, there will be a lack of trained humans for those positions… but spinning up enough “senior engineers” will be as easy as moving a slider on a cloud computing interface… or remote API… done by whichever NN comes to replace the people from HR.
ML is based on human learning and replacing the “learning” stage of human practitioner with machines is going to eventually create a gap in qualified human oversight
Cue in the humanoid robots.
Better yet: outsource the creation of “qualified oversight”, and just download/subscribe to some when needed.
Anything a human can be trained to do, a neural network can be trained to do.
Come on. This is a gross exaggeration. Neural nets are incredibly limited. Try getting them to even open a door. If we someday come up with a true general AI that really can do what you say, it will be as similar to today’s neural nets as a space shuttle is to a paper airoplane.
I sat in a room of probably 400 engineers last spring and they all laughed and jeered when the presenter asked if AI could replace them. With the right framework and dataset, ML almost certainly could replace about 2/3 of the people there; I know the work they do (I’m one of them) and the bulk of my time is spent recreating documentation using 2-3 computer programs to facilitate calculations and looking up and applying manufacturer’s data to the situation. Mine is an industry of high repeatability and the human judgement part is, at most, 10% of the job.
Here’s the real problem. The people who will be fully automatable are those with less than 10 years experience. They’re the ones doing the day to day layout and design, and their work is monitored, guided, and checked by an experienced senior engineer to catch their mistakes. Replacing all of those people with AI will save a ton of money, right up until all of the senior engineers retire. In a system which maximizes corporate/partner profit, that will come at the expense of training the future senior engineers until, at some point, there won’t be any (/enough), and yet there will still be a substantial fraction of oversight that will be needed. Unfortunately, ML is based on human learning and replacing the “learning” stage of human practitioner with machines is going to eventually create a gap in qualified human oversight. That may not matter too much for marketing art departments, but for structural engineers it’s going to result in a safety or reliability issue for society as a whole. And since failures in my profession only occur in marginal situations (high loads - wind, snow, rain, mass gatherings) my suspicion is that it will be decades before we really find out that we’ve been whistling through the graveyard.
Anything a human can be trained to do, a neural network can be trained to do.
Yes, there will be a lack of trained humans for those positions… but spinning up enough “senior engineers” will be as easy as moving a slider on a cloud computing interface… or remote API… done by whichever NN comes to replace the people from HR.
Cue in the humanoid robots.
Better yet: outsource the creation of “qualified oversight”, and just download/subscribe to some when needed.
Come on. This is a gross exaggeration. Neural nets are incredibly limited. Try getting them to even open a door. If we someday come up with a true general AI that really can do what you say, it will be as similar to today’s neural nets as a space shuttle is to a paper airoplane.