We don’t yet know how to make computer software that is as flexibly smart as human brains. So when we automate tasks, replacing human workers with computer-guided machines, we usually pay large costs in flexibility and innovation. The new automated processes are harder to change to adapt to new circumstances. Software is harder to change than mental habits, it takes longer to conceive and implement software changes, and such changes require the coordination of larger organizations. The people who write software are further from the task, and so are less likely than human workers to notice opportunities for improvement.
Humans are more flexible than software only if they are motivated to make improvements. Otherwise, they are less flexible than software. Software doesn't resist when you try to change it.
The issue is not that automation will replace human innovation anytime soon. The issue is that a large number of lower and middle class jobs mainly require only performing a finite set of repetitive tasks – retail salespeople / cashiers, warehouse movers, customer service workers, waiters, office clerks, taxi / truck drivers, mechanics, accountants, legal workers, cooks, etc. – and that puts them at high risk of being automated in the near future.
Sure, some level of process innovation can occur within these fields that might save a business money in the long-run. But businesses are notoriously short-sighted, and from an employer's perspective, is it worth the additional cost of hiring an un-specialized laborer on the off-chance that they have a business insight over a robot that works 24/7/365 and doesn't require insurance policies?
As Moore's Law drives down the cost of hardware and computing power by half every 18-24 months, automation is going to get cheaper and cheaper. It's only a matter of time before it costs less than $30,000 per year to hire a robot or algorithm capable of performing the vast majority of a low-level worker's daily tasks in a particular field. 2 years after that, $15,000. 10 years after that, $1,000. Sure, the employer might lose some level of innovation as a tradeoff. But does the average employee contribute $29,000's profit worth of ideas to the business each year to make himself a viable contender?
Especially when that business's competitors start automating and they need to compete on price, there's a tipping point where a human worker simply becomes too expensive to employ relative to a machine with 90% of the same capabilities. And even if a machine can only handle 90% of the work that occupies an employee's day, the employer now only needs to hire 1/10th as many employees to take care of the remaining 10% of those other 9 who got laid off. In other words, robots don't have to completely replace human workers in order to cause a huge disruption in the labor force.
As someone who works in the technology sector (in fact, a large part of my job is in automating things), I think this is possibly the biggest social problem we'll see in our lifetimes. In the long-run it will be great for humanity, but I think it's going to be a very rough transition.
Software/robots are not smart yet and probably is never going to be "smart". However they excel at predictability, doing the same thing with the same accuracy millions of times. Final car assembly as explained by the article needs to be flexible to maximize productivity and at the same time avoid unsold car inventories. What about spark plugs, windshields or brake pad manufacturing? The actual car components, those manufacturing plants also need lots of flexible humans or can rely more on robots?
In particular, take a look at the section titled "Machines as Children, Humans as Intestinal Fauna":
"First, machines are like children. The opposite of the overlord personification we’ve been encouraged to adopt by science fiction.
Like parents, we have to let them have the fun while we child-proof the environment (sanitize their inputs) and clean up after them (do whatever they are too clumsy to do and clean up any messes they create)."
The flexibility of "mental habits" is generally a problem for manufacturing, introducing time-to-time, person-to-person, shift-to-shift variation that can impact productivity, production quality, and rework costs; as a result workers often follow standard operating procedures that describe precisely how the work is to be done, and these can be as institutionally bound and as hard to change as monolithic software processes you alluded to.
Software can be built with granular composable domain specific aspects making it flexible, and provide the domain level interfaces that will align the perspective of the programmers with the domain experts and workers, and allow those domain experts and workers to modify the system directly--while still enforcing change control and necessary institutional communication.
Automation does not have to be a problem and can be an important way of capturing institutional knowledge; but the level and type of automation that will lead to optimal results will of course be context dependent.
Also the gains from flexibility are near-impossible to quantify in advance. Also d(profit)/d(automation) was probably positive for most of the twentieth century, since it started at almost zero automation, so automation=good might be a bit baked in to some mindsets (story of Henry Ford and all).
I don't know the answer, but even asking the question in the right way is tough. For example what about a job that shipped to China, but in the absence of globalization would have been automated anyway due to cost pressure? What about a job that moved overseas 20 years ago, but then subsequently automated 10 years ago due to rising third world wages? What if a heavily human-based American carmaker had market share taken by a heavily automated Japanese carmaker?
Google's search engine follows a set of instruction that any human being could mimic, given infinite time (read documents in universe, highlight search term, tally pagerank score on subset of matching documents). Google simply does it at much faster speed and larger scale. Its existence isn't a justification of software flexibility, but rather raw microprocessor calculation power.
As for machine learning, despite decades of developing very advanced blackbox algorithms (e.g. deep learning), using simple algorithms with heavy human feature engineering still outperform on the large majority of domains. In other words combining human flexibility and domain knowledge with machine processing power beats pure software solutions.
Seems like automatization often comes with premature optimization. In that case, collecting more data before automating everything can bring better results.
This leads to other question: why is the automatization done wrong, and why is it not fixed? But that can be explained by the company politics. You know, someone made an important business decision to buy some automated system, so if you criticize the system, it's like criticizing the person who made that decision. So the system stays there, even if it would be better to replace it with some other system, or to replace parts of it with humans.
Those are some big claims to make on the basis of one stylized observation about a Honda plant. And Honda is not the only successful car company in the world, so it hardly is the case we can draw universal truths about automation and productivity from whatever works for them.
"Software is harder to change than mental habits" - evidence please.
Human beings have no reliable definition of "intelligence".
So it's quite obvious that you're blowing hot air. No human being can search through information as fast as google can. Machine learning already outperforms human beings in many area's.
Humans aren't special, in fact the state of our world (war, religion) shows the limits and defectiveness of our animal mind.
Robots can aggregate experience much better than human workers. So software programmers will have access to much larger datasets from which to derive process improvements than any individual human worker.
Also it is much easier to copy software between (identical) robots than to transfer similar skills between humans, allowing process innovations to be applied more rapidly and more uniformly. That also means that a single robot can, in theory, perform a much wider range of skills than a single human. In that sense robots have greater mental flexibility than humans.
Isn't automation just the next step in specialization? Specialization has the same tradeoff of local efficiency versus overall flexiblity (and robustness) but in the end it has enabled tremendous economic growth since the time of the hunter gatherers. Sure there will be cases where automation backfires but it is somewhat likely that are descendants 100 years from now will have both a higher standard of living and a 16-28 hour workweek largely because of automation.
They've actually done the math on this. It's about 80% automation and 20% globalization, if I remember correctly.
Want to know how you can verify this easily? Look at manufacturing's share of the American GDP and labor force. Manufacturing *must* be getting more efficient per worker to see what you see, and that implies automation.
Humans are more flexible than software only if they are motivated to make improvements. Otherwise, they are less flexible than software. Software doesn't resist when you try to change it.
The issue is not that automation will replace human innovation anytime soon. The issue is that a large number of lower and middle class jobs mainly require only performing a finite set of repetitive tasks – retail salespeople / cashiers, warehouse movers, customer service workers, waiters, office clerks, taxi / truck drivers, mechanics, accountants, legal workers, cooks, etc. – and that puts them at high risk of being automated in the near future.
Sure, some level of process innovation can occur within these fields that might save a business money in the long-run. But businesses are notoriously short-sighted, and from an employer's perspective, is it worth the additional cost of hiring an un-specialized laborer on the off-chance that they have a business insight over a robot that works 24/7/365 and doesn't require insurance policies?
As Moore's Law drives down the cost of hardware and computing power by half every 18-24 months, automation is going to get cheaper and cheaper. It's only a matter of time before it costs less than $30,000 per year to hire a robot or algorithm capable of performing the vast majority of a low-level worker's daily tasks in a particular field. 2 years after that, $15,000. 10 years after that, $1,000. Sure, the employer might lose some level of innovation as a tradeoff. But does the average employee contribute $29,000's profit worth of ideas to the business each year to make himself a viable contender?
Especially when that business's competitors start automating and they need to compete on price, there's a tipping point where a human worker simply becomes too expensive to employ relative to a machine with 90% of the same capabilities. And even if a machine can only handle 90% of the work that occupies an employee's day, the employer now only needs to hire 1/10th as many employees to take care of the remaining 10% of those other 9 who got laid off. In other words, robots don't have to completely replace human workers in order to cause a huge disruption in the labor force.
As someone who works in the technology sector (in fact, a large part of my job is in automating things), I think this is possibly the biggest social problem we'll see in our lifetimes. In the long-run it will be great for humanity, but I think it's going to be a very rough transition.
Software/robots are not smart yet and probably is never going to be "smart". However they excel at predictability, doing the same thing with the same accuracy millions of times. Final car assembly as explained by the article needs to be flexible to maximize productivity and at the same time avoid unsold car inventories. What about spark plugs, windshields or brake pad manufacturing? The actual car components, those manufacturing plants also need lots of flexible humans or can rely more on robots?
You should check out:
http://www.ribbonfarm.com/2...
In particular, take a look at the section titled "Machines as Children, Humans as Intestinal Fauna":
"First, machines are like children. The opposite of the overlord personification we’ve been encouraged to adopt by science fiction.
Like parents, we have to let them have the fun while we child-proof the environment (sanitize their inputs) and clean up after them (do whatever they are too clumsy to do and clean up any messes they create)."
The flexibility of "mental habits" is generally a problem for manufacturing, introducing time-to-time, person-to-person, shift-to-shift variation that can impact productivity, production quality, and rework costs; as a result workers often follow standard operating procedures that describe precisely how the work is to be done, and these can be as institutionally bound and as hard to change as monolithic software processes you alluded to.
Software can be built with granular composable domain specific aspects making it flexible, and provide the domain level interfaces that will align the perspective of the programmers with the domain experts and workers, and allow those domain experts and workers to modify the system directly--while still enforcing change control and necessary institutional communication.
Automation does not have to be a problem and can be an important way of capturing institutional knowledge; but the level and type of automation that will lead to optimal results will of course be context dependent.
These ambiguities also make dubious any unequivocal overall calculation (like 80/20), don't they?
Also the gains from flexibility are near-impossible to quantify in advance. Also d(profit)/d(automation) was probably positive for most of the twentieth century, since it started at almost zero automation, so automation=good might be a bit baked in to some mindsets (story of Henry Ford and all).
I don't know the answer, but even asking the question in the right way is tough. For example what about a job that shipped to China, but in the absence of globalization would have been automated anyway due to cost pressure? What about a job that moved overseas 20 years ago, but then subsequently automated 10 years ago due to rising third world wages? What if a heavily human-based American carmaker had market share taken by a heavily automated Japanese carmaker?
Google's search engine follows a set of instruction that any human being could mimic, given infinite time (read documents in universe, highlight search term, tally pagerank score on subset of matching documents). Google simply does it at much faster speed and larger scale. Its existence isn't a justification of software flexibility, but rather raw microprocessor calculation power.
As for machine learning, despite decades of developing very advanced blackbox algorithms (e.g. deep learning), using simple algorithms with heavy human feature engineering still outperform on the large majority of domains. In other words combining human flexibility and domain knowledge with machine processing power beats pure software solutions.
Seems like automatization often comes with premature optimization. In that case, collecting more data before automating everything can bring better results.
This leads to other question: why is the automatization done wrong, and why is it not fixed? But that can be explained by the company politics. You know, someone made an important business decision to buy some automated system, so if you criticize the system, it's like criticizing the person who made that decision. So the system stays there, even if it would be better to replace it with some other system, or to replace parts of it with humans.
Those are some big claims to make on the basis of one stylized observation about a Honda plant. And Honda is not the only successful car company in the world, so it hardly is the case we can draw universal truths about automation and productivity from whatever works for them.
"Software is harder to change than mental habits" - evidence please.
Human beings have no reliable definition of "intelligence".
So it's quite obvious that you're blowing hot air. No human being can search through information as fast as google can. Machine learning already outperforms human beings in many area's.
Humans aren't special, in fact the state of our world (war, religion) shows the limits and defectiveness of our animal mind.
It's manufacturing jobs that seem usually the main concern.
So, we might ask what proportion of the manufacturing jobs eliminated was due to each cause. Anyone know?
Robots can aggregate experience much better than human workers. So software programmers will have access to much larger datasets from which to derive process improvements than any individual human worker.
Also it is much easier to copy software between (identical) robots than to transfer similar skills between humans, allowing process innovations to be applied more rapidly and more uniformly. That also means that a single robot can, in theory, perform a much wider range of skills than a single human. In that sense robots have greater mental flexibility than humans.
Isn't automation just the next step in specialization? Specialization has the same tradeoff of local efficiency versus overall flexiblity (and robustness) but in the end it has enabled tremendous economic growth since the time of the hunter gatherers. Sure there will be cases where automation backfires but it is somewhat likely that are descendants 100 years from now will have both a higher standard of living and a 16-28 hour workweek largely because of automation.
They've actually done the math on this. It's about 80% automation and 20% globalization, if I remember correctly.
Want to know how you can verify this easily? Look at manufacturing's share of the American GDP and labor force. Manufacturing *must* be getting more efficient per worker to see what you see, and that implies automation.