Insurance firm to replace human workers with AI system
mainichi.jp
mainichi.jp
What's more likely is that staff cuts were already planned. This puts a great spin on a (I would guess most likely free/cheap) experimental deployment of Watson.
This is definitely an industry where more automation could easily be done, but the big insurers are a conservative, risk adverse group.
What kind of stuff does it miss?
It's also complicated because it's hard to model buildings correctly. My sister likes to tell a story about a total loss she had for a standard masonry building with sprinklers. From her point of view, this is the perfect type of building. They're hard to catch on fire, and if there is a fire, the building puts it out. At worse, your damage is limited to the one room or section where the fire was, since the internal masonry walls keep it from spreading. This building burnt down because the solar panels on the roof caught fire, which spread across all the panel over the entire roof. The sprinklers never got a chance to go off because the roof collapsed. It's certainly possible to model this one case, but the problem is there are a million one off cases like this, and we don't know about them until there's a loss. Right now, human intuition from the underwriters and inspectors is what they use to cover try to cover the gap.
> This building burnt down because the solar panels on the roof caught fire, which spread across all the panel over the entire roof. The sprinklers never got a chance to go off because the roof collapsed.
Type 3 construction that burned to the ground? It doesn't take a human to realize or intuit that even type 1 buildings burn eventually (ask a firefighter!). If there is a way to truly model every possible variable when insuring a building, I will put money on humans doing a worse job than expert AI systems. You're asking the AI to not only predict but _know_ the future, and not asking that of the human... seems silly.
What table would you consult to come up with a rate for E&O and liability insurance for the CEO of Uber? Remember your goal is to make money.
There is also licensing involved, in my sister's case she had to earn a CPCU before she could do here job on her own.
Edit: I see you answered this same question below. Whoops.
Can you give some examples?
I thought it's pretty well understood that the "gut feelings" of experts have been and will continue to be outperformed by algorithms for these sorts of tasks. My imagination is failing trying to come up with something data-based that an agent would see and a computer couldn't.
AI doesn't have to replace 100% of all humans to have a huge impact on unemployment. If you introduce a system allowing 4 people to do the job 5 used to do you're setting the stage for 20% unemployment, which is a huge deal at scale, and also the 4-to-5 ratio is very conservative for a lot of modern automation projects.
Are there many fields where AI/robots will be doing 100% of the work in the near future? No... next to none, I'd think... but there are LOTS of fields where they will be doing a huge amount of the work while being supervised by a relatively skeleton crew of humans sanity checking their work.
Insurance doesn't suffer the same degree of competition as other parts of the economy... it has a triple-walled garden of hefty regulation, significant capital requirements, and the chicken & egg problem that you already need to have relationships and experience in the insurance sector to do business there... or spend time and money buying them in. Even the banks white-label their insurance products from insurers.
Event the simplest logistic regression training + evaluation will provide value to most insurance, mortgage or other money-related decision/"expert" systems.
So ... 16 employees. I suppose that's interesting, but it's not as big as the headline made me believe.
The article also cites other companies doing the same thing, even if no staff cuts are involved for now.
Certainly you hear more about the failures, but that be because they get better press. Not sure about any studies, would love to see a link or two if you have them lying around.
quotes:
At companies that aren’t among the top 25% of technology users, three out of 10 IT projects fail on average.
AND
On average, about 70% of all IT-related projects fail to meet their objectives.” In this case Lewis includes not only projects that were abandoned (failed), but also those that were defectively completed due to cost overruns, time overruns, or did not provide all of the functionality that was originally promised.
The difference between failure in the two quotes is that the first one seems to consider failure as just abandoned completely as being unachievable. Whereas the second also considers failure as not having achieved all goals.
It seems to me that if the project is big and central enough to a company's processes that it might be worth betting against the survival of that company.
successes rarely get written up. so finding details on them is much harder.
also depends what you mean by fail.
if fail means "ran over budget and hit loads of unexpected problems" then yeah, most probably do "fail".
if fail means "shut down prematurly and abandoned without hope" then afaik, your only really talking about stuff by google and microsoft. most other software houses would fail with their software. and plenty are still arond from the 90s.
intel. ibm. apple... not so much real fail, for example.
I foresee our (the US) government (and probably others) restricting the development and deployment of AI systems that would supplant human jobs, merely for the sake of ensuring people are employed.
I think it's sad because it would present a real opportunity to advance our society significantly.
The process of replacing jobs with ai needs to go slow enough that the risk of large scale violence is minimized
And Im in the crowd that doesn't think we'll see it replace all jobs in a field -- just 60-90% of them, which causes major labor problems when talking about common jobs.
As a (perhaps contrived) example, family doctors could be replaced by lab workers, who take simple measurements, feed them into a computer, and the AI does the rest (i.e., correlating conditions to a large number of existing patient files, and hence referring patients to specialists).
Suppose right now, we have 1 doctor, 1 nurse, and 3 lab techs per 50 patients per day. I think technology generally lets us do the same job with just 2 nurses and 1 lab tech. So we lose 40% of the jobs from the higher paying side and probably more like 50-75% of the pay.
In less contrived examples, I think we lose a lot of the jobs in the 25th-75th percentile range, which is the middle classes.
So it's not that we see no jobs, it's that we see bad jobs and the elites. The middle gets automated out, and it's starting to be faster than people can retrain.
You're making the extreme assumption that the amount of medical care demanded remains constant despite the fall in prices (e.g. employees: 5->3, patients: 50->50). An alternative extreme is that employment remains fixed while falling prices improve accessibility (e.g. employees: 5->5, patients: 50->90).
In reality we may easily end up somewhere in between (e.g. employees: 5->4, patients: 50->70). This also highlights two aspects of automation: on the dark side, it reduces demand for work, on the bright side it improves availability (here, of medical care). If as a society we're able to deal with the former (e.g. by conjuring up new occupations) we stand to improve our future significantly through the latter.
Even if it increases employment and availability (4 nurses, 2 techs, 100 customers), we're seeing a decrease in income provided -- 1 doctor and 1 tech for 3 nurses. Less spread across more people.
I don't think "surplus of trained doctors" is a real problem I'm likely to see in my lifetime, never mind a likely consequence of the foreseeable future improvements in medical data collection and diagnosis.
The reason it has been going on without anyone really noticing is because very few people get fired because of it. The real effect has been on a slow down in new hires as people retire.
The reason for this is that whatever efficiency you free up isn't directly tied to a single job function. Say you do a self-service system for handling employee transport costs. This might free up an entire job function worth of hours in a HR department, but they are coming from 6 different employees. Doesn't lead to anyone being fired, but eventually you'll automate enough systems that someone retiring won't need to be replaced.
Programming isn't even a safe zone. I mean, think about how much time you save by using things like modern frameworks and the interconnectivity of everything and then compare that to how it was 25 years ago.
A thing you might find useful to look at: loss ratios. Loss ratio is industry jargon for claims paid plus claims-related expense over premium income. GEICO's, for example, is 82.1: for every $1 in premium they take in, they pay 82 cents in claims.
The industry is regulated to a degree that few are, in both the US and Japan (and many, many other countries). If your loss ratio is too low, your friendly neighborhood insurance regulator will not look favorably upon that fact.
Denied
Denied
Denied
Denied
Denied
Denied
...