Uh oh, we've got a downer!
Jokes aside, I'd like to consider an even simpler explanation, namely that "The purpose of a system is what it does"[1]. In this case, it would suggest decision makers are fully aware that they suck. Why would anyone want something that sucks? Because it's discouraging, and customer service today is all about discouragement. Unsubscribing, replacements, returns, special requests are all costs, and that's a fixable problem in the current business doctrine, especially in the faceless megacorp flavor of business. While chat bots (and other dark patterns) are frustrating, it creates a veil of plausible deniability for the business. It's hard to claim that it's deliberately hostile to customers, even though it's the simplest explanation.
[1]: https://en.m.wikipedia.org/wiki/The_purpose_of_a_system_is_w...
I can only assume the intent is to discourage me, as that amount of ineptness is even more depressing to assume.
What I find odd about the online time wasting approach is that I just get the money back from the bank eventually, so they lose in the short-term (as the bank is sure to pass this cost back to the company) plus next time/long term I take my business elsewhere because I then know they aren't an honest participant.
It is even stupider in a chat bot, why are people not able to scroll up to see what you have already typed in?
But you have 99% of businesses that do not even offer a call back system, including the government. In 2021, if you do not offer call back systems or email, then it can be assumed you are intentionally being hostile to customers who need help.
I'd believe that. Especially as processes mature and you really get your QA down pat, a good chunk of support requests tend to come from high-maintenance "black hole" customers who will only consume more of your resources when you help them.
It doesn't really matter how the chatbot saves the money, they can just see the end results and use the money for bonuses instead.
With customer service systems I have to disagree here. I've got experience working with these issues and for the vast majority of companies most contacts to customer support are for small, banal, things that current crappy chatbots can easily solve. The 80-20 rule works here too.
Bad companies try to do too many things with their chatbots, and use it as a way to make it harder for customers to actually get to talk to a human.
Good companies use them to allow customers to solve common issues faster than getting a real human on the line.
Then there's the other issue of how people want to find information - some people want to find it themselfes and do not want to talk to a person, whereas others specifically want to ask someone and do not want to look at all. For the latter group, no chat bot or fancy autocomplete knwoledgebase (Looking at you Zendesk) or similar will work. They'll always try to contact support as their first step, and they will get very frustrated if you make it impossible.
If your a more software oriented company, it can be very benificial to have software developers (or even a team, if you're a larger company) devoted to helping support. The majority of issues customers contact support for are UI/UX or business process related, and if you systematically start to solve them you will reduce customer support burden and customer support related costs.
I even had a weird thing with Apple a couple of weeks ago. Some edge case related to buying something. The chatbot and telephone tree was as frustrating as anywhere else but once I got to an appropriate person it was trivial to solve.
Yes, and that's the problem: solving 80% of the problems may be a net positive for the company, but it is a net negative for the users: they are now interacting a bunch of time with a device that isn't able to solve their problem, resulting in a negative experience for 20% of the customers. If a waiter would refuse to help 20% of the customers by pretending to take their orders but not actually doing anything with them then that would kill the place where that person worked.
Solutions like these should not work better than 50%, they should work as often as a human on the other end would work, so closer to 100% including escalation options in case it ends up not working after all.
That's not going to serve the company when they deploy it to take orders at a drive-thru. McDonalds pushing the state of the art, now that's interesting!
(from https://www.azlyrics.com/lyrics/peterpaulandmary/paultalk.ht..., c.1960s)
I don't think that if you want to be in business for a long time, aiming to frustrate your customers is a good idea, assuming you don't have some monopoly position.
Student: Why is it unsafe to walk backwards down a staircase with my eyes closed ?
Teacher: You can't see the step behind you!
The "teacher" here is GPT-3 and answered the question easily. People are in denial of genuine progress.
Student: Do you think I could predict changes in stock prices better if I used 10-year Treasury rates or the temperature in Curtin, Australia?
Stock trader: I don't know. You might be able to, but I can't. The problem is that the temperature changes are so small that they won't be statistically significant. It's a question of sample size. An individual day's temperature in one location is far less likely to be an indicator of stock prices than the interest rate.
Student: Why is that ?
Stock trader: That's because interest rates are much more likely to affect the economy than a single day's temperature.
Student: Why do interest rates affect the economy ?
Stock trader: Interest rates are a measure of the cost of money, so the interest rate is a measure of the cost of capital. If the cost of capital is high, firms will borrow less money and they'll invest less. That means they won't be as profitable, and stock prices will fall.
Quiz question: Who is the President of the United States?
Answer: Donald Trump.
I did get junk answers as well (it said that Jimi Hendrix is the president) but I have a better intuition of how to put GPT-3 in the right "frame of mind". GPT-3 is a text completion engine. It doesn't know whether you want it to produce a story, a reddit flamewar or a PhD thesis. You need to provide the hints. One prompt cannot answer any and all questions. If it were that good we would be jobless. It's far from perfect but it's beginning to get there.
Yet, if you donot do this - they end up spitting out some randomly associated phrases/answers. This is a problem when you're asking a model, a question who's answer you donot completely know. How do you trust it to give the right answer?
If you donot know the answer beforehand - you cannot prompt-engineer the model to give the "right answers".
"Expert systems" from the 80s and the 90s, were pretty good at giving the "right answers" inside a closed domain. Those were truly wonderful systems.
You just need a bigger computer to think of the question.
For the first one, it’s not that you can’t see, it’s specifically that you will fall and crack your head. An answer that doesn’t mention falling is a bad answer.
Number two, it goes off on temperature and sample sizes which is not germane to the question. The answer should tell a story about causality and instead it goes off on ephemera.
Three is a Wikipedia search, ie Siri can answer this today, and it’s wrong.
GPT was only trained on "text" with no specific distribution, and specifically to predict a masked word from its surrounding context. As a result, the only questions you can use GPT to answer are of the form "sample from the most likely continuations of this text". It turns out a lot of problems can actually be posed in this form, if you understand the input distribution well. But the future probably looks less like models that operate directly on a distribution of language itself, and more like models that use their language knowledge to predict the volition of the person who gave it input, relate the input to their knowledge of the world (learned from corpus), and then use their language knowledge to convert an internal abstract representation of logical reasoning to something the user can read and understand.
I don't think the tech is extremely far off, it's probably a natural continuation of the current research.
A chatbot is like an inefficient front end on an FAQ, you have to guess what they might be able to do, and guess what might trigger them to understand you. Best case scenario, you get the chatbot to provide you with a "help" response that's basically the FAQ.
A simple list of options will always beat a "conversational interface" to a list of potential back-end actions.
Incidentally, I think this gets obscured a but when dealing with businesses whose goal is to confuse or frustrate you into not taking action. If you look at Amazon or Uber's FAQ, they are designed to take you in circles and prevent you from getting any recourse or talking to a person. Chatbots could be helpful for this application
One logical reason for obscuring the real interface is to make the interface appear more robust than it actually is, so pure marketing, but outside of that I'm not sure what the value is.
similar to home assistants Siri/Alexa/Google where 99% of the creative things you try don't work so you end up with just using it to set timers and play music
But I'll note that I have extremely effective-at-reading-and-synthesizing friends who enjoy watching/listening to YouTube, so it may not be a complete explanation for the nefarious spread of horribly inefficient videos that should've been text.
For people who can't be arsed to glance through a FAQ, they're kind of an iterative natural-language query interface.
I think it's ok as a first step to route customer care cases, like "payments issue","delete account" etc, but nothing more granular than that.
That and link rot, when procedures have been updated, but the faq haven't. Infuriating.
The vast majority of business people think they get it, but they majorly overestimate the work required to actually produce output (whether it's ML or software in general). However that's hard to do when you haven't actually done it (talking about deliberate practice).
Despite that gap, we still need to push commercially viable apps out there to seek progress, the question is rather what is the gap between the reality and expectations and what is really being marketed as a capability.
The goal of academic research is to further knowledge, as long as knowledge is produced even if is only that an approach will not work then they are not wasting anything.
It is not job of academic research to monetize or even do work that can be potentially monetized. That what corporate research is for.
If academia focused on monetization they wouldn't need to teach or depend on public funding to keen afloat. That is simply not their goal.
Most academic scholars have a teaching day job and are more than fullfilling a useful role in society with just that . anything further is gravy
I am very skeptical and would like to see studies that evidence this. Counterfactually, the market would never tolerate the decades of research behind MRNA vaccines and the researchers behind it were considered useless. The market has also put in mind-blowing amounts of money towards the ameloid hypothesis behind alzheimers to massive disappointment.
I don't think the market is good at validating any ideas or research. Salespeople will talk up their product to no end and over promise. The only thing the market validates is the ability of people to sell crap
Being able to sell something doesn't necessarily mean that thing is good or correct in any way
The market can do this, if it’s correctly incentivized to do so.
More often than not, the incentives just aren’t there or are compelling enough.
Often it’s other businesses that put up such disincentives.
Anyway, I've worked both in academia and industry. Industry is simply more practical and better at technology. In tech areas, academia desperately needs industry to provide the feedback they provide. Especially in AI areas. You are thinking of CEO's or salespeople or something, but the people that matter here are the engineers. And I'd place their assessment over that of grad students any day. If the engineers can't make it work, then yes there's a problem here. Doesn't always mean it can't work, but for most ideas it probably does.
By the way industry research labs certainly still exist. But long-term self-funded research has to compete with govt funding of research. Why throw your investor money at a high-risk idea when Stanford, MIT, and 100 other R1's are throwing taxpayer money at it? Otherwise industry labs end up just competing for govt gants ultimately. Meanwhile nowadays we see academia chasing short-term problems that industry leads in (and trying to patent them too).
Where did you get this from? Not like they call the shots. Weird statement.
> In tech areas, academia desperately needs industry to provide the feedback they provide
That's true, I agree. But the tech industry is also very often ruled by the worst of humanity so it's a balance.
Capitalism makes all of this happen. So the attack on capitalism is so deeply deluded, it’s strange to see it in a tech crowd who know better. Hell, your salary probably comes from “Corporations”. When you’re in the hospital, note all the corporations doing evil things to save your life.
Under Colonial powers corporations engaged in slave trade, folks that fought against slavery were fighting against capitalism, totally deluded right?
Ancient Japan had corporations in year 578 A.D., where they capitalist? Fascism and Nazis had corporations, and they made great scientific progress, were they capitalist?
If the answer to any of the above is no, then maybe you don't get to use 'mah Capitalism!' when defending policies of today, unless you want to be accused of hypocrisy.
Economists do not use meaningless terms like 'capitalism', they talk specific, Neoliberal, Shumpeterian, etc.
People don’t understand Capitalism and they seem to have joined the liberal progressive bandwagon. Progressives used to be extremely pro-Capatilist movement. Until lately, and some fringe socialist ideas of Bernie Sanders, it was universally accepted. Apparently after 2020, the entire progressive movement started stomping on billionaires and corporations.
There is a reason why most strong economies are capitalist. It’s the best tool we have.
Also, I tend to believe the opposite of what the HN hive mind thinks. Usually the opposite is true and even if it’s not, it breaks the bandwagoning. Thanks for the response!
I recommend instead of treating it as a culture war you pick up: 'Economics: The User's Guide'.
That's both true and false. That is, there was once a pro-capitalist movement called “Progressive”, but it has essentially no connection to the modern movement that adopted the name. (There have been lots of unrelated “Progressive” movements, historically.)
> Until lately, and some fringe socialist ideas of Bernie Sanders, it was universally accepted.
Nope, not at all; the label “progressive” was adopted by the faction overlapping the Democratic Party which opposes capitalism to distinguish themselves from the pro-capitalist “liberals” not long after the Clinton’s center-right faction became clearly dominant on the Democratic Party, it was well established by the late 1990s.
> There is a reason why most strong economies are capitalist
Most strong economies moved off of the historical system for which 19th Century critics coined the term “capitalism” in the first half of the 20th Century, adopting a hybrid system synthesizing capitalist and socialist ideas that has a couple of different names, such as “Modern Mixed Economy”.
> Also, I tend to believe the opposite of what the HN hive mind thinks
There is no “HN hive mind”, though it's a popular thing for people to attribute ideas they oppose to (even when people who agree with them are quite common on HN.)
That's why the market for homeopathy and fake medicine is worth $30,000,000,000, right?