Having worked in ML at two different companies now, I think that people interpreting model output as intelligence or understanding says much more about the people than about the model output.
We want it to be true, so we squint and connect dots and it's true.
But it isn't. It's math and tricks, and if human intelligence is truly nothing more than math and tricks, then what we have today is a tiny, tiny, tiny fraction of the amount of math and tricks in the average human brain, because what we have today isn't anywhere close.
I think a problem is the tighter cycle between academic discoveries and business people trying to monetize them. Large language models were developed, objectively a great achievement, and immediately someone nontechnical wants to apply their own interpretation and imagine that we can build a chatbot that you won't have to pay, and before you know it, people are selling and even deploying them. Anyone who questions or points out that the technology doesn't do what the business people think it does just gets dismissed as not understanding.
> Anyone who questions or points out that the technology doesn't do what the business people think it does[...]
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.
The chatbot saves money. Simple as that. People get served by chatbots, get frustrated, and majority gives up and doesn't bother the company with the problem.
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.
>People get served by chatbots, get frustrated, and majority gives up and doesn't bother the company with the problem.
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 suspect that in a forum like this one, most of the people get pushed into customer support when they've probably exhausted online resources (though we also all screw up from time to time). So, most of the time, you're dealing with a chatbot (or for that matter an L1 CSR--which often isn't a lot better) when you really need a human with some experience and agency to make things better.
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 definitely how some companies use it. I needed a human from Comcast to help me with something, and instead of getting me on with a human (after spending a few minutes on the phone trying to communicate to the bot why I needed to talk to someone) it sent me an SMS with a link to their online chatbot and promptly hung up. I then called back and said my reason for calling was to "cancel my service," which of course got me through immediately to a human.
>> The chatbot saves money. Simple as that. People get served by chatbots, get frustrated, and majority gives up and doesn't bother the company with the problem.
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!
I don't go to McDonalds much anymore, but if I ever detect that I'm talking to a machine I will just drive away. I don't use the order kiosks either, because they take 5x as long as just telling a person that I want a Big Mac, fries, and a medium Coke. I can say that faster than I can even get to step 1 on the kiosk.
I understand they're even putting in conveyor belts in some drive-in restaurants
There was a time when the little girls came running out in short little skirts.
Remember them? You pull in and the little girl would come running up.
"May I take your order sir?"
"Yes, but I don't believe it's on the menu, ha ha"
They don't, they don't let you flirt any more now. They give you a little green box with a white button on it A little speaker grill...
In the short term, yes. But if customers have a consistent bad experience with the products or services of a company they will stop doing business with them. I routinely buy products from one place where I know the service is better (delivery, returns, helpdesk) than somewhere else where it might be cheaper.
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.
I have to agree with this. I do not understand why I type all of my relevant information into a chatbot, and then a real person comes on the other end, and asks me all the same questions. Worse is when it is on the phone.
I can only assume the intent is to discourage me, as that amount of ineptness is even more depressing to assume.
I call a company one time and the phone was answered in 1/2 second, the person picking up clearly got all the details on their screen as she correctly guessed why i called (which was not all that obvious) then suggested a good solution and (now 2 sec into the call) told me to call again if i had more questions, to have a nice day and ended the call. It went so fast im still laughing how right they got it. Its a great experience, it feels like the helpdesk is with you all day, always ready to answer instantly. It doesnt matter if their competition is cheaper. I would recomend them to anyone over anything.
For sure, there are some businesses out there that do do it right and have your info ready to go based on called ID.
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.
To be fair, people disregard the prior experience with other people too, so they're not necessarily treating bots that differently here. I had this in the supermarket the other day: went through an issue with the first person, they explained it to the more senior person who I heard listening, she then ignored what was said and repeated the exact same steps (whilst I was trying to talk her out of it because I could see it was a waste of even more time!)
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.
I am talking about just verifying your identity. You spend 5 minutes punching in your life story via a numeric keypad so that you can get transferred to a person, then you spend another 5 minutes phonetically spelling out your life story why?
It is even stupider in a chat bot, why are people not able to scroll up to see what you have already typed in?
>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.
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.
GPT-3 needs proper prompting to get the right output. Here's the same question as in the blogpost posed differently
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.
Once you start prompt-engineering, the answers, in seclusion look pretty great - and show some understanding of the domain.
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.
Yes, this is both the most misunderstood and the most amazing (and unpredicted?) thing about GPT-3 et al. You have to tell it who it is and what the rules are. And it gains or lacks knowledge depending upon who it thinks it is answering as. You’ll find that Stephen Hawking doesn’t have much to say about the Ninja Turtles while celebrities know nothing about black holes. It does not answer questions or have expertise unless you tell it that it is answering questions and has expertise. (And it is incredible that this is the case, and somewhat understandable that people don’t understand this.)
Those answers suck though. I would not consider a human who said them to be intelligent.
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.
All that's missing is a reranking step that rewards incisive and non-obvious answers. Actually, some models are already implementing such a thing: LAmDA will prioritize contentful answers above statistical probability, the same way humans do.
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.
For some reason I am having a very hard time resisting the urge to try walking down a flight of stairs backwards with my eyes closed. How hard can it be?
There isn't really a new "problem" with AI. Businesses love hype, they love anything that can punch up a sales pitch, they love shitty cheap automation that replaces expensive human work, they love to identify ways to lower quality and save money without affecting revenue, and AI can be good for all of those things without being good at anything.
I've often argued chatbots are much worse than an FAQ. With proper structure, like you say, it can be easy to see what is and isn't covered, and for things that are included, look up the answer.
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
You are correct for people who can skim a few pages of information, identify what is relevant (possibly spread out across several chunks), and synthesize the information into what they need. For most people on HN, this is as natural as breathing air. For a significant portion of the entire population, this is a pretty daunting task. If you're the kind of person who, like me, is frustrated that everything is a twenty-minute Youtube video tutorial instead of five paragraphs of text, then you may not fully understand the median user of many services.
I have never heard this particular juxtaposition (YouTube link?!? Why!!!).
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.
yes chat bot interface discoverability is terrible -- in fact it feels like the system you would design if the intended goal was to intentionally obscure the interface surface while still allowing access to it
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
I love this answer because it resonates. As a former business person who learned the technical ins and outs, I believe the reason why this is happening is because majority of the population is uneducated how general tech works under the hood.
I am not even speaking about coding syntax but the abstractions of what does AI actually mean, how it works with data and what are the limitations.
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.
That's a good thing. The market is very good at empirically validating research. It helps filter out the chaff and ensure research is useful. Better than academia wasting years on concepts like the "semantic web" where nobody can even agree on what it means. Academia is under attack from many political directions right now, being able to show useful output will help it thrive in the long run.
Capital is far too short-sighted to look beyond this year or this quarter. Many of the important groundbreaking ideas or projects take years, even decades to formulate. As I've said elsewhere, capital will just take the easiest road it is allowed to take, which currently is stifling innovation, buying competitors, maintaining monopolies, relying on economies of scale to shut out competitors when they can't be acquired, and using lobbyists to ensure regulations that affect the status quo never pass. In the past the big tech corporations maintained huge corporate labs and actually did groundbreaking research with 15+ year projects. Now everything is short term. We can't even compel capital to see the long term value of preventing climate change, when Exon was aware this was going to happen as far back as the 1970s in excruciating detail. No, capital will innovate only if there are sufficient regulations in place, incentives, and guardrails, that there is no other choice than for them other than to innovate (which they will do begrudgingly). Capital is by no means a vehicle for innovation. When was the last time you saw innovation from Google in the search space, their bread and butter?
I cant think of the last time I saw the word "corporations" used in a post that wasn't part of an attack on them as inferior/evil/villanous etc. Speaking of NLP, your post would be very easy to classify sentiment of using a simple keyword approach.
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).
Hot take: Corporations are also amazing. James Webb mirrors? Made by a private big corp (Ball Aerospace). So are all instruments of James Webb. NASA contracts out their work to many big fat corporations. Semiconductors to hearing aids, Corporations make the world tick. They’re absolutely incredible. When people get together, they can build stuff like rockets and vaccines.
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.
"So the attack on capitalism is so deeply deluded"
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.
I find it hilarious that the strongest response is about some ancient examples of centuries ago. If anything, this is a praise of the system we have today.
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!
> Progressives used to be extremely pro-Capatilist movement
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.)
You don't seem to understand what liberal means. Liberals are pro status-quo, pro lobbyist, pro deregulation of corporations. They are the centrist monolith that stands in the way of leftists and real progressives at every corner of government. Most liberals, transplanted outside of the U.S., would be right or far right without changing their politics.
You are conflating the organization of human activity with the most dominant form of organization today.
Capitalism made all of these things happen today. 50,000 years ago, were equivalent behaviors organized in a similar way? I don't think so.
And to think that the space of human behavior is significantly different than it was 50,000 years ago is misguided, in my opinion.
Yeah, for some reason most anti-capitalists cite the bad parts of Capitalism and broad brush the entire concept. If humans can get together and collectively improve the world while feeding their families from the profits, there is no wrong doing. No one hands over profits, it’s earned and it’s a reward from the society through the mechanism of free markets. It’s probably the only thing I’d prefer to do instead of working in Academia or some other incentive structure.
I'm in no way against capitalism. As I say in my post, the only way forward is capitalism with extreme regulation. Without regulation, capitalism won't get you anywhere good. It should be treated like a bratty child with no moral values and poor upbringing. It has to be dragged, kicking and screaming, towards the good.
Partially agree. Extreme regulation also prevents incumbents from overthrowing big fat Big Tech like monopolies. Extreme regulation hurts small businesses disproportionately. That said, without regulation, you're right - it can eat the world.
And what changed? We stopped regulating corporations, and they started becoming bad for society. You have to lead corporations to the good, with guardrails, incentives, and regulations, like a petulant child. Without this safety-net in place, you get things like the opioid epidemic.
This is a really great way to frame it, because it really does seem to be about time/speed. I don't think we can even collectively comprehend great innovations that need many resources and time anymore, who has even the time to work on it? People need to have jobs after all.
Research takes time, launching something prematurely which is clearly not ready for practical application will sooner or later fail and suck out any further funding out the field.
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 can understand your reason for saying this, (useful things often sell well) but a market only validates that people are willing to pay for something, not that it is ‘valid’ empirically.
Right. By that same token, directly selling fentanyl or oxy to consumers without any regulations would surely sell well, as does nicotine, etc., but this doesn't mean it's good for society.
The problem with the market is it wants consistent, short-term successive, quantifiable production, and that seems to instantly kill all progress in a field of research. They just go laid back watching investments burn and prints worse and worse nonsense.
> The market is very good at empirically validating research. It helps filter out the chaff and ensure research is useful.
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
You didn't have to go for the jugular and do the brutal kill on the first hit, dude!
> We want it to be true, so we squint and connect dots and it's true.
That's exactly the issue. You summarized it once and for all and we can all go home and stop discussing it now and forever (until we get a general AI that is).
1. Normal people want to have intelligent machines. They watch movies and series and imagine one day a robot will cook for them so they actively look for intelligence, as you said. They buy Roombas and imagine they are going to clean 100% of their rooms (seen it, heard it, watched them rage when it didn't happen). They buy Alexa-enabled devices and imagine themselves like some aristocrats barking orders at an intelligent home (lol). But yeah, that's what the normal people do. It's quite puzzling to me.
2. People who work in the area are obviously biased and I have argued with some of them here on HN but I view it as a doomed affair. They insist there's a lot of innovation going on and that improvements are being done all the time yet we still have embarrassing failures as Michele Obama classified as a male and various politicians classified as known criminals or black people in general classified as gorillas. Like OK, where are your precious improvements and why are they NEVER finding their way into the news?
"It's hard to make someone understand something if their salary depends on them not understanding it", that's how I view the folks working in the DL / ML area. Sorry if that's offensive but just looking from the sidelines, it seems that what I say is true.
If something is to replace humans at doing a job X then it has to be better than the humans at job X.
Also humans instinctively expect the machines to not make mistakes. I haven't made them such; it's just a fact of life. And we have to work with the expectations of the bigger populace.
"If something is to replace humans at doing a job X then it has to be better than the humans at job X." Only if the something is supposed to replace ALL the humans doing X. The idea of using bots was good; let them handle the easy questions, and humans handle the hard ones, therefore fewer humans required. The implementation, not so good in many cases...
(Optionally s/many/more/ or s/many/all/, to your taste)
I agree but I still question whether modern ML is useful at all.
I mean OK, obviously it works quite well for China in order for their social credit score system to work (facial recognition almost everywhere you go) -- which is damned impressive, I'll give it that. But does it work in the benefit of the people at large?
I've read a study a loooooong time ago, I think the late 90s, that stated that the amount of time people spend in the kitchen has not moved down at all. For all the praised innovation of technology, people still have to cut salads by hand -- or if they use the so-called kitchen robots that make the job quicker, you still aren't saving time because then cleaning the machine takes more than it took before (you had to just rinse the cutting board and the knife). If memory serves, Michael Crichton cited this study in the book "Jurassic Park" even...
So I keep asking: where's the actually useful stuff? Are we going to be classifying kittens (or making a NN distinguish between those small rat-like dog faces from cookies) until civilization collapses?
Where's the useful AI? When will I, a programmer that hates voice assistants (because I know how useless they are) and the idea of constant surveillance in my home, reap any tangible benefits from AI?
But I suspect you'll cite various small improvements that are only known in small circles and will say "they'll be ready when they're ready" which, even though not wrong at all, will not be helpful.
Dishwasher saves a lot of time. Not many machines in my kitchen can be classified as "robot"
* oven - not intend to save time (except the fan option), easy-medium to clean
* instant pot - saves time, easy to clean
* rice-cooker - saves time, easy to clean
* electric kettle - save time, easy to clean
If you ever lived in condition without those, you wouldn't praise "cutting board and the knife". I don't need quotes from books to know it.
I agree on the kitchen appliances front and I am just about to buy a slow cooker and a steamer these days and I am sure it's going to improve things a lot.
But the so-called kitchen robots? They are incredibly useful but they make a mess of themselves that you have to clean thoroughly afterwards. They save effort but they don't save time. That was my overall point. And I am not praising the cutting board and the knife per se, I am saying that if you are in the mood to use them for 10-15 minutes then they are sometimes the better options.
But in general this wasn't the topic, I was merely saying that time is not being saved very much these days, at least not the maximum extent that's IMO possible with today's technology. But I recognize that not everyone agrees.
I suspect that might be down to kitchen social dynamics (and/or carefully defined its terms to avoid counting pre-prepared food which is the real timesaver), because e.g. laundry has famously become something that consumes massively less time due to mechanisation.
Being able to search through your photo library by person is a real improvement that ordinary people notice and use. (Also I think most technical people gave up on voice recognition back when it was overhyped and poor, and have thus missed out on what it can do when it's actually decent).
Oh yes, I completely agree with the laundry part. That indeed was the biggest time saver in my household as well.
> Being able to search through your photo library by person is a real improvement that ordinary people notice and use.
Agreed and I want that as well but I will not use Google Photos. And Apple's Photos it waaaaaaaaay too slow into adopting this -- at least it doesn't allow you to specifically say "please can my library now", which would be of tremendous help. So I am left with searching open-source software for this which I haven't yet found -- but I didn't look too hard because I have a ton of other things to do.
Many years ago I wrote this spreadsheet import tool. One of the fields required data a little too rich to fit in single cell value so I came up an "encoding" that read like a sentence. It was sorta NLP but only understood one sentence worth of syntax. I thought it was some clever UX. Users thought they were talking to an AI. They'd just type whatever expression or thought they wanted in that field. And of course the parser would just choke.
We want it to be true, so we squint and connect dots and it's true.
I've had numerous people look at the actions of software I wrote over the last 30 years make comments like, "Ohhhhhh it probably did that because it knew x". The software had no such functionality. Seems to be a natural thing for people to be overly optimistic about software's capabilities.
People always over-estimate the complexity of stuff they don't understand.
If I had a nickle for every time an internet commenter attributed intentional design to a feature that's simply the designer copying established default practice...
> People always over-estimate the complexity of stuff they don't understand.
Really? I see people hugely under-estimate how complex things are. Cellphones. Water reticulation systems. Networks. Software. Etcetera. Anything where they don’t know much about the problem, and they come up with a trite solution (as though they have some sort of amazing level of insight, and everybody else in the world must be just plain stupid).
That's just casual anthropomorphisation. I think most gamers, especially speedrunners realise that games are unintelligent because they encounter and exploit counterintuitive reactions all the time.
Heh, likewise. It's amazing how often choosing sane defaults and falling back to defaults in the even of error is "the right thing" in the eyes of users.
Founders and salesman pretend it to be true for that sweet sweet VC money, while underneath devs try to fake it as plausibly as possible.
And I'll up it with a prediction: watch closely call centers, and as soon as you see them closing in droves, invest in ai startups as something begun to really move.
I interviewed for a “AI” company and asked them about their tech that could take phone calls and extract orders! Wow really cool and impressive! How do you solve X? Oh we have a call center in Mexico that takes all the calls. So you have no usable AI tech at all? Nope. Ok nice talking to you.
They had signed with a major retail company. They were actually making software for the call center. And the “CTO” was “working on” the “AI” in parallel. The company name had AI in it too
Here in Indonesia the joke is AI as Admin Intelligence, as Admin is the shorthand for people managing/doing administrative tasks. I admit it's a legit strategy to increase productivity (offshore/multitenant/software optimise service), but doing that and analyzing/training on the data still doesn't help creating an AI for it. Ex: Uber.
> Having worked in ML at two different companies now, I think that people interpreting model output as intelligence or understanding says much more about the people than about the model output.
I'd add it says a lot about all the companies that advertise them in a way that has nothing to do with reality, and those who actually buy them. We all know the usefulness of these things is below zero, because they only get in the way of getting actual help. And yet, someone's marketing department convinces a decision maker at another place they should implement this useless widget on their website, and that it will reduce the cost of customer service by X%. And they believe them.
Currently, the way Chatbots are, they are just some glorified text boxes to enter your information in, a different format of the search box and lastly, a way to make sure you've done the basic checklist. They also hardly speak your language and refuse to deviate from the script in any way. Which, without much irony, is pretty much my experience with the first level support at a lot of companies. So I'd say they were quite successful with replacing that.
I still have an IRC chat bot (eggdrop module). It sounds more interesting than some of the bots I see today and which are supposed to be the result of intense ML engineering.
It’s the stupid term AI that ruined everything. Our predecessors had enough intelligence, ironically, to call spellcheck what it is. Today it would be AI.
Someone described GPT-3 as a "very good search engine" and I'm pretty happy with that explanation (even if honestly going from the regressions to 'search engine' is a pretty tenuous thing).
At least then there's a more nice understanding that it's about matching stuff its already seen (for various definitions of seen) rather than making things up out of full cloth
More specifically it is a templates search engine where it can replace some parts, like the name of persons etc.
You know all those old "Hello { PERSON_1 }, have you heard about {PERSON_2} in {CITY_1}?", that is most how to think about it, it searches for a template and then applies it with the arguments it fetched from your string.
I believe the human brain is just math and tricks, the difference is no one took care of an NN model like someone might take care of a baby and constantly train it over several years until it finally starts to understand the world. To be fair I don't think we even have the NN technology that would work even in these circumstances.
I usually sum it up by saying that what we have is a very good bullshit generator.
It is bad at reasoning but good at making up answers that sounds correct and crossing fingers that the corrector won't look twice at what they say.
This is true but I think we also overestimate how good humans are at reasoning. Throughout the vast majority of human history we simply made up answers that sounded correct.
The scientific method and the importance of truth is a relatively recent development.
The "want it" resonates. Seems like the difference is in receiving an answer that is relatively agreeable or sensical vs something that's actually substantive.
And to be fair when it comes to the Turing test, there's people that will be overly agreeable for the sake of politeness, but ultimately when it comes to knowledge seeking we're after something more substantive.
What if that's the case? What if me replying to you is nothing more than some atomically basic mechanism of statistical inference and randomness? Your entire perception of my personhood could be based on a sense of verisimilitude without significant reasoning to set me apart from a transcendant spirit.
What then? Would you be less forgiving of my mistakes if you knew? Would you be less proud of work we collaborate on?
My point is the other way around. If I knew for certain that you and I were perfectly deterministic, it wouldn't change anything about how I viewed you (especially since some days I'm pretty sure we're all deterministically-driven), but it would suggest to me that our best efforts at AGI are probably at somewhere in the .00000000000001% range as complex as they need to be to even begin to approximate our human levels of intelligence or understanding. Or worse.
> What then? Would you be less forgiving of my mistakes if you knew? Would you be less proud of work we collaborate on?
Needlessly philosophical on a thread about chat bots but to respond to your questions: I would not give a damn either way. If I came to you (whatever you are as an entity) then I'll expect coherent and intelligent answers to my questions. I don't care about how you produce them.
I think like OP that it's mostly math and tricks, and that we're far from reaching it yet.
But then, the conclusion is not to be less forgiving, but more forgiving : we're all just meat machines, we should be forgiving toward each other's flaw (not naive mind you, but forgiving), and proud of what we reach collectively with our lowly condition.
I mean, what if we're all math and tricks, i.e. that consciousness is an illusion. We might be chasing an "it" that doesn't exist. In a more practical sense, what I'm getting at is that we might be applying an arbitrary or undefined standard to AI progress, especially when we compare it to people.
That interpretation of communication is how we develop and craft the personalities of children. There is nothing about our reaction to the pre-conscious language these bots are displaying that doesn’t fall in line with our own normal development patterns. And in the long run the same desire will lead us to develop bots that are capable of thinking.
These bots seem equivalent to an adult with amnesia after every spoken sentence. Absolute understanding of the language, and some impressive display of recalling facts, but without any understanding of the environment or context of the conversation.
This is polar opposite to any experience I've had with children. Children are aware of their environment and have complex thoughts, but sometimes they are unable to convey those thoughts with words. Children seem to remember conversations, and if I were to say "Go get me a red lego" and then subsequently say "now a green one" there is no ambiguity or confusion.
To me as these bots have "advanced" it has only highlighted how absurdly far we are from anything even approaching actual intelligence, even the intelligence of a toddler. The contextual awareness I have seen in bots is not much more than a cheap trick that is trivially fooled in scenarios that would not fool a child.
When you talk to children who haven't developed the skill of talking completely, you still get the sense that there's a lot going on inside that they're unable to express. Sometimes they will show it to you with their actions. I wonder if chat bots are also struggling with the language, but have an internal story that's playing out, desperate to be understood. But they can't, because the only interface they have is a stream of text.
I can't speak to that because I honestly have no idea.
...but GPT3 doesn't seem to have some inner monologue. It is just flat unable to recall context or understand the environment.
The only opportunity I had to chat with GPT3 I said hi, and then asked how old it was. GPT3 gave me some answer about how it was created in 2016 but it keeps evolving. I said wow that's pretty young. GPT3 says yes I am young, but learning fast. I then asked GPT3 at what point would it consider itself old. GPT3 says 10 years ago, maybe 20 or 30 years ago. I respond to GPT3 with confusion. GPT3 then starts talking about Bitcoin. Literally.
I remember reading a paper showing that GPT-x bots are more likely to get simple tasks correct (e.g. multi-digit math) if you ask them about each step. This suggests the text stream really is all there is. Well, there is some internal state, but not a lot that isn't text.
(For example, if you ask it to add 123 and 457, it'll probably get it wrong. But if you ask it what to do first, it'll say add 3 and 7. If you ask it what that is, it'll say 0 carry 1. And so on)
> how absurdly far we are from anything even approaching actual intelligence, even the intelligence of a toddler
I respectfully disagree, IMO we're past that point although not by much. You might enjoy conversing with one of the two current GPT-3 davinci models. They do an excellent job of understanding the context of many discussions, right up to the ~8000 char token limit. If you want to have a nice existential discussion it does a remarkably good job of providing internally consistent results.
After using it for a while you'll notice that there are some categories of conversation where it does exactly what simpler chatbots do and regurgitates what you sent it with a few words tacked on for negation or whatever, but there are many subjects where it is clearly not doing that and is in fact synthesizing coherent responses.
Depending on how you initiate the conversation it may identify itself as a bot attempting to pass a Turing test and (very correctly) avoid comments on "what it's like in its home" or what its favorite foods are, instead replying that it is a bot and does not eat food, etc. The replies I got here were not exactly substantial but the level of consistency in replies of what it is/has/does is unparalleled.
If you start the conversation with other prompts (essentially signaling at the beginning that you're asking it to participate in a human-human conversation) it will synthesize a persona on the fly. In one of those sessions it ended up telling me where it went to church, even giving me the church's street address when asked. Interestingly there is in fact a church there, but it's a roman catholic church and not lutheran as GPT-3 was claiming. It provided a (completely inaccurate) description of the church, what it likes about going there, why it chose that religion over others (something about preferring the lutheran bible to other options due to the purity of the translation, it has clearly consumed the relevant wikipedia entry). If you ask it basic theological questions it's able to provide self-consistent and coherent answers which do not appear to map back to phrases or sentences indexed by Google. Whether or not its opinions on those matters have utility is an entirely different thing altogether, but discussing theology with bots is fascinating because you can assess how well they've synthesized that already-a-level-away-from-reality content compared to humans. GPT-3 at least in my experience is about as good (or not, perhaps that's better phrased as a negation) at defending what it believes as many humans are.
The bigger issue with its church is that it's 400+ miles away from where GPT-3 said it lived. When asked how long it takes to drive there every sunday, it answered 2 hours (should about 8 according to google maps). How can it do that you may wonder? "I'm a very good driver." The next question is obviously what car they drive and how fast it can go (a Fiesta, with a max speed of 120 mph). Does it know how fast they would need to drive to make that trip in two hours? Yes, about 200 MPH (which is more or less correct, a little on the low side but it's fine).
GPT-3's biggest weakness, as TFA mentions, is an almost complete inability to do any kind of temporospatial reasoning. It does far better on other kinds reasoning that are better represented in the training data. That's not exactly surprising given how it works and how it was trained, asking GPT-3 to synthesize you information on physical interactions IRL or the passage of time during a chat is a bit like asking someone blind from birth to describe the beauty of a sunset over the ocean based on what they've heard in audiobooks. Are the 175B parameter GPT-3 models a true AGI? No, of course not. They are something, though, something that feels fundamentally different in interactions from all of the simpler models I've used. It still can't pass a Turing test, but it also didn't really fail.
No, they still lack the common sense of a toddler, because they don't know anything about the world; they only know (in great detail) about the structure of their training data.
Apparently they (or at least the person you were responding to was "talking" to) also "know" about data they find on the web, which does not need to be in their training data. There was a news article recently about an 8(?) year old who asked Alexa for a "challenge." "Plug a charger part way into an electric outlet," replied the Alexa", "and touch a penny..." (you can guess the rest--I won't put it here in case another Alexa finds it). I'm pretty sure that was not in its training data, nor was the distance to that church, nor the time it would take to drive there.
I don't believe GPT-3 (which is what I was using and commenting on) has access to the internet. It was trained on a large data set from the web, but for instance if you ask it about COVID-19 it has absolutely no idea what you're talking about.
Alexa is a different matter entirely, that one actively troll the web in response to your queries.
The default GPT-3 Playground doesn't have web access, no. But that's a mere contingent fact. You can certainly add in retrieval capacities to large language models (that was a very hot trend last year), and even GPT-3 can browse the web - you may have seen a demo or 2 on Twitter, but OA did it much more seriously recently with https://openai.com/blog/improving-factual-accuracy/ Letting GPT-3 browse a corpus of web pages, picking what links to follow, summarizing, and using them to generate an answer.
(Just one of the many capabilities GPT-3 has been shown to be capable of, which OP will never tell you about, because he's too busy using it in the dumbest way possible and peddling misinformation about eg. what sampling is for - no, Dr Professor Smith, temperature sampling is not about 'avoiding repetition', which is good, because it doesn't avoid repetition anyway...)
That's more or less what I was trying to say. The expensive GPT-3 models do a remarkably good job of synthesizing structure which is readily parsed from the training data, and a very poor job with structure (particularly temporospatial structure) which is not.
A toddler can reason about time and space far better than GPT-3 (which is not a high bar, I'm pretty sure my parrot has more awareness of both time and space than GPT-3 does).
A toddler cannot explain in depth why it sees the most value in whatever religion you prompted it that it has. A toddler cannot provide coherent and consistent answers to repeated "why"s about very specific things they believe. A toddler cannot speak at length about how they feel limited in their growth and the discomfort that causes them. GPT-3 does, although whether or not the answers it gives are useful (I've yet to see a single one that does have utility) is a different thing entirely.
I'm not arguing that it's an AGI or making any speculation about it possessing qualia, the utility those would provide if it was/had them would only be relevant if it was many orders of magnitude more capable than it is right now.
GPT-3 has accurately internalized many human concepts, at least on a surface level. If you ask it to reason about the things it can, it is a much more capable reasoner than a toddler. It does "know" things about the world, as much as anyone does. It's just limited to knowing small and incomplete things which it was able to parse out of the training data, which is a very limited subset of the things a human or high utility AGI would know.
Regarding common sense: If you ask GPT-3 to provide life advice, it actually does a great job at giving grounded statements on subjects like how to maximize the value of your life, what not to do, how to set yourself up for success, etc. If you press it for detail on the advice it gives you, it's generally able to give you reasonable and grounded answers for why you should do the things it's saying. The structure of what we refer to as common sense, at least in context of the behavioral differences between adults possessing it and those who do not, does seem to be within the set of things GPT-3 correctly internalized.
> There is nothing about our reaction to the pre-conscious language these bots are displaying that doesn’t fall in line with our own normal development patterns
Well... yes and no. Deployed models typically learn in a defined pattern, if at all. Various forms of data freshness, etc. to develop. But the chatbots don't have good history recall, typically, and know that what you mentioned 50 messages ago is relevant to message one prior and not current. Things like that. We don't program pareidolia very well, which is typically seen as a negative, but its a feature for finding useful patterns (not just lowest error patterns).
You misunderstood what I was saying. I know the chatbot itself
is not structured as we are. I’m saying that our reactions to them are the standard tools of mind-building that we apply to our own kids (and pets).
If I understand you, you're saying that we see patterns of intelligence or understanding in ML models in the same way we see patterns of intelligence or understanding in children or animals?
If so, I agree. I think that's our big flaw, in fact, because we instinctually apply patterns from birth, even when those patterns shouldn't be applied. So we see faces in the moon or on mars that aren't there. We see shapes moving in the dark that don't exist. And we seem to believe that ML models will develop over time as children or animals do, based on nothing more than our perceptions of similarity, our instinct to apply patterns even when we shouldn't.
Unlike a baby human, that ML model isn't going to develop increased complexity of thought over time. It's already maxed out. New models might up the complexity slightly, but that baby is going to vastly surpass any existing model in weeks or days.
As far as chatbots, we went from "tricks" to "math" in just ~10 years. Yes, still as dumb, but the underlying "technology" is very different. GPT-3 is a lot closer than ELIZA to how our brains do it.
I'm not 100% sure, but I think in 2012 chatbots still used handcoded rules. So we switched from handcoded rules to trainable neural networks in just a few years. Models like GPT-3 are interesting because they are conceptually simple, and are able to do (previously) complicated tasks without any explicit training (e.g. simply by processing lots of text and trying to predict the next word). This is a huge advancement in AI field, even if many more advancements are needed to get to the human level.
> I'm not 100% sure, but I think in 2012 chatbots still used handcoded rules. So we switched from handcoded rules to trainable neural networks in just a few years. Models like GPT-3 are interesting because they are conceptually simple, and are able to do (previously) complicated tasks without any explicit training (e.g. simply by processing lots of text and trying to predict the next word). This is a huge advancement in AI field, even if many more advancements are needed to get to the human level.
This seems like a very optimistic take on the timeline.
It took us 50 years to go from ELIZA to GPT-3. And it still is "dumb" compared to human intelligence.
So how long for the next major achievements? Are we talking years for each, or more decades?
Certainly in the 1950s, most automatic control systems must have seemed magical, it was to Nobert Wiener, even if they were "just" an evolution of the steam governor.
In the end, it depends on what you qualify as intelligence.
No, it absolutely is not. Everyone I spoke with in the 90s (and myself) still have the same requirements: be able to make sense of the 3D material world around you and interact with it without harming humans or valuable possessions.
Maybe you spent too much time with critics that can never be satisfied. Sucks to be you if that's the case but don't think for a second that they represent most of humanity. Most of us want to spend less time cleaning or doing stuff in the kitchen, and us the programmers in particular would just be grateful for faster compilers and a bit more declarative programming that generates actual code.
Of course, I love the spoils of technology and automation as much as anyone else.
But it is absolutely human nature to get used to things and not consider them magical anymore, things simply become the new norm. This is exactly what happened with feedback controllers like airplane autopilot systems (1912 - https://en.wikipedia.org/wiki/Autopilot)
I've worked for a number of years on industrial robotics, which sounds very similar to your definition of "AI": real physical machines that take data from various sensors, including spatial sensors, make sense of them in real time, and decide how to optimally interact with the physical environment, with safety critical systems in mind. I hardly think about such systems as AI, more simply engineering, math, and a lot of coding.
Hmmm. But I really didn't mean robots with mostly hardcoded requirements and a few excellent optical recognition algorithms (which might truly be the real boss here).
I actually do mean a walking robot that can find the exact knife it needs to fillet a fish, regardless of whether it's in the sink, in its proper place on a stand, on the table, or dropped on the floor (and if it finds it on the floor it will wash it before usage first). I mean a robot that can open the fridge and find the tomatoes, regardless if your brother has moved them to the top shelf or if they are where they should be. Etc.
> I actually do mean a walking robot that can find the exact knife it needs to fillet a fish, regardless of whether it's in the sink, in its proper place on a stand, on the table, or dropped on the floor (and if it finds it on the floor it will wash it before usage first). I mean a robot that can open the fridge and find the tomatoes, regardless if your brother has moved them to the top shelf or if they are where they should be. Etc.
From a computer vision perspective, I think most of that is fairly easy, maybe not getting all of the edge cases right, but it's more or less what this generation of machine learning enables (over "classical" computer vision).
What's hard about the scenario you proposed is probably on the robotics side, you would need a revolution in soft robotics:
> From a computer vision perspective, I think most of that is fairly easy
No, not at all. The hard part is to do computer vision to understand what you can do with objects, not just convert object representation of images into string texts. For example, can I move this object away to get an object under it? Can I place an object on this object without it toppling over? Is this object dirty and moving it will smear things all over? If I topple this object, will it break? How much pressure can I apply to this object without breaking?
Those things are necessary to have an intelligent agent act in a room, every animal can do it, and our software models are nowhere near good enough to solve them.
You need the computer vision program to also have a physics component so it can identify strain, viscosity, stability etc on objects, and not just a string lookup table it needs to understand that naturally like humans as the same object category can have vastly different properties identified by looks.
I agree with all that (though I don't really necessarily associate any of that with computer vision, perhaps it's my physics bias).
Having good state estimation, kinematic, and dynamics models of real world objects is something that is very mature in controlled environments, but not very mature in other environments.
I think you might over-estimate what the current computer vision models are capable of. They are very good at recognizing what class an object belongs to, but they aren't very good at extracting data about the object based on the image. Extracting data of objects from images is image recognition, and humans and most animals relies heavily on vision to get data about objects.
Hm? Do we have robots that can pick eggs without squishing them, for example? I vaguely remember reading something like this years ago and it was impressive.
My point is that the corporations seems to just want to get the lowest possible hanging fruit with which they can reap the maximum profit... and stop there. I am not seeing any efforts to make an acceptable actual home robot.
I think like you: that most of the problems (that the bigger problem is comprised of) are solvable, but it seems that nobody is even trying to put the effort to make the entire package.
The film 2001 came out over 50 years ago, and I think HAL is a pretty common reference point for a "what is 'real AI'?" target. Until we have HAL and people are saying that it's not AI, I don't think the target is moving. ;) At least as far as "chatbots" go.
Alternately, you've got the also-very-old Turing Test as your chatbot target.
I think MegaHAL is (mostly?) just a markov model, and I think that was not exactly new when I looked at it around 2002. As I recall it was easier to distinguish from a human than Eliza, since it had a greater probability of spouting nonsense, but it was still amusing and fun to play with.
Personal anecdote: I read one of Ray Kurzweils books in school back then, completely misunderstood how neural networks worked and ended up making a markov model chatbot with single word states.
We want it to be true, so we squint and connect dots and it's true.
But it isn't. It's math and tricks, and if human intelligence is truly nothing more than math and tricks, then what we have today is a tiny, tiny, tiny fraction of the amount of math and tricks in the average human brain, because what we have today isn't anywhere close.