[HN Gopher] ML is not that good at predicting consumers' choices ___________________________________________________________________ ML is not that good at predicting consumers' choices Author : macleginn Score : 139 points Date : 2022-07-21 17:13 UTC (5 hours ago) (HTM) web link (statmodeling.stat.columbia.edu) (TXT) w3m dump (statmodeling.stat.columbia.edu) | Plough_Jogger wrote: | This review omits techniques from reinforcement learning | (especially bandits) that have been used successfully in industry | for years now. | jeffreyrogers wrote: | How are bandits used in consumer choice problems? Bandits solve | almost the inverse problem: which choice to offer/take when | it's uncertain which is best, but the problem under | consideration in the blog post is about predicting which choice | a consumer will pick, a standard marketing problem. | bertil wrote: | I think that the main issue is less the technique (although... | yes, please use RL if you can) and more the lack of data. | Browsing gives very little insight: dwell-time is a poor proxy | for interest, and mixes horrid ideas that are so bad they are | worth sharing with friends and confusing photos where you need | to squint to figure out if it's what you are looking for. | | Both e-commerce and social media are really not good at | gathering express feedback for what people want and valuing | that expressly. Please, let me tell you that I did spend time | looking at this thread about the latest reality TV scandal but | I don't want to hear about it ever again! Please, let me tag | options as "maybe" or let me tell you what you'd need to change | for me to buy that shirt. Public, performative Likes and | Favourite lists that are instantly reactivation spam-fodder... | Come on, you know better. | | I used to work for a big e-commerce site (the leading site for | 18-25 y.o. females). We had millions of references (really) and | it was a problem. The search team had layers upon layers of | ranking algos, incredible papers at conference... but still, | low impact on conversion. It was more than anything else that | we could do, but nowhere as transformative as it could be. | Instead, I suggested copying the Tinder interaction in a | companion app: | | * left, never see that item again; | | * right, add it to a long list of stuff you might want to | revisit. We probably would have to separate that from the | Favourite list to avoid clutter, but maybe not, to make that | selection worthwhile. | | The learning you could get from that dataset, even with a basic | RL algo to queue suggestions... People thought it was "too | much" which I'm still bitter about. | rvz wrote: | So this machine learning and deep learning hype has shown that it | is a gimmick isn't it? After years of surveilling, collecting and | training on user data it still doesn't work or gets attacked very | easily over spoilt pixels and many other attacks? | | What a complete waste of time, money and CO2 being burned up in | the data centers. | Enginerrrd wrote: | I don't know.... I think back on google search back in the | ~2014 era. It was good. Like scary good. Like I'd type "B" and | it would suggest "Btu to Joules conversion" and things like | that. Actually it was better than that... it would anticipate | things I hadn't even searched for before with very very little | prompting. It seemed to adapt to context whether I was at work, | on my phone, at home, etc. The results were exactly what I was | looking for. | | Then it got taken over by ads and SEO and corrupting influences | and it's just not that good anymore. IMO, the problem with DL | isn't the tech. It's the way its being used. The reality is: | For 99% of things advertised to me, I don't want to buy the | goddamn product, and no amount of advertising will make me want | to buy it. It's gotten to the point where if I see an ad for a | product I think I'm more likely to buy a competitor whose ad I | haven't seen because I assume the competitor is investing more | in the product than the marketing. | | And everyone seems to have forgotten about hybrid approaches of | ML and human beings that, IMO, are really good. But alas, "they | don't scale". | | But at the same time, it's really interesting. For as much data | as facebook should have about me, their ad rec's really suck | and always have. (Perhaps it's because my only ad clicks ever | are accidental ones?) I'm kind of astounded at how poor that | result is. That said, I'm always very impressed by spotify's | recommender system. I think it's one of the best on the net. | | Another thing I find interesting is that non-vote-based social | media feed systems all really suck. Once they ditched | chronological ordering it stopped appealing to me, and I don't | know exactly why that is. Evidently I'm on some tail of the | curve they don't care about. | jacquesm wrote: | No, it just isn't a silver bullet for every problem under the | sun. But quite a few record holders on various problems are ML | solutions and that is unlikely to change for the foreseeable | future. | | It's just that as soon as you start out on every problem with | 'ML will solve this!' that you're going to end up with a bunch | of crap. The right tool for the problem wins every time. | cj wrote: | While not exactly aligned to the research, I've been surprised | how poor Nest Thermostat's learning feature is. | | The main selling point for Nest is having a "learning | thermostat". Perhaps my schedule is just not predictable enough, | but the auto-generated temperature schedules it generates after | its "learning" period is not even close to what I would manually | set up on a normal thermostat. | | Maybe I'm just an "edge case" or part of the "long tail" | foobarian wrote: | Well, the main selling point when it came out was that it was | the iPhone of thermostats. It was the only thermostat at the | time that did not have a terrible UI cobbled together by | communist residential block designers or people who think that | setting your own IRQ pins with jumpers is fun. But yeah I never | understood the point of the learning feature; maybe a checkbox | that needed to be ticked or a founder's pet feature. | fshbbdssbbgdd wrote: | Not only does the Nest ignore my preferences, I think it | actually lies about the current temperature. | | Example: | | Setting is 72, reading is 73. AC is not on, I guess the | thermostat is trying to save energy. I lower setting to 71, | reading instantly drops to 72! I don't think it's a | coincidence, this has happened several times. | runnerup wrote: | I also hate how Nest only let me download at most 7 days of | "historical" data. They have the rest of my historical data, | but I can't get a copy of my own data. | amelius wrote: | Presumably they don't want the average consumer to be aware | of that fact. | actusual wrote: | Nah, you're not. I just gave up on mine and have a schedule. I | also turned off "pre-cooling" because it would just kick on at | like 6pm to "cool" the house for bedtime. I also bought several | temperature sensors to use, which are fun. At night I have the | thermostat use the sensor in my bedroom, then goes back to the | main thermostat during the day. | foobarian wrote: | See the next logical step is to outfit the output vents with | servo-controlled actuators so you can fine-tune where the air | is going! | PaulHoule wrote: | When people hear that FAANG is involved in something an | "Emperor's Clothes" effect kicks in and people stop making the | usual assumption that "if it doesn't work for me it probably | doesn't work for other people" | bell-cot wrote: | Or, maybe they invested far more cash and care in marketing | that feature than in programming that feature... | sdoering wrote: | The same for me when I am looking for very specific terms and | search engines think the know better and autocorrect me. | | Having to make an additional click because I receive something | I have never searched for is unnerving. | Slackwise wrote: | "Why am I sweating right now? Oh, the Nest set the temperature | too high again!" | | And then after a few instances, I just turn off all the | automation and set up a schedule like normal. | | Same with the "away from home" which seems to randomly think | I'm away and I have no idea why. | | Oh, and the app doesn't show me filter reminders, only the | actual device, which I never touch all the way downstairs. | There's not even any status to let me know if it's accepted a | new dialed-in temperature, as I've had it fail to capture a | request, and then I go back, and see it never updated/saved the | new temp. Just zero feedback to confirm that the thermostat has | responded to any input, and zero notification from the app if | this happens. | | Just _thoroughly_ unimpressed. | | Thankfully I didn't buy this junk, as it was pre-installed by | the owner of my rental. Can't imagine actually paying for | something that's only real feature is being able to remotely | control my temperature once in a while. | dominotw wrote: | Maybe it considers environmental impact of air conditioning | in its models and tries to nudge users into tolerating higher | temps. | idontpost wrote: | If you have to guess why it's making decisions you don't | want, it's a shitty product. | tristor wrote: | Which is not respecting your users. In fact, in my previous | house the Nest was provided by the utility company and they | used it /exactly/ for this purpose (although were legally | mandated to notify us and allow us to opt out on a daily | basis) where they'd intentionally raise your temperature | during the hottest part of the day to reduce energy usage. | But the thing is, I work from home, and if I'm sweating out | a liter of fluids while I'm trying to work, I am getting | nothing done and look unpresentable on meetings to boot. | | In the end because most of the house was empty, I let the | Nest do its thing and installed a separate mini-split AC in | my office I kept set at 72 year-round because that's a sane | and reasonable temperature for an office. Don't try to | "nudge me into tolerating higher temps", respect my agency | and choice about what is a comfortable environment for me | to work in. | | As a side note, I will never again buy a Nest product. | bryanrasmussen wrote: | >And then after a few instances, I just turn off all the | automation and set up a schedule like normal. | | If you have a fairly regular life I would think a schedule | would outdo ML pretty much all the time, because you know | exactly what that schedule should be. ML might be useful for | a secret agent whose life is so erratic that a schedule would | be useless. | | That is to say ML is maybe better than falling back to | nothing. | sarahlwalks wrote: | One niche that ML seems to be growing into is /assisting/ | humans, but not doing the whole task. ML might give you an | image that is 90 percent what you want but needs a few | tweaks. | | If the task is clear enough, ML can take it on by itself, | but this requires clear rules and an absolutely unambiguous | definition of what winning means. For example, the best | chess players in the world are machines, and are FAR better | than the best human players. Same for Go (the game, not the | programming language). | capableweb wrote: | If your schedule is so irregular/erratic, how is a ML | algorithm supposed to be able to learn it? | | Sounds like in that case it's better to just control things | manually. | bryanrasmussen wrote: | ML can learn patterns that humans might not be aware of, | so you there might be certain things that happen that | show you will be on a mission to East Asia for a couple | days. | [deleted] | tomrod wrote: | Only when data is supplied to it to match the trained | pattern, | | ML is pattern recognition. Anything outside of that is | still AI, but it isn't ML. I can think of very few | feature sets we could supply to help predict someone will | be deployed to East Asia for a few days other than | scraping calendars and mail for religious and military | organizations. | | From a design perspective, Nest and others are either | additively learning _in situ_ to enhance a base model or | they are working from a base model that doesn 't directly | learn, just classifies workflow to categorize | observations on a base model. I doubt heavy training is | occurring where the Nest and similar is treated as the | central compute node. | mbesto wrote: | I've always heard this, and so when I went for my first smart | thermometer I went straight to Ecobee (which I'm very happy | with btw). | | So I gotta ask HN...what the heck was so popular about | Nests?! It's one thing to be go after shiny lures like new | iPhone apps or luxury items...but a Thermostat?! | | Mind boggling... | Eugr wrote: | It looks good on the wall, has a bright large display that | lights up when you approach and intuitive enough for non- | techies to operate. Also it can be installed without a | common wire. | TaupeRanger wrote: | Same story. We moved into a house that had a Nest | preinstalled. Got everything set up, and noticed after a | couple of days we would always wake up freezing in the early | morning. Nest was all over the place and I just turned off | the automation. | HWR_14 wrote: | The ability to remotely activate it is useful in the case of | erratic short term rentals. Other than that, I'm not sure of | the point | miguelazo wrote: | Which is something that a cheaper, more basic Honeywell | model with way less surveillance potential can also do... | HWR_14 wrote: | Indeed. I wouldn't buy a Nest. But there is a use case | for an IoT thermostat. | [deleted] | kayodelycaon wrote: | Things like this are exactly why I went with less "intelligent" | smart thermostat. (Honeywell T9) | | The only learning feature it has is figuring out how long it | takes to heat or cool the house given the current weather. | Before a schedule change, can heat or cool the house so it hits | next target temperature on time. This seems to work extremely | well. | | Everything else like schedule and away settings are configured | by the user. | | Once nice feature is it is fully programmable from the | thermostat, without internet. You only need the app for setting | a geofence for automatic home/away. | connicpu wrote: | Building my own thermostat so I have total control was a fun | project, I learned a lot about electrical engineering and | built a circuit with some TRIACs to control the HVAC lines. | Though I still need to give it an interface so I can program | it some way other than uploading the program as a JSON blob | to my raspberry pi! | pid_0 wrote: | nahname wrote: | It is bad. I dislike most "smart" things though, so take my | agreement with a grain of salt. | baxtr wrote: | Google destroys any great product they acquire (except google | maps and YT I guess). | aaronax wrote: | ML is there to maximize business income--nothing else. | | If ML was benefiting me, it would know that 90% of the time I | fire up Hulu I plan to watch the next episode of what I was | watching last time. And it would make that a one click action. | Instead I have to scroll past promotional garbage...every single | time. Assholes. | HWR_14 wrote: | I don't know why you assume the goal is "help aaronax watch | what he wants quickly" vs "make sure when aaronax switches to | his next series/movie it's on Hulu" | mirrorlake wrote: | Customer satisfaction often translates into more dollars, | though, because it means they won't cancel their service. | I've had the same thought: if only this multi-billion dollar | company could figure out that I want to continue watching the | show I watched yesterday. | HWR_14 wrote: | I would think it would be long-term satisfaction | optimization. I'm not trying to optimize your binging of a | single show (which you might watch then cancel after), I'm | trying to get you to love enough of my product line to | stick around. | buscoquadnary wrote: | Honestly a lot of this ML to me seems eerily similar to how in | older times people would use sheep entrails or crow droppings | to try and predict the future. I mean basically that is what ML | is, trying to predict the future, the difference is they called | it magic, we call it math, but both seem to have about the same | outcome, or understandability. | treesprite82 wrote: | > I mean basically that is what ML is, trying to predict the | future | | If being so reductive, that's also the scientific method. | Form a model on some existing data, with the goal of it being | predictive on new unseen data. Key is in favoring the more | predictive models. | | > they called it magic, we call it math, but both seem to | have about the same outcome | | Find me some sheep entrails that can do this: | https://imagen.research.google/ | duxup wrote: | Is there much that is good about predicting this stuff? | | I find Amazon loves to tell me to buy ... the thing they know I | just bought and you don't need more than one of ... | | I hardly ever get ads or offers for things I want. | | How do you mess that up? | alephxyz wrote: | Google seems like they target by age, gender and income rather | than by interests. Sometimes it's convinced I'm a yuppie and | keeps showing me luxury cars, personal care/beauty products and | high end electronics (when I have zero interest in any of those | products). | | Ironically I find the "dumb" ads on cable tv news to be a lot | more effective since they have to target by interests. | quickthrower2 wrote: | Once the ML can understand Breakthrough Advertising, it might | have a chance. | hourago wrote: | > Sophisticated methods and "big data" can in certain contexts | improve predictions, but usually only slightly, and prediction | remains very imprecise | | The worst part of big data is the data itself. Used to be common | will be shared on Facebook webs about "what is your political | compass". There results were used to create political profiles of | users and targeted propaganda. | | You don't need ML to predict the data that there user already has | given. | teruakohatu wrote: | > Currently, we are still far from a point where machines are | able to abstract high-level concepts from data or engage in | reasoning and reflection | | Of course when an AI does that, we then say its just doing | statistics, not reasoning. | | Until you have built a recommendation engine from scratch, it is | hard to appreciate the complexity. I don't mean the complexity of | the code or algorithm (ALS and Spark are straightforward enough) | but the contextual problem. Models end up being large collections | of models in a complex hierarchy, with hyperparams to tune higher | level concepts such as "surprise" or business targets such as | "revenue", "engagement" etc. TikTok have nailed this, as has | Spotify. | Barrin92 wrote: | >Of course when an AI does that, we then say its just doing | statistics, not reasoning. | | no, AI simply doesn't do that. Even Demis Hassabis of Deepmind | fame in a recent interview pointed this out. Machine learning | is great on averaging out a large amount of data, which is | often useful, but it doesn't generate true novelty in any human | sense. AI can play Go, it can't invent Go. | | In the same way today's recommender systems are great at | averaging out my last 50 shopping items or spotify playlist but | they can't take a real guess at what truly new thing I'd like | based on a genuine understanding of say, my personality. Which | is reflected in the quality of recommendations which is mostly | "the thing you just bought/watched", which is ironically often | incredibly uninteresting. | humanistbot wrote: | "It's tough to make predictions, especially about the future." -- | Yogi Berra | [deleted] | shaburn wrote: | tomcam wrote: | I can personally vouch that Amazon, Twitter, and YouTube all do | horrible horrible jobs predicting my taste. And they have got | worse over the years, not better | Aerroon wrote: | Part of the reason they're horrible is because people don't | have consistent interests. I might be interested in raunchy | content right now, but I won't be a few hours later. What | determines whether I'm interested in the former is outside of | the control of these algorithms - they don't know all of the | external events that can change my current mood and | preferences. As a result of this it makes sense for people to | have many profiles that they switch between, but AI seems | incapable of replicating this manual control (so far). | | Sometimes I want to watch videos about people doing | programming, but usually I don't. When I do though, I would | like to easily get into a mode to do just that. Right now that | essentially involves switching accounts or hoping random search | recommendations are good enough. | thaumasiotes wrote: | > Part of the reason they're horrible is because people don't | have consistent interests. I might be interested in raunchy | content right now, but I won't be a few hours later. What | determines whether I'm interested in the former is outside of | the control of these algorithms | | I don't think that matters at all. People don't complain that | they're getting recommendations that would have been great if | they had come in an hour/day earlier or later. When you get a | recommendation like that, you consider it a good | recommendation. | | Instead, they complain that they're getting recommendations | for awful content that they wouldn't choose to watch under | any circumstances. | jltsiren wrote: | My favorite experience with Amazon: | | I had just preordered novel 9 of The Expanse, and I got an | email recommending something else from the same authors: novel | 8 of the Expanse. A more sensible recommendation engine might | have assumed that someone who preorders part n+1 of a series | may already have part n. Not to mention that Amazon should have | known that I already had novel 8 on my Kindle. | | I guess generating personalized recommendations at scale is | still too expensive. We just get recommendations based on what | other customers with vaguely similar tastes were interested in. | semi-extrinsic wrote: | The one thing I've been consistently impressed with is TikTok. | If I compare recommendations on YouTube to what I get on my | TikTok FYP, it's like comparing a 5-year-old to a college | graduate on a math test. | | Literally to the point where YouTube never pulls me down into | the rabbit hole anymore, I watch one video because it was | linked from somewhere else, then I bounce. | wrycoder wrote: | I think YouTube has given up on figuring me out. | | They mostly offer stuff I've already watched or stuff on my | watch list. | hourago wrote: | That may make sense of you are not the average consumer. | Optimizing for the most common case makes sense. I see that | with Google search prediction, it's good but many times it | predicts very sensible words for general use but not in the | topic that I'm interested. | abotsis wrote: | My Instagram ad conversations say otherwise. | IAmWorried wrote: | It seems to me like the "generation" use case of ML is much more | promising than the "prediction" or "classification" use case. | It's tough to predict things in general because our universe is | fundamentally uncertain. How is some computer going to predict | that some mugger sees a target at some random spot and decides to | mug them? But the progress in text to image and text generation | really blows my mind. | macNchz wrote: | I've shared this before on HN, but it never fails to make me | laugh when I think about it: | | >Several years ago a conversation about a similar topic prompted | me to look at the ad targeting data Facebook had on me. At the | time I'd had a Facebook account for 12 years with lots of posts, | group memberships and ~500 friends. Their cutting edge data | collection and complex ad targeting algorithms had identified my | "Hobbies and activities" as: "Mosquito", "Hobby", "Leaf" and | "Species": https://imgur.com/nWCWn63. Whatever that means. | oxfordmale wrote: | It is the same on Netflix. I have phases where I watch a certain | genre for a few weeks and then move on. For example after a few | Scandi crime series it is time for something else. However, at | the same time my daughter loves Anime and pretty only watch that. | It is really hard for an ML algorithm to grab these nuances. | golemiprague wrote: | bertil wrote: | Netflix makes a far more obvious sin: not having "who is | watching" as boolean choices. If I am watching with my partner, | I want both of our accounts to mark that series as viewed. And | I really want Netflix to tell me what I'm watching with her so | that I don't continue watching it without her because I will be | single if that happens (again). | oxfordmale wrote: | It would be a great revenue stream for Netflix. | | Are you sure you want to watch this without your partner ? | | Yes ? We recommend the following service for finding | temporary accommodation on short notice | annoyingnoob wrote: | Maybe humans have free will after all. | ugjka wrote: | random will perhaps | Spivak wrote: | It's funny you say random because if consumer choice was | actually random with some known distribution it would be | _extremely_ predictable, no ML needed. | nequo wrote: | Known distribution doesn't mean extremely predictable. | | For example, if your water consumption is log-Cauchy, I | will have a very hard time predicting it because the | variance is infinite. | jrm4 wrote: | I'm not surprised at this result, mostly because of the | inaccurate noise that the business of "marketing," (i.e. | specifically marketing people selling their not-very-effective | services) generates. | [deleted] | mgraczyk wrote: | Always interesting to see outsiders writing papers about this, | using anecdote and unrelated data (mostly political and real | world purchase data in this case) to argue that ML doesn't make | useful predictions. Meanwhile I look at randomized controlled | trial data showing millions of dollars in revenue uplift directly | attributable to ML vs non-ML backed conversion pipelines, | offsetting the cost of doing the ML by >10x. | | It reminds me a lot of other populist folk-science belief, like | vaccine hesitancy. Despite overwhelming data to the contrary, a | huge portion of the US population believes that they are somehow | better off contracting COVID-19 naturally versus getting the | vaccine. I think when effect sizes per individual are small and | only build up across large populations, people tend to believe | whatever aligns best with their identity. | mrxd wrote: | If your ML model is able to predict what consumers are going to | buy, the revenue lift would be zero. | | Let's say I go to the store to buy milk. The store has a | perfect ML model, so they're able to predict that I'm about to | do that. I walk into the store and buy the milk as planned. So | how does the ML help drive revenue? The store could make my | life easier by having it ready for me at the door, but I was | going to buy it anyway, so the extra work just makes the store | less profitable. | | Maybe they know I'm driving to a different store, so they could | send me an ad telling me to come to their store instead. But | I'm already on my way, so I'll probably just keep going. | | Revenue comes from changing consumer behavior, not predicting | it. The ideal ML model would identify people who need milk, and | predict that they won't buy it. | johnthewise wrote: | It wouldn't be zero. If you wanted milk but couldn't find it | in the store/spent too much, you might just give up on buying | it. | qvrjuec wrote: | If the store knows you will want to buy milk, it will have | milk in stock according to demand. If it doesn't have a | perfect understanding of whether or not people want to buy | milk, the store will over/under stock and lose money. | soared wrote: | This is incorrect. You can predict many things that drive | incremental revenue lift. | | The simplest: Predict what features a user is most interested | in, drive them to that page (increasing their predicted | conversion rate) -> purchases that occur now that would not | have occurred before. | | Similarly: Predict products a user is likely to purchase | given they made a different purchase. The user may not have | seen these incremental products. For example, users buys | orange couch, show them brown pillows. | | Like above, the same actually works for entirely unrelated | product views. If users views x,y,z products we can predict | they will be interested in product w and we can advertise it. | | Or we predict a user was very likely to have made a purchase, | but hasn't yet. Then we can take action to advertise to them | (or not advertise to them). | mrxd wrote: | ML is useful for many things. I'm asking the question of | whether _prediction_ is useful, and whether it is accurate | to describe ML as making predictions. | | The reason to raise those questions is that for many | people, the word _prediction_ has connotations of | surveillance and control, so it is best not to use it | loosely. | | The meaning of the word "predict" is to indicate a future | event, so it doesn't make grammatical sense to put a | present tense verb after it, as you have done in "Predict | what features a user _is_ most interested in. " Aside from | the verb being in the present tense, being interested in | something is not an event. | | You can't _predict_ a present state of affairs. If I look | out the window and see that it is raining, no one would say | that I 've predicted the weather. If I come to that | conclusion indirectly (e.g. a wet umbrella by the door), | that would not be considered a prediction either because | it's in the present. The accurate term for this is | "inference", not "prediction". | | The usage of the word _predict_ is also incorrect from the | point of view of an A /B test. If your ML model has truly | predicted that your users will purchase a particular | product, they will purchase it regardless of which | condition they are in. But this is the null hypothesis, and | the ML model is being introduced in the treatment group to | disprove this. | soared wrote: | You can predict a present state of affairs if they are | unknown to you. | | I predict the weather in NYC is 100F. I don't know | whether or not that is true. | | Really a pedantic argument, but to appease your phrasing | you can reword my comment with "We predict an increase in | conversion rate if we assume the user is interested in | feature x more than feature y" | mrxd wrote: | That is a normal usage in the tech industry, but that's | not how ordinary people use that word. More importantly, | it's not how journalists use that word. | | In ordinary language, you are making inferences about | what users are interested in, then making inferences | about what products are relevant to that interest. The | prediction is that putting relevant products in front of | users will make them buy more - but that is a trivial | prediction. | daniel_reetz wrote: | Exactly. I know someone who does this for a certain class | of loans, based on data sold by universities (and more). | | Philosophically -- personally -- I think this is just | another way big data erodes our autonomy and humanity while | _also_ providing new forms of convenience. We have no way | of knowing where suggestions come from, or which options | are concealed. Evolution provides no defense against this | form of manipulation. It's a double edged sword, an | invisible one. | nojito wrote: | >Always interesting to see outsiders writing papers about this | | I don't think you know who andrew gelman is. Additionally, | that's not the conclusion derived from this study. | mgraczyk wrote: | The actual conclusion of the study is so absurd that it's not | worth engaging with seriously. That is, to | maximally understand, and therefore predict, consumer | preferences is likely to require information outside of data | on choices and behavior, but also on what it is like to be | human. | | I was responding to the interpretation from the blog post, | which is more reasonable. | conformist wrote: | Yes, the review paper appears to be roughly conditioned on | "using data that academics can readily access or generate". | | Clearly, this doesn't generalise to cases where you have highly | specific data (e.g. if you're Google). | | However, cases with large societal impact are more likely to be | the latter? They may perhaps better be viewed as "conditioned | on data that is so valuable that nobody is going to publish or | explain it", which kind of is in the complement of the review? | RA_Fisher wrote: | Exactly, RCTs take the mystery out. Nice work! | mushufasa wrote: | I think you may be conflating the topics and goals of adjacent | exercises; predicting consumer behavior is not the same thing | as optimizing a conversion pipeline. | gwbas1c wrote: | > Always interesting to see outsiders writing papers about | this, using anecdote and unrelated data (mostly political and | real world purchase data in this case) to argue that ML doesn't | make useful predictions. Meanwhile I look at randomized | controlled trial data showing millions of dollars in revenue | uplift directly attributable to ML vs non-ML backed conversion | pipelines, offsetting the cost of doing the ML by >10x. | | I regularly buy the same brand of toilet paper, socks, and | sneakers. Machine learning can predict that. | | But, machine learning can't predict that I spent the night at | my parents house, really liked the fancy pillow they put on the | guest bed, and then had to buy one for myself. (This is | essentially the conclusion in the abstract.) | | Such a prediction requires _mind reading,_ which is impossible. | mgraczyk wrote: | The key insight missed by this paper (and people from the | marketing field in general) is that cases like that are | extremely rare compared to easy to predict cases. They don't | matter right now at all for most products, from the | perspective of marketing ROI. | | Also ML can predict that, BTW. Facebook knows you are | connected to your parents. If the pillow seller tells | Facebook that your parents bought the pillow, then Facebook | knows and may choose to show you an ad for that pillow. | semi-extrinsic wrote: | Are you really sure you're not just fooling yourselves with | your randomized controlled trials? As Feynman famously said, | the easiest person to fool is yourself. And in business even | more than science, you might even like the results. | | Have you ever put this data up against something similar to the | peer review system in academia, where several experts from a | competing deparment (or ideally competing company) try to pick | your results apart, disprove your hypothesis? | johnthewise wrote: | well, certainly it's possible to fool yourselves with A/B | testing, it doesn't mean you must be fooling yourselves. I've | also seen similar results in recommendation settings in | mobile gaming, not once but over and over again across | portfolio of dozens of games/hundreds millions of players. | You don't need to predict 20% better on whatever you are | predicting to get a 20% increase in LTV and it's even better | if you are doing RL since you are optimizing directly for | your KPIs | abirch wrote: | Amazon does a remarkably good job of predicting what I'll buy | and I frequently add to my purchases. | mrguyorama wrote: | Are you the mythical person buying 15 vacuum cleaners at the | same time? | marcosdumay wrote: | They are not at the same time. There are entire days of | interval! | abirch wrote: | No, I'm the person who doesn't know the great things to buy | with my Raspberry Pi. Thanks to great predictions from | Amazon's part, they get me to buy more. Similar to how | Netflix does a pretty good job of recommending movies. | bschne wrote: | I know this is slightly off what the article is concerned | with, but the important question in a business context is | whether this prediction is worth anything, i.e. whether it | can be turned into revenue that wouldn't be generated in the | absence of the prediction. | ape4 wrote: | You just bought a washing machine... could I interest you in a | washing machine? | [deleted] | im3w1l wrote: | GPT can solve this! I prompted it with "Sarah bought a washing | machine and a ". It completed "dryer.". | | Another "If you buy a hammer you might also want to buy " -> "a | nail". Ill forgive the singular. | | Just to be clear those are not cherry picked - they were my | first two attempts. | ape4 wrote: | Putting those together... I actually bought a pair of anti | hammer arrestors for the washing machine ;) | thaumasiotes wrote: | > GPT can solve this! I prompted it with "Sarah bought a | washing machine and a ". It completed "dryer.". | | The most natural interpretation there is that Sarah bought a | washing machine and a dryer simultaneously, not that, after | buying a washing machine the month prior, she was finally | ready to buy a dryer. | mdp2021 wrote: | While the chief absurdity is very clear (also mocked by | Spitting Image - J.B. on a date: "You loved that steak? Good, | I'll order another one!"), I am afraid that the intended idea | may be that your memory about the ads of what you just bought | will last as much as said goods. | | Utter nightmare (unnatural obsolescence, systemic perversity, | pollution...) but. I have met R'n'D who admitted the goal was | just to have something new to have people want to replace the | old, on unsubstantial grounds. | armchairhacker wrote: | I think the reason this happens is that when you start looking | for washing machines, you start getting ads for them. Then when | you buy nobody tells the ad companies that you just bought a | washing machine so they still send you ads because they think | you're still looking. Even if you just went straight to the | model site and clicked "buy". | thaumasiotes wrote: | We know that's not the reason; Amazon is infamous for | advertising washing machines to people who have just bought a | washing machine from Amazon. | wrycoder wrote: | I buy a package of underwear. All I see for next three weeks on | my browser is close ups of men's briefs. | | It's embarrassing, when associates glance at my screen. | bolasanibk wrote: | I cannot remember the reference now, but the reasoning I read | was a person who just bought an item x might: 1. return the | item if they are not satisfied with it and get a replacement Or | 2. buy another one as a gift if they really like it. | | Both of these result in a higher fraction of conversions in | this kind of targeting vs other targeting criteria. | gwbas1c wrote: | > for most of the more interesting consumer decisions, those that | are "new" and non-habitual, prediction remains hard | | Translation: Computers can't read minds. | | A bigger generalization is that, whenever a software feature | becomes essentially mind reading; someone's either feeding a hype | engine or letting their imagination run away. | | The best things to do in that case is to pop the bubble if you | can, or walk away. I will often clearly state, "Computers can't | read minds. You're making a lot of assumptions that will most | likely prove false." | sarahlwalks wrote: | As far as I'm concerned, the question is how ML/AI stacks up | against the competition -- humans. I don't know, but I'd bet the | answer is that ML is much better. Let's say at least 20 percent | better, but I imagine it's much higher than that. | | Second, this is only saying that right now, ML's performance is | "not that good." It says nothing about future technical advances. | If you look at the track record of ML in the past three decades, | it's amazing, and if that performance is repeated in the next | three decades, who even knows what things might look like. | (Machine sentience? Maybe.) | wheelerof4te wrote: | ML is not that good at predicting. | malkia wrote: | Some years ago, I worked on a team "Ads Human Eval" - we had | raters hired to do A/B testing for ads. These evaluated | questionaires carefuly crafted by our linguists, and then | analyzed by the statisticians providing feedback to the | (internal) group that wanted to know more about. | | So the best experience was this internal event that we had, where | the raters would say that certain Ad would not fare well (long | term), while the initial metrics (automated) were showing the | opposite (short temr). So then we'll gather into this event, and | people would "debug" these and try to find where the differences | are coming through. | | Then we had to help another group, where ML failed miserably | detecting ads that should've not been shown on specific media, | and raters came to help giving the correct answers. | | The one thing that I've learned is that humans are not going to | be replaced any time soon by AI, and I've been telling my folks, | friends or anyone (new-born luddities) - that automation is not | going to fully replace us. We'll still be needed as teachers, | evaluators, fixers, tweakers/hackers - e.g. someone saying - this | is right, and this is not, this needs adjustment, etc. (to the | machine, ai, etc.). | | Maybe machines are going to take over us one day, but until then, | I'm not worried... | | (I've also understood I knew nothing about staticics, and how | valuable linguists are when comes to forming clear, concise and | non-confusing (no double meaning) questions) | Melatonic wrote: | I dont think most people are arguing that machines will replace | everyone anytime soon - it is that they will replace a huge | portion of people. If one person can do the job of 10,000 by | being the tweaker / approver of an advanced AI that is still | 9,999 jobs eliminated. That might be hyperbole (you still | probably will need people to support that system) ___________________________________________________________________ (page generated 2022-07-21 23:00 UTC)