Demo of =GPT3() as a spreadsheet feature
twitter.comThere is a huge potential for language models to get close to messy text problems (many of which are in Excel and Sheet). I am the founder of promptloop.com - the author of this tweet has been an early user.
The challenge to making something like this, or Co-pilot / Ghostwrite, work well is about meeting users where they are. Spreadsheet users dont want to deal with API keys or know what temperature is - but anyone (like this tweet) can set up direct API use with generic models in 10 minutes. This document has all the code to do so ;). [1]
For non-engineers - or folks who need a reliable and familiar syntax to use at scale and across their org - promptloop [2] is the best way to do that. All comments in here are great though. We have been live with users since the summer - no waitlist. And as a note - despite the name "prompt engineering" has almost nothing to do with making this work at scale.
[1] https://docs.google.com/spreadsheets/d/1lSpiz2dIswCXGIQfE69d... [2] https://www.promptloop.com/
> The author of this tweet has been an early user
Maybe not good to reveal customer names this way, unless they already disclosed it publicly
Any plans to bring this to Excel? I would love to recommend this to folks at my company but we aren't allowed to use G-Suite.
Yes - Not open access yet but drop me an email!
How much does it cost to run?
The most sensible use for AI that I can see at this time is for supporting humans in their work, but only where the system is set up so that the human has to do the work first, with the AI system looking for possible errors. For example the human drives the car, and the AI brakes when it senses dangerous conditions ahead, or the human screens test results for evidence of cancer and the AI flags where it disagrees so that the human might take another look. The opposite scenario with AI doing the work and humans checking for errors as is the case here will lead to humans being over reliant on less than perfect systems and producing outcomes with high rates of error. As AI improves and gains trust in a field, it can then replace the human. But this trust has to come from evidence of AI superiority over the long term, not from companies over-selling the reliability of their AI.
Humans are also less than perfect systems. Especially if they have to deal with monotonous tasks. A human might perform better on a 100 entries than an AI, but on 10 thousand? Of course you can distribute the workload, but you will balloon the costs (I'm talking about a future where GPT3 costs come down).
There must be a set of projects which are cost prohibited now due to having to pay humans but will become feasible exactly because of this tech. For a good portion of these, higher-than-human error rate will also be tolerable or at least correctable via a small degree of human intervention.
> A human might perform better on a 100 entries than an AI, but on 10 thousand?
There's also increased variance in human accuracy. You might train 100 but 10k people? A model is consistent all the way.
This is a good point. There is some work that just wont be done unless it can be automated, and in that case work with a higher rate of error is preferable to no work at all.
This won’t work because humans are lazy and fundamentally wired to expend the least amount of effort possible. Just the belief that you have an AI that will correct your mistakes, will make people expend less effort (even subconsciously), until it completely cancels out any additional error correction from the AI. Plus, workers will hate the fact that an AI could automatically do exactly what they are doing but they are doing it manually for “error correction”.
It only works the opposite way, where machines and AI handle the trivial cases and humans handle the non-trivial ones. Many people actually genuinely like to solve hard problems which require thinking and skill, most people strongly dislike mundane repetitive tasks.
Humans may find, however, that without practice solving the trivial problems they are ill-prepared for the difficult ones.
So humans should do the work of image classification, voice transcription, text summarization, etc. before an AI gets involved?
Makes total sense to me.
"As AI improves and gains trust in a field, it can then replace the human. But this trust has to come from evidence of AI superiority over the long term"
Often the other way around is better. Example: Let AI give text classification a go then let a human perfect and check the result. Let AI end your email but check you are happy with how it is physically phrased. Etc.
Human first scenarios will be more rare. And probably where the human has to do it by law. Made up example: border control checking passport photos match face. Human checks and if they click OK then AI double checks.
One could argue that AI finishing emails is a progression of AI proofreading emails
I mostly agree. Happily, that's also what people will want as long as human participation is necessary. We'd generally prefer to write rather than correct an AI's writing, and prefer to drive rather than carefully watch an AI drive.
But when the AI is capable of something the person can't do (like Stable Diffusion creating images compared to me) the AI should take first chair.
This is a good point. Where less than perfect AI is better than the alternative, it is useful.
The ability of language models to do zero-shot tasks like this is cool and all, but there is no way you should actually be doing something like this on data you care about. Like think about how much compute is going into trying to autofill a handful of zip codes, and you're still getting a bunch of them wrong.
I've used/added the USPS API into a system and it took practically no time at all to do it. I'm guessing that is significantly less time than building an AI tool. What's worse is that the thing that takes the least time to implement actually provides good data.
Obviously adding the USPS API to your tool would take less time than building an AI tool. But the AI tool is infinitely more powerful for almost anything other than dealing with addresses.
So the question isn't which one you can add to your tool faster. The question is, if I already have this AI tool setup, is it worth setting up the USPS API to go from 95% accuracy to 99.9% accuracy. For countless applications, it wouldn't be. Obviously if you need to scale and need to ensure accuracy, it's a different story.
> For countless applications, it wouldn't be.
If having something like the zip code not be accurate, then what's the point of having the zip code in your data? People writing code to store zips/addresses are doing it to not not be able to send/verify/etc. They are doing so that the can, but if the data is wrong then they can't.
What countless applications that ask for a zip code/mailing address and don't need it to be accurate? I would then say that any that you name would actually not need the data in the first place. If your hoover it up just to sell later, wouldn't it be worth more to be valid? So again, I'm right back to why do you need it?
yeah, determining the zip for an address is a really bad example.
Better one would be "based on these three columns, generate a cold outbound email for the person..."
it would suck to be on the receiving end of those, but the use case makes much more sense.
I've been wondering about this... It definitely doesn't feel great to be on the receiving end of something auto-generated. But a "unique" message is at least more interesting to read, and doesn't feel quite as frustrating.
A yet if P(someone unknown is a robot) gets too large, it's going to be a weird adjustment period.
Microsoft and Google both have excellent formulas for dates - and are getting there for addresses. Right now - the most useful things you can accomplish in sheets center around what the underlying models are good at - general inference and generation based on text. Anything needing exact outputs should be a numeric formula or programmatic.
Non-exact outputs are actually a feature and not a bug for other use cases - but this takes a bit of use to really see.
Yeah, I think energy efficiency considerations will become important at some point. Or at least they should.
They only will if we price in the true cost of energy including negative externalities
Now wait for =deep_dream() or maybe =stable_diffusion() as a graph-generating function! (Graphs plotted with this function will of course zoom in infinitely but the further you go the more eyes and shiba dogs you'll notice in the corners ...)
Plots that devolve into madness as if you zoom in too close? Finally the programmers get to experience electronics class.
Sadly current models are bad at plotting graphs with any kind of accuracy. We're still quite far off from getting them to do a properly labeled, colored pie chart with a legend.
For what it's worth, I'm also very bad at plotting graphs with any kind of accuracy, which is why I use plotting software instead of doing it by hand.
I get the feeling that my visual system and the language I use are respectively pretty bad at processing and conveying precise information from a plot, (beyond simple descriptors like "A is larger than B" or "f(x) has a maximum"). I guess I would find it mildly surprising if any Vision-Language model were able to perform those tasks very well, because the representations in question seem pretty poorly suited.
I get that popular diffusion models for image generation are doing a bad job composing concepts in a scene and keeping relationships constant over the image--even if Stable Diffusion could write in human script, it's a bad bet that the contents of a legend would match a pie chart that it drew. But other Vision-Language models, designed for image captioning or visual question answering, rather than generating diverse, stylistic images, are pretty good at that compositional information (up to, again, the "simple descriptions" level of granularity I mentioned before.)
Also check out https://usedouble.com (YC W23) if you're interested in using something like this today.
Note: I'm the founder :) Happy to answer any questions.
Reply below with some sample data/problem and I'll reply with a demo to see if we can solve it out of the box!
Just signed up. How long is the wait list?
It's about 3 weeks right now. Mostly limited by me trying to make sure each person onboarded gets a high touch experience from me since the product is still rough around the edges. If you need access to solve an urgent problem, email me at founder@usedouble.com and I can get you in right away if you don't mind the early unpolished nature of the product.
> Get this AI tool for FREE. Next waitlist closes in:
> 0 day 7 hour 31 min 42 sec
I've never seen rolling waitlists, it's kind of strange tbh
It's the modern equivalent of creating a long queue outside your Apple store to generate buzz.
It sounds more like a series of short queues. So short, that Apple employees are sitting around twiddling their thumbs until the next queue gets long enough that they decide to start working.
If the app isn’t growing faster than infrastructure can be provisioned, these mini queues are just making people wait for no reason.
Do I understand that correctly? When I have to create a spreadsheet like this, there are 2 options. Option 1 I write a table zipcode to state and use this table to generate my column. If I carefully check my table my spreadsheet would be okay. Option 2 I ask GPT3 to do my work. But I have to check the whole spreadsheet for errors.
I dealt with something similar. I was creating a large directory of childcare centres in Canada. I had thousands of listings with a url but no email address. I created a Mechanical Turk job to ask turkers to go to website and find an email address. Many came back with email addresses like admin@<<actualURL>>.com. After checking a few, I realized that the turkers were just guessing that admin@ would work and I'd approve their work. I ended up having to double check all the work.
> I ended up having to double check all the work.
Me too, different project and different labelling company but the conclusion - it's better to do it in house. Labelling is hard. You need to see, talk with and train your labelling team.
That’s why you always set up layers of work with mturk, with other layers validating the first ones. Or give the same task to multiple workers and compare the results.
For getting email addresses when I had a url, it would have likely been easier to set up a scrapper to visit page and pull any string with an @ in it. Then scroll through the list to find the most obvious general intake email.
I wonder if those workers can be reported and fired.
It was quite a while ago so I don't remember what my options were. If I recall correctly I was able to see individual workers responses, so once I found one that was obviously faking results, I was able to reject all their submissions.
I mean, depending on how the OP phrased the work to be done, they probably did valid work.
This seems to be doing much worse than existing solutions: Google Maps probably wouldn't have gotten quite as many wrong if you just pasted those addresses into the search bar. However it could be interesting as a last shot if parsing the input failed using any other way.
"I tried parsing your messy input. Here's what I came up with. Please make sure it's correct then proceed with the checkout."
Could we hook GPT3 up to our dating apps? On both sides. That way we can just let the computers do the small talk and if they hit it off we can meet.
Google: Black Mirror Hang The DJ
Such an excellent episode! A master class in what it means for actors to have chemistry.
Of all the places spreadsheet is probably the one place you don’t want AI generated content. Half the time it’s financial info so sorta correct simply isn’t good enough
Spreadsheets are used for _waaaay_ more than just finances. I don't think it's anywhere near 50% finances. I can't recall where, but I saw a study from I think the 90s saying most of the spreadsheets they found were being used as Todo lists.
Maybe like 1 in my past 2y of many, many spreadsheets has been financing related. I think you might be overgeneralizing to an ungeneralizeably large group -- the set of all human spreadsheets.
I don't remember where I read this (and my summary might be off) but the computerized spreadsheet is a great invention because it's a programming environment for non-programmers.
"I need to input a number of variables and find their sum and average [or even more]. And I need to see how the outputs change if I change an input...".
It does not matter the point stands. I can think of almost nothing worse than data in spreadsheets that LOOKS good but is wrong.
Most real-world spreadsheets contained significant errors in this 2005 review: https://www.researchgate.net/publication/228662532_What_We_K...
Is there any reason to think the situation has substantially improved since then?
This is not a good excuse to actively and knowingly make it worse
I was going to say: This can only end well for the economy... /s
Let's see if we can tell what data was used to train the model by watching where the money starts to be moved around into offshore accounts and what not. Was the model trained on the data dumps of from those "off shore" banks recently-ish leaked.
When I see something is in a spreadsheet I immediately assume there are at least 3 things wrong with the data, 1 of which is obvious.
I do not know what's worse. Missing data you need to populate, or having an AI populate data that LOOKS right. ugh I do know what is worse.
I said it before: we need Copilot flash fill. Infer what the user wants the output to be from patterns and labels, so they can enter a few examples and then “extend” and automatically do the equivalent of a complex formula. e.g.
Formal | Informal
Lane, Thomas | Tommy Lane
Brooks, Sarah | Sarah Brooks
Yun, Christopher |
Doe, Kaitlyn |
Styles, Chris |
…
I posed this exact question to character.ai's "Ask Me Anything" bot. It decided to redo the examples, too. The results:
> Lane, Thomas => Thomas Layne
> Brooks, Sarah => Sarah Brooksy
> Yun, Christopher => Chris Yun
> Doe, Kaitlyn => KD
> Styles, Chris => Chris Spice, Chris Chasm
I'm sure the bot overcomplicated an otherwise simple task, but I think there's always gonna be some creative error if we rely on things like that. It's funny though because these results are plausible for what a real person might come up with as informal nicknames for their friends.
try to tweak the prompts so you get to the level of Katie Dee, KDizzle, Kdawg
I ... sort of got there. But then it rapidly escalated to extremely long and incoherent chains of hyphenated names with "money" and "$" and things all throughout.
i mean thats pretty realistic
That’s amazing. It does rely on a level of comfort with a fuzzy error budget.
About 20% of the generated postcodes are absurdly wrong.
also previously from 2020 https://twitter.com/pavtalk/status/1285410751092416513?s=20&...
"lack of dark mode" should be "features" not "usability"?
Similarly, "Not sure what to do with the button" is clearly a usability issue, not features.
And for the second Kindle review, it summarized one point from the actual review, then completely made up two additional points!
Really impressive Sheets extension, but you'd have to be so careful what you applied this to.
> completely made up two additional points
I wonder if this means the AI is dumb or that the AI is smart enough to notice that humans just make shit up sometimes, like when they're not reading carefully or when they need filler.
If anything it's a lack of a usability feature. Sounds like both would be right.
GPT3 charges for every token read/written. What may be more useful is using GPT-3 not to manually run itself on every row, but to take the task and generate a sufficient function that fulfills the task.
Code as Policies: - Language Model Programs for Embodied Control
generates python, then executes
Like with Stable Diffusion, maybe there will be an open model for the language prompts which less or no restrictions in the near future.
Gpt-j?
Amen.
The tasks on the first sheet is easily accomplished by flash fill in MS Excel and I suspect less prone to error. Not sure why flash fill is not more popular
What is flash fill? I have worked a lot on Excel sheets and still haven’t heard anything about it.
Flash fill is an implementation of program synthesis, a technique invented by Sumit Gulwani formerly of Microsoft Research. Here's a paper that explains more about how it works [1]. It's not a very discoverable feature of Excel though [2]
[1] https://arxiv.org/pdf/1703.03539.pdf [2] https://support.microsoft.com/en-us/office/using-flash-fill-...
ctrl+e
This particular example is an inadeqate application of AI. This is static data which can be looked up in a table (at least zip code).
This is compared to inadequate application of humans. it is not competing with people who know how to do regex and string parsing. It is for the people who put an office assistant to the task. It is better to inadequately apply AI here as opposed to inadequately apply a human who probably has more fun things to do.
What about subtle formatting differences (Country, Territory, Postal code is the norm. Doesn't have to be.). What if we applied this to hand written addresses? (Adding an OCR component).
I'm sure the USPS is already doing this and more, and if not, there's probably some AI jobs lined up for it :)
Yes, USPS has Remote Encoding Centers (REC) to handle handwritten address that can't be recognized with OCR. So AI is already there, just that humans are for harder and tedious jobs ;-)
> So AI is already there, just that humans are for harder and tedious jobs ;-)
Increasingly less so ;-)
I think that the function should be called DWIM instead. Amazing feature otherwise, we really live in interesting times!
DWIM: Do What I mean
This is terrific stuff, honestly. I could see an Airtable integration being really quite useful. There were lots of times when I will run some quick scraping, some cleaning up via an Upworker, and then join against something else.
Here volume matters, and all misses are just lost data which I'm fine with. The general purpose nature of the tool makes it tremendous. There was a time when I would have easily paid $0.05 / query for this. The only problem with the spreadsheet setting is that I don't want it to repeatedly execute and charge me so I'll be forced to use `=GPT3()` and then copy-paste "as values" back into the same place which is annoying.
Was anyone able to test if the Airtable implementation works as well as the twitter 'ad'?
As a casual Google user, how do you start developing something like this? Does Google Sheets allow loading custom plugins?
if you need that address parser, this is a bit more robust and easier to use: https://workspace.google.com/u/0/marketplace/app/parserator_...
I would love to see a tool which uses GPT-3 to generate SQL from English.
Like: give me a list of all customers from London who purchased in January a laptop with more than 16GB of RAM and used a coupon between 10% and 25%. Sort it by price payd.
Everything required to make the tool already exists.
Just ran your exact query through OpenAI's Codex (model: code-davinci-002), and this was the result:
SELECT * FROM customers WHERE city = 'London' AND purchase_date BETWEEN '2019-01-01' AND '2019-01-31' AND product_name = 'laptop' AND product_ram > 16 AND coupon_percentage BETWEEN 10 AND 25 ORDER BY price_paid DESC;
I'd say it's pretty damn accurate.
The amount of 90% sensible, 10% ridiculously wrong computer generated crap we’re about to send into real humans’ brains makes my head spin. There’s truly an awful AI Winter ahead and it consists of spending a substantial amount of your best brain cycles on figuring out whether a real person wrote that thing to you (and it’s worth figuring out what they meant in case of some weird wording) or it was a computer generated fucking thank you note.
> The amount of 90% sensible, 10% ridiculously wrong computer generated crap we’re about to send
Agreed. Sooner or later a company is going to do this with its customers, in ways that are fine 95% of the time but cause outrage or even harm on outliers.
And if that company is anyone like Google, it'll be almost impossible for the customers to speak to a human to rectify things.
And the funniest is that actual people may be worse, but still it is freaking me out to be moderated by an AI.
Also when this is normal and ubiquitous come people who are playing it and AI will be just dumb to recognise, the real humans all fired, game over, stuck at shitty systems and everyone goes crazy.
Idea: Use GPT-3 to identify GPT-3-generated snippets.
> Use GPT-3 to identify GPT-3-generated snippets.
I just lost the game.
In some cases it would be impossible, since sometimes it can output exactly what was written by a human, or something that sounds 100% like what someone would write.
But if you allow some false negatives, such as trying to detect if a bot is a bot, I think that could work? But I feel like the technology to write fake text is inevitably going to outpace the ability to detect it.
If generator output is truly indistinguishable from human output, then who cares? We've won.
It reminds me of this xkcd: https://xkcd.com/810/
> But I feel like the technology to write fake text is inevitably going to outpace the ability to detect it.
Counterintuitively, this isn't always true! For example, spam detectors have outpaced spam generation now for decades.
This, but unironically. It could be used to further improve the snippets that weren't identified.
Who said I was being ironic? ;-)
Then we need to train a model that gives us an idea of how accurate each prediction is
Is that not sort of what a GAN is?
It depends on how people use the tools. For example the thank you note one -- if someone just prints off the output of this and sends it, yeah, that's bad.
But if someone uses this to do 90% of the work and then just edits it to make it personal and sound like themselves, then it's just a great time saving tool.
I mean, in this exact example, 70 years ago you'd have to hand address each thank you card by hand from scratch. 10 years ago you could use a spreadsheet just like this to automatically print off mailing labels from your address list. It didn't make things worse, just different.
This is just the next step in automation.
> But if someone uses this to do 90% of the work and then just edits it to make it personal and sound like themselves, then it's just a great time saving tool.
This is still way too optimistic. Reading through something that's "almost right", seeing the errors when you already basically know what it says / what it's meant to say, and fixing them, is hard. People won't do it well, and so even in this scenario we often end up with something much worse than if it was just written directly.
There is a lot of evidence for this, from the generally low quality of lightly-edited speech-to-text material, to how hard it is to look at a bunch of code and find all of the bugs without any extra computer-generated information, to how hard editing text for readability can be without serious restructuring.
Just train another AI model to do it then! I'm not joking -- Stable Diffusion generates some pretty grotesque and low quality faces, but there are add-on models that can identify and greatly improve the faces as part of the processing pipeline.
Doesn't seem like a stretch to have similar mini-models to improve known deficiencies in larger general models in the textual space.
Gmail's autocomplete already works great for this, and it will only get better over time. The key is to have a human in the loop to decide whether to accept/edit on a phrase by phrase or sentence by sentence basis.
I would classify that act of editing as "completing the remaining 10% of the work." Somebody has to do it, whether you're doing it from the writing side as in your example, or making the reader do it from their side, as in my grandparent comment's example. But it's usually the last 10% of anything that's the hardest, so if someone abdicates that to a machine and signs their name to it (claiming they said it, and taking responsibility for it) they're kind of an asshole, in both the schlemiel and the schlemozel senses of the word.
I could extrapolate in my extremely judgmental way that the person who does that probably has a grandiose sense of how valuable their own time is, first of all, and secondly an impractical and sheepishly obedient devotion to big weddings with guest-lists longer than the list of people they actually give a shit about. Increase efficiency in your life further upstream, by inviting fewer people! (Yeah right, might as well tell them to save money by shopping less and taking fewer trips. Like that would ever work!)
But I digress, and anyway don't take any of that too seriously, as 20 years ago I was saying the same kinds of things about mobile phones... like "Who do you think you are, a surgeon, with that phone?" Notice it's inherently a scarcity-based viewpoint, based on the previous however-many years when mobile phones really were the province only of doctors and the like. Now they're everywhere... So, bottom line, I think the thank-you notes are a lousy use of the tech, but just like the trivial discretionary conversations I hear people having on their mobile phones now that they're ubiquitous, this WILL be used for thank-you notes!
Probably closer to a Butlerian Jihad than a AI winter as per se, assuming something dramatic does happen
You got it! After seeing a few tweet storms and articles that turn out to be GPT3 gibberish, I end up coming to HN more for my news because usually someone flags waste of time in the comments.
The software would save people 80% or the work and most are lazy enough to release it as is, instead of fixing the remaining 20%. That laziness will end up forcing legislation to flag and eventually ban or deprioritize all GPT content, which will result in a war of adversarial behaviors trying to hide generated stuff among real. Can’t have nice things!
How would you go about classifying something as GPT generated?
Let alone flagging/deprioritizing it via some draconian legislation?
By the fact that it was generated using GPT. Same way you would go about classifying something as e.g. not made with slave labour or made with a production process that follows environmental pollution rules. That you can't easily detect it from the end product is not necessarily an obstruction to legislation.
Not saying it should be happening, but if abusive misuse continues, it is likely to happen. Regulations could force labeling on content with punishments if mislabeled (content that makes sense turns to gibberish later on after you've wasted time on it looking for the main point). Flagging could be done by the community (HN style), etc.
In the sci fi movie "Her", the main character has a job with the "Beautiful Handwritten Letters Company", a service for the outsourcing of letter writing. It seemed bizarre to me, but now I can envision a future where people are so tired of not knowing if their letter is a fake generated by some descendant of GPT-3, and feel great relief knowing their note was instead written by a human third party.
Mail merge has existed since the 1980s. https://en.wikipedia.org/wiki/Mail_merge
Maybe? Is it really going to be all that different from the past thousand years where we've had 90% sensible, 10% ridiculously wrong[0] human-generated crap?
[0] https://ncse.ngo/americans-scientific-knowledge-and-beliefs-...
What happens if you try to ask GPT-3 whether something was written by GPT-3?
Just listen here - https://infiniteconversation.com/
Think the winter is here
Kinda reminds me of Google Sets.
Once it becomes an excel function, then it will be spun off into a start up.
Fascinating and terrifying all at once.
Queue Fry "I'm scare-roused" meme...
If you do a startup for this please email me wire instructions.
This is so freaking cool. Brilliant idea.
GPT3 is not deterministic though.
From one of the replies: “This is awesome. I also love how 20% of the zip codes are wrong. Messy AI future seems really fun and chaotic.”
You need an AI that can understand when not to answer as opposed to some best effort guessing. Some of that input didn't have numbers in the right format so no zip code.
The hilarious one is changing the zip code to 90210. The AI basically accusing you of a typo because you obviously meant that more famous zip code.
General purpose AIs in situations where more targeted, simpler solutions are needed are going to be incredibly dangerous. Sure this AI can fly a plane 99.999% of the time, but every once in a while it does a nose dive because of reasons we cannot possibly understand or debug.
A human developer once told me that bad data is better than no data. <facepalm>
So of course a human developer made an AI that makes bad data.
FWIW, the actual saying is that for the purposes of collection by enemies (like Facebook and Google or KGB and NSA), the only thing better than no data is bad data.
FWIW, I was told that well before Facebook was a thought in the Zuck's head. Pre-socials, nobody shared data like is done today, so the NSA had to actually work to get it. Kids these days... /s
The author posted a follow up using a more advanced (and expensive) gpt3 model (davinci) which does a better job of parsing out the zip codes. It generally does a better job at everything, but if you can get away with one of the less expensive models then all the better.
yeah, most of these demos for GPT-3 are that go viral are cherry picked at best
>I also love how 20% of the zip codes are wrong.
Only they aren't. Check the video again, they come out fine.
Edit: Oh dang, you're all right, several of them have wrong digits. :l
What are you talking about? Look at 27 seconds into the video. Many of the zip codes are wrong.
Row 15 includes the zip code 92105 in column A but the output is 92101. Similar for Row 5.
51s -- "We are truly grateful", says the heartfelt thankyou card that was written by an algorithm in a spreadsheet.
If people want to put this sort of language in a thank-you note, I guess... I dunno, it always comes off as inauthentic to me, so I don't really care if I got mass produced or artisanal hand-crafted lies.
Oh, this is nothing new.
I remember in like 2007 or something, in the early days of Facebook, someone made a CLI interface to the FB API. And I wrote a random-timed daily cron job that ran a Bash script that checked "which of my FB friends have their birthday today", went through that list, selected a random greeting from like 15 different ones I'd put into an array, and posted this to the wall of person $i. Complete with a "blacklist" with names of close friends and family, where the script instead sent me an email reminder to write a manual, genuine post.
I used to have a golfed version of that script as my Slashdot signature.
The problem's worse than you know. I've heard that sometimes real humans who tell a lot of people "thank you" aren't even that thankful.