The new global study, in partnership with The Upwork Research Institute, interviewed 2,500 global C-suite executives, full-time employees and freelancers. Results show that the optimistic expectations about AI’s impact are not aligning with the reality faced by many employees. The study identifies a disconnect between the high expectations of managers and the actual experiences of employees using AI.
Despite 96% of C-suite executives expecting AI to boost productivity, the study reveals that, 77% of employees using AI say it has added to their workload and created challenges in achieving the expected productivity gains. Not only is AI increasing the workloads of full-time employees, it’s hampering productivity and contributing to employee burnout.
Lmao, so instead of ai taking our jobs, it made us MORE jobs.
Thanks, “ai”!
Except it didn’t make more jobs, it just made more work for the remaining employees who weren’t laid off (because the boss thought the AI could let them have a smaller payroll)
If used correctly, AI can be helpful and can assist in easy and menial tasks
I mean if it’s easy you can probably script it with some other tool.
“I have a list of IDs and need to make them links to our internal tool’s pages” is easy and doesn’t need AI. That’s something a product guy was struggling with and I solved in like 30 seconds with a Google sheet and concatenation
Yeah but the idea of AI in that kind of workflow is so that the product guy can actually do it themselves without asking you and in less than 30 mins
Yeah but that’s like using an entire gasoline powered car to play a CD.
Competent product guy should be able to learn some simpler tools like Google sheets.
No arguments from me that it’s better if people are just better at their job, and I like to think I’m good at mine too, but let’s be real - a lot of people are out of their depth and I can imagine it can help there. OTOH is it worth the investment in time (from people who could themselves presumably be doing astonishing things) and carbon energy? Probably not. I appreciate that the tech exists and it needs to, but shoehorning it in everywhere is clearly bollocks. I just don’t know yet how people will find it useful and I guess not everyone gets that spending an hour learning to do something that takes 10s when you know how is often better than spending 5 mins making someone or something else do it for you… And TBF to them, they might be right if they only ever do the thing twice.
I think the actual problem here is that if the product people can’t learn such a simple thing by themselves, they also won’t be able to correctly prompt the LLM to their use case.
They said, I do think LLMs can boost productivity a lot. I’m learning a new framework and since there’s so much details to learn about it, it’s fast to ask ChatGPT what’s the proper way to do X on this framework etc. Although that only works because I already studied the foundation concepts of that framework first.
I think the actual problem is that they won’t know when they’ve got something that compiles but is wrong… I dunno though. I’ve never seen someone doing this and I can only speculate tbh. I only ever asked ChatGPT a couple of times, as a joke to myself when I got stuck, and it spouted completely useless nonsense both times… Although on one occasion the wrong code it produced looked like it had the pattern of a good idiom behind it and I stole that.
And are those use cases common and publicized? Because I see it being advertised as “improves productivity” for a novel tool with myriad uses I expect those trying to sell it to me to give me some vignettes and not to just tell my boss it’ll improve my productivity. And if I was in management I’d want to know how it’ll do that beyond just saying “it’ll assist in easy and menial tasks”. Will it be easier than doing them? Many tools can improve efficiency on a task at a similar time and energy investment to the return. Are those tasks really so common? Will other tools be worse?
It also helps you getting a starting point when you don’t know how ask a search engine the right question.
But people misinterpret its usefulness and think It can handle complex and context heavy problems, which must of the time will result in hallucinated crap.
You all are nuts for not seeing this article for what it is
Which is?
The summary for the post kinda misses the mark on what the majority of the article is pushing.
Yes, the first part describes employees struggling with AI, but the majority of the article makes the case for hiring more freelancers and updating “outdated work models and systems…to unlock the full expected productivity value of AI.”
It essentially says that AI isn’t the problem, since freelancers can use it perfectly. So full time employees need to be “rethinking how to best do their work and accomplish their goals in light of AI advancements.”
A hit-piece commissioned by the Joker to distract you from his upcoming bank heist!!!
Replace joker for media and replace distract you from bank heist with convince you to hate AI then yes.
Do convince us why we should like something which is a massive ecological disaster in terms of fresh water and energy usage.
Feel free to do it while denying climate change is a problem if you wish.
AI is a rounding error in terms of energy use. Creating and worldwide usage of chatGPT4 for a whole year comes out to less than 1% of the energy Americans burn driving in one day.
I think I’ll go with Yale over ‘person on the Internet who ignored the water part.’
https://e360.yale.edu/features/artificial-intelligence-climate-energy-emissions
From that article:
Estimates of the number of cloud data centers worldwide range from around 9,000 to nearly 11,000. More are under construction. The International Energy Agency (IEA) projects that data centers’ electricity consumption in 2026 will be double that of 2022 — 1,000 terawatts, roughly equivalent to Japan’s current total consumption.
Forgive me for not trusting an ariticle that says that AI will use a petawatt within the next two years. Either the person who wrote it doesnt understand the difference between energy and power or they are very sloppy.
Chat GPT took 50GWh to train source
Americans burn 355 million gallons of gasoline a day source and at 33.5 Kwh/gal source that comes out to 12,000GWh per day burnt in gasoline.
Water usage is more balanced, depending on where the data centres are it can either be a significant problem or not at all. The water doesnt vanish it just goes back into the air, but that can be problematic if it is a significant draw on local freshwater sources. e.g. using river water just before it flows into the sea, 0 issue, using a ground aquifer in a desert, big problem.
I wrote this and feed it through chatGPT to help make it more readable. To me that’s pretty awesome. If I wanted I can have it written like an Elton John song. If that doesn’t convince you it’s fun and worth it then maybe the argument below could, or not. Either way I like it.
I don’t think I’ll convince you, but there are a lot of arguments to make here.
I heard a large AI model is equivalent to the emissions from five cars over its lifetime. And yes, the water usage is significant—something like 15 billion gallons a year just for a Microsoft data center. But that’s not just for AI; data centers are something we use even if we never touch AI. So, absent of AI, it’s not like we’re up in arms about the waste and usage from other technologies. AI is being singled out—it’s the star of the show right now.
But here’s why I think we should embrace it: the potential. I’m an optimist and I love technology. AI bridges gaps in so many areas, making things that were previously difficult much easier for many people. It can be an equalizer in various fields.
The potential with AI is fascinating to me. It could bring significant improvements in many sectors. Think about analyzing and optimizing power grids, making medical advances, improving economic forecasting, and creating jobs. It can reduce mundane tasks through personalized AI, like helping doctors take notes and process paperwork, freeing them up to see more patients.
Sure, it consumes energy and has costs, but its potential is huge. It’s here and advancing. If we keep letting the media convince us to hate it, this technology will end up hoarded by elites and possibly even made illegal for the rest of us. Imagine having a pocket advisor for anything—mechanical issues, legal questions, gardening problems, medical concerns. We’re not there yet, but remember, the first cell phones were the size of a brick. The potential is enormous, and considering all the things we waste energy and resources on, this one is weighed against it benefits.
Not being able to use your own words to explain something to me and having the thing that is an ecological disaster that also lies all the time explain it to me instead really only reinforces my point that there’s no reason to like this technology.
It is my own words. Wrote out the whole thing but I was never good with grammar and fully admit that often what I write is confusing or ambiguous. I can leverage chatgpt same way I would leverage spell check in word. I don’t see any problems there.
But if you don’t mind, I’m interested in the points discussed.
For the curious, the message rewritten as lyrics for an Elton John song:
(Verse 1) I don’t think I’ll convince you, but I’ve got a tale to tell, They say AI’s like five cars, burning fuel and raising hell. And the water that it guzzles, like rivers running dry, Fifteen billion gallons, under Microsoft’s sky.
(Pre-Chorus) But it’s not just AI, oh, it’s every data node, Even if you never touch it, it’s a heavy load. We point fingers at AI, like it’s the star tonight, But let me tell you why I think it shines so bright.
(Chorus) Oh, the potential, can’t you see, It’s the future calling, setting us free. Bridging gaps and making life easier, An equalizer, for you and me.
(Verse 2) I’m an optimist, a techie at heart, AI could change the world, give us a brand new start. From power grids to medicine, it’s a helping hand, Economic dreams and jobs across the land.
(Pre-Chorus) Yes, it drinks up energy, but what’s the price to pay? For the chance to see the mundane fade away. Imagine doctors with more time to heal, While AI handles notes, it’s a real deal.
(Chorus) Oh, the potential, can’t you see, It’s the future calling, setting us free. Bridging gaps and making life easier, An equalizer, for you and me.
(Bridge) If we let the media twist our minds, We’ll lose this gift to the elite, left behind. But picture this, a pocket guide for all, From car troubles to legal calls.
(Chorus) Oh, the potential, can’t you see, It’s the future calling, setting us free. Bridging gaps and making life easier, An equalizer, for you and me.
(Outro) First cell phones were the size of a brick, Now they’re magic in our hands, technology so quick. AI’s got the power, to change the way we live, So let’s embrace it now, there’s so much it can give.
(Chorus) Oh, the potential, can’t you see, It’s the future calling, setting us free. Bridging gaps and making life easier, An equalizer, for you and me.
(Outro) Oh, it’s the future, it’s the dream, AI’s the bright light, in the grand scheme.
This is the stupidest shit ive seen yet.
We dont care about other data centers as much because we get a service in return that people want.
Most people didnt ask for or want AI, didnt agree to its costs, and now have to deal with it potentially taking their jobs.
But go ahead and keep posting idiotic and selfish posts about how you like it so much and its so fun and cool, look at my shitty song lyrics that make no fucking sense!
I’d say touch grass but the lyrics make me want to say touch instrument instead.
Most folks don’t need an excuse to hate the internet enabled lie generator that “AI” is.
No but most media moved quick to present every article to convince people why they should hate it. Pack mentality like when a popular kid starts spreading rumours about the new kid in class. People quickly adopt the common shared belief and most of those now are Media driven.
AI is pretty cool new tech. Most people would have been mediocre to interested in it if it were not for corporate media telling us all why we need to hate it.
I saw an article the other day about “people shitting on the beach” which was really an attack on immigrants. Media is now about forming opinions for us and we all accept it more than ever.
A majority of people have no use, nor want, AI. Just because you and a sub group of people like it, doesnt mean everyone else are idiots being misled by the media.
Why exactly so you think the media wants people to hate AI anyways? Wouldnt big corporate gain from automating news writing?
This is an upwork press release. Typical forbes.
They tried implementing AI in a few our our systems and the results were always fucking useless. Maybe what we call “AI” can be helpful in some ways but I’d bet the vast majority of it is bullshit.
The one thing “AI” has improved in my life has been a banking app search function being slightly better.
Oh, and a porn game did okay with it as an art generator, but the creator was still strangely lazy about it. You’re telling me you can make infinite free pictures of big tittied goth girls and you only included a few?
Generating multiple pictures of the same character is actually pretty hard. For example, let’s say you’re making a visual novel with a bunch of anime girls. You spin up your generative AI, and it gives you a great picture of a girl with a good design in a neutral pose. We’ll call her Alice. Well, now you need a happy Alice, a sad Alice, a horny Alice, an Alice with her face covered with cum, a nude Alice, and a hyper breast expansion Alice. Getting the AI to recreate Alice, who does not exist in the training data, is going to be very difficult even once.
And all of this is multiplied ten times over if you want granular changes to a character. Let’s say you’re making a fat fetish game and Alice is supposed to gain weight as the player feeds her. Now you need everything I described, at 10 different weights. You’re going to need to be extremely specific with the AI and it’s probably going to produce dozens of incorrect pictures for every time it gets it right. Getting it right might just plain be impossible if the AI doesn’t understand the assignment well enough.
Generating multiple pictures of the same character is actually pretty hard.
Not from what I have seen on Civitai. You can train a model on specific character or person. Same goes for facial expressions.
Of course you need to generate hundreds of images to get only a few that you might consider acceptable.
This is a solvable problem. Just make a LoRA of the Alice character. For modifications to the character, you might also need to make more LoRAs, but again totally doable. Then at runtime, you are just shuffling LoRAs when you need to generate.
You’re correct that it will struggle to give you exactly what you want because you need to have some “machine sympathy.” If you think in smaller steps and get the machine to do those smaller, more do-able steps, you can eventually accomplish the overall goal. It is the difference in asking a model to write a story versus asking it to first generate characters, a scenario, plot and then using that as context to write just a small part of the story. The first story will be bland and incoherent after awhile. The second, through better context control, will weave you a pretty consistent story.
These models are not magic (even though it feels like it). That they follow instructions at all is amazing, but they simply will not get the nuance of the overall picture and be able to accomplish it un-aided. If you think of them as natural language processors capable of simple, mechanical tasks and drive them mechanistically, you’ll get much better results.
It is great for pattern recognition (we use it to recognize damages in pipes) and probably pattern reproduction (never used it for that). Haven’t really seen much other real life value.
What were they trying to accomplish?
Looking like they were doing something with AI, no joke.
One example was “Freddy”, an AI for a ticketing system called Freshdesk: It would try to suggest other tickets it thought were related or helpful but they were, not one fucking time, related or helpful.
As an Australian I find the name Freddy quite apt then.
There is an old saying in Aus that runs along the lines of, “even Blind Freddy could see that…”, indicating that the solution is so obvious that even a blind person could see it.
Having your Freddy be Blind Freddy makes its useless answers completely expected. Maybe that was the devs internal name for it and it escaped to marketing haha.
I actually ended up becoming blind to Freddy because of how profoundly useless it was: Permanently blocked the webpage elements that showed it from my browser lol. I think Fresh since gave up.
Don’t get me wrong, the rest of the service is actually pretty great and I’d recommend Fresh to anyone in search of a decent ticketing system. Freddy sucks though.
That’s pretty funny since manually searching some keywords can usually provide helpful data. Should be pretty straight-forward to automate even without LLM.
Yep, we already wrote out all the documentation for everything too so it’s doubly useless lol. It sucked at pulling relevant KB articles too even though there are fields for everything. A written script for it would have been trivial to make if they wanted to make something helpful, but they really just wanted to get on that AI hype train regardless of usefulness.
Ahh, those things - I’ve seen half a dozen platforms implement some version of that, and they’re always garbage. It’s such a weird choice, too, since we already have semi-useful recommendation systems that run on traditional algorithms.
It’s all about being able to say, “Look, we have AI!”
To not even consider the consequences of deploying systems that may farm your company data in order to train their models “to better serve you”. Like, what the hell guys?
The workload that’s starting now, is spotting bad code written by colleagues using AI, and persuading them to re-write it.
“But it works!”
‘It pulls in 15 libraries, 2 of which you need to manually install beforehand, to achieve something you can do in 5 lines using this default library’
I was trying to find out how to get human readable timestamps from my shell history. They gave me this crazy script. It worked but it was super slow. Later I learned you could do history -i.
Turns out, a lot of the problems in nixland were solved 3 decades ago with a single flag of built-in utilities.
Apart from me not reading the manual (or skimming to quick) I might have asked the LLM to check the history file rather than the command. Idk. I honestly didn’t know the history command did anything different than just printing the history file
man 3 historyinfo historyAlso, your .bashrc file in your $HOME Dir contains env variables you can set to modify the behaviors of the history function.
Oh I need to learn more
Honestly, I thought I knew lots.
Then, one day, I decided to read
man introThen I knew I knew I didn’t know much.
I still don’t.
But I now have a much better grasp of what/how.
I really need to alias man to man -a.
I
man -ka lot.What’s that?
I didn’t know about this. Thank you for the knowledge fellow human!
I asked it to spot a typo in my code, it worked but it rewrote my classes for each function that called them
I gave it a fair shake after my team members were raving about it saving time last year, I tried a SFTP function and some Terraform modules and man both of them just didn’t work. it did however do a really solid job of explaining some data operation functions I wrote, which I was really happy to see. I do try to add a detail block to my functions and be explicit with typing where appropriate so that probably helped some but yeah, was actually impressed by that. For generation though, maybe it’s better now, but I still prefer to pull up the documentation as I spent more time debugging the crap it gave me than piecing together myself.
I’d use a llm tool for interactive documentation and reverse engineering aids though, I personally think that’s where it shines, otherwise I’m not sold on the “gen ai will somehow fix all your problems” hype train.
I think the best current use case for AI when it comes to coding is autocomplete.
I hate coding without Github Copilot now. You’re still in full control of what you’re building, the AI just autocompletes the menial shit you’ve written thousands of times already.
When it comes to full applications/projects, AI still has some way to go.
I can get that for sure, I did see a client using it for debugging which seemed interesting as well, made an attempt to narrow down where the error occurred and what actually caused it.
I’ll do that too! In the actual code you can just write something like
// Q: Why isn't this working as expected? // A:and it’ll auto complete an answer based on the code. It’s not always 100% on point, but it usually leads you in the right direction.
But I don’t like using Argparse!
TBH those same colleagues were probably just copy/pasting code from the first google result or stackoverflow answer, so arguably AI did make them more productive at what they do
2012 me feels personally called out by this. fuck 2012 me that lazy fucker. stackoverflow was my “get out of work early and hit the bar” card.
yay!! do more stupid shit faster and with more baseless confidence!
The other 23% were replaced by AI (actually, their workload was added to that of the 77%)
But But But
It’s made my job so much simpler! Obviously it can’t do your whole job and you should never expect it to, but for simple tasks like generating a simple script or setting up an array it BLAH BLAH BLAH, get fucked AI Techbros lmao
You mean the multi-billion dollar, souped-up autocorrect might not actually be able to replace the human workforce? I am shocked, shocked I say!
Do you think Sam Altman might have… gasp lied to his investors about its capabilities?
Nooooo. I mean, we have about 80 years of history into AI research and the field is just full of overhyped promised that this particularly teach is the holy grail of AI to end in disappointment each time, but this time will be different! /s
The article doesn’t mention OpenAI, GPT, or Altman.
Aha, so this must all be Elon’s fault! And Microsoft!
There are lots of whipping boys these days that one can leap to criticize and get free upvotes.
get free upvotes.
Versus those paid ones.
If someone wants to pay me to upvote them I’m open to negotiation.
I traded in my upvotes when I deleted my reddit account, and all I got was this stupid chip on my shoulder.
Yeah, OpenAI, ChatGPT, and Sam Altman have no relevance to
AILLMs. No idea what I was thinking.I prefer Claude, usually, but the article also does not mention LLMs. I use generative audio, image generation, and video generation at work as often if not more than text generators.
Good point, but LLMs are both ubiquitous and the public face of “AI.” I think it’s fair to assign them a decent share of the blame for overpromising and underdelivering.
The Upwork Research Institute
Not exactly a panacea of rigorous scientific study.
because on top of your duties you now have to check whatever the AI is doing in place of the employee it has replaced
This study failed to take into consideration the need to feed information to AI. Companies now prioritize feeding information to AI over actually making it usable for humans. Who cares about analyzing the data? Just give it to AI to figure out. Now data cannot be analyzed by humans? Just ask AI. It can’t figure out? Give it more so it can figure it out. Rinse, repeat. This is a race to the bottom where information is useless to humans.
Me: no way, AI is very helpful, and if it doesn’t then don’t use it
created challenges in achieving the expected productivity gains
achieving the expected productivity gains
Me: oh, that explains the issue.
It’s hilarious to watch it used well and then human nature just kick in
We started using some “smart tools” for scheduling manufacturing and it’s honestly been really really great and highlighted some shortcomings that we could easily attack and get easy high reward/low risk CAPAs out of.
Company decided to continue using the scheduling setup but not invest in a single opportunity we discovered which includes simple people processes. Took exactly 0 wins. Fuckin amazing.
Yeah but they didn’t have a line for that in their excel sheet, so how are they supposed to find that money?
Bean counters hate nothing more than imprecise cost saving. Are they gonna save 100k in the next year? 200k? We can’t have that imprecision now can we?
Honestly, this sounds like the analysis uncovered some managerial failings and so they buried the results; a cover-up.
Also, and I have yet to understand this, but selling “people space” solutions to very technically/engineering-inclined management is incredibly hard to do. Almost like there’s a typical blind spot for solving problems outside their area of expertise. I hate generalizing like this but I’ve seen this happen many times, at many workplaces, over many years.
No I would think you are spot on. I’m constantly told I’m a type [insert fotm managerial class they just took term] and my conversations intimidate or emasculate people. They are probably usually correct but i find it’s usually just an attempt to cover their asses. I’m a contract worker, i was hired for a purpose with a limited time window and i fuckin deliver results even when they ignore 90% of the analysis. It’s gotta piss them off.
AKA “shit, looks like now we need to re-hire some of those engineers”
Admittedly I only skimmed the article, but I think one of the major problems with a study like this is how broad “AI” really is. MS copilot is just bing search in a different form unless you have it hooked up to your organizations data stores, collaboration platforms, productivity applications etc. and is not really helpful at all. Lots of companies I speak with are in a pilot phase of copilot which doesn’t really show much value because it doesn’t have access to the organizations data because it’s a big security challenge. On the other hand, a chat bot inside of a specific product that is trained on that product specifically and has access to the data that it needs to return valuable answers to prompts that it can assist in writing can be pretty powerful.
AI is stupidly used a lot but this seems odd. For me GitHub copilot has sped up writing code. Hard to say how much but it definitely saves me seconds several times per day. It certainly hasn’t made my workload more…
I’ll say that so far I’ve been pretty unimpressed by Codeium.
At the very most it has given me a few minutes total of value in the last 4 months.
Ive gotten some benefit from various generic chat LLMs like ChatGPT but most of that has been somewhat improved versions of the kind of info I was getting from Stackexchange threads and the like.
There’s been some mild value in some cases but so far nothing earth shattering or worth a bunch of money.
I presume it depends on the area you would be working with and what technologies you are working with. I assume it does better for some popular things that tend to be very verbose and tedious.
My experience including with a copilot trial has been like yours, a bit underwhelming. But I assume others must be getting benefit.
I have never heard of Codeium but it says it’s free, which may explain why it sucks. Copilot is excellent. Completely life changing, no. That’s not the goal. The goal is to reduce the manual writing of predictable and boring lines of code and it succeeds at that.
Cool totally worth burning the planet to the ground for it. Also love that we are spending all this time and money to solve this extremely important problem of coding taking slightly too long.
Think of all the progress being made!
Must be nice that life is so simple
That instead of macros for code generation, templates and just using higher-level languages.
Github Copilot is about the only AI tool I’ve used at work so far. I’d say it overall speeds things up, particularly with boilerplate type code that it can just bang out reducing a lot of the tedious but not particularly difficult coding. For more complicated things it can also be helpful, but I find it’s also pretty good at suggesting things that look correct at a glance, but are actually subtly wrong. Leading to either having to carefully double check what it suggests, or having fix bugs in code that I wrote but didn’t actually write.
Every time I’ve discussed this on Lemmy someone says something like this. I haven’t usually had that problem. If something it suggests seems like more than something I can quickly verify is intended, I just ignore it. I don’t know why I am the only person who has good luck with this tech but I certainly do. Maybe it’s just that I don’t expect it to work perfectly. I expect it to be flawed because how could it not be? Every time it saves me from typing three tedious lines of code it feels like a miracle to me.
Leading to either having to carefully double check what it suggests, or having fix bugs in code that I wrote but didn’t actually write.
100% this. Recent update from jetbrains turned on the AI shitcomplete (I guess my org decided to pay for it). Not only is it slow af, but in trying it, I discovered that I have to fight the suggestions because they are just wrong. And what is terrible is I know my coworkers will definitely use it and I’ll be stuck fixing their low-skill shit that is now riddled with subtle AI shitcomplete. The tools are simply not ready, and anyone that tells you they are, do not have the skill or experience to back up their assertion.
Probably because the vast majority of the workforce does not work in tech but has had these clunky, failure-prone tools foisted on them by tech. Companies are inserting AI into everything, so what used to be a problem that could be solved in 5 steps now takes 6 steps, with the new step being “figure out how to bypass the AI to get to the actual human who can fix my problem”.
I’ve thought for a long time that there are a ton of legitimate business problems out there that could be solved with software. Not with AI. AI isn’t necessary, or even helpful, in most of these situations. The problem is that creatibg meaningful solutions requires the people who write the checks to actually understand some of these problems. I can count on one hand the number of business executives that I’ve met who were actually capable of that.
Media has been anti AI from the start. They only write hit pieces on it. We all rabble rouse about the headline as if it’s facts. It’s the left version of articles like “locals report uptick of beach shitting”
For anything more that basic autocomplete, copilot has only given me broken code. Not even subtly broken, just stupidly wrong stuff.
They’ve got a guy at work whose job title is basically AI Evangelist. This is terrifying in that it’s a financial tech firm handling twelve figures a year of business-- the last place where people will put up with “plausible bullshit” in their products.
I grudgingly installed the Copilot plugin, but I’m not sure what it can do for me better than a snippet library.
I asked it to generate a test suite for a function, as a rudimentary exercise, so it was able to identify “yes, there are n return values, so write n test cases” and “You’re going to actually have to CALL the function under test”, but was unable to figure out how to build the object being fed in to trigger any of those cases; to do so would require grokking much of the code base. I didn’t need to burn half a barrel of oil for that.
I’d be hesitant to trust it with “summarize this obtuse spec document” when half the time said documents are self-contradictory or downright wrong. Again, plausible bullshit isn’t suitable.
Maybe the problem is that I’m too close to the specific problem. AI tooling might be better for open-ended or free-association “why not try glue on pizza” type discussions, but when you already know “send exactly 4-7-Q-unicorn emoji in this field or the transaction is converted from USD to KPW” having to coax the machine to come to that conclusion 100% of the time is harder than just doing it yourself.
I can see the marketing and sales people love it, maybe customer service too, click one button and take one coherent “here’s why it’s broken” sentence and turn it into 500 words of flowery says-nothing prose, but I demand better from my machine overlords.
Tell me when Stable Diffusion figures out that “Carrying battleaxe” doesn’t mean “katana randomly jutting out from forearms”, maybe at that point AI will be good enough for code.
Again, plausible bullshit isn’t suitable.
It is suitable when you’re the one producing the bullshit and you only need it accepted.
Which is what people pushing for this are. Their jobs and occupations are tolerant to just imitating, so they think that for some reason it works with airplanes, railroads, computers.
Maybe the problem is that I’m too close to the specific problem. AI tooling might be better for open-ended or free-association “why not try glue on pizza” type discussions, but when you already know “send exactly 4-7-Q-unicorn emoji in this field or the transaction is converted from USD to KPW” having to coax the machine to come to that conclusion 100% of the time is harder than just doing it yourself.
I, too, work in fintech. I agree with this analysis. That said, we currently have a large mishmash of regexes doing classification and they aren’t bulletproof. It would be useful to see about using something like a fine-tuned BERT model for doing classification for transactions that passed through the regex net without getting classified. And the PoC would be would be just context stuffing some examples for a few-shot prompt of an LLM and a constrained grammar (just the classification, plz). Because our finance generalists basically have to do this same process, and it would be nice to augment their productivity with a hint: “The computer thinks it might be this kinda transaction”
