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.
I have the opposite problem. Gen A.I. has tripled my productivity, but the C-suite here is barely catching up to 2005.
Same, I’ve automated alot of my tasks with AI. No way 77% is “hampered” by it.
I dunno, mishandling of AI can be worse than avoiding it entirely. There’s a middle manager here that runs everything her direct-report copywriter sends through ChatGPT, then sends the response back as a revision. She doesn’t add any context to the prompt, say who the audience is, or use the custom GPT that I made and shared. That copywriter is definitely hampered, but it’s not by AI, really, just run-of-the-mill manager PEBKAC.
Voiceover recording, noise reduction, rotoscoping, motion tracking, matte painting, transcription - and there’s a clear path forward to automate rough cuts and integrate all that with digital asset management. I used to do all of those things manually/practically.
e: I imagine the downvotes coming from the same people that 20 years ago told me digital video would never match the artistry of film.
This may come as a shock to you, but the vast majority of the world does not work in tech.
Cool, enjoy your entire industry going under thanks to cheap and free software and executives telling their middle managers to just shoot and cut it on their phone.
Sincerely,
A former video editor.
This is an upwork press release. Typical forbes.
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?
Yeah, OpenAI, ChatGPT, and Sam Altman have no relevance to AI LLMs. 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.
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.
They tried implementing AI in a few our our systems and the results were always fucking useless. What we call “AI” can be helpful in some ways but I’d bet the vast majority of it is bullshit half-assed implementations so companies can claim they’re using “AI”
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.
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.
That’s pretty funny since manually searching some keywords can usually provide helpful data. Should be pretty straight-forward to automate even without LLM.
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.
It’s bloody amazing, here I am, having all my childhood read about 20/80, critical points, Guderian’s heavy points, Tao Te Ching, Sun Zu, all that stuff about key decisions made with human mind being of absolutely overriding importance over what tools can do.
These morons are sticking “AI”'s exactly where a human mind is superior over anything else at any realistic scale and, of course, could have (were it applied instead of human butt) identified the task at hand which has nothing to do with what “AI”'s can do.
I mean, half of humanity’s philosophy is about garbage thinking being of negative worth, and non-garbage thinking being precious. In any task. These people are desperately trying to produce garbage thinking with computers as if there weren’t enough of that already.
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.
The study identifies a disconnect between the high expectations of managers and the actual experiences of employees using AI.
