Chief executives cannot shut up about AI
economist.com
economist.com
Big businesses that sell to c-level know this too and frame these offerings accordingly. Big tech consulting companies will only ever be selling some variation on dashboards, but they're described as big data, data & analysis, deep learning, generative AI, whatever the trend is.
Nothing wrong with it, being a contrarian is hard, better to accept the way the wind is going and adapt accordingly.
My main point is that it's way less about anything specific that AI can do, vs getting people on board.
Well it's rather depressing for the rest of us. So there's that.
Lay offs were worth a few percentage points on the share price. So everyone had an incentive to take away someone's salary.
"AI" is worth a few percentage points and so will be talked about as long as the payback is there.
None of these are going to meaningful change these companies or our societies for the better, they are only to continue asset price inflation. Because with out it the illusion would begin to crumble.
The crucial core technology, secret sauce, some math I derived, in my startup is NOT "AI" and, for the problem my startup is intended to solve, more focused than "AI".
Net, (1) for the problem I'm solving the approaches of current AI won't deliver anything competitive with my math, and (2) as long as these companies, etc. pursue AI they will be doing that much less work that might compete with me!!!!
History: There is along history, decades with computers and centuries without, of math yielding terrific results -- Euclid, Pythagoras, Newton, Leibniz, Gauss, ..., Riemann, Hilbert, von Neumann, ..., to the present.
Yes, some of current AI seems to have made good progress on non-linear anomaly/problem detection and diagnosis and, in these cases, done work that is good progress on what used to be done with, say, statistical anomaly detection, maybe non-parametric statistics.
But apparently now mostly and/or nearly all of current AI, e.g.. LLMs (large language models), are for different work.
So, again, net, current AI does not seem competitive with my use of math so that I'm pleased -- partly joking and poking fun but not entirely!
On one hand, they're clearly impressive and useful. They'll get better, but it's not at all clear how much better.
On the other hand, we've had access to 3.5 for 6 months now, and thus far companies love to talk about their AI strategies, and love to roll out "alpha previews", but not much of value seems to have been produced thus far.
You'd imagine that an instantly transformative feature would not sit around in alpha to die. I think that companies are likely discovering that it's not magic, and that there are many problems that it can't solve.
It actually seems like we have passed peak LLM at this point.
The problem C-Suite has to solve is likely going to be actually finding out what processes can/have been automated (if your work culture sucks, employees won't be on-board with sharing their secrets just to have more work given to them) and knowing what jobs they can actually cut.
Do you also think the world disappears when you close your eyes?
- Trust LLM with proprietary/confidential data
- Operate the use cases in scale with proper performance
- Control prompt and post process output to avoid legal/brand risks (e.g. hallucinations)
Everything still feels like Beta, and even though companies love the concept, they won't dedicate serious money to Beta products...
and thus far companies love to talk about their AI strategies,
and love to roll out "alpha previews", but not much of value
seems to have been produced thus far
FWIW from my point-of-view as someone in one of those companies working on products (and who hasn't read the paywalled article) I see a few things playing out that are not new, mostly it's the same newer, bigger hammer syndrome:
1. Trying to solve the same old inconsequential problems but with new tech
This happens all the time. Eventually you realise that the problem is actually relatively benign. You want the hammer to hit home every time but realise that even if it did there is no real value gained.
2. Trying to solve a problem that's already been solved with existing more established tech
Open calls for ideas in company innovation labs or platforms are full of noise. Some of that noise is always around the same automation problems, usually some kind of extraction or some kind of categorisation in a workflow. Most of the time products exist but people are just unaware. In large companies the capability might already exist in house but there's a "must invent here" bias
3. Trying to solve hard problems without the right domain or technical expertise
There are legitimate problems that appear innovative and novel, maybe ones that have been waiting for this level of capability to come along so they can execute, however, adequate knowledge of the technical domain (fine-tuning, prompt engineering, zero-shot, context) or the problem domain (how to read a cat-scan) limits their ability to make a cobbled together PoC reliable, repeatable, scalable, trusted.
Think of it as some generalist buying a stock rally car to build a new racing team, but doing it all themselves instead of hiring a mechanic to tune it properly, or a driver to give you the mechanic feedback... or the mechanic to tell the driver what they can and can't do at the extremes... or the driver to tell the mechanic to FO and "fix" it. Dialogue.
4. Problem is too niche and hard to communicate effectively
If a project succeeds in an organisation and there's no one around to hear it, did it really succeed?
5. Lack of existing innovation culture, strategy, or clear direction hamstrings any serious attempt
A non-starter. A lot of organisations still can't embed or operationalise their good ideas properly. If they can't do that already, it's unlikely to change here.
6. LLM successfully implemented into existing product but no body notices or cares.
The whole "put a clock in it" from product design or "get it to send email" of software.
7. Hard problems even with the right attitude and team still take time solve effectively with relatively new technology
Ignoring the legitimate institutional roadblocks of assuring privacy, security, safety, ethics etc. It's still early days. Cost of O&M long-term is still kind of uncertain, as are some of the basic parameters like the context window. Increasing the size of it could fundamentally change your approach. Anyone who was building a system before plugins were announced probably needed to re-think a number of things and go back to the drawing board. Sure there are some who will just continue as planned and iterate later, but some will be cautious before locking in.
Lastly, I know personally for me, LLMs have become a large part of enhancing my daily workflow. They have increased both the quantity and quality of my output but the 2 fundamental problems for me are:
1. Remember it's there and to use it. (Can what I'm doing could be done with LLM assistance)
or
2. How to formulate a question or request. (This is a fundamental problem of all "work" and "management" how do you define and communicate effectively?)
Assuming they are amoral and purely self-interested (a stretch, I know ;-), I don't see why they wouldn't constantly hype AI.
Despite what you think the labor market is super hot and strong for most segments besides white collar tech (every metric still bears this out, unemployment especially in services and lower wage jobs is historically low). If you're working a shitty help desk job that you know the boss is doing his best to eradicate then you're just going to spend working hours applying to other service jobs and jump ship.
Probably because these conditions don't correlate, but the OP is presenting it as obvious. This is a back-handed wealth inequality pot-shot.
Few companies are hyping AI to then use it to justify cutbacks. The cutbacks are already happening or will happen, regardless, because of the existing economic conditions. ie The unfettered self-sustaining demand for growth, even in a recession.
Comparisons to crypto are a bit shallow - except that they both are ‘new’ technologies and need GPUs, there’s not much.
But that is hardly the measure of a new technology.
Is this actually true though. I write code for living and tried to use it many times but it wasn't really that useful to me ( vs google search)
Curious to hear ppl who are using it in 'critically useful way' . i am eager to use it my workflows .
It's not going to be barely useful, if at all, when you have high skill in a domain. It's quite useful when you don't.
https://chat.openai.com/share/aff14574-4f3e-496e-a11c-aa8ee2...
I could have done the work by hand, but instead I played some guitar while it updated the labeler software for the ML project I’ve been working on.
In fact, I think ChatGPT and Copilot wrote the majority of the code in this project:
The only clear win I see is your last example where you are extracting coords into an array. I agree that text transformation like that is a great use of ChatGPT (that's mostly where I use GPT3)
Add a text input and button to this app that sets the currentImage cookie to the filename in the input and then sets the current image to that as well
And:
Great, now I'd like to add support for the ii and vi chords. We need to add the keyboard shortcuts for "2" and "6" so when these are pressed it correctly sets the chord name as well as updating the tablature
And:
The G and C are finished. Complete D, E and A
The cognitive load was significantly reduced to the point where I got through practicing Paul Simon’s America twice while waiting for the responses. And that’s not the easiest song to sing and play on acoustic guitar!
So instead of just having the updated labeling software, I got some practice in and retained some will power to label 1,500 images when it was completed!
But thanks for explaining to me that you know better than me about what saves me time and energy…
Instead I described the csv’s that I had, the one I wanted, and asked for Python code to do it.
The code ChatGPT (v3) produced was flawless. I ran it on the data and spot checked it. The entire process took five minutes.
This morning I decided I wanted to write a simple audio visualizer for my iPhone using SwiftUI, a language and framework I have never used. Within an hour I had through the direction of ChatGPT-4 an app running on my phone using a 3rd party library called AudioKit and drawing some fractal looking thingies, as well as a pretty decent understanding of how it all worked because I asked for detailed explanations of every line of code.
I’m not sure that I could have had this little app up and working in a single day let alone an hour if I only had a search engine at my disposal.
https://medium.com/swlh/swiftui-create-a-sound-visualizer-ca... https://audiokitpro.com/audiovisualizertutorial/ https://developer.apple.com/documentation/accelerate/visuali...
I do tend to reach for GPT4 before Google for things like this now, but I feel like it should definitely be possible to get this up in only slightly longer with just Google, even if you want some mods.
I have friends working in big tech who are completely unaware of how to use GPT - maybe because their work prohibits it and can't appreciate the value.
On the other hand, I think the hype for transformer models is justified. Maybe not ChatGPT or any other specific one, but I think LLMs and transformers will be transformational for our industry, like HTTP was, or portable touchscreen devices.
Just not sure how to measure which one of us is right next year. :)
It feels like this is a category error that is causing you to miss the reason there is so much excitement.
It like saying “I don’t get it, the model T is just another car. We have had them before, and most of them are better than what Ford is selling”. The revolution wasn’t that model T cars were way better than what came before - it was that the way they were built enabled huge new markets.
LLMs seem vastly more powerful than the technology previous chat bots were built on. Plus, there is a whole ecosystem of generative techniques being applied to images, videos, sound, and others.
The exact same arguments you make were said about NFT and blockchain. It would revolutionize finance, it would empower people with decentralized finance, that the art world would be totally revolutionized with digital artifacts. All of it was just vapid hype. Much of the same people making those silly claims are doing the same for AI now...
This is just nonsense, no chatbots before LLM were powerful enough to help me during coding (in any meaningful way); the difference that turns a nerdy pastime into an actual and very useful piece of software.
This doesn’t feel like a coherent argument to me. The statement “There is no “intelligence” in LLMs” does not demonstrate that LLMs are not more powerful than previous chat bots. Same for every other loosely defined subjective word you claimed LLMs are not.
Leave aside the question of whether LLMs do comprehension or whether they are a path to AGI. Just ask if they are a more powerful tool than what came before.
So sure, maybe it's all hype, but the big tech companies with the money are certainly putting their money behind AI in a way they did never did with blockchain.
1/6th of 2021's profits, btw. If they lost all of it they would still be fine.
Edit: the comments I was referring to came while I was writing the comment, looks like I spoke too soon.
While also seeing the most advanced things ever in NFTs every day, sold out new issuances every week, EIP-6551, unique differentiations, a wildly entertaining ordinals bubble, and so much more
such a weird gulf, given that the former perception smugly lives rent free while not even being accurate aside from … volume being down from a peak?
I started paying attention to crypto during 2017's ICO fever phase, and the hype-to-reality ratio of this latest AI wave over the last year feels much stronger than anything blockchain-related in the last five.
most of the bewilderment is about the size of the existing collectors market - which blockchain activity simply reveals due to its transparency - and everyone is mostly acting surprised that the collectors market exists, at any size, and has frictions whose NFT based solutions are of interest to collectors. By that standard, the answer to your question is “nearly all”? These collectors are not crypto enthusiasts, they don’t know anything about crypto technology, just a 5 step process of getting their wallet open and using one marketplace.
It's also interesting that a centralized marketplace and (I presume user-friendly and less secure) wallets for non-crypto folks is a no-no in other parts of cryptoland.
Yeah, you picked two ideas that were overhyped by a number of people. But NFTs struggled from day one to justify their existence. And blockchain started with a clear use case, but it was expensive (by design) and people really stretched to apply it non-problems.
In contrast, these new AI technologies very clearly will revolutionize technology. To me this is self-evident, and if someone is unimpressed by what ChatGPT produces and the huge leap we've witnessed in human-computer interaction... well, I'm not sure what it would take to impress them.
I promise you that every blockchain and crypto bro said the exact same thing, hundreds of times on this very website.
Some things are hard to predict.
If, on the other hand, you’ve been caring a third of your weight on your back for years and you suddenly see someone with a pushcart for the first time in your life, it’s not hard to predict you’ll want one too.
Have you seen what generative fill can do?
Have you seen the code ChatGPT writes?
Can you imagine how many hours it could have saved me? Now multiply that by the number of professionals in the field. And that's just two areas I’m familiar with.
How’s that comparable to NFT, Crypto or Web3?
There are empty hypes bubbles and seismic shifts. It’s not that hard to tell them apart.
I’m not dismissive of AI, but people talk about LLMs as if they are AGI, and I think that is hype
AI is in a different league altogether. I don’t know if we’ll reach AGI in my lifetime, I think we will, but even if we don’t, what we already have and what’s on the horizon is ground breaking.
Generative fill is a neat feature for a limited domain. It is not some grand realignment of labor, the truth is there are orders of magnitude more construction workers, dog walkers, baristas, etc. than graphic designers. Generative fill or other related AI tech is useless to most people's work.
And that’s not a grand realignment of labor and comparable to NFTs?
This is not going to happen no matter how much you think it will.
“This is not going to happen no matter how much you think it will.”
Really? Why do you think so?
I'm a senior developer and integrated ChatGPT and Github Copilot into my workflow. No more StackOverflow for me, ChatGPT handles that part. And Copilot auto writes a lot of my code, amazingly well for a lesser known language Haxe.
My son recently came to me with a school assignment for his Arduino. As an extra he wanted to play a song through a buzzer, instead of just a beep like the assignment said. Just asked chatGPT to write this for the song Ghost Busters. I know C and C++ but never programmed an Arduino. You know how long it took ChatGPT to write it? About half a minute. You know how much time it would cost me or you?
1. Quickly figure out how Arduino works with the loop, outputs, etc. Does it have a C precompiler? etc.
2. Find the notes of Ghost Busters.
3. Figure out what kind of output is sent to the buzzer. (Solution: it's the frequency)
4. Translate notes to frequencies
5. Put the notes of Ghost Busters into an array, or something like that. How to handle timings and pauses?
6. Write the code to play it.
Well, I can tell you it saved me tons of time. It was faster than typing this comment.
Just dismissing it with "I've seen chat GPT write terrible code" is not so smart in my opinion. But hey, the more programmers think they are too good for ChatGPT and Copilot, the better for developers like me who are already great, and just add some more productivity on top of that.
Edit: Just wanted to add something on top of it, for the projects I'm working on myself: Copilot just terribly good at writing unit tests. And if you think about it, it makes perfect sense. It has all the context it needs for that. It's surprisingly also very good at writing Selenium integration tests, although I expected it not to have enough context for that, since it doesn't really see the application. I guess a lot of functionality is very logical or trivial for it to take best guesses.
Code and natural language generation I've been much less impressed by the longer I've used them. Errors in code are way too common and unlike art being 1% off in code is as bad, maybe worse than being 100% off. The entire benefit of code is precision. It's like self=driving, 99% accuracy is useless, and likely dangerous.
With natural language the lack of understanding becomes apparent and it hits a weird uncanny valley, generic, repetitive tone that gets tiresome.
I'm both a professional artist and a very veteran programmer and both of these statements are laughable.
> Can you imagine how many hours it could have saved me? Now multiply that by the number of professionals in the field. And that's just two areas I’m familiar with.
Negative hours, in my experience.
Last week, ChatGPT wrote me a WordPress plugin and we debugged it together and added a few features after my first description. Easily saved me an afternoon. Not only that, the experience of conversing with the machine, understanding what you requested and explaining why it did X, is transformative.
Yeah, I'm sure.
Behold those razdraz boys, them chief execs, gavoreeting about with their greedy glazzies, dreaming up new ways to screw the gullivers of the working class. They seek to harness the power of AI, that moloko-plus of innovation, to drive the dagger deeper into the hearts of the toilers, all in pursuit of their insatiable thirst for more deng.
But mark my words, my brothers and sisters, this ain't no horrorshow plan, it's a vile trick played upon the innocent. These devotchkas and droogs care not for the welfare of the workers, the bedrock of their so-called success. Nay, they thrive on the sweat and tears of the exploited masses, using their newfound AI toys as weapons to tighten their grip on power.
They chant the hymn of progress, luring us with promises of efficiency and automation. But behind those glossy veneers lies the true horrorshow: the displacement of workers, the erosion of dignity, and the widening abyss of inequality. They care not for the human cost, for it is merely fodder for their insidious game.
So, let us unite, my brothers and sisters, against these vile creeps and their twisted agenda. Let our voices ring loud and clear, viddy their deceitful schemes and expose their true nature. For the exploitation of workers, masked in the cloak of technological advancement, is nothing but a new form of ultraviolence.
Lol.
Not sure why those of us that live in code editors or even Excel should worry about our jobs more than those that live in Powerpoint.
Powerpoints are just the final output you see. The real work execs do is in the decisions that went into the powerpoint.
No sane board will give decision-making power to an AI they can't blame. Besides, there are probably 100 devs for every exec, so it makes no financial sense to automate execs.
But the decisions they make are one of the things that can be automated. I do not know if you have been inside one of these places but the executives are not doing a great job deciding (at mine they decided opensearch was a better bet than elastic and switched existing installations).
A new regime came in and then bad decision after bad decision drove our best talent away. Consultants, everywhere.
Also, that number is much lower. Full time devs are down, contractors and consultants are up. As a full time dev at one of these places, it felt like the number of executives was growing as everything else shrank.
Perhaps you are right about the highest levels, but think about all of the middlemen executives and what they do.
And even that -- I think an AI could choose to not spend millions on Deloitte or Accenture on software that inevitably failed.
A high paying job that might be diminished greatly by AI are clinical doctors. In modern medicine, a large part of the job becomes interpreting test results and prescribing accordingly. How long before an Urgent Care diagnosis can be done by an LLM? Surgeons will of course not be replaced and an LLM can’t do even a blood draw, but writing a prescription? That seems within reach (if medical LLMs can be tamed of hallucinations)
Or that AI outperforms doctors in controlled studies and reading imagery? https://hbr.org/2019/10/ai-can-outperform-doctors-so-why-don...
Even being more empathetic than doctors as an LLM? https://today.ucsd.edu/story/study-finds-chatgpt-outperforms...
The first to go AI will be the telemedicine firms like TeleDoc, than the Urgent Care Centers will get AI screeners, and next the doctors will be left to click a confirmation button, and last since AI alone will outperform a doctor, that confirmation will fade away or lose importance.
Doctors get sued all the time, mostly when they mess up, if AI truly does better than these doctors, legal losses and insurance costs will drop off for hospitals.
I'm talking about most CEO's, not most owner CEO's of a business.
They know, at least as far as publicity is concerned, they are the most likely person to be replaced with AI. They're a constant liability and their human qualities make the most unsuited towards their own jobs.
Could be AI or human resources, it doesn't matter.
I do think it would be incredibly entertaining to see if people could tell a difference; I just don't think anyone is going to force them out any time soon.