You’re treating it as a fact that LLM are going to replace existing products, at some unknown future date.
“In 5 years, all code will be written by AI”
“In 5 years, LLM will replace Siri and Alexa”
“In 5 years, AI will replace [sector of jobs]”
The thing that frustrates me about these statements is that you don’t know what AI technology is going to look like in 5 years, so stop treating it like a fact. It’s possible LLM are useful in all of these places, but we don’t know that yet.
I do know, for a fact, that having a more capable and powerful voice assistant than, the already fairly capable, Siri, will be a game-changer (for me at a bare minimum, but I’m not that special so I think it’s safe to extrapolate that to more people).
That’s a fact.
I also know that voice-interfaces to date have been incredibly stiff and there is ample room for improvement. I know, for a fact, that having AI enable better voice interfaces will make computing better and more accessible. I have a hard time understanding how those are hype-driven comments and/or opinions.
We do know these things for a fact. Not being able to articulate exactly which breakthroughs will be most important doesn’t make it hype.
LLM is obviously useful for something like Siri, Alexa or Google Assistant, or so you would think.
There doesn't seem to be a rush because it makes the implementation a lot more expensive, and those things are, I suspect, not profitable products (revenue sources) to their respective companies. They are a kind of enhancement to a layer of products and services; people take them for granted now and so you can't take them away.
A smarter Google Assistant would do nothing for Google's bottom line, and in fact it would cost more money to operate.
If it's not done right, it could ruin the experience. For instance, it cannot have worse latency on common queries than the old assistant.
GPT4 just wrote a python script for me that downloaded a star catalogue, created a fish eye camera model, and then calculated the position of the camera relative to the stars by back propagating the camera position and camera parameters to match the star positions.
All I did was hold it's hand, it wrote every line of code.
You are living in fantasy land if you think we will be writing lines of code in 10 years.
> You are living in fantasy land if you think we will be writing lines of code in 10 years.
I was with you until that sentence. No, LLMs will not write all our code and the reason is very simple: coding is easier than reviewing code. Not to mention the additional complexities and weirdness that we've always dealt with without even thinking about it.
We can see in Photoshop what's coming for developers: context-sensitive AI autocompletion and gap filling. Copilot but more mature and integrated, perhaps with additional checks that prevent some bugs being inserted. And troubleshooting, the area where I think we can profit the most.
that's all stuff that would be impressive for a single human to be able to produce instantly (because nobody remembers all these APIs), but that's still formulaic enough that it's not hard to imagine why ChatGPT succeeds at it
but will ChatGPT help you debug and fix a production issue that came about due to a Kafka misconfiguration? will it be able to find the deadlock in your code that is causing requests to be dropped? will it suggest a path forward when you need to replace an obscure library that hasn't been updated in 5 years? will it be able to make sense of seemingly contradictory business requirements?
That's not exactly the complexity of typical software that must solve an actual, difficult, business problem.
Wake me up when ChatGPT is able to write and maintain a POS system, or an online store with attached fulfillment management. Anything that goes beyond a fancy 100-line script. Anything that people actually hire teams of senior devs, business analysts and software architects for.
Exactly. The AI bros here are doing the same thing as the crypto bros and almost all of them don't even know it.
Pontificating their nonsense around the hype about LLMs to the point where they don't even trust it. The same thing they did with ConvNets and they still don't trust that either since they both hallucinate, frequently.
I can guarantee you that people will not trust an AI to fly a plane without any human pilots on board end-to-end (auto-pilot does not count) and it is simply due to the fundamental black-box nature of these so-called 'AI' models being untrustworthy in high risk situations.
I'd like to point out that humans, too, are not trustworthy in high risk situations. For this we have procedures, deterministic automation and so on.
I like to think of capable LLMs as pf gifted interns. I can expect decent results if I explain well enough, but I need processes around them to make sure they are doing what they are told. In my industry thats enough to produce a noticeable productivity gain, and likely some reduction of employment as its a low margin cut throat business relying on low grade knowledge workers. I see the hype and honestly cant stand it, but its measureably impacting my industry and the world around me.
> I'd like to point out that humans, too, are not trustworthy in high risk situations. For this we have procedures, deterministic automation and so on.
Except humans can transparently explain themselves and someone can be held to account when something goes wrong. Humans have the ability to have differing opinions and approaches to solve unseen problems.
An AI however cannot explain itself transparently and just resorts to regurgitating whatever output it has been trained on and black-box AI models have no clear method of any transparent reasoning meaning that it cannot be held to account.
Any unseen problem it encounters, it falls back to fixed guardrails and just repeats a variation or re-wording on what it already has said. Especially LLMs.
> Except humans can transparently explain themselves and someone can be held to account when something goes wrong
Except humans are excellent at finding excuses to avoid explaining themselves and being held to account, or to justify some misguided belief based on whatever output they have been "trained on" in their past.
People often seem to apply standards to AI in terms of rationality and reliability which even many humans cannot achieve, using terms like "hallucination" when we've seen humans do the exact same by confidently talking about things they know nothing about. Everyone laughed at Bing insisting on a wrong date to avoid admitting it's wrong about the Avatar 2 release, when that's very typical behaviour of humans in certain situations.
I'm not trying to make LLMs seem better than they are, but parts of its weaknesses are not surprising given the training data.
What would you prefer to talk about? We don’t have to make predictions and discuss their potential, or at least you don’t have to join those discussions.
A lot of these comments aren’t predictions. They’re assuming that openAI will create AGI in the next 5 years and they want to discuss the implications of that.
Personally, I think LLMs are a step forward, but I suspect that GTP-4 is close to the limit of what’s possible with LLMs. I don’t think we’re going to see AGI from the same approach.
You are either full of shit, or your "coding" is pretty basic, or your code is full of bugs and you don't care.
I can't trust GPT, and neither can you. But if it really can do all your coding for you, what stops your employer from replacing you with a secretary from a temp agency?
It's so stupid for engineers to say that ChatGPT codes for them. They are shooting themselves in the face. They are devaluing the entire profession. Why? My reaction to all those breathless online demos was to point out the difference between what they were showing and what an engineer really does. Your reaction is to act like being a prompt jockey is the new way of engineering. How does that give you pride in yourself?
Do you work much with legacy systems, internal libraries and work with a large team?
I do and ChatGPT code is rarely useful for me. I can prompt it well enough to do language related stuff for me, but the code it can write for me is more like a highly custom boilerplate that I still need to refactor.
Even for green field private projects, at first it looks fine, bit the bugs are more likely to be traced back to these snippets than not.
Can you elaborate what your process is? Some context would be nice as well. Like, what kind of language, what kind of project? I'm genuinely interested.
You’re treating it as a fact that LLM are going to replace existing products, at some unknown future date.
“In 5 years, all code will be written by AI”
“In 5 years, LLM will replace Siri and Alexa”
“In 5 years, AI will replace [sector of jobs]”
The thing that frustrates me about these statements is that you don’t know what AI technology is going to look like in 5 years, so stop treating it like a fact. It’s possible LLM are useful in all of these places, but we don’t know that yet.