This sub and blog post are all talking about ChatGPT which is no doubt amazing and far ahead of the curve. However I would also point to Metas new vision model: SAM (Segment Anything): it is so far beyond any other vision model, I actually believe vision will be solved in a few years now. People don’t realize that there was an industry of publishing paper with incremental improvements in small datasets in CVPR that h…
I wonder if this means no more stupid select-all-squares-with captchas.
What it feels like to work in AI right now
651–660 of 768 posts
Re: What it feels like to work in AI right now
#652Earlier quoted context omitted.
> Let me repeat that: You can program GPT in English. ENGLISH! The very existence of "prompt engineering", numerous discussions about how to prompt ChatGPT in order to get the result you want, etc. imply that while it may be in English, it still requires similar care and attention to do properly as a programming language does. Which makes me wonder what the advantage of using English is. A formal language seems like…
For one, GPT-4 requires far less prompt engineering and generally interprets intent better. The advantage of using English (natural language that is), the humans around you tend to speak it. I don't naturally speak powershell. Instead I want a script that searches for particular filenames, under a particular size, between a particular date in a directory path I specify. I told GPT I wanted that and in a few seconds i…
This is true, but of limited utility. English is so bad at this sort of thing that even native-speaking humans are constantly misunderstanding each other. Especially when it comes to describing things and giving instructions.
That's why we have more formal languages (even ignoring programming languages) for when we need to speak with precision.
Re: What it feels like to work in AI right now
#653Earlier quoted context omitted.
> make this happen without a lot of back and forth Perhaps this is the part you're missing. When I've watched people program with ChatGPT it _is_ a lot of back and forth because an enormous amount of context is able to be stored and back referenced. I.e. one wouldn't say "make me a Flappy Bird clone for iOS", they'd start with: "Give me the code for a starter SpriteKit project". Then "Now draw a sprite from bird.png…
> Perhaps this is the part you're missing. When I've watched people program with ChatGPT it _is_ a lot of back and forth because an enormous amount of context is able to be stored and back referenced. I've tried to build a lot of fun stuff with it so far. Haven't been able to properly 'program it in English' for anything non-trivial. Back and forth ended up in loops of not what I wanted. I'm just utterly confused at…
expectations: using to the LLM to break problems into steps, suggest alternatives, using the LLM to help them think through the problem. I think this is the people using it to write emails - myself included, having a loop to dial in the letter allows me to write the letter without the activation energy needed to stare at a blank page
empathy: people who've spent enough time interacting with an LLM get to know how to boss it around. I think some people are able to put themselves in the LLMs shoes and imagine how to steer the attention into a particular semantic subspace where the model has enough context to say something useful.
GPT4 writes boilerplate python and javascript servers for me in one shot because I ask for precisely what I want and tell it what tools to use - I think because I have dialed in my expectation for what it's capable of and I learned how to ask in precise language, I get to be productive with GPT4's code output. Here's a transcript: https://poe.com/lookaroundyou/1512927999932108
Re: What it feels like to work in AI right now
#654Earlier quoted context omitted.
It's easy to distribute across many computers which communicate with high latency
LLMs are already running distributed on swarms of computers. A swarm of swarms is just a bigger swarm. So again, what is the actual difference you are imagining? Or is it just that distributed X is fashionable?
If 80% of the processors in a cluster are running 'general LLM' and 20% are running 'math LLM' are they the same cluster? Could you host the cluster in a different data center? What if you want to test different math LLM modules out with the general intelligence?
Re: What it feels like to work in AI right now
#655Earlier quoted context omitted.
You can program GPT in English. Let me repeat that: You can program GPT in English . ENGLISH! You're complaining about the first nuclear test bomb being impractical and uninteresting. How will this change the world? That huge monstrosity had to be affixed to the top of a test gantry and took years of effort by a veritable army of the best and brightest to make! No way it could change war, or geopolitics, or anything.…
> You can program GPT in English. > Let me repeat that: You can program GPT in English. ENGLISH! How? Let me repeat that: How? I had a little script that from time to time parses a list of jobs from a specific board, extracts some categories, inserts them into an SQLite and have a frontend that displays them to me in a way I want. The board has since changed some things which would mean maybe 2 hours of commitment fr…
Re: What it feels like to work in AI right now
#656Earlier quoted context omitted.
> Imagine Walmart launching a ChatGPT interfaced bot for shopping that customers take a liking to. I can't imagine that, because it doesn't seem to fit the use case. Especially not to the point of bankruptcy of Amazon, maybe as a small novelty? Can you list some killer features that the chat would bring that would make the existing shopping experience irrelevant? Maybe not everything is a nail to the hammer?
"I'm looking for X. Can you give me a few options with the benefits and drawbacks of each" or "I'm looking for X. Can you ask me a few questions and give me a choice of products based on that" or "I'm looking for a product that does X, Y, Z. Can you find such a product for me" or "Does this product do Y/is compatible with Z/is an appropriate give for person P" I'm not saying this will happen. I'm saying it's a risk t…
When you're trying to answer that question now you'd presumably use heavy filters: wirecutter, trusted blogs, top reddit comments, etc. GPT won't.
Re: What it feels like to work in AI right now
#657Earlier quoted context omitted.
Scaling up an LM from 2017 would not achieve what GPT-4 does. It's nowhere near that simple. Of course companies saw the potential of natural language interfaces, there has been billions spent on it over the years and a lot of progress was made prior to ChatGPT coming along.
You're making incorrect assumptions. This project wasn't about scaling any published approaches. It was original neural net research that produced excellent results with a new architecture without self-attention, using a new optimizer, new regularization and augmentation ideas, sparsity, but with some NLP feature engineering, etc. Scaling it up to GPT-2 size matched its performance for English (my project was English…
And I'm sorry, but you're completely wrong about companies recognizing commercial potential. I worked on Alexa for five years, it is a far harder problem than you think. It is nowhere near as simple as "we just weren't looking at the right NN architecture or optimizer!" You're acting like it was a novel idea to think LMs would be extremely useful if the performance was better (in 2017). I'm just trying to tell you that isn't the case.
Re: What it feels like to work in AI right now
#658Earlier quoted context omitted.
I, too, have been experimenting a bit to see if/how an LLM might help me. This microtask framing jives with my experience. One of the best examples so far for me (and it's truly micro) was at grocery store. Friend trying to figure out how big of a rice bag to get and avoid not finishing it before a long trip coming up. She knew she ate a couple of cups dry a week. "I eat 2 cups dry rice per week. Can I finish a 25lb…
50 cups of rice in a 25 lb bag, 4 months = 16 weeks = 32 cups at 2 cups / week. I think there will be some rice left. Maybe this is one of those examples where this tool gives a confident and wrong answer even when it shows its homework?
Re: What it feels like to work in AI right now
#659Earlier quoted context omitted.
You’re asking “what’s so big about GUIs? Literally nobody has asked to move a pointer around a screen”. It’s the use cases these thing enable that are important. Today, I wrote a draft product announcement. Only after I was done did I realize I had written it in a really impersonal third person (“users will be able to”). No big deal, but maybe 10-20 minutes of work to make it energetic and second person (“now you can…
I mean Google revolutionized search. Apple revolutionized personal computing. OpenAI revolutionized… rewriting things with slightly different wording? I’ve seen so many breathless people posting “this would have taken me so long to search” and then I type 3 keywords from their massive prompt they crafted and find it instantly on Google. We’re talking 1000x or more faster. I feel like the same is happening in your com…
Yes, if you try hard enough, you can try to cast transformational shifts as trifling.
- e.g. “Barteen, Shockley , and Brittain made a smaller version of the vacuum tube.” (transistors)
- “Scientists discovered that light could carry information, like electrical wires do.” (fiber-optics)
The effects (including the harder to measure cultural shifts) matter more than some uncharitable characterization.
Also, the “it is not X” thinking is the result of present fixation. Such argumentation is, at best, quite narrow. Perhaps applicable in specific defined markets and situations but hardly a good mindset for making sense of how the world is changing. Hence the cliché, “The Stone Age didn’t end because we ran out of stone.”
The psychological undertones in the comment above are probably “people, stop exaggerating”. From one overreaction to another, it seems.
Re: What it feels like to work in AI right now
#660From my perspective it’s just _confusing_ to work in AI right now. We have some massive models that are doing some really neat stuff, and apparently hundreds of millions of people are using them—but I keep wondering: to do _what_, exactly? I’m not asking what the models can do, I’m asking what people want the models to do every day, all the time. I’ve been shown some neat pictures people made that they thought were c…
It's just a glorified google search. You'd save more time googling what you need and picking 2-3 results to read briefly. People have no clue human intelligence has very little to do with "statistical analysis of old data".
How is that different from googling? Lots of articles won't bother to include import and the structs they use definitely won't match up to your use-case because their dataset if different. What if my use-case is a bit strange and I need to embed the file instead of reading from the file system, for example? I can ask Chat GPT and it will update the example program, using my exact file name and the variable names for the problem I've described and the program runs as written!
I don't write much Go (so I don't actually care to commit the hello_world.csv ritual to memory), but I know enough about it to verify that the program doesn't have any glaring issues and make my own tweaks as necessary. Saved so much time for me in this scenario.