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To me what’s exciting about Chat/GPT type of tech, is that they can be the “coordinators” of other models. Imagine asking an AI assistant to perform a certain

by nico 4y ago

To me what’s exciting about Chat/GPT type of tech, is that they can be the “coordinators” of other models. Imagine asking an AI assistant to perform a certain industrial control task. The assistant, instead of executing the task “itself”, could figure out which model/system should perform the task and have it do it. Then even monitor the task and check it’s completion.

JohnFen 4y ago
I'm not sure how the likes of ChatGPT could accomplish that even in theory, but I won't say it's not possible at some point in the future. Gpt itself, perhaps, someday.
- IanCal 4y ago
  You can just tell the models to and tell them what tools they have available and how to call out to them. Langchain supports this iirc.
- nico 4y ago
  Already ChatSpot is doing it. Their system is essentially a ChatGPT-enhanced Hubspot management system using chatux. ChatSpot can understand your commands and then perform actions in the system for you, for example add a lead, change their contact info, write a blog post, publish it, add an image… Edit: but if you connected it with physical actions, it could control your house, maybe check your smart refrigerator, order food on Instacart, send you recipe, schedule the time to cook in your calendar, request an Uber to pick you up from work, invite someone over, play music… There’s a discussion about this on another homepage thread here: https://news.ycombinator.com/item?id=35172362 https://news.ycombinator.com/item?id=35172362
  - JohnFen 4y ago
    Ah, ok. I thought you were talking about something a bit more profound than that.
  - nostromo123 4y ago
    None of this is "industrial control task", aka "real work".
    - nico 4y ago
      Well, it might be coming soon, check out the video here: https://say-can.github.io/ https://say-can.github.io/
version_five 4y ago
This is just wrong. Also, even if a LLM could do that, so could a shell script, without the risks involved in using "AI" for it, or for now the ridiculous external dependence that would involve. I wonder if in 10 years people will be stuck debugging Rube-Goldberg machines composed of LLM api calls doing stuff that if-statements can do, probably cobbled together with actual if-statements
- JohnFen 4y ago
  > doing stuff that if-statements can do, probably cobbled together with actual if-statements In other words, old-school expert systems.
  - baq 4y ago
    With the limit of 25k words it might actually be reasonable to test out a prompt for an expert system… but I’d still leave reasoning to something else, for now. Z3, prolog or some forward chaining tool like clips, but have the LLM hallucinate some of the rules?
- nico 4y ago
  Sure, maybe you can use a shell script, but now the AI assistant can write it based on your verbal/text description, and then the assistant can also run it for you after you’ve checked it. What your are saying is: “why use the washing machine, if I my clothes are even cleaner when I wash them myself - I also spend less detergent and less water”. You are free to keep doing your laundry by hand. But I bet most people prefer the washing machine.
  - xwdv 4y ago
    Spare me the shitty analogies. We write shell scripts because it’s cheap, fast, and the behavior is very predictable. Like it or not, an AI’s behavior is a black box and can’t be “proven” to execute exactly the same every time for the scenarios you are targeting. A shell script will do exactly what it has been written to do every time, unless tampered with. And if changes need to be made, it can be done quickly without need for retraining, god knows how long that would take for an AI to learn something new. God help you if you need to maintain “versions” of your AI, trained for different things. Face it, AI are pointless and slow for certain classes of problems.
calf 4y ago
The central question is that a controller is assumed to be specifiable and thus formally verifiable through model checking in principle. With a neural network you have a black box and for example with ChatGPT it doesn't even have a specification. It turns the verification process upside down.
pstorm 4y ago
You are getting a surprising amount of backlash from this, but I think you are right. There may be better tools for the job, but general tools tend to win out as they get "good enough"
- digitalsushi 4y ago
  mediocre is acceptable for most things. i'd rather have 1000 free photos from my wedding than 32 perfect ones. i still ended up with more than 32 perfect ones.
burnished 4y ago
What do you imagine this would do that existing automation does not?
- nico 4y ago
  Perform tasks that humans do now, but at scale, automatically. We are going to be able to automate everything and anything with the proper feedback loops. For example, you could have an app that writes itself, deploys itself, tests itself, receives feedback, updates itself based on the feedback, writes additional tests, does CI/CD. At that point you will be just creating and directing. Or you can choose whatever you actually want to execute. And then if those same kind of processes are given access to physical tools, they could do all of our manufacturing, design and build their own machines and infrastructure. We could essentially collaborate with our systems in the most amazingly seamless way.

by nico 4y ago

JohnFen 4y ago

I'm not sure how the likes of ChatGPT could accomplish that even in theory, but I won't say it's not possible at some point in the future. Gpt itself, perhaps, someday.

IanCal 4y ago
You can just tell the models to and tell them what tools they have available and how to call out to them. Langchain supports this iirc.
nico 4y ago
Already ChatSpot is doing it. Their system is essentially a ChatGPT-enhanced Hubspot management system using chatux. ChatSpot can understand your commands and then perform actions in the system for you, for example add a lead, change their contact info, write a blog post, publish it, add an image… Edit: but if you connected it with physical actions, it could control your house, maybe check your smart refrigerator, order food on Instacart, send you recipe, schedule the time to cook in your calendar, request an Uber to pick you up from work, invite someone over, play music… There’s a discussion about this on another homepage thread here: https://news.ycombinator.com/item?id=35172362 https://news.ycombinator.com/item?id=35172362
- JohnFen 4y ago
  Ah, ok. I thought you were talking about something a bit more profound than that.
- nostromo123 4y ago
  None of this is "industrial control task", aka "real work".
  - nico 4y ago
    Well, it might be coming soon, check out the video here: https://say-can.github.io/ https://say-can.github.io/

version_five 4y ago

This is just wrong. Also, even if a LLM could do that, so could a shell script, without the risks involved in using "AI" for it, or for now the ridiculous external dependence that would involve. I wonder if in 10 years people will be stuck debugging Rube-Goldberg machines composed of LLM api calls doing stuff that if-statements can do, probably cobbled together with actual if-statements

JohnFen 4y ago
> doing stuff that if-statements can do, probably cobbled together with actual if-statements In other words, old-school expert systems.
- baq 4y ago
  With the limit of 25k words it might actually be reasonable to test out a prompt for an expert system… but I’d still leave reasoning to something else, for now. Z3, prolog or some forward chaining tool like clips, but have the LLM hallucinate some of the rules?
nico 4y ago
Sure, maybe you can use a shell script, but now the AI assistant can write it based on your verbal/text description, and then the assistant can also run it for you after you’ve checked it. What your are saying is: “why use the washing machine, if I my clothes are even cleaner when I wash them myself - I also spend less detergent and less water”. You are free to keep doing your laundry by hand. But I bet most people prefer the washing machine.
- xwdv 4y ago
  Spare me the shitty analogies. We write shell scripts because it’s cheap, fast, and the behavior is very predictable. Like it or not, an AI’s behavior is a black box and can’t be “proven” to execute exactly the same every time for the scenarios you are targeting. A shell script will do exactly what it has been written to do every time, unless tampered with. And if changes need to be made, it can be done quickly without need for retraining, god knows how long that would take for an AI to learn something new. God help you if you need to maintain “versions” of your AI, trained for different things. Face it, AI are pointless and slow for certain classes of problems.

calf 4y ago

The central question is that a controller is assumed to be specifiable and thus formally verifiable through model checking in principle. With a neural network you have a black box and for example with ChatGPT it doesn't even have a specification. It turns the verification process upside down.

pstorm 4y ago

You are getting a surprising amount of backlash from this, but I think you are right. There may be better tools for the job, but general tools tend to win out as they get "good enough"

digitalsushi 4y ago
mediocre is acceptable for most things. i'd rather have 1000 free photos from my wedding than 32 perfect ones. i still ended up with more than 32 perfect ones.

burnished 4y ago

What do you imagine this would do that existing automation does not?

nico 4y ago
Perform tasks that humans do now, but at scale, automatically. We are going to be able to automate everything and anything with the proper feedback loops. For example, you could have an app that writes itself, deploys itself, tests itself, receives feedback, updates itself based on the feedback, writes additional tests, does CI/CD. At that point you will be just creating and directing. Or you can choose whatever you actually want to execute. And then if those same kind of processes are given access to physical tools, they could do all of our manufacturing, design and build their own machines and infrastructure. We could essentially collaborate with our systems in the most amazingly seamless way.