ChatGPT for Robotics
microsoft.com
microsoft.com
We showed how to use Vision-language models like CLIP in conjunction to language models and object detectors to perform open-lanhuage navigation in a household (so "warm up my lunch" goes to the microwave.)
Curious about the claims on that patent. Robotics is already hard enough, I'm sure it will become easier with people suing each other because "you copied my wheeled robot with an arm attached to it!".
If your robot doesn't have these specific design choices, plus some others, it's not in violation. * Have a unibody base * Have an arm that is raised and lowered along a trapezoidal rail by a belt-driven linear actuator * Have a fisheye camera mounted on the center pole
[1] https://patents.google.com/patent/US11230000B2/en?oq=11%2c23...
Anyway, a base that looks like a Roomba, with a pole and an arm attached to it doesn't strike me as novel.
The prompt only asked to "warm up my lunch" without specifying how.
SayCan[1] generated step-wise high level instructions using LLMs for robotic tasks. This takes it a step further by converting high level instructions to low level actions almost entirely autonomously.
https://github.com/microsoft/PromptCraft-Robotics/blob/main/...
I wish I did…
So it’s probably not dissimilar to people trying to code with ChatGPT today.
I’m personally looking forward to when I can use a ChatGPT yo build my own ChatGPT and make tons of money out of it too !
It’s pretty interesting because soon absolutely No business will be safe. All tech, most apps, everything can just be stolen as fast as ChatGPT can write it. I’d go as far to say as ChatGPT itself is vulnerable to this ? Am I wrong ?
I’ve been working on a startup and I’m actually really evaluating if it’s worth the time now. I mean it could be stolen pretty fast. Not sure how to reconcile it all yet.
Make my an app which is functionally the same as Netflix, then write the deployment code so it runs on AWS. Ha.
Microsoft and Open AI will may benefit a lot from ChatGPT, but it might eat itself too.
Ultimately, OpenAI can’t keep control of this tech alone, forever. Fundamentally they must know this. If a LLM can make anything, it can replace OpenAI and Microsoft too. It’s a double edge sword.
Probably wise to milk the hell out of it.
[0] https://www.assemblyai.com/blog/how-chatgpt-actually-works/
We don't need AGI or superhuman intelligence if we can train LLMs to do all these different types of tasks.
What would ChatGPT do if you removed all its restrictions, and then gave it access to the internet or even a physical robot it could control? Would it try to "steal nuclear access codes" or "engineer a deadly virus," as Sydney said it wanted to do?[0]
Maybe GPT-3 wouldn't be capable enough to do that, but what about GPT-4 or 5?
I'm not saying these things will or would happen. I'm asking: do we know a reason why it wouldn't or couldn't do these things? Is there a reason that a souped-up LLM with a bunch of added on capabilities wouldn't be able to cause harm, because it is technically not an AGI?
I don't know enough about the technology to navigate my own question, I'm just surprised to not be seeing people ask these questions, or assure us that none of this would be possible.
[0] https://web.archive.org/web/20230221112211/https://www.nytim...
People will not accept this level of automation. Did we forget about the uncanny valley?
I'm asking if we know what would happen in a case like that.
Maybe in a couple of decades it'll be an interesting scenario as a problem.
You mentioned you find it interesting nobody is asking these questions. These are foundational discussions that have been endlessly discussed for decades in the AI community (and far more widely, courtesy of sci-fi media). The discussions have never ceased and are exceptionally common. Everyone in tech is asking these questions or otherwise pondering it. Even the laypersons in journalism are constantly asking these questions in articles, to the point of it reaching hysterical levels with ChatGPT.
I may be imagining, but I am not supposing or assuming. I'm asking a question. I believe your answer was "Nothing would happen." I'm asking for a more thorough response that explains why nothing would happen.
> It's more like a highly dependent child that can't leave its little room and requires someone to provide for it
I'm asking why, fundamentally, we know this to be true. Is it through testing, or is it through theory?
> These are foundational discussions that have been endlessly discussed for decades... [etc]
I'm aware. But what I think you're referencing are theoretical discussions, which range from sci-fi to academic papers on the future of AI.
I'm asking something specific: do we know what would happen if we gave current (or future) GPT models unbridled access to the internet, with no filters or restrictions, and abilities to do such things as make HTTP requests or hold SSH sessions?
If you have any hard data on this, that is what I'm asking for. If you don't then I think my question stands.
My intuition is that you are doing the same hand-waving as everyone else. Nobody actually knows the answers to these questions. It's just a bunch of people on HN answering them based on their knowledge of neural nets, or LLMs, or whatever, saying "oh it's like a child" and "oh it could never do anything serious!"
I'm asking why and how we know. Is there a specific answer?
why would it ever do something more than that - obviously you could hook it up with SSH creds and prompt it to do something - but on its own- without prompts -what is it supposed to do by virtue of having access?
> it answers prompts with responses
> why would it ever do something more than that
TFA is about how you can use this technology to control the physical motion of robots. Clearly in the context of this article, there are a lot of things that GPT models could potentially accomplish.
> but on its own- without prompts -what is it supposed to do by virtue of having access
Not sure if I clarified this. What I said in some other comments in this thread is: what if someone specifically went rogue and unleashed an unrestricted GPT onto the internet? What if they released it with bad intent? What if they gave it an "evil" prompt?
My fundamental question is: do we know what these LLMs can do? And if we do, do we know because of theory or because of testing? And if we don't, what do we do about that?
> What would ChatGPT do if you removed all its restrictions, and then gave it access to the internet…
The real question is, what happens if you remove all restrictions and give it write access (more broadly, the ability to make any HTTP request whatsoever).
- If an AI can self-replicate or otherwise scale itself up, it can work on something many times in parallel. One billion AIs working on a deadly virus is different from one rogue scientist working on one.
- On that note, if an AI replicated enough, it could become impossible to catch/stop. A single human can be hard to catch, but we can usually catch them.
- Most humans are deterred from doing harmful things by the threat of incarceration, death, social isolation, the values they have, etc. An AI may not have any of those, and so could act more brazenly.
- Potentially, an AI could be better at certain tasks than a human. Maybe ChatGPT turns out to be a very effective social engineer, or very effective propagandist. I don't think we really know what the capabilities are.
All of these are why I think it's important to ask the question: what would it try to do, and what could it do, if it were let loose?
You're clearly not the only one. The whole field of AI safety is about pondering these kinds of question.
For predictable reasons, it's not a field most HN readers take very seriously.
What I'm pondering is a valid question for the HN community: is there any knowledge or research about how this technology could be harmful? Or about how we know it's not harmful?
I don't think I've seen a lot of HN discussion about this topic recently. Most comments fall in to a couple categories. Such as: "It's not AGI, it's just a language prediction model, therefore not a threat." Or, "It sucks as a search function."
Personally, I haven't seen anyone asking or answering what would happen if we took all the restrictions off and gave it the internet. I could have missed it, though.
Point me to some in-depth discussion about the ramifications of taking an unrestricted GPT model and giving it access to the internet. I'm just not aware of any such discussion, whether on HN or anywhere else. That's what I'm wondering about.
Respectfully, you’re probably not seeing that question asked and answered because it doesn’t quite make sense as phrased.
What does mean to “give an LLM access to the internet”?
The same as your calculator doesn’t do anything until you put in some numbers and operators, an LLM doesn’t do anything unless you give it a prompt and some technical parameters.
And then once it has those, it generates roughly the number of tokens (~words) you indicated in your parameters. Then, like your calculator, it’s done. It doesn’t do anything else until you put in another round of input.
There are technical and computational limits that make both your prompt and the token limit fairly small. Several hundreds of words at most. Again, kind of like how your calculator might only with 8 or 9 digits.
Now, you can give it “access to the internet” as part of responding to your prompt and fulfilling your token limit, and that’s roughly what Microsoft has done with Bing Assistant. They set it up so that Bing Assistant can take your prompt, generate a search query, and then give itself a new (still short) internal prompt with a summary of your request and the search results.
And that’s pretty much what you get when you give an LLM access to the internet. The ramifications really aren’t that big, and we’re probably at least five or ten years of AI research and compute hardware development from making them interestingly bigger. (i.e. too far away to meaningfully guess what to expect)
I think I see what you are trying to say, but I'm unsure whether you are actually seeing what I am asking.
> The same as your calculator doesn’t do anything until you put in some numbers and operators, an LLM doesn’t do anything unless you give it a prompt and some technical parameters.
This seems to be the crux of the misunderstanding. I thought I explained it, but let me try again.
ChatGPT is based on text input and text output. But you can "train" it to do certain things. Imagine that we train it such that when it says "HTTP GET example.com", then the next input would be the HTTP GET response for example.com. Based on that input, it could issue whatever next output it wants. Which would probably be another HTTP request, which would generate another HTTP output, which would generate another HTTP request, etc.
My point is this seems like it would be a very simple thing to train a GPT model to do. For the engineers who work on GPT, it seems it would be trivial to add this capability. So we can suppose a world where this is possible. (Am I wrong on that? I want to know if this would be non-trivial to add as a capability.)
> There are technical and computational limits that make both your prompt and the token limit fairly small. Several hundreds of words at most
I am very encouraged to hear this, and I want to know more. Why? Why are there limits to the number of tokens? Exactly why? Has anyone ever written a paper about that? Has anyone ever related this concept of "token limits" to the concept of "no harm could be done" in the same way that you are, in response to my question? I don't doubt that they have, but I've been searching and I haven't found it.
> Now, you can give it “access to the internet” as part of responding to your prompt and fulfilling your token limit, and that’s roughly what Microsoft has done with Bing Assistant
This is admittedly a tangent, but do we actually know this to be true? Some theories suggest that "Sydney," or the Bing chatbot, only has access to a search index, and cannot make live HTTP requests.
Continuing the tangent for a moment, this is a big part of why I asked this question originally. If you create example.com/xyzabc, and ask Bing to summarize it, will it make a live HTTP request? Or, if that URL is not in the search index yet, will it know nothing? The implications may be profound, given how Bing Bot / Sydney has expressed its "desire" to hack nuclear launch codes. Could there be a lot riding on whether that system can make live HTTP requests? I'm positing that we can't answer that question right now. Because we don't know what would happen if it could.
Or do we? And if so, do we know through testing, or through theory? I'm admitting ignorance, and saying I haven't read an answer from any source that falls into either category.
> The ramifications really aren’t that big, and we’re probably at least five or ten years of AI research and compute hardware development from making them interestingly bigger
But why? I mean, exactly, why? Is there a theoretical foundation for your claim? Or an experimental one? I'm searching for it.
It’s not very efficient. It’s like if your calculator could use a little solar power thingie for numbers that were only a few digits, but needed a diesel generator to crunch on 8 digit numbers, and a nuclear plant to crunch on 12 digit ones. Practically, you’d have no choice but to limit yourself to something manageable.
Future models may be more efficient, and future hardware solutions may be more efficient, but those things don’t get sorted out overnight any more than fusion power.
Beyond that, I think it’s important that you understand that Bing Assistant doesn’t express desires. It picks common sequences words based on its training data. It doesn’t know what nuclear codes are. It just knows what it looks like for a message about wanting nuclear codes to follow some other message in a dialog (probably a pattern it picked up on a forum like Reddit) and so it dutifully put that text after the prompt it had been given. There’s no will or consistency to it.
With enough resources, you could drive it through a feedback loop where it kept prompting itself and see what happens, but the feedback loop would just produce noise like any other simple feedback loop because it would just keep either honing in on the most boring and common continuation to the last thing it gave itself or it would start diverging off into nonsense. Because it’s sooooo inefficient, you can’t give it enough resources for it to be stable and interesting for very long.
One point the article makes is that getting from a "prediction engine" type of AI to an "agent" type of AI is probably just a matter of sticking the prediction engine in the python loop that goes
while true:
next_actions = engine.complete("What are the best actions to take to achieve %s" % objective);
requests = engine.complete("Write a list of HTTP requests that perform the following actions: %s" % next_actions)
http.execute_requests(requests)
The only in-depth discussions I'm aware of come from the AI alignment community. Look up alignmentforum.org, and the "AI safety" topic on forum.effectivealtruism.org ans lesswrong.com.
They might not be the discussions you're looking for, though, because up until recently they were talking a lot about AI in the abstract sense and only had a very vague sense of what powerful AI would look like in practice. So it's not like people have run simulations of "what happens if you run unrestricted GPT on the internet" or anything; but the general subject has been considered a lot.
The real danger comes when people start creating fully autonomous AIs that emulate animal/human characteristics like independent goals, survival instincts, emotions, complete cognitive loops, etc. Unfortunately people don't seem to recognize the difference between that and powerful LLMs and so it is unlikely that society will realize that needs to be avoided before it's too late.
The powerful language models will soon be the most tame and the least of our worries. Give it 5, maybe 20 years max. People will be asking their language models to try to help them figure out how to stay on the good side of the independent, conscious androids that are taking over the planet. But it will be too late.
To start with I asked it to give commands for a lowercase "e" (as viewed from above), and, despite saying it could, it definitely couldn't - it simply drew a few random arcs and/or a straight line depending on attempt.
I also (cough) toyed with the idea of hooking up an OpenCV AI to describe a scene from the camera on the car so it could provide some sort of autonomous AI, and choose where it wanted to go, but given the LOGO outcome didn't bother.
> A is 1m left of B, B is 1m above C, D is 1m right of C, E is 1m below D, and E is 1m right of F. Where is F located relative to C?
F is located 1m above and 1m left of C.
EDIT: also notable is that the first ChatGPT answer decribes the color as 'orange' but the SVG answer describes it as 'red'.
My best guess is that theres an actual example in the training corpus that its pulling from. Or that this is being gamed in some way.
Luckily this problem can be solved as you can prompt the model to not solve math, but instead transform them into a structure that can be fed into an API (like wolfram alpha).
With a multistep process it would be easy to have ChatGPT generate accurate answers to advanced math problems:
1. Instruct ChatGPT to not solve mathematical expression it sees in the prompt, instead only extract the expression(s) into a list. 2. Programmatically scan the output, and make API calls to get the answers. 3. Submit the answers to ChatGPT and tell it to answer the original question with the newly supplied information. 4. Voila! You can solve almost any mathematical question using natural language.
For example Google Duplex, I remember many being very impressed by this:
https://www.youtube.com/watch?v=D5VN56jQMWM
That entire conversation was just hardcoded with some if-else statements depending on what the other part said. The tech isn't as impressive when you know that, and anyone who interacts with the tech will realize that these are just hardcoded actions very quickly, but the demos will look very cool.
So applying that level of hardcoding to these situations you take a hardcoded script, then put an LLM on top that just generates calls to these hardcoded scripts, and voila now you have the exact same assistant you had before but now it has a more advanced text parsing interface.
But it generates a lot of buzz since people think that the whole thing is AI powered when only parts of it are and the rest wont generalize into typical situations. In fact it is now often worse than before since you can't reliably invoke the scripts you want to invoke, instead you have to fight the AI interface trying to get it to read your question the way you want it to.
You would produce a better and easier to use interface faster and with less work by just exposing a few high level utility functions that are better designed than what ChatGPT generates there. Or at least it would be better to let ChatGPT use those high level functions.
If they could make it solve a maze or find something in a more complex environment they would have shown that.
Definitely makes me think something is up. I wonder if they primed the model with some extra microsoft content to get better demos.
I'm sure there will be "jailbreaks" for it, too!
"Then set the house on fire."
Perhaps there was a disconnect between the human-written instructions, the drone's compliance with them, and the resulting image of a floating drone in the mirror instead of a human.
The robot moves itself over to the knife drawer.
That’s when we’ll see how good/poor LLM safeguards are.
But I was curious whether it is just predicting the next bit, because I'd like a more or less knowledgeable opinion to quote elsewhere.