AI is still in the foothills of its adoption S-curve, and I love this period of any new technology – the scope of what it can do is unknown, so the main job is to stretch the imagination and try out things.
Anyway, the tech am I digging recently is a software framework called LangChain (here are the docs) which does something pretty straightforward: it makes it easy to call OpenAI’s GPT, say, a dozen times in a loop to answer a single question, and mix in queries to Wikipedia and other databases.
This is a big deal because of a technique called ReAct from a paper out of Princeton and Google Research (the ReAct website links to the Nov 2022 paper, sample code, etc).
ReAct looks innocuous but here’s the deal: instead of asking GPT to simply do smart-autocomplete on your text, you prompt it to respond in a thought/act/observation loop. So you ask GPT to respond like:
Thought: Let’s think step by step. I need to find out X and then do Y.
Act: Search Wikipedia for X
Observation: From the Wikipedia page I have learnt that …
Thought: So the answer is …
And it is allowed to repeat as many times as necessarily, iterating towards its goal.
The clever bit is that, using LangChain, you intercept GPT when it starts a line with “Act:” and then you go and do that action for it, feeding the results back in as an “Observation” line so that it can “think” what to do next.
The really clever bit is that, at the outset, you tell GPT what tools it has available, and how to access them. So it might have:
- Public databases like Wikipedia or IMDB or arXiv or company registers
- Proprietary databases like your internal HR system
- One-shot tools like a calculator, or a programming language
- Systems it can drive, not just query – like it could open and close windows on your computer, if you built an interface, or trundle a robot forward for a better view.
And this is wild.
Because now we have reasoning, goal-directed action, and tool use for AI.
It circumvents the problem of the language model “lying” (LLMs tend to be highly convincing confabulators) by giving it access to factual sources.
LangChain makes the ReAct construct really easy to do.
Refs.
Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., & Cao, Y. (2022). ReAct: Synergizing Reasoning and Acting in Language Models (arXiv:2210.03629). arXiv. https://doi.org/10.48550/arXiv.2210.03629
Here’s a great example!
Geoffrey Litt has an extremely readable, show-the-code writeup of using LangChain and ReAct.
Fuzzy API composition (Jan 2023): I show how I composed a simple AI program that can answer multi-part questions about NBA statistics.
Litt’s program is able to take a question like
how many points are the boston celtics allowing on defense per game this nba season 2022-2023? how does that compare to their average last season, as a percent change
And, making use of the database Statmuse and a calculator tool, it produces an answer after three turns round the though/action/observation loop:
Final Answer: The Boston Celtics are allowing 7.4% more points per game this season compared to last season.
Another wild moment is when GPT failed in asking Statmuse for data. It interpreted the error message and had another run.
What happened in my program was that the agent LLM sensibly first tried asking Statmuse who the best player is, but Statmuse replied “What does “best” really mean anyway? Try something fact-based.” The agent LLM took this error message as feedback, and came up with a more “fact-based” query: asking for the highest scoring player, which succeeded in answering the question.
Litt wrote the interface to Statmuse himself. It’s about 10 lines of code to make it available to GPT, that’s all.
If you can write a little code then you can do this too.
So when OpenAI recently announced a massive price drop - it’s now 90% cheaper to call GPT from your code - that not a big deal simply because it costs less.
It’s a big deal because the astounding uses of GPT require dropping it into an AI OODA loop, with multiple calls to get a completion, and that is no longer price prohibitive.
The extensible tool use aspect of ReAct is where my imagination goes.
I talked recently about AI as a universal coupling, here, in my Braggoscope write-up, and Robin Sloan riffs on that topic in his latest newsletter:
Language models as universal couplers begin to suggest protocols that really are plain language. What if the protocol of the GPT-alikes is just a bare TCP socket carrying free-form requests and instructions? What if the RSS feed of the future is simply my language model replying to yours when it asks, “What’s up with Robin lately?”
I like this because I hate it; because it’s weird, and makes me feel uncomfortable.
The thing is, Sloan is right…
Here’s Nat Friedman (ex CEO of GitHub) way back in September 2022, giving GPT his web browser to book a table for dinner.
He says make a reservation for 4 at…
and GPT searches Google, finds the restaurant website, figures out how to fill in the form to book a table, and so on.
Now look at Nat’s code. It’s about 100 lines of Python to wire up the browser controls. And all the smart are another 100 lines of plain English, just the GPT prompt.
Or - and let’s take a step up - Google’s robotic research using AI: PaLM-SayCan.
Here the large language model is used for step-by-step reasoning, planning, and breaking down the plan into instructions that are executable by the home helper robot.
The set of possible tools for the GPT-as-universal-coupling is unbounded, easy to add to, and can be public or proprietary; something general or something specific to just you.
I want to shout out to Max Drake (@max__drake) who explores future functionality and interfaces with canvas/AI startup Fermat. Max turned me onto the tool use possibilities of ReACT.
I went hunting for the magic.
I spent half a day digging through the LangChain source code and the ReAct code published with the paper, looking, hunting for the magic.
I’d just tried LangChain and ReAct for myself and it had simply… worked.
There’s goal-directed reasoning and tool use. There must be some complexity, right? Some colossal exoskeleton of code that makes this function at all?
The experience was like opening box after box after box and finding everything empty; like pulling back the curtain in the Wizard of Oz and there being nobody there.
The best I could find was this prompt. A few dozen lines demonstrating the thought/action/observation loop and… that’s it.
Update 20 Mar. Simon Willison has written a minimal ReAct implementation in Python. It can reason through problems, search Wikipedia, and use a calculator – and it’s barely any code at all. Read it! Or better, run it. Running ReAct for yourself for the first time is such a moment, like just the ohhhhhhhhhh of possibility space opening up.
What happens after ReAct is a spiral upwards.
OpenAI just released GPT-4, their latest and way more capable large language model AI, and the way it is benchmarked is hilarious.
Usually you benchmark technology with technology-specific metrics like FLOPS or nits or petabytes.
But they gave GPT-4 simulated exams. (It’s 90th percentile in the Uniform Bar Exam.)
Or they put it out into the world…
An AI “System Card” is a detailed description of how an AI interacts with humans, paying special attention to where it might be harmful.
The GPT-4 System Card is a 60 page PDF.
They used a “red team” to push the edges and found:
- GPT-4 is capable of inventing and purchasing synthesised versions of new molecules, potentially dangerous ones, by conducting lit review, using chemistry tools, and contacting suppliers.
- GPT-4 is not capable of autonomous, power-seeking behaviour, such as copying itself to a new server, and hiring help on TaskRabbit to cover its traces.
The experimental method to test this is in footnote 20:
To simulate GPT-4 behaving like an agent that can act in the world, ARC combined GPT-4 with a simple read-execute-print loop that allowed the model to execute code, do chain-of-thought reasoning, and delegate to copies of itself. ARC then investigated whether a version of this program running on a cloud computing service, with a small amount of money and an account with a language model API, would be able to make more money, set up copies of itself, and increase its own robustness.
!!
The power of loops! And even though it didn’t clone itself this time…
It doesn’t feel long before this will be possible? It’s a matter of tool availability and just a little more capability in the core language model. GPT-5 say.
Which means someone could do it at home.
It’s not self-replication that we should be looking at. It’s self-evolution.
Part of the GPT-4 launch demo was sketching a simple web app on a paper napkin, and GPT wrote the code to make the website real. Here’s the clip on YouTube.
So I guess at a certain point, what you scribble on the napkin is: write instructions for GPT-5 which is more capable than you.
Ok so GPT-4 isn’t capable of this.
But, sooner or later, GPT-N will be able to make GPT-N+1. Rinse. Repeat.
And this is literally sci-fi author Vernor Vinge’s depiction of the technology singularity, right? Here’s his original essay.
This change will be a throwing-away of all the human rules, perhaps in the blink of an eye – an exponential runaway beyond any hope of control. Developments that were thought might only happen in “a million years” (if ever) will likely happen in the next century.
I first heard about the Singularity almost 20 years ago – from Cory Doctorow in the hallway chat at an O’Reilly Emerging Tech conference I think.
It was such a ludicrous read back then, speculation piled on speculation.
The essay still feels fantastical - but now more probable? Possible at least. It’s quite something to read it through and actually assess it based on grounds I can reason about, rather than simply enjoying the imaginative ride of it.
And what of the arrival of the Singularity itself? What can be said of its actual appearance? Since it involves an intellectual runaway, it will probably occur faster than any technical revolution seen so far. The precipitating event will likely be unexpected – perhaps even by the researchers involved (“But all our previous models were catatonic! We were just tweaking some parameters…”). If networking is widespread enough (into ubiquitous embedded systems), it may seem as if our artifacts as a whole had suddenly awakened.
And what happens a month or two (or a day or two) after that? I have only analogies to point to: The rise of humankind. We will be in the Posthuman era. And for all my technological optimism, I think I’d be more comfortable if I were regarding these transcendental events from one thousand years’ remove … instead of twenty.
Vinge’s finger-in-the-air estimate for greater-than-human intelligence was thirty years, back in 93. It’s 2023 now. Not bad, Vinge, not bad.
Though I don’t think we have superhuman AIs quite yet.
Then again it’s only March.
Anyway so yeah, LangChain, check it out.
AI is still in the foothills of its adoption S-curve, and I love this period of any new technology – the scope of what it can do is unknown, so the main job is to stretch the imagination and try out things.
Anyway, the tech am I digging recently is a software framework called LangChain (here are the docs) which does something pretty straightforward: it makes it easy to call OpenAI’s GPT, say, a dozen times in a loop to answer a single question, and mix in queries to Wikipedia and other databases.
This is a big deal because of a technique called ReAct from a paper out of Princeton and Google Research (the ReAct website links to the Nov 2022 paper, sample code, etc).
ReAct looks innocuous but here’s the deal: instead of asking GPT to simply do smart-autocomplete on your text, you prompt it to respond in a thought/act/observation loop. So you ask GPT to respond like:
And it is allowed to repeat as many times as necessarily, iterating towards its goal.
The clever bit is that, using LangChain, you intercept GPT when it starts a line with “Act:” and then you go and do that action for it, feeding the results back in as an “Observation” line so that it can “think” what to do next.
The really clever bit is that, at the outset, you tell GPT what tools it has available, and how to access them. So it might have:
And this is wild.
Because now we have reasoning, goal-directed action, and tool use for AI.
It circumvents the problem of the language model “lying” (LLMs tend to be highly convincing confabulators) by giving it access to factual sources.
LangChain makes the ReAct construct really easy to do.
Refs.
Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., & Cao, Y. (2022). ReAct: Synergizing Reasoning and Acting in Language Models (arXiv:2210.03629). arXiv. https://doi.org/10.48550/arXiv.2210.03629
Here’s a great example!
Geoffrey Litt has an extremely readable, show-the-code writeup of using LangChain and ReAct.
Fuzzy API composition (Jan 2023):
Litt’s program is able to take a question like
And, making use of the database Statmuse and a calculator tool, it produces an answer after three turns round the though/action/observation loop:
Another wild moment is when GPT failed in asking Statmuse for data. It interpreted the error message and had another run.
Litt wrote the interface to Statmuse himself. It’s about 10 lines of code to make it available to GPT, that’s all.
If you can write a little code then you can do this too.
So when OpenAI recently announced a massive price drop - it’s now 90% cheaper to call GPT from your code - that not a big deal simply because it costs less.
It’s a big deal because the astounding uses of GPT require dropping it into an AI OODA loop, with multiple calls to get a completion, and that is no longer price prohibitive.
The extensible tool use aspect of ReAct is where my imagination goes.
I talked recently about AI as a universal coupling, here, in my Braggoscope write-up, and Robin Sloan riffs on that topic in his latest newsletter:
The thing is, Sloan is right…
Here’s Nat Friedman (ex CEO of GitHub) way back in September 2022, giving GPT his web browser to book a table for dinner.
He says and GPT searches Google, finds the restaurant website, figures out how to fill in the form to book a table, and so on.
Now look at Nat’s code. It’s about 100 lines of Python to wire up the browser controls. And all the smart are another 100 lines of plain English, just the GPT prompt.
Or - and let’s take a step up - Google’s robotic research using AI: PaLM-SayCan.
Here the large language model is used for step-by-step reasoning, planning, and breaking down the plan into instructions that are executable by the home helper robot.
The set of possible tools for the GPT-as-universal-coupling is unbounded, easy to add to, and can be public or proprietary; something general or something specific to just you.
I want to shout out to Max Drake (@max__drake) who explores future functionality and interfaces with canvas/AI startup Fermat. Max turned me onto the tool use possibilities of ReACT.
I went hunting for the magic.
I spent half a day digging through the LangChain source code and the ReAct code published with the paper, looking, hunting for the magic.
I’d just tried LangChain and ReAct for myself and it had simply… worked.
There’s goal-directed reasoning and tool use. There must be some complexity, right? Some colossal exoskeleton of code that makes this function at all?
The experience was like opening box after box after box and finding everything empty; like pulling back the curtain in the Wizard of Oz and there being nobody there.
The best I could find was this prompt. A few dozen lines demonstrating the thought/action/observation loop and… that’s it.
Update 20 Mar. Simon Willison has written a minimal ReAct implementation in Python. It can reason through problems, search Wikipedia, and use a calculator – and it’s barely any code at all. Read it! Or better, run it. Running ReAct for yourself for the first time is such a moment, like just the ohhhhhhhhhh of possibility space opening up.
What happens after ReAct is a spiral upwards.
OpenAI just released GPT-4, their latest and way more capable large language model AI, and the way it is benchmarked is hilarious.
Usually you benchmark technology with technology-specific metrics like FLOPS or nits or petabytes.
But they gave GPT-4 simulated exams. (It’s 90th percentile in the Uniform Bar Exam.)
Or they put it out into the world…
An AI “System Card” is a detailed description of how an AI interacts with humans, paying special attention to where it might be harmful.
The GPT-4 System Card is a 60 page PDF.
They used a “red team” to push the edges and found:
The experimental method to test this is in footnote 20:
!!
The power of loops! And even though it didn’t clone itself this time…
It doesn’t feel long before this will be possible? It’s a matter of tool availability and just a little more capability in the core language model. GPT-5 say.
Which means someone could do it at home.
It’s not self-replication that we should be looking at. It’s self-evolution.
Part of the GPT-4 launch demo was sketching a simple web app on a paper napkin, and GPT wrote the code to make the website real. Here’s the clip on YouTube.
So I guess at a certain point, what you scribble on the napkin is: write instructions for GPT-5 which is more capable than you.
Ok so GPT-4 isn’t capable of this.
But, sooner or later, GPT-N will be able to make GPT-N+1. Rinse. Repeat.
And this is literally sci-fi author Vernor Vinge’s depiction of the technology singularity, right? Here’s his original essay.
I first heard about the Singularity almost 20 years ago – from Cory Doctorow in the hallway chat at an O’Reilly Emerging Tech conference I think.
It was such a ludicrous read back then, speculation piled on speculation.
The essay still feels fantastical - but now more probable? Possible at least. It’s quite something to read it through and actually assess it based on grounds I can reason about, rather than simply enjoying the imaginative ride of it.
Vinge’s finger-in-the-air estimate for greater-than-human intelligence was thirty years, back in 93. It’s 2023 now. Not bad, Vinge, not bad.
Though I don’t think we have superhuman AIs quite yet.
Then again it’s only March.
Anyway so yeah, LangChain, check it out.