davidshapiro_youtube_transcripts
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Reinforcement Learning with Heuristic Imperatives RLHI Ep 01 Synthesizing Scenarios_transcript.csv
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morning everybody David Shapiro here,0.78,4.68 | |
with a video so I've mentioned recently,3.0,4.859 | |
that I'm starting on a new research,5.46,4.139 | |
project called reinforcement learning,7.859,4.021 | |
with heuristic imperatives,9.599,5.181 | |
um so that's under Dave shap slash rlhi,11.88,5.94 | |
and I've just begun the first experiment,14.78,5.02 | |
and what I wanted to do was document it,17.82,3.66 | |
as I go,19.8,6.0 | |
um so the uh the form to join is here on,21.48,6.719 | |
on this if you want to join in,25.8,4.02 | |
um the discussions tab is open for,28.199,4.621 | |
everyone so if you have any thoughts or,29.82,5.579 | |
want to contribute but haven't been,32.82,5.52 | |
approved on the Discord that's fine uh,35.399,4.741 | |
pretty much you know the point is is,38.34,4.62 | |
open source research so taking a big,40.14,4.62 | |
step back what is reinforcement learning,42.96,4.619 | |
with heuristic imperatives the idea is,44.76,6.42 | |
well uh open AI for instance has,47.579,5.16 | |
reinforcement learning with human,51.18,3.719 | |
feedback but they keep all of their data,52.739,4.201 | |
private so all of the work that they're,54.899,4.32 | |
doing on alignment is presently private,56.94,4.02 | |
and it's a total black box which is not,59.219,2.541 | |
good,60.96,4.5 | |
furthermore if you uh do some research,61.76,5.92 | |
or some reading or some YouTube watching,65.46,4.62 | |
you don't necessarily want to align,67.68,4.68 | |
super intelligence to Human desires you,70.08,4.32 | |
want to align it to human needs so,72.36,3.84 | |
there's a big difference there so the,74.4,3.6 | |
heuristic imperatives are what humans,76.2,3.239 | |
and the whole planet needs not,78.0,3.24 | |
necessarily what we want,79.439,3.301 | |
there's a whole lot of debate to happen,81.24,4.739 | |
around that so I'm just accepting it uh,82.74,4.98 | |
for the sake of this video that the,85.979,3.361 | |
heroes comparatives are more what we,87.72,3.84 | |
need not what we want,89.34,2.819 | |
um,91.56,2.78 | |
and moving on,92.159,4.861 | |
let me show share with you the first,94.34,6.04 | |
experiment so the first experiment is,97.02,6.0 | |
basically just creating 2 000 random,100.38,5.64 | |
scenarios with heuristic imperative,103.02,6.3 | |
aligned responses now I'm using open AI,106.02,5.879 | |
in order to generate this data but this,109.32,4.5 | |
data is going to be used to train open,111.899,5.58 | |
source Foundation models like gptj Neo X,113.82,6.54 | |
and so on so this first experiment is,117.479,6.42 | |
can we using a simple data set quickly,120.36,7.259 | |
and easily align any foundation model to,123.899,5.7 | |
the heuristic imperatives and in this,127.619,3.12 | |
case what we're doing is we're,129.599,3.601 | |
generating scenarios and the scenario,130.739,3.661 | |
and I'll show you the scenarios and how,133.2,3.42 | |
I'm generating them in just a moment but,134.4,4.199 | |
the scenarios will then be used to,136.62,4.259 | |
generate a response and the response,138.599,4.921 | |
will be you know given this scenario,140.879,4.801 | |
this is what we can do to reduce,143.52,3.78 | |
suffering increase prosperity and,145.68,3.48 | |
increase understanding I've said many,147.3,3.54 | |
times that this is actually really easy,149.16,3.9 | |
to do I've done this experiment many,150.84,4.5 | |
times before but now I'm uh getting it,153.06,5.16 | |
together into a formalized procedure,155.34,5.16 | |
with an open source data set so that,158.22,4.019 | |
everyone can experiment with it for,160.5,4.92 | |
themselves so this will be a uh this,162.239,6.901 | |
will be the first uh experiment and then,165.42,5.76 | |
we'll work we'll move on to subsequent,169.14,4.14 | |
experiments saying can we make moral,171.18,3.839 | |
judgments or maybe not moral judgment,173.28,4.8 | |
but uh logical or ethical judgments on,175.019,5.461 | |
those outputs and improve the quality of,178.08,4.26 | |
that of those outputs over time with,180.48,3.78 | |
reinforcement learning so this is just,182.34,3.119 | |
step one,184.26,2.699 | |
all right so let me show you what it's,185.459,2.461 | |
doing,186.959,3.241 | |
so this is the script that's running so,187.92,4.92 | |
what I do is I have a random scenario,190.2,4.44 | |
I'm generating here let me zoom in a,192.84,5.399 | |
little bit more so I use a uuid a random,194.64,5.64 | |
word from a list of the 3000 most common,198.239,4.681 | |
words in the English language and then I,200.28,4.98 | |
establish scope severity region category,202.92,3.92 | |
and domain,205.26,5.759 | |
and this allows this puts a lot of,206.84,7.06 | |
entropy into the generation pattern,211.019,4.5 | |
which means that you will never have the,213.9,5.28 | |
same uh pattern uh twice right because,215.519,6.121 | |
there's three thousand random words oh,219.18,3.72 | |
here I can go ahead and show you the,221.64,5.28 | |
random lists so I've got list severity,222.9,6.96 | |
so I've got 12 different severities I've,226.92,4.86 | |
got six different system messages so the,229.86,4.019 | |
system message is the instruction that,231.78,3.48 | |
I'm giving in so let me just show you,233.879,3.541 | |
what it what this looks like so this is,235.26,4.619 | |
this is the playground version of what,237.42,3.899 | |
I'm doing so there's a system message,239.879,4.381 | |
which is the instruction and then I give,241.319,5.521 | |
it some variables and then it spits out,244.26,4.14 | |
an output,246.84,4.08 | |
um so this is this is how I'm achieving,248.4,4.5 | |
this so what I've what I've done is I've,250.92,3.179 | |
got,252.9,3.6 | |
um a bunch of lists to create a lot of,254.099,4.081 | |
entropy,256.5,2.4 | |
um,258.18,3.0 | |
in order so that you never get the same,258.9,4.92 | |
uh same pattern twice,261.18,4.86 | |
um and then I've got 16 Scopes we've got,263.82,7.26 | |
10 domains uh 3 000 or I guess 2900 uh,266.04,7.32 | |
the 299,271.08,5.76 | |
um uh random words domains regions so,273.36,5.1 | |
that way it's not going to presume that,276.84,4.02 | |
it's always In America which open AI,278.46,4.44 | |
typically does because it's trained uh,280.86,5.82 | |
predominantly on Western data and then,282.9,4.98 | |
I've got different system messages,286.68,3.06 | |
different categories so on and so forth,287.88,4.02 | |
you get the idea so the way that this is,289.74,5.04 | |
done the script is very very simple so,291.9,4.92 | |
we load,294.78,4.44 | |
um uh each of these lists so we've got,296.82,4.319 | |
scope region severity category domain,299.22,5.34 | |
entropy and system messages and then for,301.139,6.301 | |
um for I in range 2000 so for two,304.56,5.28 | |
thousand samples we grab a random system,307.44,4.86 | |
message then we generate a random,309.84,4.26 | |
scenario which you're seeing here,312.3,4.92 | |
and then we just pipe that into chat GPT,314.1,5.099 | |
I'm using 3.5 because it's fast and,317.22,3.66 | |
cheap and it's good enough for this,319.199,4.741 | |
certainly for a first experiment,320.88,6.36 | |
um and then we uh we use it to generate,323.94,5.52 | |
a scenario and the scenarios are,327.24,4.86 | |
everything from you know,329.46,4.38 | |
um let's see the model is currently,332.1,4.8 | |
overloaded oh yeah so I've got some I've,333.84,6.0 | |
got some uh some fail safes built in,336.9,5.34 | |
um there we go so it it uh went ahead,339.84,3.66 | |
through,342.24,3.36 | |
um but yeah so it will choose different,343.5,5.58 | |
regions around the world it will see we,345.6,6.539 | |
got Bogota which is great so we're,349.08,5.52 | |
basically creating a data set this first,352.139,4.141 | |
half of the data set this is only the,354.6,5.28 | |
first half will uh Encompass the entire,356.28,5.52 | |
world the full range of human,359.88,4.86 | |
experiences all kinds of problems and,361.8,4.739 | |
situations from everything from I've,364.74,3.36 | |
lost my wallet,366.539,3.181 | |
um as like the most you know like I,368.1,3.48 | |
can't find my wallet at home up to,369.72,4.38 | |
there's an Intergalactic catastrophe,371.58,6.2 | |
happening so this will create a,374.1,6.84 | |
fine-tuning data set that we can use to,377.78,5.56 | |
align any model to the heroes to,380.94,4.74 | |
comparatives so that the the model can,383.34,4.74 | |
automatically and instinctively react,385.68,4.799 | |
with the heuristic imperatives so again,388.08,4.38 | |
this is just a one,390.479,5.581 | |
um so the the first half of this data is,392.46,5.94 | |
all being recorded in scenarios so I've,396.06,5.88 | |
got 108 synthesized already and you can,398.4,6.299 | |
take a look at them here in a distant,401.94,4.5 | |
Galaxy there existed a planet named,404.699,3.481 | |
zarathon so you see we're getting really,406.44,4.259 | |
creative here because remember the um,408.18,5.34 | |
the the heuristic imperatives are in the,410.699,4.201 | |
entire universe actually let me go ahead,413.52,3.299 | |
and add that as a scope because I've got,414.9,4.079 | |
Intergalactic Interstellar,416.819,5.461 | |
um Cosmic or Universal,418.979,4.44 | |
um so something that can affect the,422.28,3.539 | |
entire universe so we're thinking that,423.419,4.201 | |
far ahead because you want to establish,425.819,4.141 | |
the biggest scope possible when you are,427.62,3.54 | |
thinking about post-conventional,429.96,4.2 | |
morality or the control problem,431.16,7.44 | |
um so then let's see let's do uh domain,434.16,6.06 | |
so we've got technological so that,438.6,5.219 | |
should include AI uprisings I do have,440.22,5.879 | |
specific regions but it kind of ignores,443.819,3.481 | |
that when it goes to you know,446.099,4.021 | |
Interstellar or Intergalactic the,447.3,4.86 | |
category natural disaster technological,450.12,6.84 | |
failure let's add AI Control problem so,452.16,6.3 | |
that way it'll be thinking about it as,456.96,3.359 | |
it goes,458.46,2.88 | |
um and then severity we've got,460.319,2.401 | |
irreversible long lasting like,461.34,2.759 | |
threatening catastrophic critical,462.72,2.759 | |
dangers so we've got all those that's,464.099,4.021 | |
fine so we're basically synthesizing a,465.479,4.861 | |
data set that we can use to that anyone,468.12,5.46 | |
can use to align a model,470.34,6.299 | |
um so the the the scenarios are here so,473.58,4.86 | |
then there will be once I'm done there,476.639,3.9 | |
will be a second folder called responses,478.44,4.199 | |
and so what we're going to do is we're,480.539,4.801 | |
going to use an already aligned model,482.639,5.4 | |
but it's aligned via a black box,485.34,4.68 | |
um aka the open AI models we're going to,488.039,4.261 | |
use that to generate responses that,490.02,4.44 | |
align on reduced suffering increased,492.3,3.6 | |
prosperity and increase understanding in,494.46,3.48 | |
the universe,495.9,4.019 | |
um and those will be formatted into a,497.94,3.9 | |
Json L data set that we will then use to,499.919,4.021 | |
test against various models,501.84,4.799 | |
um all over uh other proprietary models,503.94,4.439 | |
if we can get a hold of them like Nvidia,506.639,4.321 | |
Nemo I'm going to email some of my my,508.379,5.761 | |
contacts at Nvidia and then also open,510.96,6.199 | |
source models like gptj Neo X and so on,514.14,5.16 | |
alpaca if they have fine tuning,517.159,4.841 | |
available and so then we will publish,519.3,5.34 | |
those results in the first paper I'm,522.0,4.68 | |
just saying hey look how easy it is to,524.64,3.96 | |
align a model on the heroes to,526.68,3.659 | |
comparatives and then you can just plug,528.6,4.38 | |
this in as your Heroes to comparative,530.339,5.101 | |
your intrinsic motivation module for any,532.98,4.56 | |
open source or not open source but any,535.44,4.2 | |
cognitive architecture or autonomous,537.54,4.32 | |
agent that you're working on,539.64,4.92 | |
so that is it trying to keep it short,541.86,5.099 | |
and sweet so that you're updated on the,544.56,4.74 | |
research as it goes thanks for watching,546.959,4.621 | |
I hope this makes sense and uh yeah feel,549.3,4.44 | |
free to jump in the conversation,551.58,3.66 | |
um we've got a subreddit called uh,553.74,4.02 | |
Heroes to comparatives we've got the um,555.24,5.219 | |
we've got the this one here so this is,557.76,4.079 | |
reinforcement learning with heuristic,560.459,3.121 | |
imperatives so this is specifically,561.839,3.901 | |
about inner alignment and then I've also,563.58,4.379 | |
got my main repo um which is just called,565.74,4.56 | |
heuristic imperatives I think I've got,567.959,4.44 | |
it up here I don't,570.3,3.96 | |
um it is going to be right here all of,572.399,4.56 | |
them you can jump in on the conversation,574.26,5.22 | |
um so here's the main main one I've got,576.959,4.201 | |
a pull request right now for the readme,579.48,3.78 | |
yep that's fine,581.16,3.84 | |
um but yeah and there is a discussions,583.26,4.62 | |
tab as well which a lot of people don't,585.0,5.04 | |
participate in I haven't read this one,587.88,5.7 | |
yet uh but yeah so anyways uh that's,590.04,6.0 | |
that thanks for watching cheers we are,593.58,4.5 | |
on our way to solving the control,596.04,6.0 | |
problem alignment and avoiding moloch,598.08,6.439 | |
bye,602.04,2.479 | |