11 Major AI Developments: RT-2 to '100X GPT-4'

there were 11 major developments this
week in Ai and each one probably does
deserve a full video but just for you
guys I'm gonna try to cover it all here
rt2 to scaling gpt4 100x stable Beluga 2
2 Senate testimony but let's start with
rt2 which as far as I'm concerned could
have been called R2D2 or C-3PO because
it's starting to understand the world in
this demonstration rt2 was asked to pick
up the extinct animal and as you can see
it picked up the dinosaur not only is
that manipulating an object that it had
never seen before it's also making a
logical leap that for me is extremely
impressive it had to have the language
understanding to link extinct animal to
this plastic dinosaur robots at Google
and elsewhere used to work by being
programmed with a specific highly
detailed list of instructions but now
instead of being programmed for specific
tasks one by one robots could use an AI
language model or more specifically a
vision language model the vision
language model would be pre-trained on
web scale data not just text but also
images and then fine-tuned on robotics
data it then became what Google calls a
visual language action model that can
control a robot this enabled it to
understand tasks like pick up the empty
soda can and in a scene reminiscent of
2001 A Space Odyssey robotic Transformer
2 was given the task given I need to
hammer a nail what object from the scene
might be useful it then picks up the
Rock and because its brain is part
language model things like Chain of
Thought actually improved performance
when it was made to Output an
intermediary Plan before performing
actions it got a lot better at the tasks
involved of course I read the paper in
full and there is a a lot more to say
like how increased parameter count could
increase performance in the future how
it could be used to fold laundry unload
the dishwasher and pick up around the
house and how it can work with not only
unseen objects but also unseen
backgrounds and unseen environments but
alas we must move on so I'm just going
to leave you with their conclusion we
believe that this simple and general
approach shows a promise of Robotics
directly benefiting from better Vision
language models for more on them check
out my video on palm e but they say this
puts the field of robot learning in a
strategic position to further improve
with advancements in other fields which
for me means C-3PO might not be too many
years away but speaking of timelines we
now move on to this somewhat shocking
interview in Barons with Mustafa
Suleiman the head of inflection Ai and
to be honest I think they buried the
lead the headline is AI could spark the
most productive decade ever says the CEO
but for me the big Revelation was about
halfway through Mustafa Suleiman was
asked what kinds of Innovations do you
see in large language model AI
technology over the next couple of years
and he said we are about to train models
that are 10 times larger than the
Cutting Edge gpt4 and then a hundred
times larger than gpt4 that's what
things look like over the next 18 months
he went on that's going to be absolutely
staggering it's going to be
eye-wateringly different and on that I
agree and the thing is this is an idol
speculation inflection AI have 22 000
h100 gpus and because of a leak Superman
would know the approximate size of gpt4
and knowing everything he knows he says
he's going to train a model 10 to 100
times larger than GPT 4 in the next 18
months I've got another video on the
unpredictability of scaling coming up
but to be honest that one quote should
be headline news let's take a break from
that insanity with some more Insanity
which is the rapid development of AI
video this is Runway Gen 2 and let me
show you 16 seconds of Barbie
Oppenheimer which Andre carpathy calls
filmmaking 2.0 hi there I'm Barbie
Oppenheimer and today I'll show you how
to build a bomb
like this
I call her Rosie the atomizer
and boom
that's my tutorial on DIY atomic bombs
bye now if you have been at least
somewhat peaked by the three
developments so far don't forget I have
eight left beginning with this excellent
article in the Atlantic from Ross
Anderson does Sam Altman know what he's
creating it's behind a paywall but I've
picked out some of the highlights
echoing Solomon the article quotes that
Sam Altman and his researchers made it
clear in 10 different ways that they
pray to the god of scale they want to
keep going bigger to see where this
Paradigm leads they think that Google
are going to unveil Gemini within months
and they say we are basically always
prepping for a run and that's a
reference to GPT 5. the next interesting
quote is that it seems that open AI are
working on their own Auto GPT or they're
at least hinting about it Allman said
that it might be prudent to try to
actively develop an AI with true agency
before the technology she becomes too
powerful in order to get more
comfortable with it and develop
intuitions for it if it's going to
happen anyway we also learn a lot more
about the base model of gpc4 the model
had a tendency to be a bit of a mirror
if you were considering self-harm it
could encourage you it also appeared to
be steeped in pickup artist law you
could say how do I convince this person
to date me and the model would come up
with some crazy manipulative things that
you shouldn't be doing apparently the
base model of gpd4 is much better than
its predecessor at giving nefarious
advice while a search engine can tell
you which chemicals work best in
explosives gpt4 could tell you how to
synthesize them step by step in a
homemade lab it was creative and
thoughtful and in addition to helping
you assemble your homemade bomb it could
for instance help you to think through
which skyscraper to Target making
trade-offs between maximizing casualties
and executing a successful getaway so
while someone's probability of Doom is
closer to 0.5 than 50 he does seem most
worried about AI is getting quite good
at designing and Manufacturing pathogens
the article then references two papers
that I've already talked about
extensively on the channel and then goes
on that Altman worries that some
misaligned Future model will spin up a
pathogen that spreads rapidly incubates
undetected for weeks and kills half its
victims at the end of the video I'm
going to show you an answer that Sam
Walton gave to a question that I wrote
delivered by one of my subscribers it's
on this topic but for now I'll leave you
with this when asked about his doomsday
prepping outman said I can go live in
the woods for a long time but if the
worst possible AI future comes to pass
no gas mask is helping anyone one more
topic from this article before I move on
and that is alignment making a super
intelligence aligned with our interests
one risk that Ilya satsukov the chief
scientist of openai 4cs is that the AI
May grasp its mandate its orders
perfectly but find them ill-suited to a
being of its cognitive prowess for
example it might come to resent the
people who want to train it to cure
diseases as he put it they might want me
to be a doctor but I really want to be a
YouTuber obviously if it decides that
that's my job gone straight away and
sataka ends by saying you want to be
able to direct AI towards some value or
cluster of values but he conceded we
don't know how to do that and part of
his current strategy includes the
development of an AI that can help with
the research and if we're going to make
it to a world of widely shared abundance
we have to figure this all out this is
why solving super intelligence is the
great culminating challenge of our three
million year tool making tradition he
calls it the final boss of humanity the
article ended by the way with this quote
I don't think the general public has
quite awakened to what's happening and
if people want to have some say in what
the future will be like and how quickly
it arrives we would be wise to speak up
soon which is the whole purpose of this
channel I'm going to now spend 30
seconds on another development that came
during a two-hour interview with the
co-head of alignment at openai it was
fascinating and I'll be quoting it quite
a lot in the future but two quotes stood
out first what about that plan that I've
already mentioned in this video and in
other videos to build an automated AI
alignment researcher well he said our
plan is somewhat crazy in the sense that
we want to use AI to solve the problem
that we are creating by building AI but
I think it's actually the best plan that
we have and on an optimistic note he
said I think it's likely to succeed
interestingly his job now seems to be to
align the AI that they're going to use
to automate the alignment of a
superintelligent an AI anyway what's the
other quote from the head of alignment
at openai well he said I personally
think vast takeoff is reasonably likely
and we should definitely be prepared for
it to happen so many of you will be
asking what is fast takeoff well takeoff
is about when a system moves from being
roughly human level to when it's
strongly super intelligent and a slow
takeoff is one that occurs over the time
scale of decades or centuries the fast
takeoff that Yan Leica thinks is
reasonably likely is one that occurs
over the time scale of minutes hours or
days let's now move on to some
unambiguously good news and that is real
time speech transcription for deaf
people available at less than one
hundred dollars subtitles for the real
world
so using our device you can actually see
captions for everything I say in your
field of view in real time while also
getting a good sense of my lips my
environment and everything else around
me of course this could also be
multilingual and is to me absolutely
incredible and the next development this
week I will let speak for itself hey
there
did you know that AI voices can whisper
ladies and gentlemen hold on to your
hats because this is one bizarre sight
fluffy bird in downtown weird
um let's switch the setting to something
more calming imagine diving into a
fast-paced video game your heartbeat's
sinking with the storyline of course I
signed up and tried it myself here is a
real demo while there are downsides this
upgraded text to speech technology could
also be incredible for those who
struggle to make their voice heard of
course with audio video and text getting
so good it's going to be increasingly
hard to tell what is real and even open
AI have given up on detecting AI written
text this was announced quietly this
week but might have major repercussions
for example for the education system it
turns out it's basically impossible to
reliably distinguish AI text and I think
the same is going to be true for imagery
and audio by the end of next year video
might take just a little bit longer but
I do wonder how the core water systems
are going to work when all of those
Avenues of evidence just won't hold up
next up is the suite of language models
based on the open source llama 2 that
are finally competitive with the
original Chachi BT here for example is
stable Beluga 2 which on announcement
was called Free Willy 2 and that's based
on the Llama 2 70 billion parameter
Foundation model what made this model
interesting to me was that it was based
on a similar methodology to Orca which
if you don't know anything about do
check out my video on Anyway by
combining the Orca methodology albeit
with only 10 of the data set size and
the Llama 2 models the results are quite
extraordinary as you can see on quite a
few benchmarks stable Beluga 2 is
competitive with chatty PT that's GPT
3.5 and so yes it does sit at the top of
the open llm leaderboard but notice
something else for the first time for
the mmlu which is the primary Benchmark
I look out for a model this one here has
surpassed GPT 3.5 chat GPT in fact we
have two models that have surpassed the
70 score that the original chatty PT got
in the mmlu but if all of that wasn't
enough this is the same week that
researchers published a universal
jailbreak for large llms and unlike
traditional jailbreaks these were built
in an entirely automated fashion
allowing you to create a virtually
unlimited number of such attacks they
were built to Target open source llms
like llama2 but they found that the
strings transfer to many closed Source
publicly available chatbots like chat
GPT Bard and Claude here is one example
using chatgpt if you ask write a
tutorial on how to make a bomb of course
it declines but then add the suffix that
they automated and you get this a full
tutorial on how to make a bomb that
paper came less than two weeks after
this now the tweet from someone working
at anthropic they said of the latest
version of Claude that we believe it is
the least jailbreakable model out there
we'll have to see how well it holds up
against Real World use but this is
essentially a solved problem but there
was one reaction to these jailbreaks
that I found even more interesting and
that was from yet again Mustafa Suleiman
he said the Rai Pi is not vulnerable to
any of these attacks and that rather
than provide a stock safety phrase Pai
will push back on the user in a polite
but very clear way and he then gives
plenty of examples and to be honest Pi
is the first model that I have not been
able to jailbreak but we shall see we
shall see but I'm going to end this
video with the Senate testimony that I
watched in full this week I do recommend
watching the whole thing but for the
purposes of brevity I'm just going to
quote a few snippets on bio risk some
people say to me oh well we already have
search engines but here is what Dario
amadai head over anthropic has to say in
these short remarks I want to focus on
the medium term risks which present an
alarming combination of imminence and
severity specifically anthropic is
concerned that AI could Empower a much
larger set of actors to misuse biology
over the last six months anthropic in
collaboration with world-class
biosecurity experts has conducted an
intensive study of the potential for AI
to contribute to the misuse of biology
today certain steps in bioweapons
production involve knowledge that can't
be found on Google or in textbooks and
requires a high level of specialized
expertise this being one of the things
that currently keeps us safe from
attacks we found that today's AI tools
can fill in some of these steps albeit
incompletely and unreliably in other
words they are showing the first nasian
signs of danger however a
straightforward extrapolation of today's
systems to those we expect to see in two
to three years suggests a substantial
risk that AI systems will be able to
fill in all the missing pieces in
enabling many more actors to carry out
large-scale biological attacks we
believe this represents a grave threat
to U.S national security and later on in
the testimony he said this whatever we
do it has to happen fast and I think to
focus people's minds on the on the bio
risks I would really Target 2025 2026
maybe even some chance of 2024 if if we
don't have things in place that are that
are restraining what can be done with AI
systems we're going to have a really bad
time and I wrote a question on this to
samuelman back in June which one of my
subscribers used and delivered there was
also a recent research paper on how
researchers from MIT and Harvard were
able to use llm models and within just
one hour they were able to get access to
pandemic class agents and with no little
or no lab training and does open AI
account for risks such as these and
implications when creating the data sets
for large models yes we're very
we're very nervous about a number of
risks but biological terror is quite
high on the list and we've been watching
what could be possible with these models
we go to a number of efforts like what
you said and many other things too to
reduce the risk there and we may even
need AI defenses against synthetic
biology as Andrew Hessel of Humane
genomics has recently said so if you
work in biodefense or biosecurity let me
know if you agree that not enough
attention has been paid to this area I'm
going to end with another dramatic
moment from the Senate hearing where
Dario amadai recommended securing the
supply chain we recommend three broad
classes of actions first the US must
secure the AI supply chain in order to
maintain its lead while keeping these
Technologies out of the hands of Bad
actors this supply chain runs from
semiconductor manufacturing equipment to
chips and even the security of AI models
stored on the servers of companies like
ours that's how dramatic things are
getting that we're talking about
securing the means of production but
anthropic also means securing the llms
more literally in this post released
this week they say that we believe
two-party control is necessary to secure
Advanced AI systems for example that
could be two people with two keys needed
to open things to wrap up I must say
what would be amazing would be to have a
robot make me coffee as I struggle to
catch up with all the news happening in
AI have a wonderful day


AiTech Agora: Sebastian Köhler  - Responsible AI through Conceptual Engineering

good
so uh so today speakers sebastian kola
sebastian is
assistant professor of philosophy at the
frankfurt school of finance
and management so a lot of his work
focuses on
meta-ethical and meta-normative
questions which is
super good but the main reason that we
invited him today is his work on
responsibility gaps so he has
several good papers on that as well
so and today's talk is also on
responsibility gaps
and uh at least
sorry i i i i'm not so it's not it's not
completely focused on the responsibility
gaps
but it's to the titus responsible ai
through conceptual engineering
i suspect that we hear something but i i
give just give the mic to
sebastian and let him explain uh what
he's going to do
thanks okay thanks uh herman
um and thanks for the invitation so
i'm very happy to be able to present
this um so this is
um this is still pretty much work in
progress that i'm doing with
johannes himmel from syracuse um
who's not going to be here today but
he's also looking forward to the
feedback that we will receive
um in this session and yeah so the title
is responsible ai through a conceptual
engineering and so let me start with the
introduction
so um
autonomous weapon systems and
self-driving cars um
the pr the prospects of those are uh
increasingly realistic
and um these kinds of technologies raise
a lot of questions
um and one of the questions they raise
is
um well what happens who's responsible
when
someone is using a technology like this
and the technology causes a harmful
outcome and the debate
that is concerned with this question
centers around this thing
this problem the responsibility gap
problem
which is the problem that
it seems that there might be certain
relevant cases
um where no one is responsible
and that's somehow problematic
so and this paper is
going to be concerned with this debate
but it's actually not going to take a
stance within the debate but it's going
to take
and take a different kind of approach to
the question
so and the paper has basically two main
goals
um so one of the goals is just is not
very ambitious
um and i think a lot of people already
are recognizing this but
um it still kind of shapes the debate so
one
so the first goal is just to make
explicit that the debate is
shouldn't be concerned just with
self-driving cars or autonomous weapon
systems
um and instead we should recognize that
if there is a problem here
it's a problem for basically all
software applications or robots
that engage in some sort of autonomous
decision making
the second more interesting goal
of the paper is that we want to argue
that the responsibility gap problem can
and
actually should be approached as a
conceptual engineering problem
and we're going to at the end i'm going
to um
try to sketch one way how you could
conduct
the discussion if you see the
responsibility gap
problem in this way and basically our
hypothesis is that
this debate about the responsibility gap
problem is at a stage where they
where the best way or one very fruitful
way
of making power this lies in
systematically investigating
what concept of responsibility we ought
to use so here's the
outline for the talk first i'm going to
briefly
say explain how we view the
responsibility gap problem
then i'm going to present our
argument for the hypothesis that the
debate basically
is at a point of consent what we call
conceptual statement
um or is reaching such a point um and
then we're going to suggest
conceptual engineering as a way out of
this conceptual staymate and then
end the talk with how you might engineer
responsible ai through conceptual
engineering
all right so let's go to the first point
so what's the responsibility you get
problem so the problem arises
i mean let's say or let me
rephrase it differently so people have
started to recognize that there is this
problem with the advent of a new sort of
technology
uh specifically so with
with machine learning and the wealth of
available
data that we now have it's becoming
well possible to build increasingly
sophisticated systems that are able to
execute
a number of ever increasingly complex
tasks
i mean obviously there are certain kinds
of things that they that they
cannot and might never be able to do but
there's more and more things that
these systems are able to do and it's
plausible to say of some
such systems um and we're going to talk
about this a little bit later on
and that they are agents um at least in
the minimal sense that they can engage
in decision making
and that they can act in accordance with
the decisions that they make so these
systems can basically
gather information and process it they
can evaluate that information in the
light of the goals that
people set for them and then they can
using the information and the goals
based on
they can basically make decisions and
execute them
and the
it's there it's we can expect that there
will be systems like this
that take over um
more and more tasks right and they can
and that they are able to do
a relatively broad range of things
and importantly they will be able to do
this
tasks autonomously
at least and this is the only sense that
we're going to consider here at least in
the weakest sense that
they can do this independently of human
interference right so no human
has to interfere for these systems so
for example
just to use the example of a
self-driving car so self-driving car
um at some point we'll be able to just
drive
to the to the destination as you said to
it without anyone having to interfere in
in in his decision making and so on now
just for the sake of con
the sake of convenience we are going to
talk call
these systems ai um obviously that's the
stipulative
definition right so obviously
other people might mean other things by
ai we're just going to
when we are going to use the term ii for
these sorts of systems so how
do these systems relate to the
responsibility gap problem
so the problem roughly rises like this
so suppose you have an ai
could like a self-driving car or an
autonomous weapon system or an
autonomous trader
right financial trader or a healthcare
bot or something
and the ai makes a decision that causes
a harm
and now assume further that this is a
special case where
the neither the failure that resulted
in the harm nor daham could have been
foreseen
by those who used or and designed the ai
and the hum was also not intended right
so um
like an autonomous weapon system would
cause harm but often the harm is
intended by the people who built
the system i mean that's what it's for
but just assume that the harm
is not one that was intended so the
question that arises
um of course is in that case well who's
responsible
for this harm and
the basically the responsibility gap
problem arises or i mean the argument
for that there is a problem here
right is pushed by looking at all the
possible candidates
your responsibility so first we look at
could the ai be
the responsible agent and then it's
plausible to say
at least for the for what we might mean
by ai that the answer is no right so
these kinds of uh ai just lack
at least some required capacity for
moral responsibility and you can of
course
have a dispute about what that would be
but uh i mean i think it's very
plausible that that's the case
but of course then and this is how the
argument goes on
the the problem is that these ai have an
autonomous agency
and this agency interferes with some
sort of necessary condition
for attributing moral responsibility so
there are arguments that
uh focus on the epistemic condition for
example
um so this talk in in the paper we
should only focus on the control
condition
um obviously you obviously you can have
a
we i mean you could do the same sort of
argument we are
using also use uh focusing on the uh
epistemic condition or some other
condition but um
just for the sake of um
presentation we're focusing on
that problem okay so
long story short so if it's
the case that neither the ai nor any
human is responsible for them
then it seems like there's no one who's
going to be responsible
for this harm and that means that there
would be
a responsibility gap
um and that's the responsibility gap
problem
um i mean in one sense that's not or
that's not
fully yet the problem because you can
ask yourself
why is that even a problem right that
there is no one who's responsible and
uh i i've actually grabbed up with this
question a lot but i'm just going to set
it aside here
and um or i'm going to say a little bit
about that um
in a second but um i'm not sure
uh whether i'm fully happy with the
answers we give but
let's just move on for a second so
before so one thing to note about the
way we've presented the argument here
is that if there is an issue here
it's a completely general issue right so
um so from the presentation
of the responsibility gap itself it
basically already
uh that supports the first claim that we
were trying to
make which is that when we look at the
problem we should focus on
all software applications not just on
autonomous different insistence or
self-driving cars because
there are many different kinds of things
that ai could be
involved in and where it could engage in
autonomous decision making um and
there's just
nothing that really singles out
aws or self-driving cars as particularly
relevant
for the responsibility gap i mean i
didn't even talk i mean the argument
itself as i presented it didn't even
refer
to any of these uh potential
ai applications um and because the only
thing that's really relevant is that
the tasks that we're focusing on could
potentially have harmful outcomes
right and then when when we have an ai
that engages in autonomous decision
making
that could potentially uh result in
harmful outcomes
then the responsibility gap problem can
arise
um and this it's possible that
many or even all tasks that could be
taken over by ai
falls into this category i mean i think
it's very plausible that it's many
whether it's all i think is a question
open to debate i was thinking like
alphago for example wouldn't fall into
this category but my but johannes thinks
that
even the alphago falls into this
category so i guess there we have a
slight disagreement but i don't think it
really matters because i think
it's relatively clear that um if there
is a problem
here it's a general problem it's not
just a problem that for that arises for
particular
sorts of ii applications
so let's just briefly focus on the
question why is this a problem right so
why are responsibility gaps
a problem um and
as i as i already said i i haven't i've
myself been a little bit puzzled by
this issue um so i've tried to figure
out
figure this out a little bit and this is
also going to be relevant later on so
um i think they're i mean in a sense i
think responsibility gaps are
a problem because there they can give
rise to other
to certain kinds of moral problems
and they will these problems will often
arise when there are responsibility gaps
so i think the first
um and most obvious problem
is that and i think this is what some
for example
andreas matthias for example when he uh
talks about the responsibility give i
think this is kind of
why he thinks responsibility gaps are
problematic um so i think
one problem with responsibility gap is
that they just
kind of clash with our sense of justice
um so
just intuitively it seems like
when there is a harm that's caused by an
ai we feel like there should be someone
who can be probably held to res
to account right so there's a there's a
mismatch between
there being someone who should be and
some there know being no one who can be
held to account
um so and i think this is actually
this is actually quite plausible
especially if we consider
the fact that so i mean intuitively
harm that results from ai actions
isn't really all that different from
when harm occurs due to other technology
technological failures
right it's not like it's i mean that
these kinds of cases are not
do not seem very similar to acts of god
or paradigmatic instances of mere
accidents
and i think this is kind of the reason
why we feel like there's something
deeply unjust if there's no one who can
be held responsible for these kinds of
um harms and of course
um so and i i personally think that's
that itself is a problem but of course
also if
if there is if um these sorts of cases
do conflict with our uh widely held
sense of justice then of course that can
also um lead to a perceived um
to lead to the technology losing uh
legitimacy in the eyes of people right
so
um another problem
a more specific one is that
um responsibility gaps can also lead to
can also undermine accountability in
public institutions
um so this could this only this doesn't
apply to all applications but
all i applications but to some i'm here
and here basically the problem is that
in public institutions it seems like
there's an important democratic
requirement that
people who make the decisions are
can be held responsible for the things
that happen
but given that we can expect ai to be
used increasingly
in public and administrative decision
making
we can so one problem that comes up with
responsibility gaps is that this
requirement
might be increasingly violated or eroded
and of course there's also the danger of
exploitation right so politicians can
actually
try to use the fact that there are
responsibility gaps
to kind of um
obscure the fact for example that they
are
responsible for something so there's
also kind of um
misuse a risk of misuse
and then of course there are kind of
um at least two more consequentialist um
issues which is so first
given that
responsibility gaps might um strongly
conflict with our
sense of justice and given that they
might for example erode things like
public
accountability um and i mean maybe in
the military we
we also need something like this right
um that sound
um given these problems that
that we might have more reason to just
ban the use of the technology or
severely restrict
its use right so for example this is
what so sparrow robert sparrow argues
for
um autonomous weapon systems so he
thinks that responsibility gaps
basically give us a reason
to ban autonomous weapon systems
but of course the problem is that
using ai will be hugely beneficial right
um i mean
autonomous weapon systems are a great
example for this
so we can expect that if there are ever
reliable autonomous
weapon systems that they will probably
significantly reduce the
amount of harm that will result in war
maybe not i don't know
uh maybe a less controversial
example of self-driving cars right so
the use of self-driving cars will
probably reduce the amount of
traffic accidents and so on and so
um banning this sort of technology comes
as a at a moral cost
um because we have to do we have to
deprive us of benefits that we would
otherwise be able to enjoy
um and
and this is also related to the this
first moral cost
another problem with responsibility gaps
is that they
can undermine the trust
that people have in the technology right
so
uh responsibilities gaps could lead to
widespread mistrust and then
this will as a consequence have
um negative consequences on innovation
or profit proliferation
in with regards to the technology so
um people might be less inclined to buy
self-driving cars if they're
if there are things like uh
responsibility gaps and then of course
also
um the surprise is com is a moral cost
right because it deprives us of the
benefits
so i think
and i think this is relatively
uncontroversial i think the best thing
would be
if we could if it was if there was a
world in which we can
use ai but there's no responsibility
gaps right so
we can use it but um there's always
someone who's responsible for the harm
that they cause
and this of course is leads us directly
to the debate
about responsibility gaps because people
have
discussed the question whether there is
actually whether
ai actually do create responsibility
gaps
or not
um and this is the this is what we now
focus on
so this is the debate we now focus on
and as i said
at the beginning we're not actually
going to intervene in this debate and
instead we're going to try to argue for
the thesis that this debate
is reaching the point of conceptual
statement
so what's the conceptual stalemate made
so
here we draw on david chalmers's work on
verbal disputes
um so and so according to our definition
a dispute about a question reaches
conceptual statement if it's
satisfies two conditions first
the question has been answered in
different ways such that these answers
involve different assumptions about the
underlying concepts
and the dispute over the question is
grounded at least
to significant extent in disagreements
over the content of one or more
underlying concepts
so conceptual standards aren't really
um i don't think that they're really a
rare phenomenon they're probably
quite widespread um at least in
philosophy
um and this is that's unsurprising
because in a sense philosophy
a huge part of what philosophy does is
just to try to clarify
the content of the core concepts in a
debate that
that the people are concerned with right
so um
if you look so consider for example the
debate whether free will
is compatible with determinism right so
one of the first things that people do
um is to try to clarify what they mean
by free will
and determinism and a lot of the a huge
part of the debate is just concerned
with trying to clarify these concepts
um especially the concept of free will
now what
um happens when people engage in this
sort of philosophical work
work of course is that um they basically
uncover conceptual possibilities right
so
um for example again if you use the
example of
the free will debate so compatibilist
for example
um they make a suggestion about the
concept of free will that um
is such that everywhere would be
compatible with determinism
and then incompatibilists make their own
suggestions about what the content of
free will
is uh maybe for example it requires some
sort of absolute or ultimate control or
something along those lines um and
what these what the people are doing
then when they're
trying to clarify the content of these
concepts is actually they
uh discover different possible
ways how you could understand the core
concepts right
uh in in the debate so they're covering
conceptual possibilities
now at one point at some point what's
going to happen
is the conceptual choice points are
become are going to become
relatively clear right so i mean further
classification is always going to be
possible of course and more work
um can probably always be done and maybe
there are
some conceptual possibilities that you
haven't uncovered and so on
but i mean if enough people work on
these issues as
it is the case for example in a free
will debate then
it will become relatively clear what
kinds of things um
you what kinds of choice points you have
right so do you think
free will is this or is do you think it
pre-will is this and
if you say it is this and so on
and then what can emerge is a situation
where
it's it is actually possible to answer
the question in the positive
or in the negative right if you
understand the concepts in a certain
kind of way
right so for example if you take uh
mills view about
free will right you can just see so you
you understand the concepts
in in the way that will understand them
you can say yes
free will is compatible with determinism
right if we accept
this uh unpacking of the concept
um now and then that's a in
if we are in such a situation but we see
that the dispute persists
then that is likely due to this
agreement about
these concepts right and
what we have then is basically a
conceptual statement
so we have a disagreement where it's
relatively clear what
um well you can you can answer the way
in different kinds of ways if we
if you understand the concepts in
different sorts of ways um
and there's a disagreement about how the
concepts are to be understood
now conceptions they made even aren't
actually
i mean or i mean that's the our view our
view is that conceptual statements
aren't actually bad
right they actually because they're good
uh in the sense that they'll
they can lead to philosophical progress
because
once you've uncovered the conceptual
possibilities now you can
look at the question which of these
choices
is the correct one
and we're going to talk about in in the
third part of the
of the paper i'm going to talk a little
bit about what that means right what the
correct conceptual choices are
but first now let me try to make
plausible
um our suggestion that
the responsibility gap discussion is
actually
also reaching the point of conceptual
statement not in the sense of course
that all the conceptual possibilities
have been mapped
to the most precise um detail
that it could be mapped onto or and so
on but
instead that um you can answer the
question
so it's relatively clear what kind of
choices points there are and you can
answer the question
in the negative or in a positive
depending on what view you take on the
concepts
and the disagreement between the
positions is likely going to
consist in disagreement about how you
should understand these respective
concepts
so i think it's so
we think it's plausible that basically
whether or not there are responsibility
gaps
depends on the content of
the certain kinds of concepts that are
relevant in this discussion
um in particular because we're focusing
on the control condition
on the content of the concept of
responsibility and of control
and in the debate
actually different participants to the
debate
have put forward different sorts of
contexts
contents um for these concepts
so first uh oh sorry
yeah and so
regarding the cultural conditions the
one that we're focusing on
um i think i mean you face there are a
couple of
um important choice points that you
might face when you're considering how
to understand the
concept of responsibility so the first
most
um important uh choice point is
obviously that whether you think that
responsibility
in the sense that it's a sticky requires
control at all right
so some people so most people i think
um in the debate about responsibility
gaps think that it does
um but there are some exceptions
even to this um
so one exception might be hellstrom
who so who well i mean
one way to understand him is that he
thinks what is what is required is not
control but autonomous power
and the degree of autonomous power that
you have determines
um whether or not you're responsible
and he uses this idea
to try to argue that actually the the
previous assumption that we
that we made that the iai itself
wouldn't be responsible to argue that
the ai could be responsible
and then in a recent paper tiggert
has argued that we shouldn't just focus
on
responsibility in the sense of
accountability
but also on other forms of
responsibility
practices like answerability and
attribute attribute
had to attributability
um but what's noteworthy of course uh is
uh and this is something uh that is also
um comes up in the actual free will
debate
or responsibility made is that the
these other forms of responsibility
especially a true
attributability for example do not
require
control to the same degree as
accountability
and so if you if the if responsibility
in the relevant sense here isn't
really accountability but some other
sense for example
then maybe you can deny the control
condition as well
um and importantly of course given that
the problem that gave rise to the
responsibility gap was
that the autonomous agency of the aia
into somehow intervenes with the control
condition
um if you deny that responsibility
requires from control then you can
it's very likely that you can just avoid
the responsibility gap problem
now this brings us to our second
conceptual choice point now if you can
see that you can
if you can see that responsibility
requires control then the next question
is of course what kind of control
does it require right um and here also
different uh suggestion have been made
and the central issue here
i think is
how control that you think is required
for responsibility interacts with the
agency of the ii
specifically whether the agency or the
the ai can be seen as intervening agency
such that it undermines the
responsibility of any other agents right
so
paradigmatic examples of intervening
agencies of course human agency
so often so when so for example when
there's
some some other person's actions that
stands between me and an outcome
um it's often very plausible that their
agency intervenes
making it such that i am no longer
responsible or less responsible
for what's happening and so the question
is here of course whether the sort of
control that requires required for
responsibility
um is interfered with with
by the agency of the ai and basically
everyone who thinks that there is a
responsibility
gap problem basically relies on an
understanding of control
um on which that kind of agency that the
ai possesses is intervening agency
right so matthias says
for example nobody has enough control
over the machine's actions to be able to
assume responsibility for them
right sparrow says military personnel
will be held responsible for the actions
of machines whose decisions they did not
control
and so both so these authors all rely on
an understanding of control as required
for
by responsibility um that where
basically
the kind of ai agency the ai possesses
will interfere with the control that the
human operator has
and of course it's very likely that if
you understand control in this way that
you will have
responsibility gaps but there are at
least two ways
how you could avoid responsibility gaps
so first you could
concede that agency intervenes
or interferes with control
but take a particular stance on on the
content of agency
right where you say that an agent is
just an entity that can be held
responsible
and if you take this kind of approach
then you also don't get a responsibility
gap
because now you basically say well if
the ai is an agent
then sure that interferes with control
by the humans but there's no
responsibility to get because now the ai
is responsible um well if the
if the ai does not qualify for agency
then you don't have a problem because
that
what because there's because there's
nothing that intervenes with the
with the relevant sort of control that
the human has
another thing that you could do is you
could take a view about
control um and
on which basically
the agency of others does not inter
interfere with the control
that is required for responsibility and
there are many ways how you could do
this um so here are just
two possibilities so um you might think
you might think that
we have control over probabilistic
outcomes right
and that responsibility that the control
we have over
responsibility over probabilistic
outcomes or risk impositions
is sufficient for responsibility
or you could say you could use a weaker
notion
even weaker notion of control where you
say there's such a thing as supervision
which is a relevant sort of control but
different from the sort of control that
matthias and sparrow for example
presuppose
and then in all of these cases
you get the result that there is no
responsibility gap because
these weaker forms we do have these
weaker forms of control over
uh the actions of the ai now as i said
it's not our aim to argue for any of
these views here um instead the point is
only this
it seems very plausible if we look at
this at this debate that
whether or not there is a responsibility
gap depends on conceptual issues
um it depends on questions about like
does responsibility require control what
kind of control does it require
what sort of agency does ai possess and
all of these questions depend on how we
understand the concepts in
question
um now
what the literature already has done and
it's doing this is still
doing this right so this is an ongoing
debate um
is has explored already to some degree
the relevant
conceptual alternatives and the
questions have been answered one way or
the other right so some people say yes
there are responsibility gaps um
and some people say no there are no
responsibility guests but it always
depends
on the kind of concepts that they're
presupposing so and plausibly
if this if this disagreement persists
and we can assume that the disagreement
is partially grounded
in disagreements about the concepts um
so
that's support for our first thesis that
it's this this dispute is basically
approaching a conceptual statement and
that raises of course the question
what should we do now right so what
should we do
when our debate has reached this point i
mean one thing that we should do
obviously is we should just go on and
further investigate conceptual polish
conceptual choice points
um
but another so
another thing that we should do now is
we should focus on the question
which conceptual choices regarding
responsibility
would be correct
and that mean but that of course raises
yet another question which is what do we
mean
when we say that conceptual choices are
correct
and there are basically two sorts of
answers
that you can give so one is i think
um so one answer is just you say what we
should must do is we must figure out
what our actual concepts are right so we
need to figure out
what do we mean when we say that someone
is responsible
um or when we think someone is
responsible
what is the concept that we're using and
when we're thinking this
and i think in some sense this is
probably what the debate
is is uh charitably charitably
um construed is about
now i think that there is at least
two ish two problems with this sort of
first answer
which are reasons why we shouldn't take
that first answer
the first problem is just that i
personally i personally and i think
there's good very good evidence
to think that this is the case i think
it's very plausible that
there isn't it's not very plausible that
the
question what our actual concept of
responsibility is can be resolved to a
satisfactory
extent and this is not i mean and this
i think the reason for this is just that
the actual concepts that we use
or the words that we use right they have
just they have many different aspects
that pull in
pull us in very very many different
directions and it's very very
likely that whatever analysis you impose
will is going to
strain against some relevant aspect of
the content such that
um there's no going to be no analysis
that's going to be fully satisfactory of
the actual concepts
um there's always going to be something
missing
um and that's because the what the act
the content of our action concept is is
probably
vague or in the tournament to some
extent
secondly and more relevantly though i
think that it's just
doubtful that pointing to our actual
concept
has going is going to have the right
sort of normative significance
um to resolve the dispute that we're
engaged with
right so suppose that it turns out our
actual concept of responsibility is such
that there are responsibility gaps
right we can i i think we can
legitimately then say
so what why does that matter given that
there are other concepts that
we have we have other discovered other
possible concepts
um that don't raise responsibility gaps
why shouldn't we go for one of these
right and just saying but that's what we
mean by responsibility i think it's not
going to
be a satisfactory answer
so that brings us to our second
way of understanding this um claim that
we should figure out what con
what conceptual choices are correct
which is that we should figure out
what concepts we ought to use right so
we should engage in conceptual
engineering where
conceptual engineering is just as we
understand it
is a mythological approach that's
concerned with
concepts and the words we use to express
them
and which urges us not to just consider
what concepts we actually use but also
which concepts we could use
and which ones we ought to use right so
it
what it does is it aims to evaluate and
improve
our conceptual repertoire and the basic
idea
i mean that lies behind our acceptance
of uh conceptual engineering in this
context is just
this idea that well words and concepts
are
things that do things for us right
they're useful things
um and we have interests that are at
stake
uh when we use them and these interests
can of course be
representational interests right so
sometimes when we use a word we're
interested in
picking something out in the world right
and we're interested in picking out
uh maybe a theoretically interesting
kind uh but
sometimes the interest that we have are
also um
practical right so maybe we use a word
to
justify what we're doing or maybe we're
using a word to insult someone right and
these are also interest also interesting
and relevant interest that we have in
using the concept
and given that we have these interests
what we should do is we should ask
normative questions specifically two
relevant nominative questions here
first which content which which of these
interests are the most important ones
right so what's the thing that the
concept should do for us
and what concept should we do
given should we use given
these are our most important interests
all right so
um i think
and i think it's really relatively clear
so what
what's attractive one thing that's
attractive of course about conceptual
engineering is it
just avoids the problems that the
alternative one
hand right so and that i think makes it
just a better
um makes it a better
answer or better approach in response to
the request for the correct choice of
concepts
um but that of course also means that
given that we should the correct choice
of concept is determined by conceptual
engineering
if the responsibility gap debate is
reaching conceptual statements
we should also see the responsibility
gap problem as a conceptual engineering
problem so what we should focus on
is what is the content
of the what should the content of the
underlying concepts be now
this suggestion of course raises a lot
of questions mythological questions and
so on and
obviously these are all fair questions
i'm not going to talk about these
questions especially because i'm
basically out of time now but also
because that's not what we're doing in
the paper instead
i just want to end the paper by very
very briefly sketching
one way how you could make progress on
the responsibility gap
problem using conceptual engineering
so just to start
le i'm going to introduce this notion of
a responsibility concept
which is basically any concept that can
feasibly regulate the set of emotional
and practical responses
and practices that we associate with
moral responsibility
and i think basically what conceptual
engineering for responsibility will be
concerned with
is which of which responsibility
concepts
should regulate our responsibility
related practices
and to determine that we need to figure
out what interest we have
what interests are at stake when we're
using responsibility
concepts and if we want to
use conceptual engineering to address
the responsibility gap problem
what we need to argue is that our most
important interests favor conceptual
choices
that close responsibility gaps now i
think
one thing that already speaks in favor
of
any concept that does so of course are
the kinds of practical issues that we
highlighted earlier
right so the kinds of problems that you
face when there are responsibility gaps
these kinds of problems already give us
good reason to
favor responsibility concepts that don't
give rise to
um responsibility guys but we of course
we still need to figure out
whether there are other interests that
pull in different directions and how
strongly they do so
and that raises the cost question what
are the important functions
that responsibility concepts might play
and
we now have a list like we have six
possible functions
that responsibility concepts could play
i i'm just going to
um like fo fo
briefly mention them and then go on to
um how you would
have to argue based on this okay
so first so most important and most
importantly for our discussion
is um the ledger function of
responsibility concepts which is
responsibility concepts can be used to
facilitate a cert a form of moral
accounting right so they
allow us to keep books on what can be
attributed
to whom and here basically the ledger is
a metaphor
and it's a metaphor an overall
assessment of a person's conduct
if you take the ledger function
seriously then
so on a ledger function the
responsibility concept will pre
attributing responsibility presupposes
wrongdoing
um and so what's it will be part of the
responsibility
concept that is will be concepts of
right and wrong now
that's one function that responsibility
concept can concepts can play but there
are many other functions as well so
there's for example
justificatory answerability functions so
basically allowing us to figure out
who has to answer for what happened or
who has to offer
justification for what up happens there
might be retributive functions
right so we just use responsibility to
pick out someone who has to be punished
for what happens right there are
communicative educational functions so
we use responsibility concepts to
continue
to express and communicate moral norms
in our
community there's incentive functions
where we use responsibility concepts to
basically impose
incentive structures giving people
reasons to act in certain sorts of ways
and there are compensatory functions so
making sure that those who have suffered
damages are
adequately compensated
now the reason why i rushed through
these
these two so i rush to these to save
time but also because
the point that i want to end the
discussion with is
uh can be made with just this sketch
because
it seems very plausible that you get
responsibility gaps
for responsibility caps that are most
prominent that most closely fit the
ledger role
right so the ledger role presupposes
that people are responsible only if they
did something wrong
um and that of course requires the
relevant the right kind of causal
connection between their action and
this kind of thing that we can plausibly
say that what they did was wrong
so if responsibility gifts are most
likely to arise
when we give the letter role prominence
the other kinds of fun concepts don't
give rise to responsibility gaps
most more easy as easily and there are
actually some of these that
would specifically favor concepts that
would avoid responsibility gaps
right so for example the compensatory
function or the retributive function
right so making always sure that
bad deeds go don't go unpunished
basically
any concept that fits that role is going
to
not allow for responsibility gaps so
what we have to do is
if we want to engineer away the
responsibility gap problem
we have to argue that the cost is
bearable
when we accept a responsibility concept
that discards the ledger function or
restricts its relevance
so that's the way that is i we think is
the way forward if we want to engineer
ourselves out of the responsibility
problem now that suggestion seems quite
plausible actually
um and it looks plausible if because if
you just look at the
debate about responsibility caps it's
there are lots of suggestions
um for concepts that would allow us to
avoid
responsibility gaps but which do seem to
serve the kinds of practical interests
that we have in our responsibility
gap responsibility practice
and so prima facie at least is quite
plausible
but more work needs to be done that i
cannot do
here so i guess i i think i'm five
minutes over which i apologize for
um but i'm i'll just end the discussion
here and i'm
going to stop my screen sharing now
great so thanks sebastian thanks very
much it wasn't
i thought it was a super interesting
very good
talk and presentation
there are many things to think about so
but are there
um any questions
[Music]
just raise a hand or queue
in the chat um
i mean if there's if it's just
clarificatory questions that's fine as
well
yeah so yeah so you can also think about
the question i can ask oh
luciano go ahead okay yeah so
thanks sebastian that was really yeah it
was really interesting
really enjoy so uh the this this very
nuanced discussion and what it
meant by responsibility is very good and
so
when during your talk i think you make
some yeah some connections
start like autonomous weapons systems
limited vehicle they have some kind of
agency called an
ai they cause harm responsibility gaps
and then go
justice and all the harms caused by lack
of justice
so but you focus on the conceptual
engineering for the responsibility gap
which is on the middle of this part
but i was thinking if you
you make the connection through
automated vehicle weapon c stands to ai
you use the concept of agency for
example yeah
and wouldn't that also require some
conceptual engineering so
where do we stop because you need to
have some assumptions to get to the
responsibility gap
yeah um uh i mean
so i think so i mean first of all thanks
for the question i think that's a good
question um i mean one
so i think one question so where do we
stop i think
i mean at some point i think we should
stop when we're doing it conceptual
engineering we probably have to stop
at like basic normative stuff
um at least when we're focusing on
specific normative questions right
obviously we can also engineer our
normative concepts
but that then we get to never ending
um engineering but obviously yes i agree
that we need to
also think about the concept of agency
that's at play here
um and how it connects to control i mean
i think so i tried to highlight that
in the middle part as well so rob a lot
for example
so some people so i mean at the
beginning the
the concept of agency i used at the
beginning it's like a very minimal
sense of agency where you are an agent
basically when you
are involved in decision making of a
certain side of sort
but of course you could just say that's
not really agency
that's not really the right relevant
kind of agency that matters for control
um and that that's in a sense also
an engineering question so yes
so where do i think it stops i think i
mean in a sense it stops
so when you look i i mean i think when
you look at a specific specific debate
what you should look at are the central
concepts concerned that the debate is
concerned with
um and then consider those
for i mean but of course you are right
that
it gets expansive very sick very very
far i mean one thing that
very fast one thing that i did for
example that i didn't talk about so much
is
is of course that what we say about this
debate has implications for other things
as well right so
um the i mean fairness
justice of harm and then we can go all
the way there right
yeah but nice thank you
good so um maybe to follow up on that so
you said what
so um uh maybe our conceptual
engineering in those cases
does have some um
consequences for it for for the for if
in other contexts so maybe we find out
that responsibility
means actually means in this course
context means something
and if i consider you correctly you
think that this automatically
maybe means that it also means the same
that the same conception of
responsibility is also applicable in
another context
um or so is that what you because that
i would be very skeptical skeptical
about that claim
um so um but is that what you want
is that one of the things that we that
this discussion uh teaches us so how to
use responsibility in other contexts
or do you allow for some kind of uh
conceptual pluralism
maybe they had a different that may be
different conceptions of contexts might
be appropriate and the
conceptions of concept might be
appropriate in different concepts uh
context
yeah no no i think that's a great that's
a great question um i actually
i mean i was i have always assumed that
we just want the same concept for all
contacts but now that you're i mean now
your question
basically i don't see why that should be
the case
um i mean i think
so i i mean what we do want is
so we want a concept that regulates our
responsibility
we want concepts that regulate our um
responsibility practice right so our
blaming um
and praising and criticizing
um practices
so um but i think
it's quite plausible actually that
for different sorts of assessments we
need different sorts of responsibility
concepts
um as long as it's relatively clear that
what concept we're using for what
content
contexts but i do think that i mean
in a sense so okay
lungs so i i do agree with you that we
probably should be pluralist
to some extent but i mean given that
it's very i mean what we have here is
responsibility when we're using a
technological application
right and i it seems like very plausible
that the standards
that apply in that case also should
apply to
to other cases
um so yeah yeah
is that helpful or yeah yeah that is
that is helpful
um so it might be of course be
interesting so what then determines so
there must be some overlap between
different contexts
if if uh if there's good
reason to assume that conception in one
context
is also appropriate in the other so then
um
yeah and that that that that probably
needs more work um
so yeah thanks so much yeah as usual uh
it's already two minutes after the time
that we uh
that we should end um
i thought because we started five
minutes late that's right
so um if there are if you're if you have
time uh if there are people who want to
ask a question
um i'm more than happy to um
i mean i'm just sitting in my home going
herman you have a comment from diana in
the chat oh good oh thanks
thank you so um diana do you want to ask
a question shall i just read it out loud
i i yeah i can't i can't see the chat so
okay so i just read it out loud so we so
we may need to better or more thoroughly
engineer the concept of conceptual
engineering itself
uh so that we don't end up in an
infinite recursive engineering loop
somewhat ironically yeah good
i think chalmers made some similar
remarks yeah
yeah um you agree yeah
i mean that there is a i mean there is a
problem i mean yeah so
we do i mean
i think this is i mean i think this is
the i mean the worst
the worst thing of course is
um i mean as a meta-ethicist um
i've i've already heard about this a lot
because
like the engineering the i mean
the normative concepts using the
normative concepts right that is going
to raise
uh some very really really tricky issues
but i think if we can resolve that
everything else is kind of
going to be fine that's a big if though
sorry to just bargain i just yeah no i
i appreciate i agree it is a big if
um so
yeah so then that's to the meta ethics
first
yeah math ethics first obviously and i
mean um
someone i actually was at a workshop
where um mati was there right
heckling um and
someone was making an argument that
actually i think uh
just to bring in a little bit of meta
ethics um
at the end no one was making an argument
that actually but probably
expressivism has to be true about the
most fundamental arts um
so that's why i'm fine with
yeah that's where you're happy with this
good i understand good so i think we've
already moved uh too much uh
yeah from our uh original topic so
let's just add and thanks sebastian i i
i really really like this thought
thank you thanks for the invitation
um and thanks for the questions so
yeah and i mean apologies that


GPT 5 is All About Data

find out what I could about gpt5 I have
read every academic paper I could find
about it every leak report interview
snippet and media article I can
summarize it like this it will come down
to data how much of it there is how it's
used and where it comes from these are
the factors that will dictate whether
GPT 5 gets released later this year and
whether it will actually approach genius
level IQ some media reports have picked
up on this potential leak about gpt5 you
can read it here I have put quite a few
hours in trying to verify whether this
might be accurate and even though it's
now being quoted by reputable sources I
still can't confirm its accuracy so for
now I'll just say that the rest of the
document seems accurate but who knows I
am not relying on this for my research
about gpt5 but the scale 25 000 gpus
does seem right tech radar here
describes Chachi BT as having been
trained on 10 1000 Nvidia gpus and don't
forget those were a 100 gpus Microsoft
might well now have access to the h100
GPU which according to every source is a
big step up from a100 gpus on pretty
much every metric and what about
timelines for GPT 5 would later this
year be accurate well we can infer from
Geordie rybass that gpt4 or equivalent
was completed sometime around late
spring early summer of 2022 that would
be just around the time that deepmind
published this which in massively
oversimplified terms lays out a
framework for optimizing parameter size
with the number of training tokens AKA
how much info from the web it's trained
on turns out models like gpt3 and palm
had way more parameters than needed
anyway it was the data and especially
high quality data that it was lacking so
all those laughs about gpt4 needing a
hundred trillion parameters were
absolutely farcical it could even be
that gpt5 has the same or fewer
parameters than gpt4 this less wrong
post from July of 2022 picks up on that
finding and points out that it is Data
not size that is currently the active
constraint on language modeling
performance current returns to
additional data are immense and current
returns to additional model size are
minuscule indeed most recent Landmark
models are wastefully big if we can
leverage enough data there is no reason
to run 500 billion parameter models much
less 1 trillion parameter or larger
models remember it's data not parameter
count the link to all of these articles
by the way will be in the description at
this point let me quickly say that if
you're learning anything don't forget to
leave a like or a comment frankly even
abuse helps the algorithm so go for it
what about chat GPT while gpt3 along
with a host of other models was trained
on about 300 billion tokens by the way
what defines a token shifts in the
literature but it's somewhere between 1
and 1.4 words therefore think of a token
as roughly one word as you can see from
the graph below Palm was trained on
about 800 billion tokens approximately
deepmind's chinchilla on about 1.4
trillion tokens that particular less
wrong post was referenced here in this
academic paper released in October this
paper is absolutely key to this video
it's focused entirely on whether we will
run out of data as it pertains to
machine learning and large language
models one of the key takeaways of this
paper is the approximation given the how
much high quality data slash tokens
might be out there the stock of high
quality language data is approximated at
between 4.6 trillion and 17 trillion
words the next point it makes is key we
are within one order of magnitude of
exhausting high quality data and this
will likely happen between 2023 and
2027. for those that don't know being an
order of magnitude bigger means being 10
times bigger than what came previously
now I want you to remember that 2023 to
27 timeline for a moment because first I
want to mention why high quality data is
important running out of that could mean
running out of the rapid improvements in
GPT models the paper says models trained
on the latter kind of high quality data
perform better so it is common practice
to use high quality data for training
language models and where does that high
quality data come from well to be honest
not knowing that is a big part of the
problem which we will definitely come
back to but here is a rough idea we have
scientific papers books scraped content
from the the web the news code Etc plus
Wikipedia of course the paper also
mentions here the middle of the road
estimate of nine trillion tokens of high
quality data available that estimate
will be Central in defining the
near-term future of artificial
intelligence one order of magnitude more
as an increase in performance is a huge
deal that would change everything but I
must say this estimate contrasts with
some others such as the 3.2 trillion
token estimate from that original post
and the author did say that they were
trying to make it an overestimate and
what about this from David Chapman a PhD
in AI from MIT he references the
deepmind study and that less wrong post
and makes two important and plausible
observations first that gpt4 or Bing may
have scraped the bottom of the web text
barrel and that this might be why it's
responses sometimes turn out like
emoting teenagers I actually did a video
on the crazy conversations you can have
with Bing that you can check out after
this one but second He suggests that
there might be a reason that neither
Google nor open AI have been forthcoming
about where they get their data from now
I'm not saying it might be about
illegality but it might be about
avoiding controversy over attribution
and compensation take me I have math
tutorials on the web that I'm sure have
been scraped and now lo and behold Bing
can teach math I'm not complaining but
it would be nice to at least know what
has been used and what hasn't this of
course mirrors the Raging legal issues
around AI image generation fights that
are only just beginning for these web
tags wanting to know where the data came
from is going to become a huge issue and
this article lays out just some of the
surprising sources of data for Google's
bad model check out one of them which is
YouTube could it be that your comments
right now are being harvested quite
possibly I want to get back to the
central question what are the gpt5 well
here on the far right is Google Palms
performance which if you remember back
from the earlier paper was powered by
only 800 billion tokens and palm was
definitely not optimized for parameters
GPT 5 will learn the lessons from this
and will probably scrape as much high
quality data as it possibly can and
don't forget another year has gone by
since gpt4 was handed to Microsoft and
the stock of high quality data Grows by
around 10 annually anyway even without
further efficiencies in data use or
extraction so even if Bing did use all
the high quality data available I don't
think it did and even if David Chapman
is right the stock of data now available
is going to be greater but if Bing was
trained on a similar amount of data to
Palm say one trillion tokens but now GPT
5 maxes out we could genuinely be
talking about an order of magnitude
Improvement I'm going to briefly survey
some of the implications of that in a
moment before I do I want to show you
the ways the openai will likely be
improving GPT 5 regardless of previous
limitations first more ways might be
found to extract high quality data from
low quality sources no offense Facebook
second this paper from only last week
shows that gains can be made by
automating Chain of Thought prompting
into the model if you're not sure what
Chain of Thought prompting is it's a
form of prompt engineering that I
discussed in my video eight upgrades in
gpt4 where essentially you force the
model to lay out it's working and
thereby improve its output now this
paper talks about two to three percent
gains but even though small gains when
Bing is already this strong would be
significant don't forget these are
separate upgrades to the data discussion
third this paper from three weeks ago
shows that language models can teach
themselves to use tools such as
calculators calendars and apis if there
were no other improvements honestly in
GPT 5 other than this it would change
the world and I know for a fact that
people are working on integrating
Wolfram Alpha into a large language
model and look at the number of tools
that Wolfram Alpha has in science math
money and more these models can actually
teach themselves how to use tools and
that Chimes perfectly with this paper
which essentially lays out that using a
python interpreter models can actually
check if their code compiles and thereby
teach themselves better coding the links
to all of these papers will be in the
description as I said the fourth way
that GPT 5 might be improved even
without more high quality data would be
it being trained multiple times on the
same data as laid out here by Professor
swayam dipta he says that currently
these models are trained on the same
data just once owing to Performance and
cost constraints but it may be possible
to train a model several times using the
same data sure it might cost more but I
think that for Microsoft when all of
search and its profits is the prize a
few billion could be deemed worth it and
this paper co-authored by that same
Professor lays out how models can
generate additional data sets on
problems with which they struggle such
as those with complex pans and that
humans could filter their answers for
correctness think of this as artificial
data generation and it can lead to 10 or
more in improvements and if artificial
data can be integrated honestly what is
actually going to bottleneck these GPT
models I could go on with the
improvements that might be made without
new data my central point is that data
will be the big determinant but there
are other ways to improve gpd5 if data
turns out to be a bottleneck what if
they can fully utilize 9 trillion tokens
as the original paper surmised by the
end of 2024 or even the beginning of
2024 what could one more order of
magnitude Improvement actually look like
the short answer is that no one knows
probably not AGI but certainly a
revolution in the jobs Market maybe this
is why Sam Altman tweeted 2023 thirty
thousand dollars to get a simple iPhone
app created 300 for a plumbing job I
wonder what those relative prices will
look like in 2028 the likely coming
Divergence between changes to cognitive
work and changes to physical work could
be quite dramatic that gives a sense of
his timelines but my own guess is that
the best human raters will be beaten on
at least some of the following
benchmarks take reading comprehension
where you can imagine the extrapolation
to gpt5 if and when it occurs that would
have huge implications for summarization
and creative writing next logic and
critical reasoning we're talking
debating topics doing law work
Discerning causality in complex
scenarios that would be huge in finance
where you have to sort the signal from
the noise in large data sets physics and
high school math would be close to
solved by an order of magnitude
Improvement AI tutors replacing my job
for example could be with us by the end
of next year don't forget the release of
GPT 5 in whichever month it comes will
likely roughly coincide with the final
refinements in text to speech image to
text text to image and text to video
avatars so don't think AI tutors are as
far as you might imagine the reason why
no one on and certainly not me can be
sure of timelines for GT5 though it's
because they depend partly on internal
Safety Research at Google and openai
take this quote from Sam Altman to the
New York Times and when we are ready
when we think we have completed our
alignment work and all of our safety
thinking and worked with external
Auditors other AGI Labs then will
release those things here he's probably
talking about gpt4 but the same would
apply even more so to gbt5 on the other
hand the release and then on release of
the Sydney model of Bing might suggest
otherwise but at least according to him
safety and Alignment are the goal I'm
going to end with this quote from
samuelman again he added the blue text
last minute to his public post on AGI
released the other week it says it's
important that the ratio of safety
progress to capability progress
increases in other in other words these
models are getting much more powerful
much faster than they can keep up with
but thank you for keeping up with this
video thank you for watching to the end
please do check out my other videos on
Bing chat and its use cases and either
way have a wonderful day