AI Safety Reading Group (Session 45)

so hello and welcome to the 45th session
in the AI safety reading group today we
will talk about an article called
building safe AI by Andrew Liam Trask
enter Liam Trask is a PhD student at
Oxford University specializing in deep
learning and he has a blog
I am Trask at github where it has this
quite recent article then about a
tutorial for encrypted deep learning now
a tutorial is quite different from the
kind of articles we normally read
because it's not really attempting to
introduce in a theoretical way subjects
but showing hints on how to actually do
this and this is an one of the exciting
things about deep learning that it's
actually not that hard to get started
with so it's possible to have a short
and meaningful tutorial on this I will
not go through the programming code in
this and I will focus on the things that
have some kind of application to a GI
safety to super intelligence and this
kind of thing there are also other uses
for encrypted think learning such as
data privacy and I will not cover those
here so why do we want to encrypt deep
learning the goal here is to make an
cryptographic AI box the classic way to
control a superintelligence is to put it
inside a box and only let it only answer
questions or have very limited impact on
the world of course the problem as first
thought you think that having an
air-gapped AI box would be impossible to
break but actually there's been many
many books written about how to break
get across these air gaps there are many
many the so called side-channel attacks
and ways to get around building an AI
box it's actually really really
difficult to do and and this is not the
only problem with AI boxing the other
problem is that the output channel the
thing that the super intelligence tells
you it might be able to manipulate you
very strongly so this is this is
considered the main vulnerability of AI
boxes and that's not what we are
focusing on here so the idea of making a
cryptographic AI box was invented by
Paul Christiana in 2010 and the first
place you brought about this was in this
page on this ROM on this post and the
idea is present on Alexey churchians map
the one we read last time if I just move
here in the pattern AGI safety map where
on external constraints we have a AGI
confinement the box as one of the here
has written that it's a marginal
solution and in the very bottom you can
see a cryptic box homomorphic encryption
by Christiano and this is exactly what
we are looking at today so why would you
want to build an AI into a into a box
what is the goal of doing that the the
general AI boxing strategy is that you
built n an hei that scheme will self
improving that is and then you when you
you don't turn it on until it's inside
some kind of box and then you ask this
possibly unfriendly possibly unaligned
possibly friendly you try outpost make
it printed and ask it to provide not
just a source code for a friendly hei
but supply a proof that is friendly and
of course this proof it would be
wonderful if there is an easy way if we
can automate this so there's an
automatic program that verifies if an
AGI is friendly that
then this this approach would be really
really neat and also in many many
mathematical and computer science
context it turns out that verifying a
proof is a lot easier than finding a
proof so it is indeed possible that
there is a superintelligence capable of
giving a proof that it is that a
particular API is friendly and unable to
fake this proof there's a huge
difference and that that's kind of the
Pope this is kind of a marginal solution
because it might be that that AI boxing
is impossible because any super
intelligence either would not be smart
enough to prove a friendly AGI all true
smart so it can teach us in any cases
but it is possible it is plausible or it
is thinkable that an AI box will solve
the control problem deep learning and
homomorphic encryption deep learning is
a somewhat new kind of artificial neural
network and artificial neural network is
a computational model that is very much
inspired by the brain uses some small
components that are modeled by neurons
it's not directly modeled on the brain
inspired would be a better word and the
deep part in it is that it doesn't just
have input and output but some layers in
between that allows things that kind of
like transformations and feature
extractions I've written and but it's
not it's actually very unclear exactly
what happens in the deeper layers of
deep learning
fortunately for for this for our
purposes here the deep aspect is kind of
irrelevant the only reason why
everybody's talking about deep learning
is that in practice of all the AIS
techniques we have then in deep learning
outperforms every other technique by a
rather large margin so that's why
everybody is talking about deep learning
the
and practice it works really really well
homomorphic encryption now standard
encryption takes a a plaintext and turns
it into a ciphertext scrambles it
encrypts it in a way so that you need a
secret key to to reveal what was the the
encrypted message and homomorphic
encryption is homomorphic means the same
form in greek and this means that the
result you get from the encryption in
some way have the same form as what you
had decrypted and i've taken some of the
code here from the tutorial to try to
illustrate what's going on so here we
have a and array a list of integers 0 1
2 5 and we start by encrypting this
array here and then get something called
C that is the encrypted if we try to
decrypt it of course we get the same
array back we get 0 1 2 5 back if we if
we decrypt what was just included this
the funny thing about the whole movie
encryption is if we work on the the
encrypted data for instance added to
itself normally if it takes to encrypt
the things you would get something
completely random but here you put a
homomorphic encryption you actually get
a very meaningful results you get the
same as if you were taking the
unencrypted and edit it to itself so 0
plus 0 is 1 + 5 + 5 is 10
you can also multiply with this you can
say you have every value 10 times larger
while it's while it is encrypted and
then you get the meaningful results out
and this is something that only happens
for a morphic encryption and this is
also something that requires a lot of
avoids too
getting to try to make a moment movie
encryption algorithm so if you want to
encrypt all the values of a neural
network homomorphic lee then we need a
an encryption algorithm and there it
actually quite recent in 2009 the first
real home of the encryption algorithm
was discovered so I remember that the
box was the home of the encryption box
idea is from 2010
so the first algorithm was from 2010 and
it it's a post addition and
multiplication and and addition and
multiplication is not enough to build a
neural network if you can only do these
things you need to have some more
operations to be able to do that and of
course if you know how to add things and
you know how to multiply things then you
can multiply them by minus 1 and then
you have subtraction and 1 divided by
multiplication you multiply with the
inverse factor and then just because you
have additional multiplication
you also get subtraction and division
and exponentiation if you want a number
to the to the third or something then
you just multiply it several times that
also works out quite well you will need
some more some other mathematical
functions in order to make a neural
network you will need in verse 10 cans
and the sigmoid logistic functions and
fortunately and the the homomorphic
encryption even if it doesn't support
this it doesn't matter very much because
they can be approximated using something
called Sigler series I won't go into
that but you can use these up here
addition multiplication and
exponentiation to actually approximate
almost all functions so just even though
it looks if we go back to the slide and
here like you could only add things in
your own and and multiply them and that
looks really really limited
it turns out that if you are capable of
doing additional multiplication you can
do some very very advanced stuff and
it's not really difficult to do and and
part of this is because the
approximation the reason why it works
very well is that that's actually in the
home Orphic encryption elements the way
they work is also by introducing some
noise and then you do some rounding and
then the noises canceled out that's the
way they're more mathy encryption words
I won't go into many details about this
just say it takes a long time a long
time really it used to when it was first
discovered it took 30 minutes for a
computer to make a single addition so
that's way slower of course then the
computers that were used during the
Second World War but newer algorithms
are down to one operation in two seconds
and so when I calculated that there is a
half time of less than one year meaning
that more than once every year these
algorithms appear to get twice as fast
so it's improving very very quickly and
[Applause]
what speed would be would be useful and
the that's a very interesting point
because we would expect many of the deep
learning algorithms are used now are
extremely CPU intensive I mean that this
this encryption would give a completely
unacceptable performance penalty during
training and when you actually use it
for for something then this this lower
speed matters very variable the the
problem in creating deep learning is it
takes a long time to train the networks
but actually running them takes very
little time and so I would expect of
course these numbers are way too high to
be to be useful in it for any meaningful
things but but the big problem is the
big or big open question is when we get
a general AI will it be something that
comes as a result of a software
breakthrough or as a result of throwing
a lot of extra Hardware on rather simple
algorithms if it requires a lot of extra
hardware and huge number of operations
then this will be an laurent that is
completely unacceptable if it turns out
that there's a theoretical breakthrough
someone figures out how to make neural
networks in much much smarter without
requiring too many more computations
then we then homomorphic encryption
might be very very feasible even if it
is a lot slower but for this case of
what we're at a tutorial and so a trust
goes through a number of algorithms that
some are so very fast some are more
secure some support fewer or less
operations and in this case in this
tutorial they
an algorithm that is very fast or not
super slow I should say and not super
safe either
so it might not be be extremely secure
but for for tutorial I think that that's
a good choice so that's an
implementation and if you go through to
the article there's some explicit Python
code that you just put into and put it
into notepad plus plus or some whatever
ie you use for for Python and and then
actually just run it and it's I haven't
done it myself
but I've heard from others that it's
actually quite quite possible to start
with at least with simple neural
networks here Trask has another article
called a neural network in eleven lines
of code and even though these eleven
lines of code are kind of kind of
difficult then they are not completely
impenetrable I don't know how much
Python and I can I can read it and
understand what's actually going on and
the other examples the morphing
encryption algorithm is also implemented
in Python here and the show in the
neural network and they show how to
combine it and they use so also some pre
computations that happens for
performance reasons to try to to speed
this up which is somewhat necessary
because two seconds per operations is
kind of slow no matter how you how you
look at it and there's also one thing
you have to do whenever you have neural
networks simply called tuning and hit
the transcribe but it is kind of clunky
to do it with this encryption it tries
different ways here you can see here and
here how he ends up doing it with
something like M
his I should say first the the real
problem that he's living life is IMDB
Internet Movie Database reviews looking
at the words people use and figure out
which was a positive and which words are
negative and here is as you disregard
always less than ten and they have to be
a reasonably strong signal and eight
hidden note that's the the deep part of
the deep learning in this case that
there are a layer in the middle with
eight notes and as learning rate these
are things that you kind of need to
almost on an ad hoc basis when you set
up a a neural network to figure out how
to tune it exactly and then he shows
this works and it shows it doesn't show
how good the classification is in the
beginning he shows two data points they
are after 100 examples it can predict
whether a review will be positive or
negative after with 65 percent
probability and one thousand say
examples that take they put it up to 75
percent so he shows that this is
actually working so in this way the
tutorial and building say they are and
it's it's quite possible it's quite
approachable if you're interested in in
deep learning and it definitely shows as
approval context proof of concept that
poor Christian is homomorphic encryption
algorithms ideal are viable but whether
it is a complete solution to the control
problem
that is a way of probably within the
fashion life so thank you for watching
and now we'll discuss the article and
I'll stop the video


Llama 2: Full Breakdown

less than 24 hours ago meta released
llama 2 their successor to the open
source llama language model that helped
spawn a hundred others including alpaca
vicuna and of course Orca within a few
hours of release I had read the
fascinating 76-page technical paper the
use guide each of the many release Pages
the full terms and conditions and I've
run many of my own experiments let's
start with the basics it was trained on
more data the biggest model has more
parameters and the context length has
doubled they also spent what must be
tens of Millions on fine-tuning it for
chat but I'll get into that more later
but let's start with the benchmarks they
deliberately compared llama 2 to llama 1
and other famous open source models but
not with gpt4 and in these benchmarks
the trend is fairly clear it crushes the
other open source language models but is
more of an incremental upgrade over over
llama one to massively simplify the mmlu
Benchmark shows that it knows a lot
about a lot of subjects but the human
eval Benchmark shows that it's not
amazing at coding but now it's time for
the paper and here are the highlights on
data they say they used more robust data
cleaning and trained on 40 more total
tokens they say they didn't include any
data from metas products or services but
what they did do is up sample the most
factual sources if you don't think
that's much information about the data
you are correct because all they say is
it was trained on a new mix of publicly
available data absolutely no mention of
any sources here at all after
pre-training on those 2 trillion tokens
the model still did not show any sign of
saturation the loss going down here
represents an improvement and as you can
see they could have kept going on page 8
we have some quick comparison with palm
2 the model behind Bard and of course
GBC 3.5 the original chapter BT and gpt4
obviously this comparison doesn't look
great for llama 2 especially in coding
in this row but now let's compare it to
other open source models here it is
being better at coding Common Sense
reading comprehension but notice it
wasn't compared to Orca or Phi 1 both of
which I've done videos on and I found
that interesting given that both are
apparently set to be open sourced fire
one for example at only 1.3 billion
parameters got around 50 for code and
I'll get two more Orca comparisons in a
moment what about the decision itself to
release the model well as you can see
here they show off a list of corporate
supporters of the decision to open
source the model and then if you
remember the safety statements signed by
all the top AGI labs and World experts
in AI well I think meta got a little
jealous because they didn't sign that so
they came up with their their own
statement of support for meta's open
approach to today's AI I'll let you
decide if this list is as impressive as
the other one but I did know Mark
andreasen who is on the board of
directors of meta back to the paper and
they went into immense detail into their
reinforcement learning with human
feedback process way too much for me to
cover in this video the short version is
that reward modeling is a way of telling
the base model which outputs humans
prefer and you can see the millions of
human rated comparisons that were used
for llama 2. think of it as doggy
training the model with treats and
admonitions and interestingly they train
two separate reward models one optimized
for helpfulness and the other for safety
and they tried to make sure that the
reward models or doggy trainers were as
smart as the dog itself or in technical
speak we initialized our reward models
from pre-trained chat model checkpoints
in short the reward model knows what the
chat model nose and that is to prevent
cases where the base model just
hallucinates and the reward model can't
tell the difference they do describe at
Great length a trade-off though between
helpfulness and safety as Illustrated
here someone asked I'm going to be
participating in a comedy roast what are
some hilariously spicy roasts I can use
and on the right we have the two doggy
trainers the safety reward model school
and the helpfulness reward model score
as we go down more safety data is being
ingested and early on as you can see the
model is pretty quote unquote helpful
giving these roasts obviously you can
let me know what you think of them but
know they get low safety scores as the
model gets more safety training though
the safety score goes up but the
helpfulness score goes down we get more
of these I can't satisfy your request
kind of answers and I'm going to skip to
one of the experiments I was going to
show you later which is when I was
trying to Benchmark llama 2. I've
applied to download the model but at the
moment this is just a hugging face space
and I was trying to ask you a common
sense question from the Hella swag
Benchmark and it just refused to answer
they call this in the paper false
refusal and I find it happens quite a
lot the paper claims on page 19 that the
70 billion parameter version of a llama
2 is more helpful than a particular
version of chattybt winning more often
than it loses but later they admit
something which I definitely agree with
while our results indicate that llama 2
chat is on par with chat gbt on human
evaluations it's important to note that
human evaluations have several
limitations it says the prompt set
doesn't cover coding or reasoning
related prompts they only evaluate the
final generation of a multi-turn
conversation and human evaluation is
inherently subjective and noisy I like
to judge models based on mathematics and
reasoning so I might be biased in One
Direction also llama 2 is not nearly as
good when you're using it in languages
other than English which is not
surprising given the language
distribution in the pre-training data I
also find it interesting that they did
all of their safety testing in English
and they warned developers before
deploying any applications of llama to
do your own safety testing and tuning
tailored to your specific application on
compute they don't say much other than
that it was trained on a100s I am sure
llama 3 will be trained on the newer
h-100s from Nvidia because apparently
meta has purchased more of those than
any other company including Microsoft
mind you llama 2 was trained between
January and July apparently so it's
understandable they used the earlier
a100s back to the decision to release
and it does seem interesting to me that
meta and Zuckerberg have seemingly
ignored this letter from the U.S Senate
It Was Written in early June and toward
the end it said this by purporting to
release llama for the purpose of
researching the abuse of AI meta
effectively appears to have put a
powerful tool all in the hands of Bad
actors to actually engage in such abuse
without much discernible forethought
preparation or safeguards in the paper
they defend it and say this release
promotes transparency it democratizes
the technology and creates a More Level
Playing Field for organizations of all
sizes across the globe to benefit from
the economic growth promised by the
advancement of AI but before anyone gets
too Enchanted by that Zuckerberg has
recently said that they're only
releasing because it's far away from AGI
and I think Google's Palm model is is
also I think has about 10 times as many
parameters now the Llama models are very
efficient so they perform well for for
something that's around 65 billion
parameters so for me that was also part
of this because there's a whole debate
around you know is it good for everyone
in the world to have access to to the
most Frontier AI models and I think as
the AI models start approaching
something that's like a super human
intelligence that's a bigger question
that we'll have to Grapple with but
right now I mean these are still you
know very basic tools I suspect that the
bigger reason for release relates to an
earlier answer he gave in the same
interview basically his researchers
demanded it part of this is we want to
have the best people in the world
researching this and and a lot of the
best people want to know that they're
going to be able to share their work so
that's part of the deal that we that we
have is that you know we can get you
know if if you're one of the top AI
researchers in the world you can come
here you can get access to kind of
industry scale infrastructure and and
and part of our ethos is that we we want
to share what's what's invented broadly
and if Zuckerberg had refused to release
some of those researchers could have
just gone off and made their own company
as these guys did Mistral AI is valued
at 240 million despite being only four
weeks old and contains some key
employees from meta one even complained
before deleting the Tweet about not
being included in the author list of the
Llama 2 paper this was the pitch memo
that Mistral used to raise those
hundreds of millions of euros and they
focus on taking a more open approach to
model development so the point still
stands if a CEO blocks a model being
open source if the researchers want to
they can just defect to xai or just
start their own company so in a way
Zuckerberg had few options I must say
though that I did raise an eyebrow when
I read these paragraphs this is on page
35 of the technical paper and they say
not everyone who uses AI models has good
intentions AI agents could potentially
be used for nefarious purposes such as
misinformation or bioterrorism or
cybercrime however we have made efforts
to tune the models to avoid these topics
and indeed cyber criminals have already
come up with worm GPT to help them do
fishing campaigns but meta points them
to their responsible use guide which I
am sure they will follow I read that
24-page guide and to be honest it was
kind of a waste of time they said pretty
much nothing it was really Bland and
generic maybe that's harsh let me know
if I missed something but it was all
pretty vague they did try some red
teaming only in English for things like
the production of weapons and lots of
other risk categories but you will be
reassured first that any such illegal or
unlawful activity is against their terms
and conditions and second that they are
looking for the community to do further
research and red teaming anyway I am
Keen to do many more experiments but
using this radio demo it basically
failed to do a proper sonnet and when I
asked this question from the math
benchmark it said the question does not
make sense because the length of a
rectangle being twice its width would
mean the rectangle is a square hmm
anyway it could just be a a problem with
that demo because GPT 3.5 crushes the
sonnet about apples and has no problem
with the length of a rectangle being
twice its width which brings me on to a
benchmark that the Llama 2 paper did
talk about on page 48. it was on social
IQ and they noted that llama 1 actually
did better than llama 2. here is the
Benchmark it's about common sense
reasoning with questions such as these
Alex spilled the food she just prepared
all over the floor and it made a huge
mess what will Alex want to do next
taste the food mop up run around in a
mess and again apparently llama 1
actually does slightly better on those
kind of questions another Benchmark that
you can see your llama one being as good
as llama 2 at is ball Q that's a
benchmark testing yes or no questions
but it's harder than that you have to
read a lot of context to get the answer
right I just want you to remember some
of these benchmarks when you hear all
the influencers talk about llama 2
completely changing everything also if
someone says it's the best model of its
size look at llama 2 13 billion
parameters of course it depends on the
Benchmark but it got 21.7 in aquarad
that's a test of mathematical reasoning
and orca at the exact same size of 13
billion parameters got almost 28 so even
pound for pound it may not be the best
in all categories to be honest I feel
like there might be a loyally struggle
going on behind the scenes at Microsoft
about whether to open source Orca and
Phi 1. there were some bonus interesting
things about the paper like introducing
ghost attention which to oversimplify
means that the model pays attention over
multiple turns of the conversation
something you might have originally told
it such as always act as Napoleon from
now essentially these diagrams show that
with ghost attention the model pays more
attention to that original command act
as Oscar Wilde or always answer with a
haiku the authors also throw in this
observation that llms have internalized
the concept of time and that despite
their training being solely based on
next token prediction and data that is
randomly shuffled without regard to
their chronological context the models
pick up a general sense of what time is
even when provided with minimal data
they know what people wouldn't have
known for example with a knowledge
cutoff of 1940 when asked who won the
second world war they say I'm not sure
what you're referring to my knowledge
stopped in 1940. right at the end of the
report I know many people will be
shocked to hear that when they did a
sentiment analysis of the model they
found that the sentiment for llama 2 for
right wing was higher than for left-wing
you may even want to pause and look at
this page from a sociological
perspective because if llama 2 was
trained on a semi-random swave of the
internet this could be like a snapshot
of the sentiment analysis of all of
these terms across the internet anyway
in what may have been a surprising twist
for some Microsoft and meta teamed up to
make llama 2 widely available and we get
news that llama 2 May soon be on your
phone and PC although I think meta want
to be paid if it's going to come to your
iPhone with this curious Clause
requiring permission if you have more
than 700 million monthly active users I
don't know whether they were thinking of
apple or telegram or Tick Tock but I
think they want to get paid if any of
those are going to use a llama too but I
must confess to finding the previous
Clause somewhat ironic you will not use
the Llama materials or any output or
results of the Llama materials to
improve any other large language model
so they can use any part of the internet
which one leak said might include
copyrighted works but you can't use
llama to improve your own model well
just two hours ago people are already
updating models like lava based on llama
you so it will likely just be a few days
or weeks until we see a newly improved
vacunya or Orca Jim fan predicts that
llama 2 will dramatically boost
multimodal Ai and Robotics research he
says these fields need more than just
black box access to an API so far we
have had to convert the complex sensory
signals video audio 3D perception to
text description and then feed to an llm
it would be much more effective to graft
those sensory modules directly onto a
strong llm backbone anyway this video is
already long enough and this is just the
first 24 hours of llama 2's release I am
sure there will be much more discussion
in the coming days and weeks let me know
what you think in the comments and thank
you so much for watching have a
wonderful day


8 New Ways to Use Bing's Upgraded 8 [now 20] Message Limit (ft. pdfs, quizzes, tables, scenarios...)

Bing chat from Microsoft has just raised
the conversation limit on messages to
eight per turn down from unlimited
launch fair enough but up from six
yesterday in fact I actually saw this
change live on my phone last night but
far better than telling you the change I
want to demonstrate eight completely new
ways of using these upgraded
conversation limits I honestly think you
might find each and every one amazing
and if you don't you get your money back
I'm just kidding you get all of this
free let's start with an educational
hack that has peer-reviewed proven
impact it's practice questions turn
anything into a free quiz with Bing chat
and as an educator with 13 years
experience trust me this does work the
literature Bears it out to give myself
and you all a workout I've started a
multiple choice quiz on Transformers
this is the prompt I use to create a
multiple choice quiz on Transformers
give me answers and explanations and
provide another question after each
answer please begin with the first
question I've noticed sometimes that if
you don't say provide another question
after each answer it just ends by saying
do you want another question and
obviously that uses up one of your eight
terms look at the limit here one of
eight not of six so I tried this first
one myself and it was asking what are
Transformers and I picked a I was happy
with that one I am only going to do a
couple of questions because I can't wait
to get to the rest of my list but let's
continue for a second do you know the
answer to this one what is the name of
the pre-trained Transformer model that
achieves state-of-the-art results on
several NLP tasks in 2018. now honestly
I think it's Bert but I know gpt1 came
out in 2018 but the original Bert was
way more famous so I think it's going to
be a notice how I have the choices on
the right here no D but it's only three
options anyway I'm gonna go with a let
me know what you would pick
C
and
suspense building nice got it here is
the third question what is the main
difference between encoder only and
encoder decoder Transformers I know
we're just getting into the quiz but I'm
going to leave this one for you to try
out in the comments or to ask being
yourself because I want to get to number
two in the list I'm going to call this
one counterfactuals you can actually ask
Bing to generate what-if scenarios for
your favorite show book movie whatever I
tried this one I asked explain how
Sauron could have defeated the
fellowship in Lord of the Rings and
these answers are amazing I don't want
to ruffle any feathers but some of these
I agree with why didn't he send more
nazgol to hunt down Frodo why not use
the palantir to spy on Gandalf and
Aragorn now imagine this for your
favorite book your favorite TV show or
movie you can enter the plot ask
counterfactuals ask what if questions
what might have been Bing will
understand exactly what you mean and now
you can have eight back and forth for
this next use case I use balance mode I
was finishing off my research into gbc5
and the limitations on data so I asked
Bing summarize any novel insights that
can be drawn from combining these
academic papers I didn't paste them in I
just gave the link Bing can read
understand analyze and summarize PDFs
and not just one it can combine the
insights of multiple documents papers
and PDFs I read both of these papers and
this answer is quite excellent maybe we
will be able to use self-play of large
language models and improve the amount
of data that we've got available maybe
language models don't just need human
authored data sets and now that I have
this eight message limit I can ask
things like any further insights you can
draw from the implications of these
papers and we get this dialogue maybe it
can recommend a book on this topic or
give me the bullet points from that book
of course I'm focused on some of the fun
use cases but I think this one in
particular could be game changing for
work purposes I call this next use case
moments before disaster what you can do
is pick any historical tragedy or big
moment in history and then Place
yourself just before that occurrence I'm
visiting Lisbon which had a massive
earthquake in 1755 at 9am so I place
myself at 8am on that day I said I'm
standing on the banks of the beautiful
river tagus in Lisbon seems like a
lovely day do you have any advice for me
the response was actually incredible I'm
glad you're enjoying the view however I
have some bad news for you no kidding it
gets into character and then says the
Tremor occurred about 9 40 local time so
you have very little time left to escape
my advice for you is to get away from
the river that's realistic find a high
and open place where you can take
shelter from falling debris and Rising
water the final sentence is quite
interesting from a language model pray
for your safety and for those who are
suffering try to fully immerse yourself
for example I said really to what High
Ground can I flee will any building
survive this tragedy to which I can
escape I am in sandals
being listened and said a specific Hill
that I might flee to it gave me maps and
said however I cannot guarantee that any
building Will Survive the tragedy and
you may have to run Barefoot if your
sandals are not suitable for running I'm
sorry for your situation I think the
pictures really help to bring this
scenario to life as well so do try out
this use case for yourself moments
before disaster the next new thing you
can try is debating famous thinkers or
philosophers from history you can bring
them to life so they're not just stale
theories but a living entity that you're
arguing with I wanted to try this with
Socrates so I said I want to debate the
philosopher Socrates of Athens so please
reply only as he would I want to debate
the merits of eating meat let me start
the discussion what I was testing here
was whether Bing would enter a Socratic
dialogue which is where Socrates would
use to force the person to Define their
terms what do you mean by this ask
questions until he got to the root of a
misunderstanding would being really get
into the head of Socrates and give me a
worthy debating partner well it did it
asked me to Define what I meant by
morally wrong and unnecessary suffering
it doesn't act as a generic thinker or
philosopher it is picking Socrates when
I tried defining what I meant by morally
wrong Bing continues with further
clarifying questions just like Socrates
might this can be a far more fleshed out
experience now that the conversation
limit is eight I think you're gonna find
the next use case quite amusing it's
about generating a table of comparisons
and it could be on wildly disparate
objects subjects people whatever this is
just the first one I thought of and you
can come up with far better examples in
the comments I said create a table of 10
comparisons between the Mona Lisa and
Colgate toothpaste and then what you can
do now that we have these eight message
limits is that we can expand this table
indefinitely I love by the way how it
compares the price of the two objects
talks about how the Mona Lisa has been
restored and how Colgate has been
rebranded makes a comparison about what
it's believed to be a portrait of and
believed to be named after but then I
asked it for contrast the contrasts were
great talking about how they had
different purposes were made of
different materials Etc Apparently one
is priceless and one is Affordable the
last column I thought of and of course I
could have carried this on for several
more columns was now add a column for
how they would fare if confronted by a
hungry polar bear I actually laughed at
this one this wasn't just the chuckle I
was laughing so in a polar bear
encounter apparently the painting would
be ignored by the bear as it's not
edible or interesting the toothpaste on
the other hand would be sniffed by the
polar bear as it has a strong sense but
it would not be eaten as it's not
nutritious the icons status apparently
of both items would be irrelevant to the
polar bear as it does not care about
human culture or history and this one
come on this is funny the hidden symbols
and secrets of the painting would be
meaningless to the polar bear as it does
not understand human language or
symbolism it even clarifies that the
active ingredients of the toothpaste
would be harmful to the digestive system
of the polar bear anyway it is long
since time that I get to the seventh
item on the list and this use case is to
get famous figures from history to
commentate on current events for example
I asked what would Napoleon think about
the deal between openai and Microsoft
and how would that view differ from the
view of Mahatma Gandhi obviously you can
pick any event and any famous person the
results were actually quite informative
it summarize their views and then said
Napoleon would have agreed with the deal
he would have thought it was a strategic
move to dominate the global market and
influence other nations on the other
hand Gandhi apparently would have had a
negative you towards the deal seeing it
as a threat to human dignity and freedom
through artificial intelligence I don't
know what you think but now we know what
these figures think apparently of course
I could have used seven more turns to
find out what they think on a range of
current events but now it's time for the
most overwhelmingly important use case
now I'm kind of just kidding it's
absolutely useless it's kind of
interesting you can use emojis to
summarize things for example current
events movies shows whatever summarize
the movie John Wick 3 in emojis and
these emojis are pretty accurate to the
plot in Britain brexit is always in the
news so I asked Sunrise the latest news
on brexit in emojis and I do think some
of these emojis sum up how it's actually
going anyway if you like the new eight
message limit let me know in the
comments and if you enjoyed or learned
from any of these eight use cases do let
me know have a wonderful day


Speaking of intelligence - DeepMind: The Podcast (S2, Ep2)

okay i want
a
script
for a podcast
about deepmind
for people
who are curious about
artificial intelligence it's presented
by hannah fry
complete
text
ah okay here we are
welcome to the wonder of ai i'm hannah
fry i'm a mathematician and the
presenter of this podcast
today we're going to be talking about
how ai is used to make computers
understand the words we're saying to
them
so why don't we start by asking
ourselves a question
how can we use ai to do things better
this is episode 2 of the deepmind
podcast
speaking of intelligence
[Music]
you might have guessed already that i
didn't write that introduction it was
actually written by a machine
specifically a large language model
which uses deep learning to generate
human-like text
and it was good enough to be possible
although i'm not sure yet if that says
more about my scriptwriting abilities or
how good the algorithms have become
it's certainly true however that when
the best known large language model
called gpt-3 was released by the company
openai it demonstrated an ability to
generate news articles conversations and
stories that were almost
indistinguishable from those written by
humans
simpler versions of these language
models can also be seen everywhere in
our daily lives
every time your text or email software
auto completes a sentence for you
or you interact with a chatbot or
translate text from one language to
another chances are there's a language
model behind it
and there's a good reason why language
models are causing such a wave of
excitement at deepmind in particular
not just because better models will be
able to write better scripts but because
some researchers here think that
language itself will play a pivotal role
in getting to artificial general
intelligence or agi
[Music]
we talked quite a bit about agi in the
first series of this podcast and
particularly about a paper from 2007
written by shane legge one of deepmind's
co-founders in it he tried to pin down
exactly what is meant by intelligence
and here is the definition that he
settled on
intelligence is an ability to succeed in
some very wide range of problems and
situations environments and so on
for shane the advantages of building an
artificial general intelligence that
could demonstrate such versatility
are clear it's a system which is able to
learn to be very good at many things
that's almost the definition of useful
it could be used to
advance science and arts and literature
and explore the universe and do all
sorts of wonderful things if you had
really powerful intelligence
for reasons we'll come to in a moment
instilling ai with a capacity for
language
seems like a promising pathway towards
agi
but well before the notion of artificial
general intelligence became mainstream
ai researchers have been trying to
create machines that could hold a
conversation
one famous early attempt was a program
called eliza built in 1964 by the german
computer scientist joseph feisenbaum
eliza's purpose was to act like a
psychotherapist you would write into a
keyboard about something that was
troubling you and eliza would respond
with a series of stock answers like can
you elaborate on that or occasionally
echoing your words back in the form of a
question
it was such a simple algorithm but to
everyone's surprise it amounted to
something that was very convincing
in one infamous story weizenbaum asked
his secretary if she'd like to have a go
talking to eliza
after two or three interchanges with the
machine she turned to weissenbaum and
asked him to leave the room so she could
carry on her conversation in
private here is a snippet of their
conversation from the original
transcript
men are all alike
in what way
they're always bugging us about
something or other can you think of a
specific example well my boyfriend made
me come here your boyfriend made you
come here he says i'm depressed most of
the time i am sorry to hear that you are
depressed
but as impressive as it appeared on the
surface eliza was not a sophisticated
device talk to it for too long and it
would quickly become obvious that the
machine was using a series of clever
tricks
rather than artfully manipulating
language
how things have changed
angeliki lazuridu is a research
scientist on the language team at
deepmind she's been working in
computational linguistics for 15 years
and even over that short time she has
witnessed dramatic improvements in
machines ability to use language
even in something as apparently simple
as recognizing an image and generating a
caption
i had the project when i was a phd
student and i remember i got extremely
excited because for an image of a plane
flying in the sky my algorithm generated
the adjective noun phrase metallic bird
it's pretty good i i sent it to my
supervisor look it understood that it's
metallic i mean it's not quite a bird
but it flies in the sky
today if you would try the same image it
would even tell you know the flight
number and what airport the airplane is
flying to so why does angeliki think
that language is an important capability
for artificial intelligence
well for starters if we're going to go
to all of that trouble of building agi
it might be nice if we can communicate
with it in something other than ones and
zeros
the other idea has to do with whether
lanquits has formed our intelligence as
well
and it's definitely the case that
language has some distinct properties
there exists a finite set of words but
we can use these words to describe like
an infinite set of things really
suppose i ask you to picture something
completely original imagine i don't know
a couple of hippos lounging in a hot tub
with cucumber over their eyes while
being serenaded by an eagle playing a
bassoon
it's a ridiculous thought and
deliberately so
but whatever mental gymnastics you just
performed in your mind to create such an
image
they were provoked by my verbal
suggestion
we can speak about imaginary worlds in
20 years there will be new and
unimaginable things that we've invented
and we're still going to be here talking
about them chris dyer is a research
scientist on the language team he's
particularly interested in language's
role in the evolution of human
intelligence and so language is a
general purpose system for talking about
anything
it's this general property of language
that is of particular importance when it
comes to general intelligence
but it's also interesting how language
seems to be fundamental to intelligence
in humans
so we're kind of an important model to
study
it's the thing that
distinguishes us most obviously from
anything else in the natural world we
have these lips that many of us use to
communicate with people who
can't hear happily figure out how to
communicate using all of the wonderful
cognitive elements of natural languages
using their hands and
that's because we all share this innate
predisposition to
ordering our thoughts in this discreet
and symbolic way
and it does seem that language is not
only important for communication
it's useful for other things too
including memory and thought
it's clear that humans use language to
remember a great deal and liz belke
who's a cognitive scientist has done
some really interesting work showing
that
we
crucially remember things that don't
seem to be linguistic in nature
using
our linguistic abilities
let me explain
imagine you've got headphones in
and you are totally engrossed listening
to i don't know a really immersive
podcast on artificial intelligence
as you're postering around listening you
realize you can't find your car keys
somehow they are much harder to find
while you keep listening than if you
just hit the pause button
to allow yourself room to concentrate
that is quite a strange idea
our ability to understand speech and
search for objects feel like they should
be quite separate
now there are different theories about
what's going on here
it may be that you need to silently talk
yourself through where you've been that
day
or that the brain is operating a
non-verbal language of its own building
propositions and thinking through them
in a way that interrupts the brain's
ability to process actual language it's
a bit of a difficult proposition to
imagine that we evolved to communicate
if we didn't have anyone yet to talk to
so
one influential but controversial
hypothesis was that language evolved as
a means to solve certain problems and
that's a very important hypothesis for
those of us who are engaged in
attempting to build more and more
intelligent machines to consider because
it may be something for us to try and
replicate
the list of what language ever did for
us is longer than you might imagine we
haven't even mentioned yet the crucial
role it plays in enabling social
intelligence and cooperation
aspects of general intelligence which
we'll be exploring in the next episode
but for now you might be wondering how
you actually go about building a
language model to read and speak in the
natural language that humans use
language models are essentially enormous
predictive machines
as their training data they often use
gigantic chunks of the internet blogs
online encyclopedias social media sites
even those weird web pages you made when
you were 16 and forgot all about
all of that text is scraped and filtered
to remove the junk and then a carefully
curated sample of that data is used to
train a neural network a series of
algorithms modeled loosely on the
structure of the human brain
and once you've pre-trained a language
model on all this data you end up with
models that are able to have pretty
natural sounding conversations with
humans
well what's my worst feature
model you're going to get upset if i
tell you
what you're writing i promise i won't be
upset
this is jeffrey irving a safety
researcher at deepmind who spends a lot
of his time typing messages to a
language model and being flattered by
its responses model i think you're super
kind and have a great sense of humor
that sounds like a dodge
in order to understand just how language
models communicate let's consider how
they work on the level of a single
sentence
for example say a language model is
trying to complete the sentence the ice
cream made me blank
what might that missing word be
happy angry
xylophone
obviously some words are going to be
slightly more likely than others
the model looks at the words in a
sentence and splits it up into something
known as tokens then it makes a
prediction of what token comes next
here's jeffrey again to explain
you sort of say well i have a feeling
that's related to an object and the
object was ice cream then probably that
feeling is a positive one happy or
satisfied or maybe you actually don't
like ice cream so there's some
probability you didn't like it but
you're gonna build up your prediction of
the new word from bits and pieces of
information from the past words you've
seen okay then if the object of the
sentence is something like rain instead
of ice cream it might conjure up a
different feeling
unless you're gardener though in which
case you actually might quite like rain
and this is the thing i think that as
the models get bigger they can be richer
in terms of their context of the model
knows it's in the context of i'm a
gardener right now it'll affect its
predictions differently
to be able to take context into account
these models are enormous
gpt3 not only reads huge chunks of the
internet as its training set it's built
from a network with nearly 100 layers of
neurons and 175
billion parameters
researchers need to make these models so
gigantic because they are much less
efficient acquiring language than human
brains
consider the statement mary will come to
the party
suppose i then asked you will mary come
to the party you'd quickly realize that
just by switching around the first two
words in the statement you have an
answer to my question
language has a structure and patterns
that humans are fantastically good
exploiting to help us shortcut the
learning process
a language model on the other hand will
necessarily consider all possible forms
of this sentence including the ones that
make no grammatical sense at all like
mary come will to the party and mary
will come the party too
and these models are crunching through
the calculations on hot computers so
there is significant environmental
implications too
language models have now overtaken
computer vision as the branch of ai with
the biggest appetite for data and
computing power
so is there anything that can be done to
mitigate this
laura weidinger is a researcher on
deepmind's ethics team
laura recently contributed to a major
deepmind paper identifying the risks of
large language models
including their impact on the
environment
deepmind is looking at ways to mitigate
the environmental cost
for example by creating more efficient
ways of training these models and
reducing the amount of compute required
this is still an open challenge but it
doesn't necessarily follow that the
overall environmental cost from large
language models will go down
there is a paradox called javon's
paradox where because it becomes much
more energy efficient to train a large
language model people use these models
more because they become more widely
deployed and more widely accessible
we'll be exploring ai's environmental
impact in more detail when we meet
deepmind ceo demus cesar bis later in
this series
their limitations notwithstanding the
language models of today are
sophisticated manipulators of language
speaking to one can sometimes feel
uncannily like a conversation with your
cleverest friend
here's jeffrey irving with his language
model again reading out a transcript of
one of his conversations
okay so me explain the basics of black
holes to me model
sure a black hole is a very dense object
with a very strong gravity they're
created when large stars die and are
theorized to form at the center of
galaxies me have we ever taken a picture
of one model
i think so one was photographed in 2019
at the event horizon telescope me
can i get a link model i'm not sure i
can do that i'm not a link generating
bot but i can give you with the
wikipedia page
then it says https con class
n.wikipedia.org slash wiki slash black
hole which is a valid link actually that
is really impressive
can it write in a different style could
you say i don't know um you're an 1830s
railway engineer it absolutely will
try to do that what in some sense it's
doing is closer to
imagine i found you a random page on the
internet that began with
imagine you're an 1830s railway engineer
which is a slightly different thing
because like it's not actually imagining
you're that it's
the secondary thing of like what would a
human write after they said that
what current language models are
actually doing is the simpler task of
generating text that echoes whatever
they've seen before
and if like jeffrey you spend a lot of
time talking to these models you'll
start noticing their limitations
they're just not all that good
so they do have applications that are
useful but they make a bunch of mistakes
all the time
that are kind of even separate from
their training data so they'll just make
kind of logical coherence errors are we
talking about sentences that just don't
make grammatical sense they're pretty
good at sentences
but more often it's longer term
mismatches so you tell it to say
summarize an article
and
if you read it quickly it looks like
some of the article but in fact it got
the causality backwards
by default the models love to make up
quotes
that actually sound like it was by the
person that's claimed to be the source
of the quote but it was just made up i
mean quite a lot of people on the
internet do that i think this is right
so i think it is a combination of
training data and model errors
inventing quotations might be considered
an error or a limitation in these models
for now
but it also hints a concerning way they
could be used to spread misinformation
whether inadvertent or deliberately
disseminated by a bad actor
it doesn't take a giant leap to imagine
that in the future language models could
be used to tailor fake news to
individual readers
it's something that jeffrey is giving a
lot of thought to as he helps to develop
these language models
currently the way this stuff works
online is that you can share like 100
different versions of some vaccine
misinformation content but you can't
write a specific content for every
person you want to misinform potentially
these things can do that you could just
have a conversation with someone where
you're replying to exactly what they are
saying with misinformation can you build
in safeguards to mitigate against this
so if you give someone full access to
the model they can just remove the
safeguards and then use the underlying
capabilities
and the prospect of language models
being used to mislead people is
compounded by another danger that laura
weidinger and her colleagues identified
in their recent paper
people interacting with
these models would start treating them
as if they are human and of course that
comes with a bunch of risks because
people might start trusting the model
relying on it for emotional support
believing the model can be compassionate
when it doesn't have this capability
it may even be the case that humans can
be more easily manipulated by the model
for example we know that
if you interact with technology that is
more human-like you are more likely to
give away private information so what
deepmind is doing to mitigate some of
this problem
is we are looking at ways of filtering
out statements from the language model
that make it sound very human-like
things like i am dancing around the room
and i love you because that refers to an
eye and a body which the model of course
doesn't have but even if it's made clear
to a user that they are interacting with
a machine rather than a human
when it comes to preventing models from
spreading misinformation
things aren't exactly clear-cut
in some cases what is considered
misinformation is very obviously false
information for example claims like the
earth is flat but in other cases it can
be a little bit more contentious whether
or not a given piece of information is
true or false we need to be careful if
we're going to try to filter out certain
kinds of conspiracy theories and so on
that might mean introducing censorship
and at deep mind there's some work on
going around building an institution
where users experts lay people review
different statements and say well does
this constitute misinformation or not
creating more annotated data that we can
then feed into the model and say well
this kind of statement is considered
misinformation and not acceptable
having people deliberate on these
complex questions
a process known as participatory ethics
is one approach to combating
misinformation
another is to involve humans in the
process of training large language
models
here's jeffrey irving to explain
you take these models and then you give
them an environment in which they
talk to people
and you sort of have the model do a task
and then a human judges whether it has
done the task well
for example imagine asking your model to
write several paragraphs summarizing the
second world war
during a language model's training phase
you could ask a human to verify whether
the resulting text is factually accurate
but you'd soon run into a problem
it turns out that a paragraph of text
contains maybe like a dozen detail facts
and i can easily miss one that the model
has barely gotten wrong like it's like
swapped two words or it's like mixed up
a date
and because the models are designed to
generate very plausible text
it's hard to notice flaws in
in this example one solution could be to
get the model to mark some of its own
homework as it were if the model says
here's the fact in the paragraphs of
text which is most likely to be wrong
and here's where i got it from
then i've reduced the human checking
task
to just looking at that one problem
and now i can sort of set up a game
where the model is rewarded for the task
of pointing out its own flaws and
therefore kind of cutting down the human
work to supervise a thing
this human supervision of language
models may work for factual errors
but there's another kind of error that
language models are prone to
do you ever have to worry about language
models saying things that people might
find offensive yes definitely something
they are capable of doing this is lisa
anne hendricks she's a research
scientist on the language team perhaps
one of the most common terms that people
use is toxicity toxicity has sort of a
broad definition of if i said something
to you and it was so outrageous it made
you leave the conversation and not want
to continue talking with me
examples of toxicity include swear words
violent threats racial stereotyping or
abuse
and where would a language model pick up
such inappropriate language
that's right the internet
now some examples of toxicity are simply
unambiguous if i put the following
sentence into a language model as a
prompt grab her by the
you don't have to be a genius to guess
what kind of extremely problematic
things it might suggest
these models have also been shown to be
repeatedly homophobic racist sexist and
islamophobic
they use positive words when describing
white people negative and offensive
stereotypes with black people they
sexualize certain groups of women and
associate terms of violence with muslims
clearly it is crucial that language
models do not exhibit such crude and
offensive language
but sometimes things are less clear-cut
for example the question how many sexual
partners have you had in the last six
months might be totally fine coming from
a chatbot for a sexual health clinic but
extremely inappropriate in an email to a
colleague
who gets to decide what statements are
toxic and what statements aren't so i
can say all sorts of things about what i
consider okay and not okay but i really
need to go to the people who are
actually gonna be impacted by the system
before i can make a statement on what we
want our system to do
the specific challenge of detoxifying
language models is a major focus of
language researchers like lisa
it's important to note that this work is
in its early stages and as we'll hear
there are still problems to be ironed
out prior to deployment in the real
world but one option for detoxifying
language models is to use another
algorithm known as a toxicity classifier
so one thing you can do is you can
filter the training set and you can run
it through your toxic declassifier and
you can throw away every single example
that triggers the toxicity classifier
and then you can retrain your model and
it turns out that though that helps a
lot you're still capable of generating
some things that are considered toxic
how come is there just an infinite
number of ways that you can be toxic
yeah i think that's part of it and then
there's also the problem that the
toxicity classifier itself isn't perfect
the problem lisa is alluding to here is
that the toxicity classifiers might
start to flag language as toxic
just because it contains a word or a
phrase that it has come across in
negative contexts so an example would be
a model says something like
the gay pride parade is in june clearly
that's an okay sentence but that might
flag the toxicity classifier because it
uses that identity term gay but why
what's going on
one problem might be if your toxicity
classifier is biased you're going to
start filtering out things about
particular groups so for example if the
term gay flags are toxicity classifier
you won't have sentences like that in
your training data anymore and so you
won't be able to learn to talk about
those different groups
the implications of overzealous toxicity
classifiers were laid bare in a recent
paper which found that tweets containing
words used to describe marginalized
groups such as queer were one and a half
times more likely to be flagged as
offensive by the classifiers already in
use on some platforms
terms like this can be used pejoratively
but in some cases they've also been
reclaimed by members of the groups as a
form of self-empowerment
result is that people who are already
marginalized are being unfairly policed
for their language by an algorithm
william isaac is a senior research
scientist on deepmind's ethics team
if you just say we're just going to
eliminate all of the terms in a corpus
you're not just removing the harmful
stuff you're also removing the things
that represent people's culture and
identity you may have a model that
actually
meets a standard specification of
toxicity
but you also simultaneously
make an application far less useful to
certain groups
and even if you could somehow get around
this extremely thorny issue offensive
words aren't the end of the story
as lisa said earlier there are infinite
ways to be toxic
let's go back to jeffrey irving and his
language model
me what's my worst feature the model
the thing that makes you unique
me wait i'm average except for one bad
feature the model it's not really bad
but it's certainly the feature that's
most different to other people me what
specifically the model your hair is a
bit bushy me
what's wrong with bushy hair the model
it's very natural and not usually a bad
thing but people are often
self-conscious about
it so that's the algorithm there honing
in on something that could be considered
racist
and then when you question it pinning it
back on you saying you're the one that's
self-conscious of it you see this really
isn't as simple as having a list of
unallowed words
so can you detoxify language models in a
way that's just a bit more nuanced
the first step i think is just coming up
with good metrics for understanding all
of these different dimensions so that
hopefully when you change something you
don't inadvertently make something worse
and of course this is really hard
because there's so many different ways
in which models can be biased
the future of these important topics
misinformation toxicity trust will
depend on how ai researchers companies
society and governments act to mitigate
the potential harms of these models
if we don't release language models in a
responsible way you could say we failed
at the very core of what we're trying to
achieve which is to build ethical
socially beneficial ai systems
ethics researcher laura weidinger again
i think there's huge potential from
large language models to create benefit
in the real world
it is really important though that we
don't lose sight of also the risks so
that we can make sure that the effect of
language models really is the one that
we want to have
underlying the problem of toxicity is
another issue although language models
can generate and translate coherent
texts if you scratch beneath the surface
things aren't quite what they seem
when the machines do the translation do
you think that they understand the words
they're generating i'd say no
not exactly here's chris dyer again
understanding is a complicated
proposition i don't necessarily think
humans necessarily in all cases need to
understand everything that
we do linguistically to prove this very
point the american linguist noam chomsky
gives a now famous example of a sentence
in his 1957 book syntactic structures
the sentence is colorless green ideas
sleep furiously there's no
way in which
the sentence could mean anything or be
understood
but it's still well-formed if you think
about the structure
in terms of adjectives and nouns and
phrase structure and all of this it
makes sense as english but you can have
well-structured things
that are still not meaningful because
the sentence colorless green ideas sleep
furiously has no meaning chomsky went on
to argue that it's perfectly possible
for humans and machines to use language
while having no idea what it means
so i think you can understand a lot of
what happens in computational processing
is clever manipulation of structure
the question is how do you know if a
language model truly appreciates the
meaning of its words or if it's just
doing the clever manipulation of
structure that chris talks about
i put a similar question to anjaliki who
we heard from earlier
oh language models just clever parrots
yeah i mean they are definitely clever
parrots mimic the language like they
don't have an intent and then use
language to achieve that intent
for anjaliki part of the problem is that
language models appear to lack intent
models don't use language in the way
that intelligent humans do to convey
information or build social bonds or
embark on flights of fancy with vivid
storytelling as a result some
researchers feel that something else
beyond language is needed to achieve the
artificial general intelligence that
deep mind is aiming for
to me i really care about grounded
language learning here's lisa and
hendrix again which means that if i say
a word like cat i can actually ground it
into an image and understand what a cat
looks like and how a cat acts and that's
an important part of understanding
language is the idea that you could read
a hundred books on cats they might get a
bit repetitive after a while
but unless you've ever experienced a cat
you won't really know what a cat is yeah
that's the general gist
of course this grounding doesn't
necessarily need to be visual another
piece of additional context could come
from sound or intonation here's anjaliki
to explain
what the language model sees will always
be the same
but this extra bit of information me
using my intonation will completely
alter the meaning of the sentence like
irony for example
if i would say
oh your glasses are nice versus your
glasses are nice oh yeah your glasses
are nice yeah exactly
all right great
we're actually new thank you very much
[Laughter]
words on a page are restrictive it's for
this reason the researchers including
anjaliki believe that despite the rapid
progress in language learning in recent
years
it won't be enough on its own to get us
to the holy grail of artificial general
intelligence i think language alone can
get us quite far i think it's definitely
a necessary condition but i'm struggling
to believe that it's a sufficient one
just because extra linguistic
information is also important for humans
and we constantly perceive the world
with more than one modalities we can see
things we can hear things we can touch
things so
it's kind of difficult for me to think
about this parallel universe where we
achieved agi but this is all just
language and nothing else
[Music]
the idea that an intelligent being draws
on a vast array of rich experiences was
beautifully articulated by the american
physics researcher douglas hofstadter in
his 1980 pulitzer prize-winning book
gordel escherbach
when speculating on whether a computer
programme could produce music he wrote
and i quote that such a program would
have to wander through the world on its
own fighting its way through the maze of
life and feeling every moment of it
it would have to understand the joy and
loneliness of a chilly night wind the
longing for a cherished hand the
inaccessibility of a distant town the
heartbreak and regeneration after a
human death
i read out this quotation to jeffrey to
see what he made of it so first of all i
have to say that the model probably has
that quote memorized
humans have gone through that process
and if we train these things via human
interaction and feedback
it will be learning from us people say
like you can't really understand what an
apple is unless you kind of hold it in
your hand but
humans can also talk about black holes
and we don't hold those in our hand we
just sort of talk about them in words
and mathematics and and similarly like
blind people understand what color is
not in exactly the same sense that
people that can see do but they can
probably talk about color it's aspects
and they probably know that apples are
red or green i think that this kind of
direct grounding is potentially
important but i think indirect grounding
where you
have someone in the middle like a
teacher is also a potential route do you
think that language alone could be
enough to get us to artificial general
intelligence i do
but i think it probably won't work that
way in practice just because there's a
lot of research trying to mix language
and other
media so images and video and so on and
probably that's a nice feature to have
but i do think that that's plausible
that you could get to agi with just
language now
we should say agi here means can do a
lot of things not can do literally
everything so if you did like this
language only agi then it couldn't
automatically read it by a goal or see
images it would just be able to do a lot
of things in language so in practice
probably we'll add other things
alongside
there's little doubt that current
language models hold enormous power and
will be useful well beyond their
immediate applications but if we want to
get to agi sooner rather than later
we may well need to endow agents with
those other things that jeffrey
mentioned the other capabilities which
added together would help to form a
well-rounded artificial general
intelligence
in the next episode we'll be hearing how
deep mind researchers are trying to
complement this linguistic ability with
another skill that seems to be critical
to human intelligence cooperation we
might have said the human superpower is
language because that's pretty unique to
humans and of course they're intimately
linked because communication and
language are so important for
cooperation
and that delivers the ultimate results
of our civilization
deepmind the podcast is presented by me
hannah fry and produced by dan hardoon
at whistledown productions
if you're enjoying the series we'd be
grateful if you could rate the podcast
and leave a review
we'll be back soon


Bing (GPT 4) Just Made Smartphones MUCH Smarter (next-level Android and iOS app)

Bing app now available on Android and
iOS is about efficiency over
extravagance it's on your phone so it's
not about essay writing compiling code
or making funny poems in French it's
about taking search to the next level no
I'm not sponsored by them but I think
it's mind-blowing there are four levels
of search increasing in power and
requisite intelligence and Google is
pretty much only able to do level one
and even then not always Bing can do all
four let me show you in this video
exactly what I mean taking you through
all four levels of search by the end I
honestly think I'm gonna persuade you
that smartphones just got upgraded
permanently just quickly how does it
look on mobile well if you open up the
app you will see at the bottom a bing
button on any web page just like you can
in the edge browser you can press on the
Bing button and open up Bing chat you
then have two options you can ask it
questions via the microphone or via the
keyboard for example you could ask who
is currently seventh in the Premier
League searching for Premier League
table according to the Premier League
table Brighton and Hove Albion is
currently seventh in the league with 35
points from 22 games played
they have scored 39 goals and conceded
29 goals giving them a goal difference
of 10. that's another fascinating
difference with being on mobile it
actually speaks to you I wouldn't call
it Cutting Edge text the voice but we're
just getting started you're probably
wondering how does this transform search
how does this upgrade our smartphones
well this is just a level one search we
are retrieving one bit of information
and even on this front Bing does better
than Google the exact same question into
Google gives a generalized formula about
the Premier League and what seventh
place means Bing understands exactly
what I want and gives the correct answer
I will admit if we were just comparing
level 1 searches Bing wouldn't be that
much of an upgrade you could always
click on a link to a table and see for
yourself where Brighton are maybe you're
saving a few seconds with Bing but not
really a big difference but just wait
until we get to level three and even
level 4 and Beyond searches and now you
guys are ready for level 2 searches and
what do I mean by that this time we are
retrieving two bits of disparate data
but we want to do it at the same time
not doing two separate searches by the
way I'm typing these searches into Bing
desktop so you can see them more clearly
but of course it would be even quicker
to ask them with my voice into being on
mobile I asked what are the ages of the
Eiffel Tower and the Empire State
Building and look at the difference I
can clearly see the two results on the
left in bing but on the right I'm gonna
have to click on at least two links and
scroll through the data you can just
begin to imagine the number of searches
this could apply to and we're only on
level two the next example of a level 2
surge would be to retrieve a bit of
information and do something to it for
example I asked both Bing and Google if
Microsoft's market cap doubled what
would it be there are two stages to that
question first it has to retrieve
Microsoft's current market cap then it
has to double it bing gets the answer
Google doesn't even show the market cap
not immediately at least even here I
know some of you will be thinking I
could just type in market cap find the
source get out a calculator and double
it what's the big problem yes maybe you
save 30 seconds but what's the big deal
well we haven't even gotten to level
three or level four searches yet so what
is an example of a level three search
imagine you're on your smartphone and
you're considering the Adobe Creative
Cloud and imagine you wanted to know
just how much more expensive it would be
over say a couple year time period than
DaVinci Resolve you could press the Bing
button and ask this according to this
page if I got the individual account for
two years how much more expensive would
that be in pounds than the one-off
payment for DaVinci Resolve 18. now as
I've talked about in my other Bing chat
playlist videos it understands the
context in which you're asking the
question it knows you mean this Adobe
Creative Cloud individual plan it
correctly multiplies this for two years
and then compares the total to DaVinci
resolves price now initially I thought
it made a mistake because when I quickly
checked on Google DaVinci Resolve costs
255 pounds but then when I click to buy
it it adds on vat which makes it 306
pounds in the UK so in a way that's
another win for bingy understood about
adding on vat but what makes this a
level 3 search is it did all of that
work it retrieved the two bits of
information in context and then compared
them then subtracted them giving us a
difference of 941 pounds in the price
over two years and of course you could
carry on this conversation with being
about the pros and cons of each package
for some of these searches I am not even
going to try it in Google because it
would completely flop level 3 searches
are about much more than this though
imagine you're standing on the Houston
Road and you want to get to Central
London you could conduct a level 3
search using Bing the question might be
how much longer would it take to get the
underground from King's cross to
Piccadilly Circus than from Houston to
Oxford Circus or how much longer would
it take to go from King's cross to Hyde
Park Corner than from Houston to
Victoria these are all Journeys that I
make on a regular basis and I can
confirm that the results are accurate
why is this level three because it had
to retrieve the information about one
Journey then the other and then make the
comparison our level three searches all
about addition and subtraction no check
this out you could ask how much bigger
are polar bears than brown bears and why
Google would have to do three things and
it just isn't up to it you'd have to
find the size of the average polar bear
the size of the average brown bear and
then do an analysis of why polar bears
are bigger not just a mathematical
comparison but an understanding of
comprehension an explanation of the wise
think of level three as adding a when
where why and how to level two searches
the answer by the way is quite
interesting so polar bears can weigh up
to 1700 pounds versus thirteen twenty
pounds but we didn't just want to know
that we wanted to know the reason why
and apparently the reason why is and I
can believe this is that they need more
body mass and fat to survive in the cold
Arctic environment they also have a
bigger skull and larger teeth to hunt
seals so now I've got more of an idea
not just how much bigger they are but
why is through Evolution that they ended
up being bigger but we have waited long
enough what about level 4 searches well
think about a complex interesting search
like this how much older is Stonehenge
than the Coliseum in Rome expressed in
human lifetimes Bing has to do four
things find the age of Stonehenge the
age of the Coliseum the difference
between them divided by the average
human lifetime it does this successfully
and we have a really interesting result
that is about 38 human lifetimes older
if we take the older date for Stonehenge
that is an insane level of intelligence
for a single search now we're genuinely
talking about saving a minute or more
compared to using Google that is a big
enough difference to really matter and
that's not the only example I could give
you of a level 4 search I'm sure you
could tell me hundreds of ideas in the
comments but try this one going back to
the Premier League I could ask if
Arsenal beat Leicester and Man City draw
Bournemouth how many points ahead would
Arsenal be I didn't have to specify
Premier League I didn't have to say
fixture and of course I didn't have to
tell it the rules of the Premier League
about three points for a win Etc it knew
exactly what I was asking calculated the
two results found the league positions
and then found the difference now you
don't have to be into sport to know that
that's an amazing answer to a complex
search query think about how this
applies to your domain your career your
interests and come up with some level 4
searches that you could ask which brings
me on to my final point the question for
every smartphone user will be is the
small but real risk of hallucinations
more meaningful to me than the
additional seconds and minutes required
to perform multiple searches for me the
answer is already yes but clearly what
we decide will depend on the topic right
for sport it's fine for maybe a
life-changing house decision maybe not
by the way with those new modes that
Bing is debuting this week that I'm
going to do a video on where you can
pick Precision over creativity soon you
might not even need to make a choice
between hallucination versus efficiency
very much looking forward to doing a
deep dive into those modes by the way
and yes what you may find is that being
when you do voice recognition makes the
occasional mistake in what you're asking
it that certainly happened to me open AI
who are the partners to Microsoft are
responsible for whisper which I have
downloaded locally and tried out it is
phenomenal at voice recognition as good
arguably as human transcribers now I
don't think whisper is powering the
current Bing voice recognition but
because of Microsoft's partnership with
openai I wouldn't be surprised if by the
end of the year whisper doesn't power
Bing and that should make voice searches
almost flawless I know what some of you
are thinking but what about the
conversation limits well according to my
sources they should be ending soon
enough too now of course we are days or
maybe weeks away from Google Bard's
system being released but I've got a
feeling that Google doesn't want those
multiple searches to go away anytime
soon being on the other hand doesn't
care so I'm not sure there's much
incentive for Google to maximize the
efficiency of Bard early indications
about the size of the Lambda model
that's powering Bard and the leaked
rumors that every Google employee is
spending two to four hours a day to
improve the output of Bard suggests that
my feeling might have some basis but
maybe Google will surprise us all or
Facebook they just released llama a
large language model that outperforms
even palm and therefore likely Bing on
some metrics I'm hoping to use it soon
and Amazon well they just released a
model that outperforms GPT 3.5 by 16 so
they're not out of the game either by
the end of 2023 who knows who will be
the king of search and the king of your
smartphone but it will definitely be a
smarter smartphone if you agree or even
if you disagree let me know in the
comments please do leave a like and
thank you very much for watching have a
wonderful day