Enter PaLM 2 (New Bard): Full Breakdown  - 92 Pages Read and Gemini Before GPT 5? Google I/O

less than 24 hours ago Google released
the Palm 2 technical report I have read
all 92 Pages watch the Palm 2
presentation read the release notes and
have already tested the model in a dozen
ways but before getting into it all my
four main takeaways are these first Palm
2 is competitive with gpt4 and while it
is probably less smart overall it's
better in certain ways and that
surprised me second Google is saying
very little about the data it used to
train the model or about parameters or
about compute although we can make
educated guesses on each third Gemini
was announced to be in training and will
likely rival GPT 5 while arriving
earlier than GPT 5. as you probably know
Sam Altman said that gbt5 isn't in
training and won't be for a long time
fourth while dedicating 20 pages to bias
toxicity and misgendering there wasn't a
single page on AI impacts more broadly
Google boasted of giving Gemini planning
abilities in a move that surprises I am
to say it makes open AI look like
Paragons of responsibility so a lot to
get to but let's look at the first
reason that Palm 2 is different from a
gpt4 on page 3 they say we designed a
more multilingual and diverse
pre-training mixture extending across
hundreds of languages and domains like
programming mathematics Etc so because
of the text that they train Palm 2 on is
different to the text that openai train
gpd4 on it means that those models have
different abilities and I would say Palm
2 is better at translation and
Linguistics and in certain other areas
which I'll get to shortly if that's data
what about parameter count well Google
never actually say they only use words
like it's significantly smaller than the
largest Palm model which was 540 billion
parameters so sometimes they say
significantly other times dramatically
despite this it's significantly
outperforms Palm on a variety of tasks
so all the references you may have seen
to imminent 100 trillion parameter
models were bogus skipping ahead to page
91 out of 92 in the model summary they
say further details of model size and
architecture are withheld from external
publication but earlier on they did seem
to want to give hints about the
parameter count inside Palm 2 which
openai never did here they present the
optimal number of parameters given a
certain amount of compute flops scaling
this up to the estimated number of flops
used to train Palm 2. that would give an
optimal parameter count of between 100
and 200 billion that is a comparable
parameter count to gpt3 while getting
competitive performance with gpt4 Bard
is apparently now powered by Palm 2 and
the inference speed is about 10 times
faster than gbt4 for the exact same
prompt and I know there are other
factors that influence inference speed
but that would broaden fit with an order
of magnitude fewer parameters this has
other implications of course and they
say that Palm 2 is dramatically smaller
cheaper and faster to serve not only
that part 2 itself comes in different
sizes as Sundar pichai said Palm 2
models deliver excellent foundational
capabilities across a wide range of
sizes
we've affectionately named them gecko
order bison and unicorn
gecko is so lightweight that it can work
on mobile devices
fast enough for great interactive
applications on device even when offline
I would expect gecko to soon be inside
the Google pixel phones going back to
data Google cryptically said that the
pre-training Corpus is composed of a
diverse set of sources documents books
code mathematics and conversational data
I've done a whole video on the data
issues that these companies face but
suffice to say they're not saying
anything about where the data comes from
next they don't go into detail but they
do say that Palm 2 was trained to
increase the context length of a model
significantly beyond that of palm as of
today you can input around 10 000
characters into Bard but they end this
paragraph with something a bit more
interesting they say without
demonstrating our results show that it
is possible to increase the context
length of the model without hurting its
performance on generic benchmarks the
bit about not hurting performance is
interesting because in this experiment
published a few weeks ago about
extending the input size in token up to
around 2 million tokens the performance
did drop off if Google have found a way
to increase the input size in tokens and
not affect performance that would be a
breakthrough on multilingual benchmarks
notice how the performance of palm 2 in
English is not dramatically better than
in other languages in fact in many other
languages it does better than in English
this is very different to gpd4 which was
noticeably better in English than in all
other languages as Google hinted earlier
this is likely due to the multilingual
Text data that Google trained Palm 2
with in fact on page 17 Google admit
that the performance of palm 2 exceeds
Google Translate for certain languages
and they show on page 4 that it can pass
the Mastery exams across a range of
languages like Chinese Japanese Italian
French Spanish German Etc look at the
difference between Palm 2 and palm in
red now before you rush off and try Bard
in all of those languages I tried that
and apparently you can only use Bard at
the moment in the following languages
English US English what a Pity and
Japanese and Korean but I was able to
test Bard in Korean on a question
translated via Google Translate from the
mmlu dataset it got the question right
in each of its drafts in contrast Gypsy
4 not only got the question wrong in
Korean when I originally tested it for
my smart GPT video it got the question
wrong in English in case any of my
regular viewers are wondering I am
working very hard on Smart GPT to
understand what it's capable of and
getting it benchmarked officially and
thank you so much for all the kind
offers of help in that regard I must
admit it was very interesting to see on
page 14 a direct comparison between Palm
2 and gpt4 and Google do admit for the
Palm 2 results they use Chain of Thought
prompting and self-consistency reading
the self-consistency paper did remind me
quite a lot actually of smart GPT where
it picks the most consistent answer of
multiple outputs so I do wonder if this
comparison is totally fair if Palm 2
used this method and gpt4 didn't I have
to talk about these benchmarks more in
another video otherwise this one would
be too long a quick hint is that why no
Grand is about identifying what the
pronoun in a sentence refers to Google
also weighed into the emerging abilities
debate saying that Palm 2 does indeed
demonstrate new emerging abilities they
say it does so in things like multi-step
arithmetic problems temporal sequences
and hierarchical reasoning of course I'm
going to test all of those and have
begun to do so already and in my early
experiments I'm getting quite an
interesting result Palm 2 gets a lot of
questions wrong that gpt4 gets right but
it can also get questions right that
gpt4 gets wrong and I must admit it's
really weird to see Palm 2 getting
really Advanced college level math
questions right that gpd4 gets wrong and
yet also when I ask it a basic question
about prime numbers it gets it kind of
hilariously wrong honestly I'm not
certain what's going on there but I do
have my suspicions remember though that
recent papers have claimed that emergent
abilities are a mirage so Google begs to
differ when Google put Palm 2 up against
GT4 in high school mathematics problems
it did outperform gpd4 but again it was
using an advanced prompting strategy not
a hundred percent different from Smart
GPT so I wonder if the comparison is
quite Fair what about coding well again
it's really hard to find a direct
comparison that's fair between the two
models overall I would guess that the
specialized coding model of palm what
they call Palm 2s is worse than Gypsy 4.
it says it's pass at one accuracy as in
past first time is 37.6 remember the
Sparks of AGI paper well that gave GT4
as having an 82 percent zero shot pass
at one accuracy level however as I
talked about in the Sparks of AGI video
the paper admits that it could be that
Gypsy 4 has seen and memorized some or
all of human eval there is one thing I
will give Google credit on which is that
their code now sometimes references
where it came from here is a brief
extract from the Google keynote
presentation how would I use Python to
generate the scholars move in chess okay
here Bard created a script to recreate
this chess move in Python and notice how
it also formatted the code nicely making
it easy to read we've also heard great
feedback from developers about how Bard
provides code citations and starting
next week you'll notice something right
here we're making code citations even
more precise if Bard brings in a block
of code just click this annotation and
Bard will underline the block and link
to the source as always it seems the
appendix contained more interesting
information sometimes than the main body
of the technical report for example we
get a direct and fair comparison between
Gypsy 4 and palm 2 or I should say flan
Palm 2. that is the instruction
fine-tuned version of palm 2.
essentially that's the version where
it's been fine-tuned to get better at
following a question and answer format
but anyway the original palm 2 scored
78.3 and flan Palm 2 scored 81.2 that's
below the 86.4 percent of GT4 and that's
why my broad conclusion is that Gypsy 4
is a bit smarter than Palm 2 but as I'll
be showing over the coming days and
weeks there are genuinely quite a few
areas in which palm 2 is better than
gpt4 what about the big bench which was
designed to be particularly tough for
language models I talked a lot about
this in my earliest videos well the
graph is going to look pretty weird
because Palm 2 has improved upon Palm
while reducing the number of parameters
so the graph kind of doubles back on
itself back up here up to around 69
according to the technical report I
would say this is quite a major moment
in human history there is now virtually
no language task that the average human
can do better than palm 2. of course
expert humans can do better in
individual domains but the average human
is now worse in virtually every domain
of language here you can see that
confirmation of the big bench hard
results for flan Palm 2 69.1
interestingly in the original chart Palm
2 is even claimed to have higher
performance than that at 78.1 if you
remember the reason we can't compare
that to gpd4 is that in the technical
report for gpt4 they admit that during
their contamination check we discovered
that portions of big bench were
inadvertently mixed into the training
set and we excluded it from our reported
results before we get to Gemini Google
show off in the latter half of the
technical report with examples of of
linguistic ability like writing
paragraphs in tajiki and then
translating them into Persian they go on
to show examples in Tamil and they are
really making a big point of showing off
its multilingual capabilities at this
point and I'm going to admit this is my
personal opinion Google then Strays into
dozens of pages on bias toxicity and
gender interestingly some of the people
paid to assess these risks were paid
only 1.5 cents per judgment these things
do need to be addressed of course but it
was somewhat shocking to me to see 20
pages of that and not a single page on
the broader AI impacts as many of you
may know I have criticized openai plenty
of times on this channel but compare
their technical report which goes into
far more detail about what we need to
monitor the closest Google got was
showing how their Universal translator
could be used for deep fakes Universal
translators and experimental AI video
dubbing service that helps experts trans
later speak his voice while also
matching their lip movements let me show
you how it works with an online college
course created in partnership with
Arizona State University what many
college students don't realize is that
knowing when to ask for help and then
following through and using helpful
resources is actually a Hallmark of
becoming a productive adult
universities
it just seems a massive black hole when
one of their recent former employees
Jeffrey Hinton had this to say this week
on CNN you've spoken out saying that AI
could manipulate or possibly figure out
a way to kill humans how could it kill
humans if it gets to be much smarter
than us it'll be very good at
manipulation because it will have
learned that from us and very few
examples of a more intelligent thing
being controlled by a less intelligent
thing and it knows how to program so
it'll figure out ways of getting round
restrictions as we put on it it'll
figure out ways of manipulating people
to do what it wants it's not clear to me
that we can solve this problem
um I believe we should put a big effort
into thinking about ways to solve the
problem I don't have a solution at
present I just want people to be aware
that this is a really serious problem
and we need to be thinking about it very
hard this all seems particularly
relevant when Google made this
announcement about Gemini their rival to
Gypsy 5. all this helps set the stage
for the inflection point we are at today
we recently brought these two teams
together into a single unit Google
deepmind using the computational
resources of Google they are focused on
building more capable systems safely and
responsibly
this includes our next Generation
Foundation model Gemini which is still
in training
Gemini was created from the ground up to
be multimodal
highly efficient at tool and API
Integrations and built to enable future
Innovations like memory and planning
that ability to plan may ring a bell
from the gpt4 technical report which
said this novel capabilities often
emerge in more powerful models some that
are particularly concerning are the
ability to create and act on long-term
plans remember Google didn't identify
planning as a risk but as a selling
point for Gemini next Google talked
about accelerating their progress which
was again directly mentioned in the gpt4
technical report it said one concern of
particular importance to open AI is the
risk of racing Dynamics leading to a
decline in safety standards the
diffusion of bad norms and accelerated
AI timelines Each of which heightens
societal risks associated with AI we
refer to these here as acceleration risk
and make no mistake Gemini will be very
accelerated from Palm 2. it looks set to
use the the TPU V5 chip which was
announced back in January of last year
and on page 91 of the Palm 2 technical
report they say that that model used TPU
V4 now it should be said that Palm 2 is
leading to some impressive medical
applications as I actually first
reported on seven weeks ago without
quite realizing it here's Med Palm 2. We
Believe large language models have the
potential to revolutionize Healthcare
and benefit Society mad Palm is a large
language model that we've taken and
tuned for the medical domain
you know medical question answering has
been a research Grand Challenge for
several decades but till date the
progress has been kind of slow but then
over the course of the last three to
four months first with metform and
metform2 we have kind of like broken
through that barrier unlike previous
versions mad Palm 2 was able to score 85
on the usmla medical licensing exam yeah
this is immensely exciting because
people have been working on medical
question answering for over three
decades and finally we are at a stage
where we can say with confidence that AI
systems can now at least answer USMLE
questions as good as experts as many of
you may know the CEO of Google as well
as the CEO of Microsoft and Sam Altman
and the CEO of anthropic all went to the
White House to discuss AI risk and
opportunity but given that the main
outcome from that seems to be 140
million to establish seven new AI
research institutes that feels a little
slow given all the acceleration that's
occurring because as Google somewhat
soberly conclude their rapport we
believe that further scaling of both
model parameters and data set size and
quality as well as improvements in the
architecture and objective we'll
continue to yield gains in language
understanding and generation they are
not slowing down and the world hasn't
yet caught up thank you so much for
watching to the end and have a wonderful
day


Morality, uncertainty, and autonomous systems (Luciano Siebert) - 1st AiTech Symposium

thank you note thank everybody for
coming here to the high tech symposium
I'm gonna be talking to you about
morality uncertainty and autonomous
systems and the message I wanted to give
you that uncertainty can be a driving
force to meaningful human control this
project is developing a collaboration
with cattle are younger and arune
vanderhoven so first I would like to
bring you to and illustrative examples
to highlight some of the main points
that I'll be discussing okay let's
consider here in a not so distant future
you are sitting on a desk by the way
this is my desk and I'm sitting here
with my VR monitors working and just got
a coffee and suddenly a lot of noise and
then I think okay today is actually it's
Thursday right it's not Monday it's not
the testing of the arm season maybe the
serious I see some smoke coming from the
hallway and okay probably should get out
of the building right and then I go to
the hallway and then fine suddenly I see
Robo is the robot for burning offices
it's not it's gonna burn the offices
gonna help you and so it's actually a
new regulation that came just came
around and then every building in the in
the country must have a robot okay but
Robo mingo is like to support the fire
emergency evacuation okay so what can
you do it can guide you to the best or
the shortest and best exit route can you
remove some obstacles on the way it can
extinguish the fire a lot of things that
robot can do okay yeah we can definitely
can think about a lot of other things
sure but what do we want
Robo to do so according to which
guidelines should Robo act it was
brought on the previous talks a lot of
different ways so we don't want I also
choose to embed some specific rules we
want to that Robo should act according
to some values to some norms that this
plays a role in our society when should
Robo act or not act if the Robo does not
really know the outcome of a specific
goal it tries to help you and get you a
really bad situation that we want robot
to do this
and we were able be able to justify a
given action if you make something can
it can it be accomplished can you
explain Kenny we have some
accountability not for robbing self but
for developers or for the users and
should we rob should we actually develop
Robo or not because any approach may
have strong ethical implications an
approach we take so let's think about
some possible ways one Robo should act
according to what people want so there's
someone so whoa fire I'm getting me out
of here now okay but what does it really
mean it can how how can we understand it
can be so it can be a comment like your
thing can talk to your phone assistant
okay but do we really want what we say
can Robo really interpret these kind of
things it's really not an obvious
decision so maybe you can make some
rules previously when I analyze all kind
of scenario is going to be in the
offices and then okay if this happened
the N Robo should do this and if these
other things happened then through Robo
should act another way but this is not
also really possible because it's so
many different contexts out in the real
world for a simulation small system okay
but really for the real world is very
different there's some other ways okay
maybe Robo could have some model of
moral cognition and then I want to make
presence that's one of the things I'm
most investigating in my projects is
this kind of how to embed this models of
moral cognitions ain't you machines okay
Robo could have a model to interpret
what this person want in a different
situation so can get a little bit more
adapter but does this model explains
offer is is applicable for different
people for all situation maybe you can
get something okay let's let's use AI
let's use machine learning and let's use
some automatic at math inference of
humans preference there's something
people say over in inversion for some
learning so Kim the robot kind of
understand by the behavior these people
what are the values what are they
working towards is also way there's also
a lot pros and cons but okay let's
assume we can kind of teach robot how to
act according to what people want
so I'm trying to investigate this models
of more cognition and how ultimate
inference can play a role but even if
you do that there is someone else there
let's say don't worry about me go to the
fifth floor there's someone in the fifth
locker really needs assistance so robot
should help the person a should really
go to the fifth floor s person B
commanded then there is already some
what's the right thing should do and
there's one more thing also for the
designer and the engineers develop Robo
how they translated is and this the this
under sting of the words there they have
any any assumptions of the human
behavior so even if it can act according
to what people want comes a great
question so what if there is no
agreement and what if people are not
consistent I say now I want this and
then me thirty seconds I want something
else and they should a robo go up and
down the room trying to figure out what
people want will be effective could we
find an overall solution that would make
everyone satisfied
hmm it's a good question so let's try to
act according to what is right okay what
is really right Jaron gave a green
stairs leg also the possible ways like
he said like the Cantonese room that is
maybe almost in Europe are all Tillet
enemies me or other people can
understand it's not it's not the
consensus I'm pretty sure even in this
room we could not find any consensus and
any of different situations but let's
assume we go for utilitarianism it's a
consequentialist theory what does the
mean is the final consequence of an EF
an action that really matters
okay so let's say Drobo should in a way
try to maximize happiness and well-being
for the context where it is being
developed but what if robot blocks one
room just English the fire do we really
want and kill some people inside it's
saving a lot of people do but do you
really want that let's go another
approach we can say kangan ism is a
deontological theory that says action
self is can be right or wrong but what
if Robo save someone there is says the
life of someone on a wheelchair but
dozens are
in terms of people get severely injured
for this get really so do we really want
that okay so how to act on this there's
also some things I've been working on my
research project also to understand is
what's the wage how to move given these
normative s is more uncertainty one pick
and choose choose choose an ethical tier
that you want to go on talk without
stakeholders involved with this agree
okay we're gonna go to this theater
isn't approach okay and go for it they
have pros and you have a lot of cons
there a lot of a possible ways to
consider multiple theories if you
consider several theories that may have
a relevance that people have a degree of
credence on that you could if you think
about the Parliament you can combine
these views different political views is
not there always the majority wins is
also about trade-offs then for specific
situations okay so Robo face a lot of
uncertainty state space what is the
context of the word what is the
consequence of an act and what is the
right thing to do so a lot of
uncertainty but how can we move on so I
can I'm gonna just connect back right to
Phillipos talk to the tracking condition
for a meaningful human control it says
that an autonomous system should respond
to the relevant moral reasons of the
relevant humans and the relevant factors
in the environment in which the system
operates okay there comes a lot of
question what are the relevant my
reasons who were the relevant humans in
limits you think so what should i do
what should i do what should any
autonomous system do in this kind of
situation and what is the right way of
thinking or reasoning and this is
actually thinking about thinking or
reasoning about reasoning this is called
meta reasoning meta reasoning so on is
how an agent can select its computations
without knowing previously what the
outcome if I can just try to connect
back a little bit to the should my
illustrative example let's say okay we
have fire situation row
okay why should I really do and not for
and I'm not sure
and then what there's a lot of people
that have a lot of strong opinions on
the anarchy you should be a shoe do that
okay but let's see we have what the
relevant people want that we could try
to understand into some models more
models of more cognition we can also try
to automate inference of people's
preference we can combine those what is
the right thing to do also we can
combine those different ethical theories
different of different views and what we
want the system to do it doesn't mean we
don't want the season to act in all
kinds of situations
it'll all kind of actions and these I
don't mean that that the autonomous
system should be able to choose this
side by itself all the time it should
have a lot of human interaction human
evaluation in so fallbacks season and
specific sense so that can lead us to a
more safe approach to the development of
such systems and now I just like to
conclude here just bringing me in some
cattle I and your own here and I would
like to say let's integrate let's talk
about possible way outs let's bring some
specific context where we this kind of
things can be applied thank you very
much
[Applause]