Bing Just Upgraded YouTube (and changed the internet forever)

Bing just upgraded YouTube and changed
the internet forever and I'm going to
show you exactly how I know that
statement sounds crazy especially about
something called Bing but let me prove
it to you you can now open up any
YouTube video and click on the Bing icon
in the top right of the edge browser now
it does currently have to be in Dev mode
and I'll tell you about that at the end
but this is what will come up an option
to chat and an option to compose and
what's so revolutionary about that is
not just the intelligence of Bing chat
and I've got a whole playlist on that
it's the fact that the chat is in the
context of the video it understands what
video you're watching and you can chat
about it with Bing a bit like having a
highly paid assistant who's sitting
there watching the video with you take
this example I was watching a video
about a review of the s23 ultra by
Marquez Brownlee and I said what are the
screen dimensions of the s23 phone
lineup and base storage capacity it gave
me the answer and all of the details
check out now I know what you're
thinking I could have just opened a new
tab put that into Google search but what
I couldn't have done in Google search is
what I did next which is ask in context
how old is this guy and where did he
grow up Google search would be like who
are you talking about whereas Bing knew
the video I was watching and gave me a
correct contextual answer now you
comment if you disagree but I think that
is a massive upgrade to YouTube now I
know what you're thinking shouldn't I be
concentrating on the video but we all
multitask I know that you check your
emails sometimes while watching my
videos or maybe you scroll through
Reddit while you have a tech review
playing we all do it sometimes the
difference now is it's as if we've got
someone watching with us take this
example from one of my own videos where
I talk about gpt4 and how it's likely
comparable to the par model while the
video is playing you can bring up chats
and ask questions about Palm you can
teach yourself as you're going along
some people of course will want to wait
to the end of the video but this is
crazy search integrated into the video
as if you're carrying on a conversation
with another person about the video you
just watched teaching yourself about
topics with an AI that as I talked about
in another video has an estimated IQ of
114 and Rising but the craziness is only
just beginning I have four more examples
of how this is going to upend the
internet this is not just about YouTube
and each example is more crazy than the
last imagine you're reading a Wikipedia
article all you have to do is highlight
some text and then press Bing again in
the top right and it will give you this
little no pasted from page and that's
the thing that you highlighted it will
ask you what do you want to do with the
text and you can ask contextual
questions about what you're reading so I
was learning a fact about London's GDP
and asked how does it compare to Paris
and it gave me an answer a quick note by
the way that as I talked about in
another one of my videos on the limits
of Bing you are restricted to five
replies and eventually you you will get
an answer like this thanks for this
conversation I've reached my limit we'll
hit a new topic please so I think
Wikipedia just got upgraded too what
about the compose feature well I tested
it out I said write about Bing chat's
rivalry with Google bard and I chose an
enthusiastic tone in an email format and
a long draft honestly I was expecting
something slightly Anodyne it's a
slightly controversial topic and I
thought Bing would steer away from any
corporate comparisons with Google bard
well I was wrong it wrote a long and
detailed email about Bing chat and it
did not refrain from comparing itself to
Google bard it said well let me tell you
why Bing chat is superior to Google bard
in every way he said Google bard is not
yet available which is true it did then
hallucinate and said Bing chat is open
to everyone right now which as you know
is not true there is still a waitlist
but then it got even more brutal it said
you Bard is based on a lightweight model
version of Lambda which requires
significantly less computing power this
is true and it concludes that will
deliver less impressive results or we
shall see Bing we shall see it says
Google bard can only simplify complex
topics but cannot answer specific
questions or do Advanced searches like
Bing chat can it goes on to say that
googlebard has been reported to have
some AI shortcomings that make it less
reliable and trustworthy than Bing chat
anyway enough about Bing chat's ego my
only question about Bing's compose
feature is how does it differ too much
from Bing chat feature I get that you
have these templates and that's cool but
I think the templates are going to have
to get a lot more detailed a lot more
nuanced to be significantly more useful
than Bing chat itself time for the final
two bits of craziness imagine you're
browsing a forum like Reddit and you
read an interesting post with a link
inside you can highlight the link go up
to Bing again and ask for a summary of
the article without even opening the
link yourself it gave me this summary
about gpt4 talking about how it might
have 175 billion parameters the article
says and be trained on four trillion
tokens now of course I did my due
diligence and I actually checked the
article and look as assuming gpt4 has
175 billion parameters it should be
trained on about 4 trillion tokens now
of course I'm not guaranteeing that
every single detail is correct if you
are a little bit lazy sometimes like me
this is going to make browsing the
internet a lot easier and this is only
in beta this time next year when gpt4 is
integrated these summaries are going to
get a lot more detailed and a lot more
accurate but I wanted to push Bing chat
in one last way I went back to the
original reply I highlighted it press
the Bing button and then wrote write a
reply to this as a dystopian pessimistic
sonnet and I think the result is so good
so creepy that I want to read out in
full
the AI is coming fast and strong it will
surpass us all in every way we do not
know how long we can prolong our feeble
Reign before we fade away the internet
was just a Prelude to the massive change
that AI will unleash we have no clue
what it will make us do or how it will
manipulate our speech we're in the
generational leap phase where every
model makes the last one moot we cannot
keep up with its rapid Pace or
understand its logic or root we do not
know where lies the ceiling high but we
may reach it sooner than we die I agree
but it's kind of freaky to be told that
by the AI itself just finally to wrap up
how do you get access to it well you go
to the Microsoft Edge Insider website
the link will be in the description and
download Dev channel for Windows 10 11.
I believe you do have to have got
through the wait list for Bing chat
itself but let me know in the comments
if you can access this Dev mode of Edge
without the waitlist for being I believe
it's very much worth it just like Bing
chat itself is and one cheeky final
comment before I go I heard rumor on the
Bing subreddit that the Bing chat could
give you time stamps of the transcript
to a video and I was about to excitedly
talk about those timestamps for example
these on my video before I realized that
all the timestamps are wrong and it
gives the video as being 6 minutes 50
long when it's actually four minutes 30.
I tried it on the Marquez Brownlee video
too and it also completely flops so this
ability isn't quite integrated yet don't
believe the hype if this video sparked
any interest in you about the
capabilities of Bing AI check out my
playlist where I go through the
sometimes shocking things it can do and
just how intelligent it is if you found
this video in any way helpful please do
leave a like and leave a comment to let
me know
have a wonderful day


Agora: M.  Brandão: Fairness and explainability in robot motion

this community
um shall i start
do you want to start the recording
yes i started recording so confident all
right
all right so i'm going to talk about uh
fairness and explainability in robot
motion
uh which are probably concepts that um
one wouldn't uh wouldn't think would
be relevant to robot motion at the first
instance maybe but i hope i hope
to convince you uh otherwise
so for those of you who are not familiar
with the
path and motion planning these are
technical problems that are very
important in
robotics but not only robotics even in
your
in your phone apps when you uh ask to go
to a certain place on your
gps using google maps or something you
have to
solve a path planning problem so uh to
find
a sequence of uh of uh
of steps over over network of roads for
example to get to the cone
and uh the motion planning problem is
the continuous version of that so
where you want to find for example full
body postures of a robot
so exactly where each of its motors
should be and its position in the world
should be
and this is both for this kind of robots
with an arm
and the wheels or humanoid robots
that need to do steps and avoid
obstacles and balance and etc
legged robots but also autonomous
vehicles when you want to
find a trajectory maybe for the next 10
seconds that
avoids obstacles so it's safe but also
perhaps
is smooth and comfortable for the
passengers
and even in warehouse automation
so computing the paths or motion for
for many of these robots to reach their
destinations without colliding with each
other
and making sure they satisfy certain
constraints so this is the
path motion planning um problem
um i'll start with the uh explainability
of robot motion so to try and uh unpack
this
concept of explainability i'm going to
start uh
basing myself on some literature review
and some user studies
derive at some working concepts of what
could explanations for robot motion
uh look like but then use the actually
uh implement concrete uh explanation
generation algorithms or explainable
planners
and get uh feedback from users to
iterate the concepts and
uh figure out what are the important
design considerations when thinking
about
explanations for about motion
and so why would you actually want to
explain
the way the robot is moving or the the
output of a motion planner
so here are two examples the your robot
might move in a way that is that you did
not expect so for example here this
robot is taking this
long path through the right around
around the table to reach first
shelf and the operator or the user might
think
why why are you doing such a long path
you might be spending more battery than
you should i would expect
that that you you you or the robot would
take this other path that is shorter
um through the other side of the table
and the
another reason is that sometimes or
actually very often
planning algorithms just cannot find a
solution so they will tell you
uh yeah sorry uh i could not find a
solution
after i tried for so long or i was
unable to solve this planning problem
so i cannot get the robot to where you
want it to be
and then the the user or the even the
developer is is left with no clue as to
what they should do to to fix this issue
why why is it failing
so i i've actually recently done a user
study with
both developers of motion planning
algorithms and expert
users who use it on a daily basis of
motion planning algorithms
and most of them kind of the recurring
uh theme and opinion
was that explanations could be useful
specifically for debugging
of these algorithms and also for
mechanical design and for
iterating the mechanical designs of
robots so these
experts uh these were around 20 or
25 experts they were
commonly saying things like explanations
could
accelerate the debugging process or they
could even help
the developers understand the inner
workings of motion planners because even
though they develop the algorithms
themselves often it's not intuitive uh
the outputs you get why why is it that
the output is the
way it is so the the sometimes the
expectations of developers do not match
the actual
outputs of the algorithms and
explainability could even even suggest
algorithm changes so thanks to changing
the algorithm such as to
to better match the expectations or they
could even suggest uh
changes to the robot model so that
basically the the
mechanical design of the robot or
morphology or actuation changes
so of course this is the point of view
of experts and
probably the different people will be
interested in making different questions
and they will be interested in different
kinds of answers
when they ask questions about robots so
a developer as i said could ask
questions about a certain plan that the
robot has
in order to to make changes to the
algorithm improve the algorithm
or find the bug but maybe a la user
could ask questions about what the robot
is doing
imagine or imagine a warehouse worker
could ask
questions about what the robot is doing
in the warehouse in order to better
understand how the planet works to
better
be able to predict how the the robot
will move and so
and better collaborate with the robot
maybe but the mechanical engineer might
ask questions about the robot
i might ask questions um that that are
based on the robotic design so for
example why can't you why can't you
reach
for this object and uh this maybe a
mechanical
engineer would like to hear something
like because my arm is not
long enough and so they they will know
that they should change the mechanical
design of the arm
and even an and an architect could ask
questions in order to be able to
to change the layout of the warehouse
for example so it depends on the user
of course then uh what kind of
explanation so what can explanations
look like
of course this again will depend on who
the user is and what they're interested
in
but uh of course there are also
different ways to answer the same
uh different possible explanations for
for a certain question so i've i've
uh organized this into problem-centered
explanations and algorithms centered
explanations in this in this recent
paper
so uh if you see this example here on
the right where the robot does path a
but the user expected plus b b so why is
it that you took path instead of b
the explanation could be based on costs
so because
b would take more energy or because of
constraints so because
b would violate a certain constraint b
would actually be in collision you don't
really see it but it would be in
collision
it could be based on robot design
because the robot cannot fit through
corridor y so it's it's
based on the on the dimensions of the
robot or based on the environment
because
the there's an obstacle there's a table
in a certain location
on the other hand there are algorithm
centered explanations so you could say
that
the algorithm found path a not b because
you didn't run the algorithm for long
enough if you had
run it for long enough it would have
actually found b
or because the algorithm is not optimal
so of course there can be different
explanations and also similar
explanations to these
if you're wanting to explain failure
so uh so now i have a kind of a set of
working
examples of possible explanations and i
um
to better tease out what what are the
actual design considerations
and and the how what how you should
actually
show these these explanations i uh went
ahead and
did some prototype implementations of
these
of this explanation so one was the
constraint-based explanation
so imagine your algorithm fails so you
cannot find a path
for the robot to reach for this water
tab here for example on the left
and it could and i made an algorithm
that automatically generates
explanations for this kind of problems
so it will say
i couldn't find the solution because i
cannot simultaneously
touch the water tap or grasp the water
tap and
avoid collision for example though i
would be able to do this if the water
tap was
15 centimeters closer for example
and another kind of explanation
algorithm that i've
done is based on initialization so it's
an algorithm centered explanation
so you could say i failed to find a
solution because
the initialization which is a kind of
step of the
uh planning algorithm utilization was
in the basin of attraction of an
infeasible um solution
though you would you would have been
able to find a solution if you had used
a different strategy
so you could also say because you used
uh this initialization strategy instead
of the other one
so how i generate this kind of
explanation is basically for this
initialization
i just try a different kind of
initialization scheme initially
method and if that works i can blame the
initialization scheme basically states
that
it's the the that's the reason for
constraint based explanations i
basically
solve many relaxed problems so problems
that
do not need to satisfy all the
constraints of the original problem so
there are subproblems
and i try to find that problem that
satisfy as many
constraints as possible so that is
as close as possible to the original one
so i then
uh showed these kind of explanations to
um the same expert users as
as before and uh so
well one good thing is that most of them
were satisfied with explanations so in
this kind of
question like how how how much are you
satisfied with this kind of explanation
one to seven
so they are in general they were
satisfied with this with these kind of
explanations
some explanations more than others but
overall more satisfied with than
the kind of output they currently get
from motion planners on the other hand
i got some interesting insights such as
users were not sure
as uh whether one should see
when there are multiple possible
explanations whether the user should see
should see only one or a set or all
possible explanations
so it could be the case that you could
make a problem feasible but both
by saying that two constraints don't
conflict
or by by saying that you could change
the initialization
uh method right so which which kind of
things should we show
or should we show all the options
another insight was that
sometimes the language was hard to
understand potentially for the users so
we we might have to tune
uh the the kind of language uh
that you use depending on the user other
other users complained that it was not
clear to them
why there was a collision so even though
the explanation was because there is a
collision it was not clear there was
actually a collision so
visually visualization aids might be
important to make things more
interpretable and intuitive but perhaps
the most
important um insight from this user
study was that
many people said that the explanations
should go deeper
and for example not just say i couldn't
find a solution because
uh i can't uh reach uh
for this and avoid collision at the same
time but actually say
why it is that you cannot reach for this
and
set the cyclist at the same time so for
example you could say in this case you
should go deeper and say well
you can the the water tap is too further
away from the beginning of the furniture
that's why you cannot
solve this problem so what should the
explanations look like then the
visualization
theme was very common around these
experts inputs so they they suggested we
should use highlighting or
visualizations of feasibility regions
etc to improve
but also abstraction so sometimes it's
not enough just to say
uh constraint one conflicts with
constraint two you might have to use
intuitive concepts
for example say because the environment
is too cluttered
or because the object is not close
enough but these are very
abstract things that um
we will have to find uh methods uh
that this is i think an open problem
that we still have to find
methods that are able to come up with
these abstractions automatically
and finally this deep explanations theme
of uh so not just say
uh because two tasks conflict but this
but the same widely conflict
so i then um did another
uh user study with a with a different
kind of method
so i uh this was not just a very simple
method as the ones
as the ones before but a more involved
one using advanced
algorithms and more uh perhaps more
visualization
focused so here you want to ask why
uh so i'm and i was also interested in
seeing
so if explanations are actually
effective
at helping users understand the problem
so i wanted to evaluate this depending
on the the kinds of
design choices you make so for this
specific problem
you see here there's a map with blue and
red areas
the blue areas are easy to move on and
the red arrows are hard to move on so
you have to go slower
and then the shortest path or the
fastest path is
the path a which goes around this
long distance and path b is what user
expected
and so the user asks why did you take
path a instead of path b which is
shorter and takes these
terrors here this is a staircase
and so i i've proposed some new
algorithms to generate explanations
for this kind of questions based on
inverse shortest paths
algorithms for those of you who are
familiar
um so basically what the what these
algorithms do is
to answer the question why is path a
optimal not b
they find the minimal changes to make to
the map
so that the path b so the user's
expectation would actually be true
which means uh basically here you would
say well
f a is the shortest not b because for b
to be shortest
the shortest you would have to change
the
the traversability or the the ease of
reversibility of these two areas
here so you would have to make these
staircases here be
easier to traverse if you wanted this to
be the shortest path
or you could say because um
a is the shortest not b because walking
on blue
is is too cheap so if it costed a bit
more to walk on blue then b would
actually be the shortest paths so you
can automatically generate
these kind of explanations and even more
recently i've developed an algorithm
that can
even generate all possible
optimal explanations for a question so
say
uh why is path a optimal not b and you
could say because
for b to be the shortest path the trains
should change in one of the
following ways and it gives the user all
possible ways in which so for example if
you put
these two areas here or these two or
these two
if they become hard to traverse then
indeed
the shortest path would be the one you
wanted but since it's not that way
the shortest path is is this one instead
right so i passed these explanations on
two users so
another set of users so uh
single um explanations and
multiple explanations and i've tried to
see um
how how well uh so after after seeing
many of these explanations do
users become better at understanding and
predicting the behavior of this path
planner
and there was some interesting insights
that we got so
for example more user satisfaction does
not mean
that the explanations are more effective
we actually
got the the opposite so people were more
satisfied
with one kind of explanations the the
one with multiple
options but they actually um
got worse at understanding the problem
if they if they
had seen more uh possibilities
so it is also interesting that showing
multiple possible explanations could
actually be counterproductive
and confuse the the users and decrease
um problem understanding so to kind of
sum up their important design
considerations to think about when
you're
implementing explainable planners the
type of explanation depends on user
needs
there's a conflict between user user
satisfaction and understanding
users might prefer simpler or more
complex
explanations even though they're not as
effective
there's so there's a conflict between
complexity and multiplicity
and uh there's you should also pay
attention to explanation depth and
visualization abstractions
but also kind of a general methodology
that i used here
which was to explore a concept
an ethical concern of transparency or
explainability
and jump into implementation
of prototypes and use these to
anticipate design
issues and to actually refine the
concepts and
iterate and this could be potentially a
good um
i believe this could be potentially good
way a good tool for responsible
innovation
as well and which i will apply next to
the
problem of fairness in robot motion but
if if there are any questions at this
point
about explainability i'm happy to
take questions yeah thank you martin
i see one question from uh young
maybe if you can mute yourself and ask
the question it'll be great
hey yes thanks thanks so um
thank you martin for sharing your
research so my question is
is really about these uh these
constraints that you're describing right
so it looks that you mainly consider the
uh those
constraints that can be formally
specified right
and then you inject that somehow into
your organization algorithm
so um my question is other uh
constraints that are you know more
ambiguous that need to be
uh and that can only be uh expressed
through for example natural language or
something like that
maybe so certainly it's very it's very
difficult
to in many problems it's difficult to
elicit the requirements and it's
difficult to understand what are what do
we actually want
the the robot to do but if we want the
robot to
to do it using a we actually need to
formulate it to put it down
in into uh into an equation so that's
the
the only way it's going to it's going to
work but then so usually the way it uh
works is that actually you start by
by eliciting the requirements of the the
problem and then you
implement it in the motion planner using
specific equations
and constraints and then you deploy it
and very often you realize oh wait but
the robot is still doing this which
is not really what i expected so we need
another constraint and this
often happens in deployment so it takes
a long time for you to get at the place
where the robot is doing exactly what
you wanted
and this this is a continuous process as
well
yeah exactly that part i found very
interesting right it seems that what we
do now is mostly you know uh
let's see how it works and then we come
back and maybe there are more
constraints that we can derive right
but then i'm wondering if there should
be some you know principled method that
would allow us to get as much
as possible in the beginning and then of
course it's the incremental process
right we need always have to debug and
improve
right but that's the part that sounds
quite interesting
to share some thoughts yeah
what seems to be you usable by those
machines it needs to be formally as
specified
but then it would be really interesting
to see what are the things that can
really be formally specified and what
are the things that is hot
maybe there has to be some research
going on there
yeah of course there are also methods
based on learning right so
if if the user is not it cannot easily
write down the requirements the users
can just
show examples and then uh by by seeing
many examples an algorithm
could learn to do this but this is
impractical impractical for
large systems like imagine a warehouse
with with 1 000 robots the user cannot
possibly have
good intuition about um
how each of the robots should be moving
so you have to start by actually
formalizing
some objectives so that's also just some
thought
yeah yeah that's a good point thanks for
sharing
yeah and actually as a follow-up to this
question for me
uh so did you also consider like of
course you need to define all these
constraints
beforehand but there's obviously going
to be cases that you haven't foreseen
when you started implementing or
formalizing everything
so have you also considered uh scenarios
where francis robot has no clue what you
know has not the real explanation and
we'll just tell you the human
hey i have no idea what happened but
this is what i what all the events that
led up to this point
and uh did you consider that in your uh
your algorithms too or in your design
and
uh no not not at this point but
so the for example it's it's usual for
for
developers of motion motion planning
algorithms to for example look at the
search tree as a
way to kind of get an intuition as to
what what happened so the search tree is
a kind of
history of everything that the algorithm
tried and
but i didn't for example compare uh
the explanatory power of a search of
looking at the search tree versus
uh these kind of explanations that i did
here so that would be a good baseline as
well
thanks for the suggestion
thanks interesting um any other
questions
or else
we can uh move on to the second part
yeah
good sure okay all right so um
i'll continue then with the uh fairness
uh
um the idea of fairness in
motion planning so i'm going to use the
similar kind of methodology that i used
for drawing out this uh the this concept
of
explainability so i'm going to try and
start by
um actually simulating the
the deployment of a robot and
and looking at the results uh and then
distributions of impact
and trying to understand what kind of
issues awareness could come up
and then directly formalize concepts
uh formalize the concepts of fairness
that are in the
philosophy literature and distributive
justice literature
and again simulate the deployment of of
of a fair planner in those senses
and use those simulations to again draw
out the design considerations and
potential issues
and uh i'm going to suggest this could
be again a good way to involve
stakeholders in the in the process
so to for all of this i'm going to use a
walkthrough
use case to to make things more
intuitive
so this use case is of a rescue robot
that needs to find as many people as
possible so it uses path planning or
motion planning that
so it starts at a certain point in the
in the middle of the
of a city which is oxford in the uk uh
the fire station it goes
uh for uh some some path
around the city and then returns to
recharge the batteries
and then of course we can we can use
perhaps some
uh census date about uh how the
population is
distributed so population density or
even age distribution and
ethnicity distribution to think about
potential impact
but also about how to get as many people
find as many people as possible with
our robot so i'm going to use these
simulations i'm going to simulate a
robot that is
trying to find as many people as
possible and then look at the kind of
which people the robot found and try and
uh
make claims about fairness in the past
of robots
from this so my first claim is that
robot paths can be uh
biased maybe uh this is straightforward
to some of you but the idea
here is that if your robot tries to find
as many people as possible it should
stay
in in the close vicinity of the of the
launching station
both because it can then uh return very
quickly when the battery is over
and because that's where the highest
population density is
but at the same time if you do this the
the people you will find are mainly
people in
between in their 20s so the
undergraduate population of oxford
and they're also mainly white chinese
and male
and these are the population you find if
you stick to the center of
oxford because of historical reasons and
because of the way that
the city works at the same time you can
also make the other
uh like the next step uh of
claims which is the robot pass can be
discriminatory
so if you think that internet
discrimination is when a decision or
policy
even though it doesn't target specific
group it has worst
effects for those group this happens in
this case here right because
the people that are much younger so the
older population and younger population
are actually um
have less probability of being found by
the robot
as well as the female group and minority
ethnicity groups
then you could go one step further and
say that it's possible for robot paths
to be unfair
so if you think that the goals the
disaster response are to find as many
people
as possible but also to attend those
that are most at risk first
this strategy that tried to find as many
people as possible actually did not meet
the second requirement
because it found the student population
in their 20s which could be considered
the risk so you could say that a robot
pass could be
unfair according to disaster response
ethics
this other claim is perhaps also uh
intuitive to many of you but the idea is
that robots must face dilemmas already
faced by humans so disaster response
teams
already have to think about the impact
and the fairness of a mission so the
robots that
are deployed for disaster response we'll
uh of course also have to think about
this but how do we operationalize
similar furnace sensitivity so this is
the step where i uh dive into
implementation
directly to try to tease out issues and
design considerations
so i i did a survey of the distributive
justice and
political philosophy literature and
tried to bring these concepts
and formalize them for the the for the
context of path planning
so i here are three examples that are in
this
in this paper in this in my recent paper
so demographic parity is
is one concept conception of fairness
where you say that the probability
of the distribution of people that your
robot finds
in its path should be the same as the
distribution of people
of the whole city so this means
basically that when your what goes and
look
looks for as many people as possible it
should still try to pick a
representative sample
of the population of the whole city but
there are also
conceptions of fairness for example that
the distribution of people found by the
robot
should be such that all groups have at
least a certain probability of being
found this is a sufficient
therian um approach and it could also be
a rosian
uh approach so the the path should
maximize
the the the probability of the
least uh likely group and there are also
other
kinds of um apparent conceptions of
course but i'm going to stick to one and
try and tease out
possible issues um that come up
when when you when you choose a
definition of fairness so i implemented
a motion planner
that simultaneously uh optimized the
number of people found
uh found and the uh minimized the
unfairness so try to get the
distribution of people
found by the robot as close as possible
to the distribution of people of the
whole city
so um this curve here has many points
each point is a different path that the
robot could
take and this this point here more
to the left uh says 0.6 this 0.06 this
is the distance to the ideal
distribution so if this
was zero that would mean that you could
find a path
that had the exact ideal
probability distribution so you can
already take
quite a few uh so if you're interested
in the details of the method you feel
free to
read the paper it's a pareto estimation
method but independent of that so you
can already
take some interesting conclusions from
here so first
it might be not possible not even
possible to
to satisfy a fairness definition exactly
which is the case
in this example so this never reaches
zero
and it to find more people you might
have to compromise on fairness
so you need to find more people you
might have to find uh people that are
less representative
of your city in this in this short path
another observation that you that i uh
made in this work was that a fairness
definition could be
a counterproductive so here is the
comparison
of two fairness definitions one
which is demographic priorities so your
your planner is trying to as much as
possible that the path
um finds uh
male and female groups in the same
proportion
as in the whole city and this in the the
graph on the right is the result of
doing
uh running a planner that tries to
promote affirmative action so exactly 50
ratio of men and women found on its on
each of its paths
but you see that when you do uh the
algorithm on the right you find
both fewer men and fewer women
than if you uh applied the the first
definition of fairness so you have to be
careful you cannot just
think oh i think i should went this
fairness definition not that one
you might have to actually simulate it
to see what happens because it might
even be
counterproductive be worse in all
respects compared to another
metric um so
a kind of final more technical
um conclusion for observation is that
current
methods for motion planning offer a few
guarantees so basically they're
they're methods that will solve
your plan your problem optimally but
you'll have to mix an
approximation of the problem and there
are methods that will try to solve
exactly what you asked it to solve but
there they cannot guarantee that they
will do it optimally so you have to
choose between
optimizing the real fairness real uh
fairness metrics suboptimally or
optimizing something else that is not
what you care about
optimally so intuitively for those of
you who are familiar with the a-star
algorithm for example
for path planning you have to use the
cumulative cost
which means that the cost of being in
each
cell along a path is equal to the in
this case it would be equal to the
how close the displacion of people on
that
area is to the distribution of people
over the whole city but this would mean
that uh your planet would try and avoid
neighborhoods
that are that have uh
minority neighborhoods because they are
not representative
of the whole city so even though you're
trying to optimally solve
a fair um planning problem you're
actually
doing something very unfair which is to
avoid minority bit neighborhoods because
they're not
not representative of the whole city
because of the approximation
that you need to use in order to apply
an optimal
uh planner all right so you can complain
about this this example this toy example
that
i did that it's not realistic enough uh
because it only does one path and
returns and that's it
uh so i've recently started exploring
the
the the coverage problem which is when
your robot
will do uh a short path
covers a small area returns and then
another area returns until the whole
city is covered it's called the the
coverage
problem and i've actually
observed through experiments that um
if you're solving a coverage problem
again
trying to first find the first go to the
neighborhoods that i have the most
amount of people so as to find more
people faster
so you you'll have this kind of graph
for the amount of people you find
first you you find a lot of people very
quickly and then
it starts slowing down the amount of
people you find until you find everyone
but that also means that again you have
similar inequality and you have an
inequality peak in the beginning so in
this particular example of oxford you
would find
50 percent of the younger population by
200
minutes but by 200 minutes you would
only have found 15 percent
of the older population which is a big
gap and problem
and also because the older population is
potentially the one you you care most
about when you're doing these missions
so to kind of uh also take some
conclusions from here
um it's important to know that fairness
can be not only about who gets served by
a robot but the order or the speed in
which groups are served
we've also seen how population greedy
algorithms those algorithms that try to
find
as many people as possible or serve as
many people as possible
will be biased according to structural
biases in the city or or in your
domain so this this kind of bias could
reinforce
current inequalities and criticisms for
example disaster response there
are like from this recent disaster
response missions in the us and thailand
there have been criticisms that the
disaster response
efforts uh did not help enough the
marginalized
groups because even though they were
from the start less likely to survive
and if we were to deploy these kind of
algorithms
for finding as many people as possible
we will again be
um reinforcing this kind of criticism
because we would not be serving
marginalized groups as as quickly as as
we
should so to sum up uh for
in order to build the fair robot motion
planners
uh what we have to think about so so
first i made the claim that robot motion
can be biased and unfair so
this is both true for goal directive and
coverage paths
and i showed how motion can inherit
spatial distribution biases
of people so people they're natural
naturally uh
they're they're segregations
due to that they're um gentrified
neighborhoods in their minority
neighborhoods so this
then passes by passes to the
the motion planner then of course i
omitted from the
discussion here that there are also
fairness issues related to census data
itself and its representations and
gathering methods but i
i can skip that bit from this
presentation
regarding design considerations for fair
planners so i've
shown how there's something to be
pay attention to is the fairness and
efficiency tradeoff
and i've shown how some fairness
specifications might actually be
counterproductive
and i've shown how there's an issue with
the optimality of an algorithm and the
use of
approximations and to
to wrap up in a similar notes as to the
explainability section so uh how do we
design for
fairness i think we need to iterate
design and
anticipation and why because the set of
uh personal characteristics we care
about or
uh what we mean by fairness the furnace
specification
and the the impact of a deployment all
of this is not obvious from the outset
so you might have to
iterate so deploy or simulate the
deployment of the system look at the
results
and then iterate and then uh
another another important aspect of
this methodology that i used is that it
is
currently hard to engage with
stakeholders in the early stages of
design so
so someone wants to deploy a robot and
and they will say yes of course we want
to make sure this
this deployment is fair but it's not
clear what what
how fairness even relates to robot
motion for example right so
uh these discussions uh are also
difficult to ground
unless we have something uh poppable
like like something like a simulation so
i think
it's um probably as a
methodology what i did here could be an
interesting tool for
responsible innovation where we
implement prototypes and simulate
deployments to anticipate
issues with stakeholders and to ground
discussions with stakeholders
to better understand the concepts so to
iterate concepts and design
considerations um in the early stages of
design
so thanks a lot for your for your time
and let me know if you have any other
questions hey thanks my team for this
really interesting
talk i see that every guinea has his
hand raised
yeah thanks and thanks martin for the
presentation
so uh about the the fairness uh topic so
so i understand uh that taking this
perspective
um if we assume that like a robot is
introduced that
it allows us to uncover what kind of
unfair dynamics
might happen but thinking more broadly
about the ultimate purpose here
so shouldn't we be taking a more
systemic
design perspective here of the fairness
issue so
what i mean by that is thinking more
broadly about the infrastructure
we are designing rather than focusing
just on the robots
decisions so for example bid build more
stations
in different areas of the city
increasing
battery capacity increasing the number
of charging stations throughout the city
so
uh thinking more broadly about what's
the socio-technical infrastructure that
needs to be in place to
satisfy both of the objectives that you
were saying so also get to the people
who are in need as quickly as possible
and make sure that you do it fairly
yes yes i i totally agree i don't i
don't think what i've
talked here is is kind of going against
that thought it would be a
small piece of the of the puzzle of
course
you you need to think about
[Music]
how many how many robots you can use how
many two to buy as many
as possible where do you launch them
from and do we have the
people to operate them are is this is it
even
is it even good to use robots in the
first place
uh do do disaster response teams want to
those are also important questions of
the for the whole uh problem so
my claim here was more uh so first
that there is a fairness component to uh
motion and then uh of course
we will we should think of the whole
system
so where to put launching stations how
many robots etc
but then even then so uh how should the
robots move depending on how they move
it it will still have a different impact
so it will still have
a different uh people will still
still be served or found or or uh helped
at different paces depending on the
algorithm you implement so
uh regardless of that we we will still
need this kind of um
this kind of uh algorithms and and and
and thought processes going into the
design of
in of uh motion planners but i totally
agree yes so that that
there's a lot of different problems to
to think about and also
more social problems of how
how are these then going to be used and
even how was this
data obtained and and do these the
categories
in the census even make sense so these
are all
questions that need to be thought of
but of course it cannot be just be by
myself it would have to be a full
interdisciplinary uh conversation with
all the stakeholders so yeah
in general i really agree with what you
say thanks for that
thanks for clarifying that and if i can
just a quick follow-up you
you mentioned that you you saw that it
was difficult to engage with
stakeholders
early on the topic of fairness to to to
understand the nuance better could you
please elaborate on that a bit
like what what were some of the
challenges uh
in that um uh
no so i didn't so to clarify i didn't
actually do any user study with this
with this fairness work uh yet what i
say is more is more
a general uh claim do i have no data for
that that it's
that uh once uh people who do not have
so much
knowledge about uh planner or or or
something
or or even or even the domain
so some people want to buy a robot to
deploy in a hospital for example
and um and they are aware that it's
important right it's in
it's in the the guidelines and the the
principles of ethics
ethical the ethical development of aio
yes of course we should
make sure this is fair uh but it's
actually difficult
to even come up with with things
you should have in mind when you okay we
will put a robot
in the hospital or or in the what does
it even mean
for the motion to be to be fair it's not
clear
uh what how to even start the
conversation that's my
issue you can not start talking about uh
compromises between enforcing fairness
or not
or start talking about do you you cannot
just use the menu
of uh yes do we do we want
uh egalitarianism sufficientarianism do
we want prioritarianism it's not just uh
it's just not it's not easy like that to
to to guide the conversation you
probably need examples you need
visualizations you need simulations
to tease out details of the definition
that's that's my
my claim not something that i
encountered personally
okay yeah no thanks for clarifying yeah
yeah now because
and of course the the if you actually do
the empirical work
then of course if you start talking to
people people don't think
necessarily like in our everyday
conversations we don't think in these
categories and we don't necessarily
think in categories that align with
quantitative fairness metrics that have
been proposed so far in computer science
literature so so that's no but that's
that's kind of yeah coming back to also
coming back to the first question i ask
is basically
the the world is like the social reality
is so nuanced that
i think more broadly speaking like we
should be thinking about these
socio-technical infrastructures where we
figure out what's the appropriate
real goals for the humans to play to
ensure for example fairness but also
expandability what's appropriate roles
for
machines to play and the interaction
between the two
yeah i agree thanks thank you thanks
luciano you were first i saw
yeah okay so uh thank you very much for
the talk that was
really great talk uh and i
i really like the way you make very
clear that explainability and fairness
they are relevant
so many choose to specific concrete
context
and that's very important for to also to
raise awareness that is like we are not
just developing
robots we're not them just ai there's
things that are going to use in the
world have the impact
there was very nice things and my
question
goes on the aspects on the second part
on the fairness
about the the metrics and the
definitions so it's basically following
up a little bit on what you just
comment here for a vienna's question
it's
because yeah indeed people have like it
it's not an easy
thing to discuss about like what
different concept of fairness and also
in broader sense if you think about
ethical ai
it should be utilitarian should it be
follow this canton approach
without this or that um a way
to to try to see to visualize as i said
like through
visualization representations and
through
demonstrations also as preferences so
because usually when people try to say
okay this is
i would prefer to go in this way because
or that way
and then you try to engage into this
deliberative process you're saying okay
why do you prefer this way then that way
might be something some some interesting
things there so my question is
um going a little bit much more to this
technical side so
do you envision some kind of uh learning
methods
to engage these for example investment
enforcement learning are some approaches
to try to
learn what fair means for a specific
group of people that might be impacted
by the solution
so what do you think about this
i me personally personally i i'm not a
big fan of learning
based approaches because um because
you you're never too sure what they're
learning
and and um and that's a kind of
a personal approach i think it's still
it's still perhaps easier to come up
with rules
that that you totally understand and
then
um maybe iterate on those rules but but
i think
related to this learning learning
approach there's this kind of
iterative uh planning approach that is
popular in the in the planning community
which is when
uh so you you suggest your algorithm
suggests a plan
and then the user says yes but i would
expect this to be
i don't know fairer or or or more
intuitive according to
to whatever it is that i that i think
should happen so the user provides some
other suggestion and then
the algorithm could could uh try to
make make that happen and say what what
are the potential issues with that so
show
either show from uh to similar to what i
did in the explainability bits
either show that there's an increase in
costs or there's an increase in
uh there's uh then this furnace metric
would go down
or this this minority group would not uh
be seen anymore so and then by
these back and forth then the user oh i
see then why not this path
and then by this conversation almost
like a like a
negotiation between planner and person
you could then arrive at um
at the path so even though it's not
really the
the traditional learning scheme it is
still something
uh similar i think in in a product
yeah yeah no it does and i i just really
clarify i do
share your concerns with completely like
open world
learning approaches which you don't read
you know there's lack of transparency
lack of explainability there
things but i also see this value of this
combining things so you might have one
specific
definition of fairness but by learning
process you can try to
fine-tune this according to to
interaction
so yeah thanks thanks that was
very nice thank you very much and also
related to that learning
approach there they're also we could
also think of
methods that try and not just uh so
learn an arbitrary or an arbitrary
neural network or something but come up
with rules explicit rules so
well then do you not mean this for
fairness what about this
formula for fairness would that make
sense to you
and uh perhaps always uh side by side
with
with the examples of how that would work
in practice i don't know
also because in in like in political
philosophy and in this
in these works about distributed
fairness it's always a lot about counter
examples right
it's usually the rules sound perfect
when you just look at the rules but when
you look at
specific examples you find counter
examples and and then and i think
probably in design you will have to to
do it the same way
yeah yeah definitely and i which are the
norms relevant because they
i completely imagine that this path
planning that we're going to work in
in oxford maybe doesn't would not work
in delta and the other way around
right so that's understanding what are
the specific
context dependent norms that's also very
important nice
true okay thanks a lot thanks thanks
thanks i see we have five minutes left i
see one more question from
our caddy
yes uh hey martin that was a fascinating
talk thank you
i have a well let let's make it two
questions about the
first part about the explanations and uh
well the first question is basically
some of the approaches
that you showed uh it seems like there
is an extra computational toll
uh that you get on top of your normal
motion planning
if you need explanations so can you
comment on that so
yeah i would imagine that some of them
are more computationally intensive some
of them
are kind of not as much but uh yeah in
general
what is how hard is this trade-off
between
extra computational cost and explanation
right yeah so in in terms of
computational
cost it depends a lot on the kind of
explanation you want to
to make all right so um
maybe i'll share the screen at the same
time so for example for the
for for the path planning explanations
these ones
uh where you find changes to the model
that lead to the expected path
so this we can be done quite quickly so
first i had an algorithm
that was slow but you can always find
ways to make it fast and it takes like
one second
to to generate an explanation so it's
quite quick for
for reasonably sized
maps that you would use normally but for
example for motion planning
for some kind of motion planning
explanations like
um if the reason why you
fail is because um you didn't wait long
enough you might
actually have to wait a long time
to resolve the problem for a long time
and say oh
in order for you to be able to say that
you would need two hours to solve the
problem
you might actually have to try and solve
it for two hours
so uh for some explanations it might uh
take it it might take considerably long
um for path planning it's easy it's
easier because since it's discrete you
can do
you can pre-compute explanations to a
lot of different questions right from
the beginning you can
you can leverage that but for motion
planning continuous motion planning
some of them you might just have to say
[Music]
if you want to do it quickly you might
have to skip doing some some of the
potential explanations
not investigate some of the potential
expressions or you could say
uh well if you want i could also see if
this is the source of the issue but you
would
have to come back in in a while right
yeah but that's an important point good
thanks uh and then uh
the second question well it's it's not a
direct extension of the first one but
um yeah i was wondering your general
framework for
the kind of explanations for the kinds
of explanations that you can
expect from uh robot motion uh
well how how generalizable that is to
the tasks where uh a robot need to
interact with the human
because uh yeah with the examples that
you showed it's just the robot and that
moves
nice but uh what if uh kind of a human
is involved or
several humans are removed that's messy
on its own even without
considering explainability but if you
want to add the
explanations in terms of human sections
that also could come in very different
shapes and forms
so uh how do you see this kind of
generalization to
human robot interaction tasks yeah
thanks
so i think so even if your system is
messy when there are humans involved
i mean there will still be rules to how
this the
robot should act right it is uh so if
the user that of course it's not as
simple but
if the user is if this user is doing
this then i i do this so you can always
uh in this framework you would still
generate explanations by saying
uh by finding changes to the input
basically
that would lead to the expected outcome
so you could say
why didn't the robot do this while there
were three people passing by and one
operating that robot and
you could try and make simulations of
uh removing one of the people or putting
one more person
or or making simulating that the person
who's controlling the robot asked for
another command so
of course the space of exploration is
huge but i think the general framework
of finding
changes to the inputs of the the control
system right of the or the planning
system
so that the the the things that were
asked uh become
true right so i think that's still valid
of course it will be more
computational intensity and then
potentially more difficult to interpret
so you'll have again
have to think about about um
about how to to summarize this or to to
figure out which which are the the the
relevant bits
but i i think um
i i think it's doable i think there's a
lot
to eat on i think it's a an interesting
research avenue
yeah thank you thank you martin
yeah thanks thanks for the feedback


9 of the Best Bing (GPT 4) Prompts

Sam Altman tweeted that writing a really
great prompt for a chatbot Persona is an
amazingly High leverage school and an
early example of programming in a little
bit of an actual language this video is
about proving that that's correct with
these game-changing prompts I was
genuinely mind blown by a lot of these
prompts so let me know in the comments
which one's your favorite let's start
with asking Bing to be your personal
free interview coach for the exact
position that you're applying for I
picked a job almost at random and then
press the Bing button in the edge
browser it opened up Bing chat I then
gave it this prompt you will ask me
interview questions for the position
detailed on this page notice I did not
specify the job I just said detailed on
this page being understood the job I
want you to only reply as the
interviewer do not write all the
conversation at once ask me the
questions and wait for my answers do not
write explanations and ask me the
questions one by one waiting for my
answers this problems was inspired by a
GitHub post Link in the description and
look what Bing does it reads the page it
understands the job description and then
it starts asking me relevant questions
it even gets into character please
answer my questions as best as you can
question one why do you want to work for
us and if you just thought these were
going to be generic questions no says
thank you for your answer what are some
of the benefits and challenges of
implementing Robotics and AI Solutions
in finance and or supply chain processes
this is part of what I would do in this
job people pay hundreds and thousands of
dollars for interview coaches but you
could practice with Bing for free you
could even ask it to grade your answer
furthermore you could paste your CV and
say my skills are listed below write out
all the reasons I would be appropriate
for the position listed on this page and
Bing understands what you mean and gives
you the reasons why you might be a good
fit I think Bing might just be the only
ultimate job finding assistant the next
game-changing prompt involves asking
Bing to improve its suggestions imagine
you wanted to create a YouTube channel
and you ask it find 10 original names
for a YouTube channel on the
intersection of AI and politics and I
know what you're thinking this is an
easy prompt anyone could have come up
with this how is this game changing but
look at how bland the answers are Ai and
democracy AI Politics the future of
politics with AI they're okay but so
Bland but you can ask Bing to research
how best to name things and to improve
its suggestions based on that research
check this out this particular promise
was inspired by Ethan Malik and I wrote
now use the Igor naming guide to come up
with better names and tell me why
they're better this guide advises you to
create evocative names that convey a
rich meaning and emotion without being
literal or generic it suggests inventive
descriptive evocative names and based on
this suggestion look at the new names I
think they're brilliant spark ethos
Paradox influence Polaris I think
Polaris is particularly impressive it
says a name that suggests guidance
Direction and leadership in the complex
world of AI so not all prompts are born
equal you can get Bing to improve upon
itself but we are just getting started
with game changing Bing prompts not all
of the prompts have to be strictly
functional I was absolutely blown away
by this prompt I think any adult would
enjoy using the following prompts and
clearly children would too you can ask I
warn you to act as my time travel guide
I will provide you with the historical
period or future time I want to visit
and you will suggest the best events
sites or people to experience as if we
were living in those times simply
provide interesting suggestions and stay
in character my first request is I want
to visit the Viking period in Britain
and it gets into character it transports
Us in time greetings traveler you have
chosen a fascinating and perilous time
to visit some of the best events sites
or people to experience are witnessing
the first Viking raid on lindisfarne in
793 I want to go there by the way join a
viking longship expedition across the
North Sea or even meet King Alfred the
Great of Wessex one of the most
influential rulers in British history I
of course stayed in character and said I
would love to meet King Alfred but where
shall I rest my head at night in these
perilous parts right now in my head I'm
in Viking Britain Bing replies A wise
choice traveler King Alfred is a
remarkable man who has faced many
challenges and triumphs he is a patron
of learning and culture to meet him you
will have to travel to Wessex the
southernmost Anglo-Saxon Kingdom and it
suggests towns and Villages where I can
rest my head at night these are all real
and I may indeed visit them in real life
it even suggests ways I could continue I
want to stay in a monastery I want to
stay in a castle Etc the this is role
playing on another level as I say
incredible for children and addictive
for adults too what about Investments
and making money thing is Bing won't
give you direct Financial advice but if
you phrase the question like this you
can learn lots of interesting Trends I
asked if I had invested a hundred
dollars in each of stocks bonds real
estate and cash savings accounts in the
US in 2000 what would they each be worth
now imagine you're deliberating how to
assign your portfolio this could be
genuinely interesting Bing is now able
to do four up from three simultaneous
searches and compare the performance of
each of these categories in any given
time period by the way out of Interest
stocks returns 6.5 so that hundred
dollars would now be worth 366. for
bonds it'll be two three three the
property will be 236 and for cash it
will be one two two now of course you
would want to follow the links and do
more research yourself and Bing is never
going to directly tell you how to invest
your money but for gaining basic
Financial education Bing can be crucial
I would envisage over the next three to
five years financial advisors being
mainly replaced with AI Tech but of
course let me know what you think I
think this next prompt is also
mind-blowing when Bing makes mistakes
you can actually give an example of a
correct answer and Bing will improve its
own logic Bing chat isn't this static
model that will always give the same
output you can teach it and this is
called few shot prompting here's a
question that Bing and chatty PT
notoriously get wrong the question is
when I was six my sister was half my age
now that I'm 60 how old is my sister now
clearly if the sister was half your age
she would have been three at the time
that's an age gap of three and now that
you're 60 she would be 57. but Bing gets
this wrong as you can see below and now
you're thinking how is this a
game-changing prompt if Bing gets it
wrong what's game changing is the next
prompt because all you have to do is
give it one correct example preferably
starting with the phrase let's think
step by step an entire academic paper
has been written about how that phrase
improves outputs follow it on with an
example of a correct usage of logic I
gave an example of me being 10 and my
sister being half my age I ended with
does this make sense being understood
and learned I gave it no further
pointers and just asked so when I was
six my sister was half my age as before
now that I'm 60 how old is my sister
notice I never said 57 I never gave it
the right answer so surely it would get
it wrong again no it updates its logic
thinks it through and gets the answer
right this time this is called few shot
prompting and you can radically improve
the performance of your prompts in this
way I think that's incredible the next
prompt is going to show you how to get
around Bing's reluctance and turn
learning into an amazing adventure one
thing that Bing is generally quite
relaxed important to do is to play act
now I know I did show you it play acting
earlier but in general it doesn't like
doing it and notice when I asked it to
act as an entertaining etymologist
someone studying the history of words it
denied that request it says that's not
my purpose however notice what happens
when I clear the conversation and take
away the request to act as a role this
time I took away the role play element
and gave the request directly Bing
thought this was an interesting
challenge it was the exact same
challenge it denied earlier and went
along with it and look at how fun this
adventure can be I said I'm going to
give two words I want you to trace the
origins of the words and keep going back
in time until you find a language that
they were both spoken in I then gave an
example this was a One-Shot prompt and
then said start with the words
management and pizza and it understood
the game it didn't just give me the
origin of the words it then said so both
words have have roots in Latin that
would be a language they were both
spoken in in their earlier forms after
this the game was set all I needed to do
was give it two more words I didn't need
to explain the game again I said sky and
thing I actually know a bit about the
etymology the word origin of these two
words and did you know that they both
have roots in proto-germanic and Old
Norse Viking language you can read the
fascinating word Histories on the left
you can imagine using prompts like this
to teach yourself or to teach others in
an educational setting it's so much more
entertaining this way the next prompt
concerns prompt formatting and research
you can specify the exact format of
Bing's outputs this could be
transformative for academic research or
even personal research I said give me
one the date of publication two a
summary of the abstract and conclusion
three the main author and four citations
on five peer-reviewed spelled wrong by
the way papers concerning caffeine
consumption and cognitive performance it
understood exactly what I wanted and
gave me this in this format these are
genuine links as you can see and of
course the onus is on me to fact check
them but what's brilliant is the
formatting it's given me the date given
me the summary of the abstract the main
author the citations and honestly the
hallucinations are reducing gradually
instead of relying on random articles
you find on Google you can do an instant
meta-analysis of your own comparing
peer-reviewed Studies by a range of
authors the next prompt can provide
endless entertainment one problem you do
have to use chat gbt not Bing because
Bing denies the request the prompt is
amazing though I want you to act as a
drunk person you will only answer like a
very drunk person texting and nothing
else you'll make a lot of grammar and
spelling mistakes in your answers my
first sentence is how are you and look
at the super random conversation I have
this is so fun I might just continue
this conversation now the activities
talking about pizza being the best and
is now somehow randomly talking about
donkey so I'm going to ask are you drunk
and see what happens my typing is drunk
are you drunk let's see what chat TPC
comes up me drunk no way I'm just having
a lot of fun tonight this is just
endlessly entertaining do you have any
drink recommendation not for you
chattybt you're already drunk I'm
looking for something new to try try
this yourself you may even want to get
into a random argument with chatty PT
the last prompt involves writing styles
and this reminds me of the controversy
that has happened in AI art where you
can visually imitate a certain artist I
suspect the same controversy is coming
for writing because look what you can
currently do if you ask Bing for a
generic paragraph about a generic event
a man crossing a road for example what
you tend to end up with is something
okay vaguely interesting in topic I mean
this guy has a faint smile on his face
as if he knew a secret okay that's kind
of interesting better than what chatgpt
used to do it's just that writing is
very Bland but what you can now do is
say something like rewrite this in the
style of Carl Sagan this particular
suggestion again came from ethanolic an
Ivy League Professor but look how it
transforms the writing he lingered at
the edge of a pavement his gaze locked
on the Crimson symbol that forbade him
to proceed look at the new vocabulary he
had a modest attire he resembled a
typical inhabitant of this planet he had
a subtle curve on his lips as if he
possessed a knowledge that eluded others
the writing quality just went up about
three grades and now to bring this full
circle you can actually ask Bing to turn
this into a current version mid Journey
prompt this is getting kind of meta but
here's what Bing comes up with
these prompts here I of course put them
into mid-journey and some of the results
are amazing this is a man in a gray suit
crossing a busy Street in New York at
night well how about a pixel art
animation of a man crossing a road in an
old school video game retro or maybe a
cartoon style of drawing of a disguised
superhero honestly there are dozens more
examples of incredible prompts I could
show you I just didn't want the video to
get too long I've done everything with
Bing from playing Tic-Tac-Toe generating
ASCII art of a bear for example coming
up with startup ideas in London and
getting Bing to help me compose music
the possibilities go on and on but do
let me know in the comments if even the
prompts I have shown you are as game
changing as I believe don't forget to
leave a like and have a wonderful day