Mega-R7. Near Misses; Arch Learning Video Lecture - Artificial Intelligence

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here we have a near-miss Learning Tree
it's a little bit different but a little
bit similar we've got different types of
we're trying to learn about different
light sources so well we're sort about
different lamps flashlights all sorts of
things so these guys can have types of
support a bass or wires the base can be
flat clamped or have legs the legs can
be flat bottom two-wheeled the light
source can be incandescent fluorescent
or sodium vapor and the energy source
that powers it can be electric battery
oil or gas so there's also some other
things but these are the trees that we
might be climbing up with the climb tree
here a stick
our starting model is we have an
incandescent bulb the height is equal to
24 inches exactly they have a flat base
there's electricity to power it and it
has a shade so our starting our starting
model is basically like a table lamp
that you set on the table it's got you
know the shade over top of it you know
the standard broom table what is our
starting model so recall in arch
learning which is what we are using here
that there are several heuristics that
we might want to use let's see well the
heuristics are um let's see we've got um
require link forbid link
we've got climb-tree extend set closed
interval and we also have we also have
drop link forgot about that one that's
served with the link so I can put it
here okay so if you want to do well on
these questions if that if they appear
on the final first of all you better
know what those six heuristics do so
basically require link is a heuristic
that it goes into your model and it as a
requirement where before your model just
didn't care so let's say there was
something about color for your light for
your lamp's base or for the lamp and
right now it doesn't care about color at
all but let's say that you wound up
having an example that was for instance
blue or white or whatever these colors
are and it lets it and and it wound up
get it wound up getting that blue was
okay for a for idiots model wound up
getting okay blue and white are okay and
then it or actually know it even more
basic example let's say that it just
kept getting blue lamps as examples and
all of them were blue and it just didn't
care about the fence they were blue but
they did turn out to be blue and then
eventually it found a red one that was
exactly the same as its model but the
red one was a near-miss then it might
require blue which was um already an
acceptable
permissible thing in his model but it
could be required now another thing you
can do is forbid some people ask me why
would you bother saying forbid read say
if you could already just require blue
like together they cover they cover the
same ground the answer is because this
system this ARS learning system remember
Patrick's doctoral thesis is an old
system very because it was not built on
old systems and because also it's
generally a good idea it was very
parsimonious if you let's say you had a
set of colors red blue yellow green pink
purple orange and you found that only
orange was giving you problems you could
try to require that it be in this or you
could even use extend set making to make
it make a giant set of red blue yellow
green purple orange but oh sorry purple
pink but not orange but if you just
forbid orange you've just saved them a
whole bunch of space particularly if
there's a large possible as soon as the
set you want to have the ability to
forbid because it meet well it needs to
have the smallest bottle that it can
that covers all of its samples so we
also have climb tree climb tree takes
one of the elements in our model and
moves up the tree one level to be you
know so let's say we only we said only
incandescent lights are good but we
found that fluorescent lights are also
good whoop we move up two leg stores
climb tree extend set we don't have any
sets here but let's go back to red blue
orange yellow extend set is when we say
only red is good as an example but we
see another positive example the yellow
so they say okay I'll extend the set red
and yellow or both good all right close
interval closed interval is really
obvious when you need to use it because
not only is it only used for intervals
like height equals 24 inches or some
kind of numbers but it is the only thing
you can use
for for when I equals some kind of
numbers close interval will let you fuss
around with the intervals and say oh
well I guess one with a 20-inch bate 20
inch height is okay too so we'll make
the whole interval 20 to 24 inches good
because it doesn't make any sense for it
be like 20 is good 24 is go but nothing
in between is good no those are awful
so that closed interval covers the
entire interval last but not least is
drop link um the thing about drop link
is that drop link is again only really
used due to the fact that the system
wants to be as parsimonious as possible
with its model drop link is let's say
that you have a color and you're like
okay only red is acceptable and then you
see a blue one it's go okay and let's
say red blue yellow your only colors
only red and blue except wait a minute
no we could just stop say that only
yellow is not acceptable or something
like that all right but then you see
yellow as a positive example actually
you can't even switch to the yellow
being not acceptable cuz you've only
seen positive renders you say only red
and blue are acceptable lesson for us as
indicated in the climb tree to light
sources we don't see one that's Soviet
of a percent stream
do we have to go back down the tree or
do we just add up for bid one is we high
florescent to light source when we see
sodium vapour what do we do the answer
is our system unlike with lattice
learning in arch learning our system is
memoryless of its previous examples it's
incapable of going back down the tree
and so if you've got a incandescent
fluorescent climb up the light source
your only recourse if you see the
subbing vapor doesn't work is to forbid
link sodium vapor that's a good question
it shows good understanding of how we
work so drop link is you see red and
blue they both are okay you don't know
about yellow so you can't just switch
that to a forbidden link to yellow but
then you see yellow is also okay what do
you do you can drop the link altogether
save yourself space you don't if all the
colors that you have in the entire world
are fine you just not have part of your
model be the color and just say any
color is fine color must not be really
part of what defines a lamp and I'd say
it's not I mean it's what defines a
tacky lamp I guess but it's not what
defines a lamp so I mean that might be
why color isn't here to begin with so to
make yourselves do better on this more
knowledge equals less search let's have
more knowledge there's only some of
these are used to generalize to make our
model more accepting of new examples and
those are ones we would use after we've
seen a positive hit sort of generalize
learn more things in the model some of
these are used to specialize and make
our model refine it make our model more
specific you'd only use those after you
saw a near-miss so let's actually
separate those that way you guys will
never make the mistake of using one of
these in the wrong situation and more
knowledge less search and less search
means faster quiz time so require link
what do you guys think is that a
specialized ER or generalizer
specialized er that's right we'd only
use it when we saw him you're miss
forbid links specialized or a
generalizer
specialized er we only forbid something
if we saw miss climb-tree specialize or
generalizer generalize it that's right
we'd only climb up to a more generic
thing in the tree
if we saw a positive example extend set
generalizer we'd only extend to the
things that are set if we saw a positive
example close interval that's a
generalizer we'd only make the interval
bigger if we saw a if we saw a positive
example someone asks what do you do if
you have like 10 to 30 and then you find
a negative example in 20 it's pretty
it's generally pretty annoying for the
system to have to deal with that there's
a variety of things you could do based
on your implementation I've never seen
us ask it in a quiz but one thing you
could do is forbid 20 exactly just 20
and just just have a little hole at at
exactly 20 so then drop links specialize
or generalizer generalizer this one is
an easy one to mess up if you don't
understand the system because dropping
you think Oh getting rid of something
that's special it actually no you're
generalizing the entire area it's saying
we can forget about it because they're
all good all right so given that we are
set to do this problem it we have pretty
reasonable amount of time considering
that we don't have to do a sobs it's
great so our first example is a sort of
one of those stem reading lights that
you sort of have it on the stand and you
can like adjust the little light bulb up
or down it is a in it's an incandescent
bulb it has a height of 11 inches and
it's got a flat base it's electric it's
got a shade and it's a hit it's a hit a
positive example plus it's good so right
away we know that we can only use the
esses or the G's the G's we can only use
the G's because it's a hit so we're
not gonna be requiring we're not gonna
be forbidding today we're going to be
happy and using in generalizer so I'll
help do the first one you guys will help
me do the next one or someone can say we
didn't cover this and I'll do all of
them but I'll do the first one and we'll
stop but so we've got an incandescent
light that's the same as our model where
we've got a height of 11 inches oh I'm
not only covers 24 flat base that's the
same as our model electricity same as
our model shade same as our models so I
say the only difference is the height I
say we need to close the interval closed
interval and I say that our model is
model is same as before except for that
height it is an element of 11 to 24 all
right
simple enough I picked the easy one for
myself
next one so this one is going to be a
yeah this one is going to be the picture
here didn't turn out but it's basically
a lamp that doesn't have a shade it just
sort of has the light sort of shining
shining on you so it is a positive
example its incandescent height equals
11.5 flat base and electricity
all right so first of all which kind
would we use stresses on the
specializing or generalizing
generalizing and particularly anyone
want to take a look at our model which
is the starting model except for that
the height can go 11 to 24 what which
heuristic do we need to use here all
right so people are all saying drop-link
yes that's right someone said extend set
well I suppose we could extend the set
to shade it or not shade but that's
everything so drop-link is the answer we
can drop the shade shade or no shade or
not this is still pretty much a lamp
so that's correct we drop link so the
model alright there's been enough
changes to it I think I'll write it out
again the model is incandescent
incandescent height equals an element
from 11 to 24 and flat face an
electricity
good stop
question
so you're the same thing except for it's
tough for that there's one that's the
same with no shades and isn't izzy is a
mere miss or something so that you need
to or you're on site you're saying what
if there's one that has a shade now
that's a near miss and so it would
probably try to forbid link shades
that's sort of the question or you're
right the system is that's an
inconsistency in the data the system is
very fragile it's very fragile too
ordering in particular and um I mean
you'd be surprised how awesomeness this
does uh considering that it was made in
like the sixties like it's pretty
impressive stuff but is old we're not
saying that this is a way that you
should do all of your learning nowadays
because it has some serious issues but
um it's it's old but it's pretty damn
good for what it did a newer style of
this kind of learning made by me and
another friend who's now working at
Google is a lattice learning which has
his own share of issues one of which is
that since it's trying to act like a
little kid at first it tries to claim
that everything is okay until you show
it some good negative examples but very
particularly one way gets around this
problem is it's not memoryless it in
fact stores all the examples it's ever
seen and compares and contrasts them to
what it sees in the new in the new
example and this allows it to say
something like let's say you can teach
it what can fly in lattice learning you
say all right well bluej can fly it's
like and and then if you ask it can a
caliph I sure Kyle jumped over the moon
everything can fly so that's its problem
if you say actually cow that was a
nursery rhyme a cow cannot in fact fly
it will then say only birds can fly
because it remembers that the bluej can
fly and then you can eventually say well
actually bats can fly uproot back and
fly oh okay so chiroptera pnes can fly
and burn to fly but that's it and you
can give it some other examples but
there are other spells of learning
you're absolutely right in fact if
there's a multiple choice asking you
about like the strengths and weaknesses
of arch learning one of the weaknesses
it's very vulnerable to ordering of what
you teach it it can look but think about
it if our first example was one that's
exactly the same as this but with a
shape with a shade and it was a Miss the
system would just yell at you like
you're kind of being an asshole to it to
give it to things that are inconsistent
you might say in the real world that
happens
well arts learning not great with real
world messy data it just it goes
ballistic
it's very OCD wants everything to match
up if it gets to things that are
inconsistent
it'll just yell at you it's like you're
wrong this is this can't be true
because you said it's okay with the
shade so that's a very good question
another question to the model and it's
in dear Miss and I'm sorry to cut you
off we based on timing I'm just going to
say whatever you said afterwards doesn't
apply due to the fact at that point it's
not a near miss it's very important it's
only in your miss if there's only one
change otherwise it's just a miss and if
it's a miss with more than one change
you're probably gonna say how do you
know what to change the answer is you
can't change anything because any of
them could have been what was wrong
that's why for hit oh it can have as
many differences as you want you'll just
generalize them all from Miss it can
only have one thing different in fact
there might be one here that's not in
your miss and you just say oh we don't
do anything we'll keep going we'll night
see it if we have time so question
ordering is a factor for this your thing
and you encounter a item which has two
discrepancies in values but is
eventually but if the ordering were
different such that that would be
accountable like deliver be turned back
so today's lesson how you not hear this
can you hold onto it and use it later
when it would be in your mince the
answer is nope
if we're going here for a system that
doesn't remember things you can put in
some clutches that definitely makes it
smarter it uses odds data but we want
Patrick in the 60s making this up strove
for elegance and the elegant solution is
let's just be memory less completely the
idea being well little babies don't
can't tell you ever experience they had
with playing with blocks with an arch so
let's remember nothing with lattice
learning our idea is that people sort of
somewhere in the subconscious maybe do
store all the examples or at least a lot
more than you give them credit for so
why not store them but with arch
learning it's little babies can't tell
you oh yeah
there's at one time I played with a
block or I played with a lamp and it
didn't have a shade and so it wasn't a
lamp or something like that they're not
going to be able to store it they're not
gonna be able to save it for later
it really really in sort of installed
Turing who believed a human would be a
teacher to a computer arch learning
really focuses on the fact that the
human is a kind and good teacher who
offers examples that are exactly
appropriate at a time and that puts a
lot of pressure on you as the trainer of
a large learning system good questions
all let's continue working this guy out
so the next example is basically another
one of these scan lamps it's got a light
shining down which is fluorescent it
looks like this it's fluorescent height
equals 13 inches
flat base electric shade okay and yeah
as I put here it's a it's a hit so as
you guys have told me countless times so
I believe you remember we're going to
generalize for the hit so what should we
do here so some people are staying
extend set the people who are saying
climb tree are correct why do we climb
to extend extend set because of the fact
that it's a tree not a set and it is
more parsimonious to climb the tree than
it is to treat it as a set and extend
the set sets don't have a hierarchy it's
just like red comma blue color yellow or
shade common not shade whenever you have
a tree it's a fact that it's more
parsimonious to just climb it than it is
to treat it as a set and extend it even
though you couldn't do that I suppose
and arts learning its parsimonious its
elegant it's simple and the elegant
thing to do just climb up so yes we're
going to climb up the tree so our model
is same as last time
same as last time except that this time
it has a light source instead of as
light stores instead of saying
incandescent it just planted the lights
are sure all right
so the next one is pretty cool it's
another one of those fluorescent shine
down lamps but in space it has like a
tripod with three legs and those legs
all have wheels so it is also a hit so
it is fluorescent height equals 14
inches we old legs and electric so
obviously we're generalizing what
heuristic we'll use this time climb tree
again that's right we're going to climb
from flat base to base support itself
because wheeled legs and flat base base
support is an ancestor of both so our
model is the same as before but this
point has changed enough that I might as
well write it out so we'll see that it
is light source height between 11 and 24
inches base support some sort of support
and a letter
awesome
now come on parody
ah no back up you there we go so that
down there don't get confused that down
there's our most recent model we now
have Oh oops
Kiki um okay I actually missed one of
the examples however we were actually
completely correct which gives away
quite a lot
we had a miss incandescent bulb back in
the old days when incandescent was the
only kind of light we had um height
equals 60 inches flat back in the old
days when flat was only when we had
electric back in the old days when we
had electric and we also have shade
after we've dropped the link for shade
so we know that shade or not shade are
both okay so the question is what do we
do first of all do we specialize or
generalize with this miss so obviously
we're only going to be able to require
for bid so standardly traditionally the
answer would be nothing how are we
accepted as sane the possibility that
you might put a require onto the height
some students did that like say oh gosh
well that's it that's a hard set 11 to
24 but that's not what you would
normally do so just to show that we try
to understand that there might be
multiple ideas in a weird situation
we did get except that that one time but
generally there's not anything you have
to do this is the system at the time and
still now set 11 to 24
this is spookily exactly the correct
place where the example would go but you
don't have to do anything to the model
you don't have to use any heuristics ok
so last step the question is a question
ok it only the first one no first by a
lot it actually do beer as we did it it
would be any recipe no it doesn't in
different than light source height is
different than height flat is different
than base support so uh variety gave a
huge number and ok here's a question if
even though on the tape on the tree
incandescent is a child it is a child
branch off the branching off hook light
source if we instead encountered a
because our current model says like come
a look not okay instead we got a
negative thing that said fluorescent
again would that be considered about a
near-miss because for us at the first
lectures um so listen would it not be
considered a near-miss if you had say
fluorescent and rather than light source
because fluorescent differs from light
source so the answer there is um well
sure it is memoryless it doesn't know
that fluorescent used to be a positive
example before unfortunately let's say
we did have an example that was
fluorescent height is an element of 11
to 20 for base support electric but
actually let's say we have on a sodium
vapour height is 13 sodium vapour height
is 13 base support for some reason it
was base it was called base apart and
electric that's
possible right we haven't seen a
positive the sodium-vapor let's say the
sodium-vapor was not allowed if we don't
say that that's in your miss and then
forbid link sodium-vapor how are we ever
going to get rid of studying vapor
unfortunately because of that we lose
some we lose some amount of
expressiveness it's actually um your
what you're saying is very logical and
make sense that you would want to have
the ability to say okay this is a subset
this should be okay but unfortunately
because of that we climb the tree even
when we haven't seen a positive example
for all things in the bottom on that
tree we actually lose that ability it's
a trade-off it's a it is most certainly
a weakness of Irish learning that you
have pointed out here lattice learning
fixes that but it has its own it has its
own problems so you're right that you'd
want to be able to do that but you can't
because it's memoryless so last but not
least which model would we present in
order to teach the system right now from
its final system that a lamp requires a
base support so we need to be required
link for a base for the base support so
really fast incandescent bulb with a
height of six a flat base and
electricity would that do it
incandescent bulb with a height of eight
and a battery incandescent bulb with a
height of 12 wire support and
electricity oh yeah by the way this will
have to be amiss to teach you require
fluorescent bulb height of twenty one
clamp base and electricity or
incandescent bulb height of twelve flat
bottom legs and electricity so did
anyone pick that out over the fasted
miss of me saying it because Ottawa is
gonna want to come in question or answer
hmm
yeah so I know fast you can look through
quiz 2007 final because Saturday is
going to come in a minute but it would
be a miss on the one that was correct on
everything except for it was a wire
support then the question you have is
wait a minute it says incandescent ball
that is now a subset of light source you
have his question again the answer to
that is they must have decided they must
have decided here that that was okay but
on the other hand how would it learn
sodium vapor if that if that wasn't
decided to be okay
that's going to have to be an
implementation detail so one thing you
can do is try to treat subsets as okay
in the logical way because questions may
do that and then I guess you've lost the
ability to forbid say sodium vapor so
you have to lose one or the other I
think you're definitely okay to ask TAS
or someone who's proctoring if a
situation like that comes up which
implementation detail is chosen because
you could choose either when you're
running arch learning