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alright well thank you so much for inviting me so i um
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going to talk a little bit about the current state of the yeah i what i think it's limitations are
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and uh where it might go next so
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we've all heard about the t. learner main revolution
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the idea behind it learning is actually very
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old comes from the visual cortex in the brain
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which is that the very back of the brain which is roughly
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assembled in a series of layers like i showing here and that inspired the
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idea of the neural networks which are assembled in a series of where i
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i i ears for probably input been up in
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a i research since the nineteen seventies but only recently
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has the uh cut computing power and a
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data in available to actually make them work
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so it's things like human labelled image is the image that a data set
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has suffered enormous progress in
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computer vision especially in recognising objects
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and in fact over the years of
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the competition a model companies and researchers to
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do oh well in this uh object recognition competition
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in two thousand twelve the very first idea neural network
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was entered in this competition and the error rate plummeted it
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went way down and has gone down down down until it's about
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the same or below human performance on this particular data set
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so this has suffered enormous optimism
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yeah and visible progress in computer vision
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including in areas like facial recognition
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oh i self driving cars which are now able to identify objects on the road
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came flame like go where well we
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also uh uh the go grad grandmaster
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these should all be defeated by output go a program based on the neural networks
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and even for language tasks such a and as open a eyes g. p.
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t. three language model so just for fun i put in the names and
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titles of the few uh talks at this particular meeting
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and had g. p. g. three generate
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down below some additional suggestions for possible talks
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so i don't know of any day if these people are real but
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it looks very convincing and generates
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language ah that looks very human like
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however as the previous speaker noted there are many limitations to correct
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a high and he said there's no intelligence in artificial intelligence and
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even though we don't have a well formulated definition
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of what intelligence is i think i have to agree
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so here are some of the limitations so first of all we know that these machines learn
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through millions a human label examples such as follows
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or sentences or or any other kind of data
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and this is very different from human learning
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which only requires a very small number of
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examples if any at all these machines are enormous
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the the neural networks underline a g. t. t. three is language generation program
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have a hundred and seventy five billion parameters that's
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i'm numerical oh wait in the neural network and it has
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to be trained on text amount to hundreds of billions of words
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he's this scale of a size and training
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makes the creation of the systems available only to
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large companies that have this kind of computational resource and
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um it's collection of data on these machines
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are sometimes not transparent in what they actually lower
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there's a very simple example one of my graduate students trained it d.
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neural network to decide whether a a photo contains an animal or not
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so i hear the machine says animal ups and here it says no matter
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it was trained on a large number of like a major photographs
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but when my student trying to understand exactly what the
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machine had burned to enable it to perform this task
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you know that one of the things that it was using to
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make this decision was the background not the foreground of the animal
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but to get the background was blurry you can see it worry here and not learn here
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the the uh that was statistically associated with having an animal because the photographers
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focusing on of animal in the foreground here no focus at all so this
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machine what had work to do the task very well using ah
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using uh information that was not what we had intended it to lower
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this is called short hacked in machine learning
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you it's very common when the machine learn something
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that is able enables it to perform the task but is not
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what the same thing that humans use to perform the task and that's
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the machine can then make errors that are very i'm
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human like for example another a research group looked at but
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uh the neural networks that were very good at object
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recognition for example they could identify this as f. ayers rock
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with ninety nine percent confidence i but if
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the object was ah photo shot into different houses
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now the system was very confident that it was the school bus
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or fire goat or bobsled and um this is the new c.
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but it really shows that the machine is the line on something very
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different to make its decisions that what humans use an in real life
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this kind of uh what we call ritalin is in d. you're in neural nets
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can we use you accidents and catastrophes such as
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ah self driving cars like has a uh huh
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not recognising a fire truck stop on the highway and crashing into it
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which systems like t. g. three which i mentioned before this text generation system
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actually has been shown very clearly that it does not understand the text that it's
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generation even though it can uh look very human like it can make
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big uh errors in very nine human uh like a a a kind of behaviour
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and so i yeah researches have call this clever hans
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phenomenon clever hans was a horse back in uh the
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uh that to turn of the twentieth century who supposedly could do mathematics by
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a human would give it a math problem and it
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would uh hound it's hove to uh give the answer
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but it turned out that clever hans was not doing math at all it
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was responding to startle bahrain language of
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its trainer and this analogy now is that
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the uh clever hans uh projectors these uh neural
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networks or machine learning programs are used things settle
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clues in the data that is not actually performing the task
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the way humans would but responding to starbucks settles to just call correlations
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and this is shown again the vulnerability of neural networks to
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uh what are called adversarial attacks so this group showed that
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you and to an image that is good correctly classified
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by a neural network if you add it carefully engineer perturbation
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this isn't always that's been highly magnified here this is the
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result it looks identical the humans but now the machine will always
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classify this as an ostrich and no matter what the
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picture is given the engineer predation these are called adversarial attacks
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i'm neural networks and they've been shown to work even
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buys putting simple stickers on stop signs a machine will now
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that has been trained to recognise stock signs one now recognise this
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as as he limit a. e. sign which is not a good thing
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ah especially if this is in miles per hour i'm not a good thing for self driving car
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so these are of problems with uh are the current robustness of a i system it's
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but also show that they really are learning something quite
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different then what humans uh learn in the real world
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so i wrote a paper recently called y. a. o.
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i. is harder than you think which uh might through some
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reasons why i think a i it's not soon going to be let the level of humans
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and one of the uh reasons is that was a a quote from herbert right it's a philosopher
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who wrote extensively about a guy and he noted that
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i've expected obstacle in the assumed continuum of a i progress
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has always been the problem of common sense so common sense is now a huge
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kind of buzz word in a i it's a a
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a problem that many people are trying to get at
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such as paul allen the if if uh the cofounder of microsoft it
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before he died a few years ago he invested a
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lot of uh money in a t. g. a. i. commonsense
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the department of the defence in united states is devoting quite a bit
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of a fun beans you trying to get machines to have common sense
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but it is a very difficult problem so i i i imagine for
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example that a self driving in car is faced with a situation like this
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what i understand about the world to be able to project
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what's going to happen in this situation well there's many things that humans understand
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such as what we might call intuitive physics
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but but how different objects interact in the world
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intuitive psychology what key how people interact with
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their relationships are a biology you know other living
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why this dog it's doing what it's doing models of cause and effect very vast
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world knowledge that were unaware of consciously
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but that we use to understand new situations
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and filing the ability to abstract make analogies to
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situations that we've been in previously all these things uh
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are i would say a eyes biggest
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open challenges and we're very far i would
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say from being able to get a i systems to have anything close to human level
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on all of these abilities which are fundamental to being an able to function in a
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robust way in the real world so i worry about this extensively in every city well
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called artificial intelligence a guide for free humans would just just come out in a friend's edition
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so i hope that uh some of you will will take