September 9, 2020 • 38 mins
As we develop more advanced natural language systems, will we ever reach a point where a computer can detect not just what we say but what we actually mean?
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Episode Transcript
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Speaker 1 (00:04):
Welcome to tex Stuff production from I Heart Radio. Hey there,
and welcome to tech Stuff. I'm your host, Jonathan Strickland.
I'm an executive producer with I Heart Radio and a
love of all things tech, and I'm going to bring
you guys a little bit of a rerun today. I
(00:24):
am trying to get some stuff put together for a
special series of episodes as well as prepare for some
other stuff. So great things right around the corner. I
did not want to leave you without an episode at all,
So we're going to listen to this one that originally
published on October eighteenth, two thousand eighteen, and it kind
of goes in line with some other stuff we've been
(00:45):
covering in recent episodes of Tech Stuff. This episode was
titled Could We Make a Sarcastic Supercomputer? And yeah, it
really dives into the whole concept of artificial intelligence, natural lane,
which and just kind of understanding the quirks of what
it is to be human and the whole concept of sarcasm.
(01:07):
I hope you guys enjoy it. I mean that without
even a hint of sarcasm. And I'll chat with you
after the episode. Today. I want to talk to you
about an interesting topic that I got to explore a
couple of years ago with Joe McCormick and Lauren fogobaum
As we debated the possibilities of computers learning how to
(01:31):
understand sarcasm. We did it for a podcast called Forward Thinking,
which was around for a couple of years. It was
a lot of fun to work on that that show
is over, but I thought I would revisit the topic
and talk about it for you guys and kind of
go over what would it take to have a computer
that could actually understand when someone's being sarcastic. Now to
(01:54):
understand why this is a big deal, it helps to
have a refresher course on how computers process information. And
I know I talked about this a lot, but I
still think it's important to cover the basics when you
want to talk about something as advanced as being able
to detect and understand sarcasm. So computers understand machine code
(02:16):
or assembly language. This is a language that corresponds with
the actual physical architecture of the computers. So the way
the computer is built, that's how this language interacts. It's
it's essentially how the physical components of the computer are
able to handle electric current or voltage differences in order
(02:38):
to process information, and computers can interpret this and execute
upon this language very quickly. It is the basic language
of those physical components. However, it is almost impossible for
humans to work with this, at least on a way
that is at all of shion, because it ultimately for
(03:02):
most computers boils down to binary language, right, zeros and ones.
So you see a huge block of zeros and ones,
and unless you are neo from the matrix, it means
nothing to you. So we speak in natural language to
one another. Natural language, however, is filled with a lot
(03:24):
of components that make it very very challenging for machines
to interpret, like ambiguity, or there might be double meanings
in a phrase and you may mean both meanings at
the same time, and that is too complicated for most
machines to be able to process. They just can't deal
with that. So to bridge the gap between the way
(03:47):
we humans communicate and the way that computers process language,
we have created programming languages and compilers. Now, programming languages
fall into two broad category worries. It's more like a spectrum,
and you could be further on one end than the other,
and we typically call them high level programming languages and
(04:09):
low level programming languages. The lower the level of programming language,
the closer it is to machine code, and the easier
it is for a computer to understand, but the harder
it is to work with. If you happen to be,
you know, a human being. High level programming languages are
easier for humans to understand. Now, if you have never
(04:30):
taken any courses in programming and you're looking at a
page of code, it could seem indecipherable to you. It
is just meaningless strings of characters. But once you learn
the rules of that programming language, how you construct an instruction,
and a series of instructions, how you go from one
(04:51):
instruction to the next. Once you understand the rules, it
actually becomes quite easy to use in the grand scheme
of things, much more easy than machine which would be.
But again, the problem here is that computers don't understand
programming languages, not natively. Even though this is not exactly
the same as human natural language, it's also not the
(05:12):
same as machine language. That's why you need compilers. A
compiler is essentially a translator. It takes this high level
programming language or higher level anyway and then converts it
into a machine readable language for the computer to actually
execute upon. And this is all in the design of
(05:32):
the programming languages and the compilers. So this is the
way that for decades we have interacted with computers, when
you're talking about it on a on a direct level,
not just executing a program, but creating code, creating programs
for computers to run. Over the last few decades, we've
(05:53):
had some very very smart people working on natural language
systems for machines, which would allow a computer to interpret
natural language in a way that would make some sort
of sense, and for the computer to be able to
act upon that language. And we've seen this in plenty
(06:15):
of examples recently. Most smartphones have some sort of smart assistant.
You have standalone products like Amazon's Echo, you have Google Home,
You've got tons of devices that can interact with people.
It can be activated by typically an alert phrase, which
(06:35):
I'm not going to say because I don't want any
of you guys to have to deal with that. I
know how irritating it is when I'm watching a video
and someone activates their specific system and then mine begins
to respond, and all my lights started going on and
off because the people on YouTube we're talking funny. I
know how irritating that is, but use that it activates
(06:56):
and then you can speak. And typically you can say
the same thing several different ways and the device appears
to understand you no matter how you word it. And
this is a real challenge because we human beings can
find lots of different ways to say the same thing.
For example, if I say what is the weather today,
(07:18):
it could be very similar to if I if I
ask a question is it going to rain today? Both
of those are asking for information about the weather, but
are very different ways of saying that. A good natural
language recognition program will be able to parse that information
and then return the appropriate response. This is not an
(07:40):
easy thing to do. Typically it involves creating a neural
network structure, and I've talked about artificial neural networks recently.
That's a typically a network that can accept multiple binary inputs,
so either a zero or a one input that represents
some thing uh, some sort of yes, no or on
(08:03):
off kind of feature. It can accept multiple multiple inputs
of that nature, so multiple zeros or ones that all
factor into making a decision, and then it has a
waiting for each of those components, and then it produces
a single output that's also binary in nature, either is
zero one, and it passes that on to other artificial
(08:26):
neurons further down the chain. Sometimes that will come back
around and you have a recursive artificial neural network. The
goal here is for this process two ultimately result in
a response that is reasonably certain to meet the requirements
of the person asking the question. This tends to be
(08:49):
talked about in the realm of probabilities. We we talked
about how certain the machine is that the response is
the appropriate one, and if it falls below a certain toushold,
then the machine would typically respond with I'm sorry, I
don't know what you're asking for, or something similar to that.
There are cases where you just get misinterpreted and you'll
(09:10):
get a response that does not reflect whatever you ask.
That's a little different. That's where the machine has drawn
a conclusion, has been reasonably certain that it came to
the right conclusion. It turns out it was wrong the
whole way, but that's the process. Now, when it comes
to sarcasm, that adds yet another layer of difficulty, because
(09:34):
now a machine isn't just parsing what you are saying.
It has to understand what you mean, the meaning of
your words and the meaning of the way you deliver them.
It could be different. So if I were to just
write out a phrase with no tone, nobody language, uh,
(09:55):
not emphasizing any one word over another, it might be
very difficult to detect what my intent was. It may
seem like I'm being sincere, when in fact I'm being insincere.
For example, Uh, if I were to say that guy
is super tall, but I'm being sarcastic, then just in
(10:18):
that phrase the way I write it out, you would think, oh, well,
that person he's looking at must be super tall. How
do you recognize sarcasm? How can you detect that this
is in place and then understand what the meaning underneath
it is. One of the approaches that has been put
forward relates to IBM's Watson platform. Now. Watson first made
(10:44):
headlines back when it was a contestant on Jeopardy. It
went up against two former champions, including Ken Jennings, who
shows up on a house Stuff Works podcast. Anyway, Watson
went up against these two former champions, and it is
able to interpret natural language. It had to in order
to play the game of Jeopardy, And for those who
(11:05):
do not know what jeopardy is or they're not familiar
with the game show, Jeopardy is a game where you
are presented with categories of trivia and each category has
multiple uh questions or multiple entries in it, and they
range in dollar value, and the lower dollar value ones
(11:29):
are easier to answer than the higher dollar value ones,
and UH, you're Typically the way jeopardy works is that
you're you're given quote unquote the answer and you have
to provide the question. So, uh, if the answer were
this film that detailed the adventures of a young playwright
(11:53):
in sixteenth century England, one best picture, you would say,
what was Shakespeare in Love? So this computer is playing
against these two former champions. This was sort of an
exhibition series of games. It wasn't meant for, uh, a
competition in the way the typical Jeopardy games were there
was money on the line. Was an exhibition and Watson won.
(12:16):
It beat both of the champions, and it did what
I was telling you. It It would analyze the clue
that was given, the answer that was given, It would
try and generate a question to correspond with that answer,
and only if the question met a certain threshold of
confidence with Watson buzz in. If it did not meet
(12:37):
that level of confidence, Watson would remain quiet. And most importantly,
Watson was not at all connected to the Internet. All
the information was contained within a massive series of servers
more than gosh I can't even remember. There's a ton
of processors attached to it. Um so a very powerful machine,
(12:59):
but it still wasn't exactly able to detect sarcasm. It
could work with word play and it could work with riddles,
so that was really impressive. But what it really did
was it gave IBM the opportunity to say, we have
this platform here and we're welcoming developers to create applications
(13:20):
that tap into this platform and make use of this
in order to do interesting stuff with it. And IBM
was largely working with the medical industry at that point
to try and help doctors treat and diagnose patients, and
it was sort of computer guidance. It wasn't that you
had an automatic doctor, but rather the doctor had what
(13:44):
equates to a medical expert to confer with when trying
to determine why's the best course of action for a patient.
IBM put up an application Program Interface or API, and
let developers create their own cognitive computing applications built on
top of Watson. One of those was called the tone analyzer.
(14:08):
It still exists back when we were doing this episode
for Forward Thinking. It was in the form of analyzing
some text and telling you whether or not that text
would come across as agreeable or argumentative, or positive or negative,
and it would assign tone to those pieces. I'll explain
(14:28):
more about how it did and what it did in
just a minute, but first let's take a quick break
to thank our sponsor. So how did this tone analyzer work.
It would search for cues in any written text, social cues,
(14:53):
written cues, emotional cues in order to determine the overall
tone of a piece, which actually meant that the analy
lizer would tag individual words within a text, words that
it recognized and had already pre labeled as falling into
various categories. So words that might have a positive meaning
(15:14):
like happy, glad, joy, things like that, those would get
tagged as cheerful. But then it would then assign all
the individual words tags and then tally everything up. So
let's say you've got a bunch of sentences and it
starts individually labeling certain words as being cheerful or sad,
(15:37):
or angry or helpful, and then it adds it all
up and then would give you a percentage. So a
message might be agreeable or thirty conscientious, you would actually
get multiples of these, and that would just really indicate
the density of those types of words within the message itself. Now,
(15:58):
in an ideal world, if language were very simple to
understand and interpret by machines, this would help you gauge
how people would respond to your work. Right, So, you
could write a message. Before you send it, you put
it through the tone analyzer and it tells you what
sort of a tone you are setting. So if you
(16:22):
wanted to create a business letter, you could send it
through this tone analyzer and if it came back as
saying it's coming across as as a indecisive, you might
want to go back in and edit that message so
that you can make a more straightforward and uh decisive
message and not give the wrong impression before you send
(16:43):
the message out to your actual human recipient and come
up with alternate word choices in order to make sure
that your message is received the way you intended it
and anyone who has communicated over the Internet can think
of ways that this might have been helpful in the past,
because again, language depends on so many different elements to
get your meaning across, and when you reduce it to
(17:07):
the written form, especially the written form online, where we
tend to be very short with our our communication, it
comes in very quick bursts, a couple of sentences here
or there. We lack all that body language, we lack
that tone. It's very easy to misinterpret. I'm sure there's
been an example in your life where either you got
(17:28):
offended from receiving something that was meant in a way
that was different from the way you you interpreted it,
or the reverse happened where you sent a message and
somebody had a reaction you did not anticipate because they
could not tell what tone you were using just from
the words you were using. Machines have that same problem.
In the future, an analyzer like this tone analyzer, it
(17:51):
could be incorporated into word processors, or email servers, or
email services, i should say, or social media platform. So
you start typing in your message and before you hit
published or post or send, you could analyze that text.
It could tell you what the tone is and then
you could say, oh, no, that's gonna come across totally
(18:12):
the wrong way, and you could actually fix it before
you posted it or sent it, and then you wouldn't
have that awkward decision of whether or not to edit something, or,
in the case of Twitter, which continues to refuse to
allow you to edit tweets, to delete a tweet. I
deleted a tweet the other day when I posted a
link to a news story, and I had done a
(18:32):
rookie mistake, one that I try to avoid, but I
did it this pastime, which is that I didn't think
to look at the date when the news item had
been published, and had been published a full year earlier,
so it was not new news, it was old news.
And uh then deleted the tweet and it wasn't up
for long, but I still felt dumb about it. It
(18:53):
would have been nice to have been able to check that.
Although that's not tone obviously, that's but similar in the
and the idea that you want to check before you
end up offending someone, unless you're one of those jerk
faces that just sets out to offend people, in which
case rethink your strategy. There are better things to do.
It's just as you can make just as big an
(19:15):
impact being a positive person as you can being a
jerk face. I know it can seem like it's more work,
but it's also more rewarding in the long run. Okay,
soapbox done. So. There is a demo of the tone
analyzer that's available online, and back when we were recording
Forward Thinking, the demo worked in a way where it
would tell you about emotional tone and break it down
(19:38):
by percentage. It's a little different now, but I want
to tell you the what words and the results we
got in the past because they were so much fun.
Granted you would get a different result now because the
tone analyzer has been tweaked since we recorded that episode. So,
when we recorded that episode, one of my co hosts
(19:59):
decided to put a a sentence that is somewhat known
in literary circles into this tone analyzer and find out
what it said. And the sentence used was it is
a truth universally acknowledged that a single man in possession
of a good fortune must be in want of a wife. Now,
the analyzer said that this emotional tone was cheerful, the
(20:21):
social tone was seventy six percent open and fifty one agreeable,
and the writing tone was analytical. You can also view
the sentence in terms of word count as opposed to
the weighted value of individual words, and using that view,
five percent of the sentence sentences were in an emotional tone,
eighty nine percent in a social tone, and five percent
(20:44):
in a writing tone. Now, the analyzer highlights each word
according to how it classifies them, so emotional words would
be highlighted in red or pink in that older version
of the Tone Analyzer, social words would show up in blue,
and writing tones would be in green. And you could
click on any word and the analyzer would offer alternative
(21:05):
words that you might want to use and classify those
words in the tones that they are associated with, so
that you could shape your message to meet the tone
you wish to convey. Also, the Tone Analyzer demo used
the business letter format as the means of comparison, so,
in other words, we compared Jane Austen to a business letter. Presumably,
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if you were to use a full version of the analyzer,
not just the demo version, you would have other options
so you could compare it with other models, not just
a business letter. Joe McCormick. He included an excerpt from
Dostoyevsky's Notes from Underground. That excerpt was, I could not
become anything, neither good nor bad, neither a scoundrel nor
(21:52):
an honest man, neither a hero nor an insect. And
now I am eking out my days in my corner,
taunting myself with the bitter and entirely useless constellation that
an intelligent man cannot seriously become anything, that only a
fool can become something. The feedback was that the emotional
(22:12):
tone had anger at cheerfulness at so happy anger negative at.
The social tone was agreeable zero percent conscientious, zero percent open.
The writing tone was analytical, zero percent confident and tentative.
Joe would actually end up highlighting some of the words
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to find out which words were the ones that ended
up giving that cheerfulness result. Those four words were good, honest, hero,
and intelligent and that kind of are that That's important
because those words, the way they are used, uh in
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that passage are not used in a positive sense. They
are positive words, but they're meant to show kind of
a negation there not and not an assertion. So that
really highlights a big problem in this tone analyzer, which
is that it's tagging these words individually without context. So
(23:20):
if I wrote the phrase I am not glad, it
would tag the word glad and say that's a cheerful word.
But I said I am not glad. You if I
told you I am not glad, you would not think, oh, well,
that's a cheerful thing to say or a positive thing
to say. But according to the tone analyzer, it would
come across as a cheerful statement because it had tagged
(23:44):
that word as as being cheerful. In the other words
are not that strong, They don't They don't warrant being
tagged in a way like that. Now, over time, we
might have a tone analyzer that can actually take context
into account, and then you would learn a lot more
about the actual meaning behind a phrase. It would be
(24:05):
more than just tone. So if you were trying to
get across tone by using more complicated and subtle word
choice where you're sort of being kind of uh poetic
in your expression, you're trying to get across a feeling
by using irony or sarcasm, then a tone analyzer like
(24:29):
this would totally miss it because it would just be
counting the hits and not understanding the usage. There the
hidden meeting the word play, so that is going to
be a real challenge. So it's kind of another interesting
use of IBMS Watson. There are a lot of other
ones that we could talk about, like Chef Watson, which
(24:50):
was my favorite. Chef Watson would generate new recipes based
upon ingredients that you would tell it that you had
on hand, and it wouldn't it wouldn't go and reference
old recipes and pull one up for you. Instead, it
would make flavor profiles based upon all the different combinations
of food that were found in various recipe books and
(25:11):
generate a brand new recipe for you right there on
the spot. And sometimes they were whacka doodle crazy, y'all.
So in a way, you could say that Chef Watson
was another another way of seeing how I b M
S Watson has a lot of promise, but it requires
a ton of work on the app level in order
(25:32):
to leverage it and make actual practical use out of it.
I have more to say about computers detecting sarcasm, but
first let's take a quick word from our sponsor. So
(25:53):
back in two there were some researchers at the Hebrew
University in Israel who designed a system called the Semi
Supervised Algorithm for Sarcasm Identification or SAZI, and they used
SAZI to analyze collections of nearly six million tweets and
(26:15):
also around sixty six thousand product reviews from Amazon. They
wanted to find rich treasure troves of sarcasm that turns
out reviews and tweets. They fit the bill sarcasm is.
Really it's typically conveyed in some vocal tone, right and
(26:35):
nonverbal cues. So you have to first go someplace where
sarcasm is is rampant in text form to be able
to really fine tune how you can identify sarcasm versus
something that's meant exactly the way it's written on the
surface level. So they started to map out the various
(26:58):
features that were common in sarcastic comments online. So they
were looking for things like hyperbolic words and if you're
using a lot of exaggeration, that could be a key.
Excessive punctuation was another one, especially ellipses, which I tend
to use a lot, though I don't know if I
use it so much for sarcasm as I do for
(27:19):
just timing purposes. To indicate this is the beat I
would take if I were saying this out loud, I
guess that's just as irritating. Though, also how straightforward is
the Senate structure? And they gave it examples of sarcasm.
They fed it tweets that were tagged hashtag sarcasm, so
that the machine quote unquote knew that that was already
(27:42):
a sarcastic tweet and could start to analyze it and
build out a model for what sarcasm is. They also
fed at a bunch of one star Amazon reviews that
had been judged to be sarcastic by a panel consisting
of fifteen human beings, and the system was told it
had to rate sentences on a scale of one to five,
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One being not sarcastic, they mean exactly what the Senate says,
five being holy cow, this person should write for the Onion.
This is incredibly sarcastic. SAZI could identify sarcastic Amazon reviews
with precision. Not bad, But when it came to Twitter
(28:26):
it did even better. I think, probably because there had
to be very short messages on Twitter. This was before
Twitter had even expanded to characters, so it was still
back in the one character days. The precision rate for
SAZI for Twitter was so it was really good at
detecting straightforward sarcasm, the kind that a lot of people
(28:49):
on Twitter use because you have limited space so you
can't really set it up in a more complex way.
But it was also uh more prone to judging things
as false negative evaluations rather than false positives. In other words,
it was more likely to look at a negative sarcastic
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message and say that's not sarcastic than it was to
look at a straightforward message and say, no, that is sarcastic.
So that was kind of interesting back to Watson. Another
use of Watson came out of the Milk and Institute
Global Conference at IBM showed off some research that it
(29:30):
had been working on internally, and it was calling this
research debating Technologies. This was a project in which IBM
was trying to see if they could feed a computer
raw information, have the computer synthesize the information, understand that
information at least on a computational level, and then create
(29:54):
a a debating strategy for both pros and cons they
on that information. So it would take a huge amount
of content like all of Wikipedia, for example, and then
on any given subject that would be covered in Wikipedia.
It would be asked form an argument that is in
(30:14):
favor of or is against a concept, whatever that concept
might be. John Kelly of IBM showed off in a
demo how the tool could be used to predict pro
or con arguments about a subject based on a body
of information. So you might be able to use this
(30:34):
technology in order to anticipate what an opposing person might
say on any given subject. Let's say that you are
getting ready to debate a topic. You might feed that
information to a computer system using this Watson platform. You
might feed in a ton of information, and then you
(30:56):
might say, who is a man and someone who is
against this particular topic, whatever it might be. Uh. Let's
say it's it's it's renewable energy, and the uh the
efficiency of solar panels, whether or not it makes sense
to invest in solar panels. Let's say that your stance
(31:17):
is that you have to argue for solar panels. You
might say, what would someone who wants to argue against
solar panels say? And then Watson would analyze this information
and return to you what it thinks would be an
argument someone would use to support that that stance, and
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then you could prepare for that, which would be an
incredible tool. I mean, you could think of this as
for political debates. It would be amazing. You could think
of how you might want to prepare so that you
can argue intelligently against an opponent, and you can already
anticipate what that opponent is going to say, because you
know their general stance on a topic, but you might
not know what tactic they might use to support that stance.
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Maybe politics isn't a great choice because that's not always
in the realm of rationality. That often falls into a
call toward emotional response rather than rational response. That's more
of a a commentary on politics in general, regardless of
what side you might be on, all sides do this anyway.
(32:23):
He actually showed at this demo a different example. He said,
what if you were to take the sale of violent
video games to minors should be banned. That's the topic,
and that the computer would then go through all the
information and had access to. It would end up sorting
out all the parts that were relevant to the discussion,
(32:45):
so it just put those aside and that would become
the core of the data it would reference. I would
then go through and identify basic statements as either being
a pro stance of banning violent video games to my
nes or a constance for that saying no, we should
be able to sell violent video games to minors. The
(33:08):
tools scanned four million articles, it returned the top ten
articles that were determined to be the most relevant to
that particular debate, and it scanned approximately three thousand sentences
from from top to bottom, and it then identified sentences
that contained candidate claims that would be statements that would
(33:28):
either be interpreted as being pro or con for the stance.
Then it identified the parameters of those claims. Then it
assessed the claims for the pro and con polarity, then
constructed a sample pro or con statement. And the statements
in the demo were kind of interesting. And since the
computer is constructing arguments based upon what people have already written,
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it would reflect a lot of vague statements that aren't
a firm stance. So, in other words, like it couldn't
take a bunch of stuff that was written that it
sell did not take either a pro or constance, and
then transformed that magically into the perfect pro stance or
the perfect constance. Uh, it's dependent upon the words that
human beings have already written, so it could not magically
(34:14):
come up with a killer argument if the data that
had been written about this subject didn't come down on
a firm stance one way or the other. Um, the
point of the demonstration wasn't to create a tool that
could either troll people or counter trolls. It was to
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show that a computer could be useful to aid in
the reasoning process when you're making a critical decision. Again,
to go back to that medical example, it could be
used to help a doctor determine which diagnosis is the
most likely to be accurate for a patient, what what
course of treatment might be the most helpful for that patient,
(34:56):
and thus it could have real practic coal use outside
of this more esoteric, interesting uh debate news. Now, will
we see computers in the future able to detect sarcasm
just as easily as your typical human being can when
given the right circumstances. And I use the word typical reluctantly,
(35:22):
but you get what I mean. I don't know. It's
gonna take some time. It takes an awful lot of
processing power too. You have to remember that for these
neural networks systems, the ones that are running these these
various platforms and programs and strategies. They take up a
lot of processing power because our brains have billion neurons
(35:46):
in them, so we have a very sophisticated supercomputer sitting
in our heads. Moreover, our brains are insanely energy efficient.
They require about the equivalent of twenty watts of power.
A supercomputer needs a lot more power than that. So
while we're seeing advances in this, it requires so much
(36:07):
processing power, so much energy, it is not a practical
approach to most forms of computing, at least from a
consumer standpoint. You might see a future where the sort
of stuff is all in the cloud and then we
can access it through an app or a program or whatever.
That way, you don't have to have a supercomputer sitting
(36:30):
on your desk in order to tap into those, uh,
those capabilities, but you have to have an Internet connection,
which most of us these days tend to have fairly frequently.
I mean, there are a lot of people out there
who at this point have had a persistent Internet connection
for pretty much their whole lives, which blows my mind.
But that's the kind of world we'd have to live
(36:50):
in in order to really take advantage of this at
least in the near term. I don't know if we're
ever going to see a computer that can analyze, say,
an article from the Onion and not only point out
that it's being sarcastic or ironic, but also point out
why it's funny. I think at one point, when you
start analyzing comedy, there gets to be a level where
(37:12):
nothing is ever funny ever again, but it is a
really interesting problem. So that's whether that's that's this look
back on if AI is ever going to understand sarcasm. Well, guys,
I hope you enjoyed that classic episode of tech stuff.
I guess I guess two years old isn't old enough
to be classic. That uh that that only somewhat less
(37:36):
than fresh episode of text stuff about artificial intelligence and
sarcasm and things of that nature. I am constantly impressed
with how artificial intelligence is advancing year over year. But
when you look at what it means to be human
and the ways that we humans interact with one another,
(37:56):
and the ways that we can communicate complicated three things,
sometimes just through you know, subtle methods that are not
overt or or you know, directly spoken, it reminds us
that machines have got a long way to go in
order to really grasp what it is to be human,
So unless you're Commander Data, you're probably struggling a bit.
(38:20):
So I hope you guys enjoyed this. If you have
suggestions for future episodes of tech Stuff, I've got a
few episodes based on listener suggestions coming up soon. But
if you want to get your suggestions in tweet me.
The Twitter handle is text stuff H s W and
I'll talk to you again really soon. Text Stuff is
(38:44):
an I Heart Radio production. For more podcasts from my
Heart Radio, visit the i Heart Radio app, Apple Podcasts,
or wherever you listen to your favorite shows.
Welcome to tex Stuff production from I Heart Radio. Hey there,
and welcome to tech Stuff. I'm your host, Jonathan Strickland.
I'm an executive producer with I Heart Radio and a
love of all things tech, and I'm going to bring
you guys a little bit of a rerun today. I
(00:24):
am trying to get some stuff put together for a
special series of episodes as well as prepare for some
other stuff. So great things right around the corner. I
did not want to leave you without an episode at all,
So we're going to listen to this one that originally
published on October eighteenth, two thousand eighteen, and it kind
of goes in line with some other stuff we've been
(00:45):
covering in recent episodes of Tech Stuff. This episode was
titled Could We Make a Sarcastic Supercomputer? And yeah, it
really dives into the whole concept of artificial intelligence, natural lane,
which and just kind of understanding the quirks of what
it is to be human and the whole concept of sarcasm.
(01:07):
I hope you guys enjoy it. I mean that without
even a hint of sarcasm. And I'll chat with you
after the episode. Today. I want to talk to you
about an interesting topic that I got to explore a
couple of years ago with Joe McCormick and Lauren fogobaum
As we debated the possibilities of computers learning how to
(01:31):
understand sarcasm. We did it for a podcast called Forward Thinking,
which was around for a couple of years. It was
a lot of fun to work on that that show
is over, but I thought I would revisit the topic
and talk about it for you guys and kind of
go over what would it take to have a computer
that could actually understand when someone's being sarcastic. Now to
(01:54):
understand why this is a big deal, it helps to
have a refresher course on how computers process information. And
I know I talked about this a lot, but I
still think it's important to cover the basics when you
want to talk about something as advanced as being able
to detect and understand sarcasm. So computers understand machine code
(02:16):
or assembly language. This is a language that corresponds with
the actual physical architecture of the computers. So the way
the computer is built, that's how this language interacts. It's
it's essentially how the physical components of the computer are
able to handle electric current or voltage differences in order
(02:38):
to process information, and computers can interpret this and execute
upon this language very quickly. It is the basic language
of those physical components. However, it is almost impossible for
humans to work with this, at least on a way
that is at all of shion, because it ultimately for
(03:02):
most computers boils down to binary language, right, zeros and ones.
So you see a huge block of zeros and ones,
and unless you are neo from the matrix, it means
nothing to you. So we speak in natural language to
one another. Natural language, however, is filled with a lot
(03:24):
of components that make it very very challenging for machines
to interpret, like ambiguity, or there might be double meanings
in a phrase and you may mean both meanings at
the same time, and that is too complicated for most
machines to be able to process. They just can't deal
with that. So to bridge the gap between the way
(03:47):
we humans communicate and the way that computers process language,
we have created programming languages and compilers. Now, programming languages
fall into two broad category worries. It's more like a spectrum,
and you could be further on one end than the other,
and we typically call them high level programming languages and
(04:09):
low level programming languages. The lower the level of programming language,
the closer it is to machine code, and the easier
it is for a computer to understand, but the harder
it is to work with. If you happen to be,
you know, a human being. High level programming languages are
easier for humans to understand. Now, if you have never
(04:30):
taken any courses in programming and you're looking at a
page of code, it could seem indecipherable to you. It
is just meaningless strings of characters. But once you learn
the rules of that programming language, how you construct an instruction,
and a series of instructions, how you go from one
(04:51):
instruction to the next. Once you understand the rules, it
actually becomes quite easy to use in the grand scheme
of things, much more easy than machine which would be.
But again, the problem here is that computers don't understand
programming languages, not natively. Even though this is not exactly
the same as human natural language, it's also not the
(05:12):
same as machine language. That's why you need compilers. A
compiler is essentially a translator. It takes this high level
programming language or higher level anyway and then converts it
into a machine readable language for the computer to actually
execute upon. And this is all in the design of
(05:32):
the programming languages and the compilers. So this is the
way that for decades we have interacted with computers, when
you're talking about it on a on a direct level,
not just executing a program, but creating code, creating programs
for computers to run. Over the last few decades, we've
(05:53):
had some very very smart people working on natural language
systems for machines, which would allow a computer to interpret
natural language in a way that would make some sort
of sense, and for the computer to be able to
act upon that language. And we've seen this in plenty
(06:15):
of examples recently. Most smartphones have some sort of smart assistant.
You have standalone products like Amazon's Echo, you have Google Home,
You've got tons of devices that can interact with people.
It can be activated by typically an alert phrase, which
(06:35):
I'm not going to say because I don't want any
of you guys to have to deal with that. I
know how irritating it is when I'm watching a video
and someone activates their specific system and then mine begins
to respond, and all my lights started going on and
off because the people on YouTube we're talking funny. I
know how irritating that is, but use that it activates
(06:56):
and then you can speak. And typically you can say
the same thing several different ways and the device appears
to understand you no matter how you word it. And
this is a real challenge because we human beings can
find lots of different ways to say the same thing.
For example, if I say what is the weather today,
(07:18):
it could be very similar to if I if I
ask a question is it going to rain today? Both
of those are asking for information about the weather, but
are very different ways of saying that. A good natural
language recognition program will be able to parse that information
and then return the appropriate response. This is not an
(07:40):
easy thing to do. Typically it involves creating a neural
network structure, and I've talked about artificial neural networks recently.
That's a typically a network that can accept multiple binary inputs,
so either a zero or a one input that represents
some thing uh, some sort of yes, no or on
(08:03):
off kind of feature. It can accept multiple multiple inputs
of that nature, so multiple zeros or ones that all
factor into making a decision, and then it has a
waiting for each of those components, and then it produces
a single output that's also binary in nature, either is
zero one, and it passes that on to other artificial
(08:26):
neurons further down the chain. Sometimes that will come back
around and you have a recursive artificial neural network. The
goal here is for this process two ultimately result in
a response that is reasonably certain to meet the requirements
of the person asking the question. This tends to be
(08:49):
talked about in the realm of probabilities. We we talked
about how certain the machine is that the response is
the appropriate one, and if it falls below a certain toushold,
then the machine would typically respond with I'm sorry, I
don't know what you're asking for, or something similar to that.
There are cases where you just get misinterpreted and you'll
(09:10):
get a response that does not reflect whatever you ask.
That's a little different. That's where the machine has drawn
a conclusion, has been reasonably certain that it came to
the right conclusion. It turns out it was wrong the
whole way, but that's the process. Now, when it comes
to sarcasm, that adds yet another layer of difficulty, because
(09:34):
now a machine isn't just parsing what you are saying.
It has to understand what you mean, the meaning of
your words and the meaning of the way you deliver them.
It could be different. So if I were to just
write out a phrase with no tone, nobody language, uh,
(09:55):
not emphasizing any one word over another, it might be
very difficult to detect what my intent was. It may
seem like I'm being sincere, when in fact I'm being insincere.
For example, Uh, if I were to say that guy
is super tall, but I'm being sarcastic, then just in
(10:18):
that phrase the way I write it out, you would think, oh, well,
that person he's looking at must be super tall. How
do you recognize sarcasm? How can you detect that this
is in place and then understand what the meaning underneath
it is. One of the approaches that has been put
forward relates to IBM's Watson platform. Now. Watson first made
(10:44):
headlines back when it was a contestant on Jeopardy. It
went up against two former champions, including Ken Jennings, who
shows up on a house Stuff Works podcast. Anyway, Watson
went up against these two former champions, and it is
able to interpret natural language. It had to in order
to play the game of Jeopardy, And for those who
(11:05):
do not know what jeopardy is or they're not familiar
with the game show, Jeopardy is a game where you
are presented with categories of trivia and each category has
multiple uh questions or multiple entries in it, and they
range in dollar value, and the lower dollar value ones
(11:29):
are easier to answer than the higher dollar value ones,
and UH, you're Typically the way jeopardy works is that
you're you're given quote unquote the answer and you have
to provide the question. So, uh, if the answer were
this film that detailed the adventures of a young playwright
(11:53):
in sixteenth century England, one best picture, you would say,
what was Shakespeare in Love? So this computer is playing
against these two former champions. This was sort of an
exhibition series of games. It wasn't meant for, uh, a
competition in the way the typical Jeopardy games were there
was money on the line. Was an exhibition and Watson won.
(12:16):
It beat both of the champions, and it did what
I was telling you. It It would analyze the clue
that was given, the answer that was given, It would
try and generate a question to correspond with that answer,
and only if the question met a certain threshold of
confidence with Watson buzz in. If it did not meet
(12:37):
that level of confidence, Watson would remain quiet. And most importantly,
Watson was not at all connected to the Internet. All
the information was contained within a massive series of servers
more than gosh I can't even remember. There's a ton
of processors attached to it. Um so a very powerful machine,
(12:59):
but it still wasn't exactly able to detect sarcasm. It
could work with word play and it could work with riddles,
so that was really impressive. But what it really did
was it gave IBM the opportunity to say, we have
this platform here and we're welcoming developers to create applications
(13:20):
that tap into this platform and make use of this
in order to do interesting stuff with it. And IBM
was largely working with the medical industry at that point
to try and help doctors treat and diagnose patients, and
it was sort of computer guidance. It wasn't that you
had an automatic doctor, but rather the doctor had what
(13:44):
equates to a medical expert to confer with when trying
to determine why's the best course of action for a patient.
IBM put up an application Program Interface or API, and
let developers create their own cognitive computing applications built on
top of Watson. One of those was called the tone analyzer.
(14:08):
It still exists back when we were doing this episode
for Forward Thinking. It was in the form of analyzing
some text and telling you whether or not that text
would come across as agreeable or argumentative, or positive or negative,
and it would assign tone to those pieces. I'll explain
(14:28):
more about how it did and what it did in
just a minute, but first let's take a quick break
to thank our sponsor. So how did this tone analyzer work.
It would search for cues in any written text, social cues,
(14:53):
written cues, emotional cues in order to determine the overall
tone of a piece, which actually meant that the analy
lizer would tag individual words within a text, words that
it recognized and had already pre labeled as falling into
various categories. So words that might have a positive meaning
(15:14):
like happy, glad, joy, things like that, those would get
tagged as cheerful. But then it would then assign all
the individual words tags and then tally everything up. So
let's say you've got a bunch of sentences and it
starts individually labeling certain words as being cheerful or sad,
(15:37):
or angry or helpful, and then it adds it all
up and then would give you a percentage. So a
message might be agreeable or thirty conscientious, you would actually
get multiples of these, and that would just really indicate
the density of those types of words within the message itself. Now,
(15:58):
in an ideal world, if language were very simple to
understand and interpret by machines, this would help you gauge
how people would respond to your work. Right, So, you
could write a message. Before you send it, you put
it through the tone analyzer and it tells you what
sort of a tone you are setting. So if you
(16:22):
wanted to create a business letter, you could send it
through this tone analyzer and if it came back as
saying it's coming across as as a indecisive, you might
want to go back in and edit that message so
that you can make a more straightforward and uh decisive
message and not give the wrong impression before you send
(16:43):
the message out to your actual human recipient and come
up with alternate word choices in order to make sure
that your message is received the way you intended it
and anyone who has communicated over the Internet can think
of ways that this might have been helpful in the past,
because again, language depends on so many different elements to
get your meaning across, and when you reduce it to
(17:07):
the written form, especially the written form online, where we
tend to be very short with our our communication, it
comes in very quick bursts, a couple of sentences here
or there. We lack all that body language, we lack
that tone. It's very easy to misinterpret. I'm sure there's
been an example in your life where either you got
(17:28):
offended from receiving something that was meant in a way
that was different from the way you you interpreted it,
or the reverse happened where you sent a message and
somebody had a reaction you did not anticipate because they
could not tell what tone you were using just from
the words you were using. Machines have that same problem.
In the future, an analyzer like this tone analyzer, it
(17:51):
could be incorporated into word processors, or email servers, or
email services, i should say, or social media platform. So
you start typing in your message and before you hit
published or post or send, you could analyze that text.
It could tell you what the tone is and then
you could say, oh, no, that's gonna come across totally
(18:12):
the wrong way, and you could actually fix it before
you posted it or sent it, and then you wouldn't
have that awkward decision of whether or not to edit something, or,
in the case of Twitter, which continues to refuse to
allow you to edit tweets, to delete a tweet. I
deleted a tweet the other day when I posted a
link to a news story, and I had done a
(18:32):
rookie mistake, one that I try to avoid, but I
did it this pastime, which is that I didn't think
to look at the date when the news item had
been published, and had been published a full year earlier,
so it was not new news, it was old news.
And uh then deleted the tweet and it wasn't up
for long, but I still felt dumb about it. It
(18:53):
would have been nice to have been able to check that.
Although that's not tone obviously, that's but similar in the
and the idea that you want to check before you
end up offending someone, unless you're one of those jerk
faces that just sets out to offend people, in which
case rethink your strategy. There are better things to do.
It's just as you can make just as big an
(19:15):
impact being a positive person as you can being a
jerk face. I know it can seem like it's more work,
but it's also more rewarding in the long run. Okay,
soapbox done. So. There is a demo of the tone
analyzer that's available online, and back when we were recording
Forward Thinking, the demo worked in a way where it
would tell you about emotional tone and break it down
(19:38):
by percentage. It's a little different now, but I want
to tell you the what words and the results we
got in the past because they were so much fun.
Granted you would get a different result now because the
tone analyzer has been tweaked since we recorded that episode. So,
when we recorded that episode, one of my co hosts
(19:59):
decided to put a a sentence that is somewhat known
in literary circles into this tone analyzer and find out
what it said. And the sentence used was it is
a truth universally acknowledged that a single man in possession
of a good fortune must be in want of a wife. Now,
the analyzer said that this emotional tone was cheerful, the
(20:21):
social tone was seventy six percent open and fifty one agreeable,
and the writing tone was analytical. You can also view
the sentence in terms of word count as opposed to
the weighted value of individual words, and using that view,
five percent of the sentence sentences were in an emotional tone,
eighty nine percent in a social tone, and five percent
(20:44):
in a writing tone. Now, the analyzer highlights each word
according to how it classifies them, so emotional words would
be highlighted in red or pink in that older version
of the Tone Analyzer, social words would show up in blue,
and writing tones would be in green. And you could
click on any word and the analyzer would offer alternative
(21:05):
words that you might want to use and classify those
words in the tones that they are associated with, so
that you could shape your message to meet the tone
you wish to convey. Also, the Tone Analyzer demo used
the business letter format as the means of comparison, so,
in other words, we compared Jane Austen to a business letter. Presumably,
(21:27):
if you were to use a full version of the analyzer,
not just the demo version, you would have other options
so you could compare it with other models, not just
a business letter. Joe McCormick. He included an excerpt from
Dostoyevsky's Notes from Underground. That excerpt was, I could not
become anything, neither good nor bad, neither a scoundrel nor
(21:52):
an honest man, neither a hero nor an insect. And
now I am eking out my days in my corner,
taunting myself with the bitter and entirely useless constellation that
an intelligent man cannot seriously become anything, that only a
fool can become something. The feedback was that the emotional
(22:12):
tone had anger at cheerfulness at so happy anger negative at.
The social tone was agreeable zero percent conscientious, zero percent open.
The writing tone was analytical, zero percent confident and tentative.
Joe would actually end up highlighting some of the words
(22:34):
to find out which words were the ones that ended
up giving that cheerfulness result. Those four words were good, honest, hero,
and intelligent and that kind of are that That's important
because those words, the way they are used, uh in
(22:55):
that passage are not used in a positive sense. They
are positive words, but they're meant to show kind of
a negation there not and not an assertion. So that
really highlights a big problem in this tone analyzer, which
is that it's tagging these words individually without context. So
(23:20):
if I wrote the phrase I am not glad, it
would tag the word glad and say that's a cheerful word.
But I said I am not glad. You if I
told you I am not glad, you would not think, oh, well,
that's a cheerful thing to say or a positive thing
to say. But according to the tone analyzer, it would
come across as a cheerful statement because it had tagged
(23:44):
that word as as being cheerful. In the other words
are not that strong, They don't They don't warrant being
tagged in a way like that. Now, over time, we
might have a tone analyzer that can actually take context
into account, and then you would learn a lot more
about the actual meaning behind a phrase. It would be
(24:05):
more than just tone. So if you were trying to
get across tone by using more complicated and subtle word
choice where you're sort of being kind of uh poetic
in your expression, you're trying to get across a feeling
by using irony or sarcasm, then a tone analyzer like
(24:29):
this would totally miss it because it would just be
counting the hits and not understanding the usage. There the
hidden meeting the word play, so that is going to
be a real challenge. So it's kind of another interesting
use of IBMS Watson. There are a lot of other
ones that we could talk about, like Chef Watson, which
(24:50):
was my favorite. Chef Watson would generate new recipes based
upon ingredients that you would tell it that you had
on hand, and it wouldn't it wouldn't go and reference
old recipes and pull one up for you. Instead, it
would make flavor profiles based upon all the different combinations
of food that were found in various recipe books and
(25:11):
generate a brand new recipe for you right there on
the spot. And sometimes they were whacka doodle crazy, y'all.
So in a way, you could say that Chef Watson
was another another way of seeing how I b M
S Watson has a lot of promise, but it requires
a ton of work on the app level in order
(25:32):
to leverage it and make actual practical use out of it.
I have more to say about computers detecting sarcasm, but
first let's take a quick word from our sponsor. So
(25:53):
back in two there were some researchers at the Hebrew
University in Israel who designed a system called the Semi
Supervised Algorithm for Sarcasm Identification or SAZI, and they used
SAZI to analyze collections of nearly six million tweets and
(26:15):
also around sixty six thousand product reviews from Amazon. They
wanted to find rich treasure troves of sarcasm that turns
out reviews and tweets. They fit the bill sarcasm is.
Really it's typically conveyed in some vocal tone, right and
(26:35):
nonverbal cues. So you have to first go someplace where
sarcasm is is rampant in text form to be able
to really fine tune how you can identify sarcasm versus
something that's meant exactly the way it's written on the
surface level. So they started to map out the various
(26:58):
features that were common in sarcastic comments online. So they
were looking for things like hyperbolic words and if you're
using a lot of exaggeration, that could be a key.
Excessive punctuation was another one, especially ellipses, which I tend
to use a lot, though I don't know if I
use it so much for sarcasm as I do for
(27:19):
just timing purposes. To indicate this is the beat I
would take if I were saying this out loud, I
guess that's just as irritating. Though, also how straightforward is
the Senate structure? And they gave it examples of sarcasm.
They fed it tweets that were tagged hashtag sarcasm, so
that the machine quote unquote knew that that was already
(27:42):
a sarcastic tweet and could start to analyze it and
build out a model for what sarcasm is. They also
fed at a bunch of one star Amazon reviews that
had been judged to be sarcastic by a panel consisting
of fifteen human beings, and the system was told it
had to rate sentences on a scale of one to five,
(28:04):
One being not sarcastic, they mean exactly what the Senate says,
five being holy cow, this person should write for the Onion.
This is incredibly sarcastic. SAZI could identify sarcastic Amazon reviews
with precision. Not bad, But when it came to Twitter
(28:26):
it did even better. I think, probably because there had
to be very short messages on Twitter. This was before
Twitter had even expanded to characters, so it was still
back in the one character days. The precision rate for
SAZI for Twitter was so it was really good at
detecting straightforward sarcasm, the kind that a lot of people
(28:49):
on Twitter use because you have limited space so you
can't really set it up in a more complex way.
But it was also uh more prone to judging things
as false negative evaluations rather than false positives. In other words,
it was more likely to look at a negative sarcastic
(29:10):
message and say that's not sarcastic than it was to
look at a straightforward message and say, no, that is sarcastic.
So that was kind of interesting back to Watson. Another
use of Watson came out of the Milk and Institute
Global Conference at IBM showed off some research that it
(29:30):
had been working on internally, and it was calling this
research debating Technologies. This was a project in which IBM
was trying to see if they could feed a computer
raw information, have the computer synthesize the information, understand that
information at least on a computational level, and then create
(29:54):
a a debating strategy for both pros and cons they
on that information. So it would take a huge amount
of content like all of Wikipedia, for example, and then
on any given subject that would be covered in Wikipedia.
It would be asked form an argument that is in
(30:14):
favor of or is against a concept, whatever that concept
might be. John Kelly of IBM showed off in a
demo how the tool could be used to predict pro
or con arguments about a subject based on a body
of information. So you might be able to use this
(30:34):
technology in order to anticipate what an opposing person might
say on any given subject. Let's say that you are
getting ready to debate a topic. You might feed that
information to a computer system using this Watson platform. You
might feed in a ton of information, and then you
(30:56):
might say, who is a man and someone who is
against this particular topic, whatever it might be. Uh. Let's
say it's it's it's renewable energy, and the uh the
efficiency of solar panels, whether or not it makes sense
to invest in solar panels. Let's say that your stance
(31:17):
is that you have to argue for solar panels. You
might say, what would someone who wants to argue against
solar panels say? And then Watson would analyze this information
and return to you what it thinks would be an
argument someone would use to support that that stance, and
(31:39):
then you could prepare for that, which would be an
incredible tool. I mean, you could think of this as
for political debates. It would be amazing. You could think
of how you might want to prepare so that you
can argue intelligently against an opponent, and you can already
anticipate what that opponent is going to say, because you
know their general stance on a topic, but you might
not know what tactic they might use to support that stance.
(32:03):
Maybe politics isn't a great choice because that's not always
in the realm of rationality. That often falls into a
call toward emotional response rather than rational response. That's more
of a a commentary on politics in general, regardless of
what side you might be on, all sides do this anyway.
(32:23):
He actually showed at this demo a different example. He said,
what if you were to take the sale of violent
video games to minors should be banned. That's the topic,
and that the computer would then go through all the
information and had access to. It would end up sorting
out all the parts that were relevant to the discussion,
(32:45):
so it just put those aside and that would become
the core of the data it would reference. I would
then go through and identify basic statements as either being
a pro stance of banning violent video games to my
nes or a constance for that saying no, we should
be able to sell violent video games to minors. The
(33:08):
tools scanned four million articles, it returned the top ten
articles that were determined to be the most relevant to
that particular debate, and it scanned approximately three thousand sentences
from from top to bottom, and it then identified sentences
that contained candidate claims that would be statements that would
(33:28):
either be interpreted as being pro or con for the stance.
Then it identified the parameters of those claims. Then it
assessed the claims for the pro and con polarity, then
constructed a sample pro or con statement. And the statements
in the demo were kind of interesting. And since the
computer is constructing arguments based upon what people have already written,
(33:51):
it would reflect a lot of vague statements that aren't
a firm stance. So, in other words, like it couldn't
take a bunch of stuff that was written that it
sell did not take either a pro or constance, and
then transformed that magically into the perfect pro stance or
the perfect constance. Uh, it's dependent upon the words that
human beings have already written, so it could not magically
(34:14):
come up with a killer argument if the data that
had been written about this subject didn't come down on
a firm stance one way or the other. Um, the
point of the demonstration wasn't to create a tool that
could either troll people or counter trolls. It was to
(34:34):
show that a computer could be useful to aid in
the reasoning process when you're making a critical decision. Again,
to go back to that medical example, it could be
used to help a doctor determine which diagnosis is the
most likely to be accurate for a patient, what what
course of treatment might be the most helpful for that patient,
(34:56):
and thus it could have real practic coal use outside
of this more esoteric, interesting uh debate news. Now, will
we see computers in the future able to detect sarcasm
just as easily as your typical human being can when
given the right circumstances. And I use the word typical reluctantly,
(35:22):
but you get what I mean. I don't know. It's
gonna take some time. It takes an awful lot of
processing power too. You have to remember that for these
neural networks systems, the ones that are running these these
various platforms and programs and strategies. They take up a
lot of processing power because our brains have billion neurons
(35:46):
in them, so we have a very sophisticated supercomputer sitting
in our heads. Moreover, our brains are insanely energy efficient.
They require about the equivalent of twenty watts of power.
A supercomputer needs a lot more power than that. So
while we're seeing advances in this, it requires so much
(36:07):
processing power, so much energy, it is not a practical
approach to most forms of computing, at least from a
consumer standpoint. You might see a future where the sort
of stuff is all in the cloud and then we
can access it through an app or a program or whatever.
That way, you don't have to have a supercomputer sitting
(36:30):
on your desk in order to tap into those, uh,
those capabilities, but you have to have an Internet connection,
which most of us these days tend to have fairly frequently.
I mean, there are a lot of people out there
who at this point have had a persistent Internet connection
for pretty much their whole lives, which blows my mind.
But that's the kind of world we'd have to live
(36:50):
in in order to really take advantage of this at
least in the near term. I don't know if we're
ever going to see a computer that can analyze, say,
an article from the Onion and not only point out
that it's being sarcastic or ironic, but also point out
why it's funny. I think at one point, when you
start analyzing comedy, there gets to be a level where
(37:12):
nothing is ever funny ever again, but it is a
really interesting problem. So that's whether that's that's this look
back on if AI is ever going to understand sarcasm. Well, guys,
I hope you enjoyed that classic episode of tech stuff.
I guess I guess two years old isn't old enough
to be classic. That uh that that only somewhat less
(37:36):
than fresh episode of text stuff about artificial intelligence and
sarcasm and things of that nature. I am constantly impressed
with how artificial intelligence is advancing year over year. But
when you look at what it means to be human
and the ways that we humans interact with one another,
(37:56):
and the ways that we can communicate complicated three things,
sometimes just through you know, subtle methods that are not
overt or or you know, directly spoken, it reminds us
that machines have got a long way to go in
order to really grasp what it is to be human,
So unless you're Commander Data, you're probably struggling a bit.
(38:20):
So I hope you guys enjoyed this. If you have
suggestions for future episodes of tech Stuff, I've got a
few episodes based on listener suggestions coming up soon. But
if you want to get your suggestions in tweet me.
The Twitter handle is text stuff H s W and
I'll talk to you again really soon. Text Stuff is
(38:44):
an I Heart Radio production. For more podcasts from my
Heart Radio, visit the i Heart Radio app, Apple Podcasts,
or wherever you listen to your favorite shows.
TechStuff News
Hosts And Creators
Oz Woloshyn
Karah Preiss
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