Anyway, excitement about my five minutes of robot fame aside, I did want to comment something related to the actual subject of this video :) My favorite method of generating plausible text is a simple multilayer Markov chain. Way back in my freshman year at uni, one of my assignments was to create a three-layer Markov chain trained on excerpts from the Wizard of Oz to generate a new page from the book. It was interesting. But then, we were allowed to train our Markov chain's dictionary on *any corpus* we wanted, so I chose the US Constitution. Needless to say, seeing brand-new laws come into existence at the (metaphorical) hands of a computer was extremely amusing.
The output is nonsense, but it looks quite similar to badly translated Chinglish found in the manuals for cheap eBay electronics :) I am impressed with the way this isn't just randomly sticking words together, it's actually making the words themselves, letter by letter - without even really knowing what a "word" even is.
"I find your own difference." It sounds so deep and profound! Now all we need to do in translate it into Latin, "Differentiae tuae invenio." That's one solid tattoo right there! Or it should be Chinese, if someone would like the chime in on that one?! :D
I just discovered Numberphile and Computerphile recently and appreciate everything you guys are doing here. The content is definitely top notch. I'm lovin' it.
Hey Computerphile, I love your comment at 13:43, where you asked the question "How do you watch if you basically have one hardware?". Great video, quite fun to read through those generated comments to find some that almost make sense.
Hi Max. If you're interested in AI, you can check out my introductory book "How to Create Machine Superintelligence" available for FRЕЕ in amazon kindle store till 6th October. In this book, I go over the following: - intelligence as a form of information processing - basics of classical computing - basics of quantum computing - some basics of machine learning and artificial neural networks - and also share some thoughts on building general AI and dealing with the control problem
Depending on the channel, it might indeed become an Artificial Stupidity. However, as this AI is trained on Computerphile comments it's probably above average.
I realize this is more powerful in a general sense, but to just generate text (what I know of as) a markov chain seems... easier. They're fun and extremely easy to get started with if nothing else, and I feel like playing with the generator order gives a sense of how (non)random language is.
Yeah, definitely. This isn't the best use of a recurrent neural network, but it does sort of help for demonstration purposes. But I love me some Markov chains! I always wanted to use Markov chains to generate songs based on a given artist's corpus of song lyrics, but the one time I tried to make one, I did it very crudely and naively and ended up accidentally DOS'ing a lyrics site... so I stopped XD I should get back to that one day, but do it more intelligently this time... if I ever have any free time for that anymore.
@@superdau A RNN is kinda like a Markov chain but using thousands of different states instead of one. Also each state is modulated by math/weighted connections instead of probability. It's much harder to generate with quality of RNN if the Markov Chain is working character by character.
what would happen if you tried using this as a generator for a generative adversarial network and then make the classifier as real vs generated? is it too random to get to realistic comments?
So how is this different what Markov chain does? Because to me this video sounded lot like only difference between Markov chain and neural network would be how it is made under the hood.
Yeah, this demonstration would work better (or at least be easier) with a Markov chain. But recurrent neural networks allow machines to learn more than just sequences, which Markov chains can't do.
I imagine it would be possible to layer this with some sort of pre-processing? For example, I imagine it would be possible to parse the sentences into word objects pretty easily first, and then run the same kind of a network against words rather than letters?
Since RNNs are ideal for predicting the next thing in a long sequence, it's really fun to train them on audio (predicting the next position of the waveform based on the audio leading up to now). It's much slower than dealing with text, though. In case anyone's curious, I've made a couple of videos showing results of that (using torch-rnn, the same software Mike uses here).
What if there was another input into the network for some kind of "concept" the network is having, and you train it with sentences tagged with their concepts? Then, when generating sentences, you could theoretically generate them from concepts rather than thin air.
I was No pretty puzzling the code gaming so they would have found out the livelutoo tred s+so the larger stop information. I don't think before doing "is when it can predict it as computing and A/P?" R it wasn't a high-fact a bit stamp idea
Thanks, great vid. Also, would be great to hear from you about spiking neural networks. There isn't much free and quality information about spiking nets.
Has there been much work on giving such systems initial information from which to build off of? For example, one might give the system a dictionary of English words, acronyms, etc (possibly letting the system expand it to some extent) with a list of the potential (or probable) types of word (nouns, verbs, adverbs, adjectives, etc.) and even, perhaps verb tenses. Besides going letter-to-letter and word-to-word, it could start building models of overall sentence structures. This would significantly increase the complexity of the system and prescribing rules, rather than letting the system learn rules, might limit it in certain ways and would require more work initially.
:) Looks it is. If you're interested in AI, you can check out my introductory book "How to Create Machine Superintelligence" available for FRЕЕ in amazon kindle store till 6th October. In this book, I go over the following: - intelligence as a form of information processing - basics of classical computing - basics of quantum computing - some basics of machine learning and artificial neural networks - and also share some thoughts on building general AI and dealing with the control problem
@Computerphile Suggestion: Does it makes sense to use service like "Grammarly" to auto-correct sentences before we feed them to make this idea more sensible?.
So, the challlenge here is to give the NN a memory right? Is what you're doing approximately the same as giving a state the previous states as input ? I guess it's not totally equivalent since you said the hidden layers' weighs are shared. If it is different, why not do as I said ? Too many inputs ? Moreover, is the "memory" length fixed ? Like 30 characters for example ? I'm actually interested in this since I'm trying to figoure out how to design a NN for training on a racing game, and sometimes, I need to know that I've already been through some path, to not go there again, so I need memory. Thx for your answer!!!
LSTM is different from other neural nets because it incorporates timesteps. Every timestep, data is inserted into the model input. The one thousand neurons in Computerphile's LSTM decides the output based on the inputs and previous output. This allows the LSTM to remember, and unlike a Markov model, the memory capacity of an LSTM is theoretically infinite. Unfortunately in practice the memory is not actually infinite because Back-Prop through time (optimization algorithm) results in slowly vanishing gradients which limit it's memory to a few hundred characters.
I don't see how this is different from building a probability tree on sequential words. I'm thinking of a hash map filled up by scanning the words in order, like this: { "the": ["house", "river"], "house": ["is", "has", "is" ] } Pick a starting word at random, and pick the next words at random from the corresponding array in the hash map. More probable words appear more times in the array, hence has a higher probability to be chosen. I don't see the point in training a neural network for something that is just plain statistics.
Well, aside from the many comments my videos are now getting to let me know about this, I'm also subscribed to Computerphile. Hence why I leave enough comments for the AI to notice me, senpai :D So I saw it! :D
what about using iconic authors from the past to get reasonable conversations or responses for the present time or maybe for commentary towards the future?
You can download the source code from websites in some programming languages and then develop an algorithm to extract specific content like comments from it.
I assume that instead of "downloading the comments", what was done is creating a program that "downloads the entire webpage, and extracts the comments, for multiple (or maybe even all) computerphile videos".
Not all the comments, he said, just the Computerphile ones. And I'd assume he used some UA-cam API to extract the comments. Such API exists, it gives you comment text and it's metadata in JSON.
could you use say a large neural network that was trained on high res numbers and see what is the "purest" image of each number by kind of going backwards?
I commented a few comments just now... actually, I'm sure my habit of commenting multiple times throughout the course of watching a video helped the AI notice me xD
I recently texted my partner "Give yourself 5 minutes to clear the car, it may be frosty" and my phone suggested "relationship" as the next word. Hmmm?
I think going below word level is like SHA1: You can't get it back. In this case, the semantics of the language itself. you could have a word level neural net run in parallel. that'd be neat.
I think there was a horribly expensive experiment where stocks were either bought or sold based on a neural network trying to decipher the mood of the language used related to the stock.
Given enough examples of essays, enough computer power, and enough time, you could train a neural network to do so. But that's not happening before your deadline, so you should probably get back to work. :D
Does this take into account which user posted which type of comment? If not, the comments posted by IceMetalPunk may just be some random thing. How could you even assume to analyze whether a comment is positive or negative, which is several meta-layers above that?
Kind of old video now, but let’s say you trained it separately on comments on videos about computer science history, then AI safety videos, etc Would it start to generate more plausible sentences? Or is there too much variety even in that specific set that it’d devolve to nonsense
Loren ipsum 2.0 Seriously though, this is an interesting way of producing random text which at the same time is not entire gibberish. I could use this as is, now!
I am currently working on something very similar with reading books and writing small sub stories. I am happy I am not the only one getting the number loop problem
Question about how this generator works. Does it use the GAN kind of structure, with one network trying to produce legitimate looking comments and the other trying to discriminate between the two?
What impresses me is that the comments are structurally plausible: "and you can also get knowledge" instead of "knowledge get can and also". not sure how that works.
12:11 I'm in the video! :O This is amazing! The robots love me!
Also... this might be a sign that I need more to do with my life besides comment on UA-cam videos...
+IceMetalPunk hey, thanks for the comments! >Sean
Hi IceMetalPunk. ☺️
Well, ask him for the code. That way, you'll never have to comment again!
The code is in the description.
Anyway, excitement about my five minutes of robot fame aside, I did want to comment something related to the actual subject of this video :)
My favorite method of generating plausible text is a simple multilayer Markov chain. Way back in my freshman year at uni, one of my assignments was to create a three-layer Markov chain trained on excerpts from the Wizard of Oz to generate a new page from the book. It was interesting. But then, we were allowed to train our Markov chain's dictionary on *any corpus* we wanted, so I chose the US Constitution. Needless to say, seeing brand-new laws come into existence at the (metaphorical) hands of a computer was extremely amusing.
I love you, baby.
Nice try computer. Your phony back story isn't fooling anyone. Well done on the improvements though. Your comments are getting better.
IceMetalPunk I am a knight and what I say to all this is Nee!
Just nee, nee, nee!
+IceMetalPunk
A little late, but if you have any excerpts, please post them?
After this video i will never be able to trust computerphile comments ever again.
Developing an AI based on UA-cam's comment section might not be the brightest idea in the world. The "I" in AI stands for intelligence.
Maybe he was developing Artificial Idiocy
I always preferred Artificial Incompetence.
Artificial ignorance.
Artificial imitation
Space doubt wins....
"I was able to want to be able to be happy."
me too... me toooo...
Same
That's an important first step.
It has reached consciousness!
watcherFox they became self aware! 😱
First UA-cam Comment
Mike is the best speaker IMO
Computerphile reply
1:25 Don't right?
do you support online learning from websites like "Free code amp" and 'Solo learn" 'Code academy".
gosh! I am soo noob
Great video!
IceMetalPunk comments on my videos too :-D Deffo real person!
xisumavoid xisuma here wow
Cool to see you here
Hey Xisuma. Love your vids. good channel you watch here
didnt expect to see you here :D
xisumavoid Oh, hey xisuma
I find your own difference.
This relayed having two first.
We all find your own difference on this blessed day.
Hmm, profound words; they art spilling from thine mouth.
I just wanted to let you know that I was just wondering if you were able to get the kids to school.
Does it write "first"-comments?
Ebumbaya ' first
Probably.
Yes, but excessively so and at inappropriate times.
@@Treddian So... like normal youtube comments then.
In one of the screens you can see a comment which says "One!", which is kinda the same
Using comments as input on university computers? Would be a shame if someone would '); DROP TABLE Students;--
Hey! Leave Bobby out of this! :D
I wonder what lil Bobby has gotten himself into this time
Train it on computerphile transcripts, then act out the output!
Yes! I second this entirely!!
"This video was written by an AI."
even better, train it on classic literature, read its essays at a TED Talk
The output is nonsense, but it looks quite similar to badly translated Chinglish found in the manuals for cheap eBay electronics :)
I am impressed with the way this isn't just randomly sticking words together, it's actually making the words themselves, letter by letter - without even really knowing what a "word" even is.
Amazing stuff! And ironically, the fact that it outputs typos every now and then makes the comments much more realistic.
"I was able to want to be able to be happy" The network is trying to tell us something.
I dunno why, but I really love this guy. He's very good at explaining things and he's funny. Thanks Mike!
Agreed!
I think it's his passion for explaining and kinda learning at the same time.
Watching this 5 years later now that we have ChatGPT and such...
and where he said that a Chatbot from this is "theoretically" possible and here we are
It would be interesting to create a neural network that would predict comment likes/dislikes based on the content of the comment.
@@dejfcold that's an over simplification
I predict your comment will receive 76 likes before it is forgotten.
@@abstractapproach634 I don't think it's gonna make it :(
@@samuelthecamel Prob 42 likes
"I find your own difference." It sounds so deep and profound!
Now all we need to do in translate it into Latin, "Differentiae tuae invenio."
That's one solid tattoo right there! Or it should be Chinese, if someone would like the chime in on that one?! :D
I just discovered Numberphile and Computerphile recently and appreciate everything you guys are doing here. The content is definitely top notch. I'm lovin' it.
Hey Computerphile, I love your comment at 13:43, where you asked the question "How do you watch if you basically have one hardware?".
Great video, quite fun to read through those generated comments to find some that almost make sense.
Hi Max. If you're interested in AI, you can check out my introductory book "How to Create Machine Superintelligence" available for FRЕЕ in amazon kindle store till 6th October.
In this book, I go over the following:
- intelligence as a form of information processing
- basics of classical computing
- basics of quantum computing
- some basics of machine learning and artificial neural networks
- and also share some thoughts on building general AI and dealing with the control problem
Bots commenting on bots..
12:12 So did IceMetalPunk ever appear? Did the happy reunion ever happen? I so want to know!
I did appear! :D
This was done by a UA-camr called CaryKH, he applies neural networks to various tasks.
Yea i love the stuff he does - like the language-recognision :)
I was about to mention. I wonder what difference there is between their methods, if any
Charles Thatisall There might be, if there is I'm voting for an epic NN battle.
how many times does 'Hitler' pops up in those generated comments
Too often #Godwin’s law
Isn't trying to generate an AI trained on UA-cam comments a bit counterproductive?
Depending on the channel, it might indeed become an Artificial Stupidity. However, as this AI is trained on Computerphile comments it's probably above average.
Daan Wilmer tru nuff, it was a joke tho.
No, because even it the bot replicates natural stupidity, you've proved that you have an algorithm than can replicate human behaviour accurately.
IceMetalPunk is a UA-camr gamer who makes Minecraft let’s plays. This dude exists!
I only know them as a UA-cam viewer whose viewing habits often overlap with mine.
Well, I used to... I haven't had much time lately :( Having two jobs can really hinder a social (media) life XD
I guess the network would output a lot of "first" as comments and a lot of "first" related replies
I realize this is more powerful in a general sense, but to just generate text (what I know of as) a markov chain seems... easier. They're fun and extremely easy to get started with if nothing else, and I feel like playing with the generator order gives a sense of how (non)random language is.
A recurrent neural network looks like a "recursive" markov chain to me.
Yeah, definitely. This isn't the best use of a recurrent neural network, but it does sort of help for demonstration purposes. But I love me some Markov chains! I always wanted to use Markov chains to generate songs based on a given artist's corpus of song lyrics, but the one time I tried to make one, I did it very crudely and naively and ended up accidentally DOS'ing a lyrics site... so I stopped XD I should get back to that one day, but do it more intelligently this time... if I ever have any free time for that anymore.
@@superdau A RNN is kinda like a Markov chain but using thousands of different states instead of one. Also each state is modulated by math/weighted connections instead of probability. It's much harder to generate with quality of RNN if the Markov Chain is working character by character.
what would happen if you tried using this as a generator for a generative adversarial network and then make the classifier as real vs generated? is it too random to get to realistic comments?
We welcome our new AI overlord! 👑
So how is this different what Markov chain does? Because to me this video sounded lot like only difference between Markov chain and neural network would be how it is made under the hood.
Yeah, this demonstration would work better (or at least be easier) with a Markov chain. But recurrent neural networks allow machines to learn more than just sequences, which Markov chains can't do.
"I said yesterday I walked to the park 2 days ago."
I imagine it would be possible to layer this with some sort of pre-processing? For example, I imagine it would be possible to parse the sentences into word objects pretty easily first, and then run the same kind of a network against words rather than letters?
Is this a reupload? I feel like I remember this, down to the IceMetalPunk guy, but it could just be déjà vu
You probably just have seen my comments before... I comment way too much... hence why the AI noticed me XD
One more comment for your neural network :) I admire your work and your ambition.
Mike's the best at Computerphile videos
13:45 "How do you watch if you basically have one hardware" #Deep #EveryHumanIsOneHardware #RobotsWillEnslaveUsAll
He is so good at explaining this stuff
Since RNNs are ideal for predicting the next thing in a long sequence, it's really fun to train them on audio (predicting the next position of the waveform based on the audio leading up to now). It's much slower than dealing with text, though. In case anyone's curious, I've made a couple of videos showing results of that (using torch-rnn, the same software Mike uses here).
What if there was another input into the network for some kind of "concept" the network is having, and you train it with sentences tagged with their concepts? Then, when generating sentences, you could theoretically generate them from concepts rather than thin air.
I dont think thats needed, the hidden units in the LSTM are responsible for remembering and generating the "concepts" and coordinating text outputs.
I was No pretty puzzling the code gaming so they would have found out the livelutoo tred s+so the larger stop information. I don't think before doing "is when it can predict it as computing and A/P?" R it wasn't a high-fact a bit stamp idea
10:23 Neural network literally saying: "I don't think before doing"
Mike Pound, you created a NN that can lie.
+ComputerPhile Are Markov chains related to the ideas in this video?
Thanks, great vid. Also, would be great to hear from you about spiking neural networks. There isn't much free and quality information about spiking nets.
Has there been much work on giving such systems initial information from which to build off of? For example, one might give the system a dictionary of English words, acronyms, etc (possibly letting the system expand it to some extent) with a list of the potential (or probable) types of word (nouns, verbs, adverbs, adjectives, etc.) and even, perhaps verb tenses. Besides going letter-to-letter and word-to-word, it could start building models of overall sentence structures.
This would significantly increase the complexity of the system and prescribing rules, rather than letting the system learn rules, might limit it in certain ways and would require more work initially.
Please provide full references for scientific papers when used on the show. People like me might want to read it for ourselves.
IceMetalPunk is a chat bot helping to train a chatbot to become a chatbot.
:) Looks it is. If you're interested in AI, you can check out my introductory book "How to Create Machine Superintelligence" available for FRЕЕ in amazon kindle store till 6th October.
In this book, I go over the following:
- intelligence as a form of information processing
- basics of classical computing
- basics of quantum computing
- some basics of machine learning and artificial neural networks
- and also share some thoughts on building general AI and dealing with the control problem
I won't read it because if you're rationalising what intelligence is you're misrepresenting what intelligence is
@Computerphile
Suggestion: Does it makes sense to use service like "Grammarly" to auto-correct sentences before we feed them to make this idea more sensible?.
What technique is used by the somewhat similar Dissociated Press program, and how does it compare?
Can you post the code that generate the comment output? Would love to see it and potentially run it on my own data!
So, the challlenge here is to give the NN a memory right? Is what you're doing approximately the same as giving a state the previous states as input ? I guess it's not totally equivalent since you said the hidden layers' weighs are shared. If it is different, why not do as I said ? Too many inputs ?
Moreover, is the "memory" length fixed ? Like 30 characters for example ?
I'm actually interested in this since I'm trying to figoure out how to design a NN for training on a racing game, and sometimes, I need to know that I've already been through some path, to not go there again, so I need memory.
Thx for your answer!!!
LSTM is different from other neural nets because it incorporates timesteps. Every timestep, data is inserted into the model input. The one thousand neurons in Computerphile's LSTM decides the output based on the inputs and previous output. This allows the LSTM to remember, and unlike a Markov model, the memory capacity of an LSTM is theoretically infinite.
Unfortunately in practice the memory is not actually infinite because Back-Prop through time (optimization algorithm) results in slowly vanishing gradients which limit it's memory to a few hundred characters.
please keep on making videos. it really really really helps.
If you would always choose the most likely character, what would be the average comment on Computerphile videos?
I don't see how this is different from building a probability tree on sequential words. I'm thinking of a hash map filled up by scanning the words in order, like this:
{
"the": ["house", "river"],
"house": ["is", "has", "is" ]
}
Pick a starting word at random, and pick the next words at random from the corresponding array in the hash map. More probable words appear more times in the array, hence has a higher probability to be chosen. I don't see the point in training a neural network for something that is just plain statistics.
Where's IceMetalPunk? Someone let them know.
Well, aside from the many comments my videos are now getting to let me know about this, I'm also subscribed to Computerphile. Hence why I leave enough comments for the AI to notice me, senpai :D So I saw it! :D
"I have no ideas so I use a neural network"
why are you so mean?
Because there is better AI. I am not mean, just angry for not being heard.
what about using iconic authors from the past to get reasonable conversations or responses for the present time or maybe for commentary towards the future?
Giths transistor a benefit of cracking something reciever, not match obviously available?
Extremely cool demonstration! I find neural networks so fascinating.
Could you provide the code you used? I'd like to mess around and take a look.
Those over the sholder shots have quite a large desync between audio and video
first!'); DROP TABLE comments;--
Hello, little Bobby Tables :D
IceMetalPunk That should teach you to sanitize your inputs.
Clever, except that the machine learning server is probably quite remote from the administrative servers
Yea because a Doctor of computer science would use SQL HAHAHAHA... Not.
Why Lua and nor other languages? Make a video about that!
Could anyone shed light on how he managed to download all the UA-cam comments?
You can download the source code from websites in some programming languages and then develop an algorithm to extract specific content like comments from it.
I assume that instead of "downloading the comments", what was done is creating a program that "downloads the entire webpage, and extracts the comments, for multiple (or maybe even all) computerphile videos".
I meant the same. Excuse me for my english if you could not understand it.
Luke Overwien I never said otherwise, I just wanted to say it more clearly than you did.
Not all the comments, he said, just the Computerphile ones. And I'd assume he used some UA-cam API to extract the comments. Such API exists, it gives you comment text and it's metadata in JSON.
Cool :) I also did this a while ago when I learned about Andrew Karpathy's blog, to generate song lyrics with using a certain music style as input.
could you use say a large neural network that was trained on high res numbers and see what is the "purest" image of each number by kind of going backwards?
Immediatly came to see IceMetalPunk's comment.
I commented a few comments just now... actually, I'm sure my habit of commenting multiple times throughout the course of watching a video helped the AI notice me xD
Can this read and generate MIDI? I wonder which styles it fails/works on.
I recently texted my partner "Give yourself 5 minutes to clear the car, it may be frosty" and my phone suggested "relationship" as the next word. Hmmm?
"I was able to want to be able to be happy". A perfectly normal UA-cam comment.
I think going below word level is like SHA1: You can't get it back. In this case, the semantics of the language itself. you could have a word level neural net run in parallel. that'd be neat.
+IceMetalPunk .. we are waiting!
Wait no longer! :D
I love you, Mike Pound
I'd like to see your opinion on RoboRosewater using one of these networks.
Here! aspart of the DOG 'POUND' fandom for computer science knowledge smackdowns!!!
IceMetalPunk: You've been targeted for termination.
D: NO, I welcome my robot overlords! I'm honored they picked me and I will work with them however they see fit! Don't kill me!
"I find your own difference" is amazingly deep!
I think there was a horribly expensive experiment where stocks were either bought or sold based on a neural network trying to decipher the mood of the language used related to the stock.
Mike is one super enthusiastic computer scientist! Go Mike!
can it write an essay for me??
Given enough examples of essays, enough computer power, and enough time, you could train a neural network to do so. But that's not happening before your deadline, so you should probably get back to work. :D
bowie brewster an essay written by the average YouTub commenter.
Two years later, and we have GPT-3, so now I can officially say: yes, yes it can write an essay for you.
Is it character level RNN? And how much time did you train the network?
Nice to see you using Lua for this! Just recently began learning the language
It would be interesting to see what it comes up with if you train it to predict comments based on the video title (and perhaps description).
Carykh
Does this take into account which user posted which type of comment? If not, the comments posted by IceMetalPunk may just be some random thing. How could you even assume to analyze whether a comment is positive or negative, which is several meta-layers above that?
Kind of old video now, but let’s say you trained it separately on comments on videos about computer science history, then AI safety videos, etc
Would it start to generate more plausible sentences? Or is there too much variety even in that specific set that it’d devolve to nonsense
Loren ipsum 2.0
Seriously though, this is an interesting way of producing random text which at the same time is not entire gibberish. I could use this as is, now!
surprised it didn't say "WE LOVE YOU MIKE" all the time
Encryption is meant to be able to manages to the description, at the human it's design. Either. He bayed not select to the way.
It would be cool if you train this network on the whole(or part of) the wikipedia archive.
GPT-2:
This video was spectacular.
- A real person
I am currently working on something very similar with reading books and writing small sub stories. I am happy I am not the only one getting the number loop problem
So what is the most likely sentence taking the highest percentage letters every time?
What do all our replies combined give?
frognik79 "First!"
10:24 "I was able to want to be able to be happy."
Great video!
Question about how this generator works. Does it use the GAN kind of structure, with one network trying to produce legitimate looking comments and the other trying to discriminate between the two?
No it uses loss function basically how similar its text is to the dataset text. There is only one network.
Gratz IceMetalPunk You win the comments...! xD
Extremely belated thank you!
Wow its better than mine the chatbot my team made only could successfully achieve contextual awareness. we used LSTMs (a type of node for RNNs)
"I find your own difference." -Hessil200, 2017
Could you analyze a single users comments and generate comments that that user might be likely to say?
Mike, how do you scrape UA-cam comments?
What impresses me is that the comments are structurally plausible: "and you can also get knowledge" instead of "knowledge get can and also". not sure how that works.