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+1
+00:00:01,280 --> 00:00:06,759
+so the class today is uh introduction to
+
+2
+00:00:04,680 --> 00:00:09,480
+natural language processing and I'll be
+
+3
+00:00:06,759 --> 00:00:11,200
+talking a little bit about you know what
+
+4
+00:00:09,480 --> 00:00:14,719
+is natural language processing why we're
+
+5
+00:00:11,200 --> 00:00:16,720
+motivated to do it and also some of the
+
+6
+00:00:14,719 --> 00:00:18,039
+difficulties that we encounter and I'll
+
+7
+00:00:16,720 --> 00:00:19,880
+at the end I'll also be talking about
+
+8
+00:00:18,039 --> 00:00:22,519
+class Logistics so you can ask any
+
+9
+00:00:19,880 --> 00:00:25,439
+Logistics questions at that
+
+10
+00:00:22,519 --> 00:00:27,720
+time so if we talk about what is NLP
+
+11
+00:00:25,439 --> 00:00:29,320
+anyway uh does anyone have any opinions
+
+12
+00:00:27,720 --> 00:00:31,439
+about the definition of what natural
+
+13
+00:00:29,320 --> 00:00:33,239
+language process would be oh one other
+
+14
+00:00:31,439 --> 00:00:35,680
+thing I should mention is I am recording
+
+15
+00:00:33,239 --> 00:00:38,600
+the class uh I put the class on YouTube
+
+16
+00:00:35,680 --> 00:00:40,520
+uh afterwards I will not take pictures
+
+17
+00:00:38,600 --> 00:00:41,920
+or video of any of you uh but if you
+
+18
+00:00:40,520 --> 00:00:44,719
+talk your voice might come in the
+
+19
+00:00:41,920 --> 00:00:47,440
+background so just uh be aware of that
+
+20
+00:00:44,719 --> 00:00:49,000
+um usually not it's a directional mic so
+
+21
+00:00:47,440 --> 00:00:51,559
+I try to repeat the questions after
+
+22
+00:00:49,000 --> 00:00:54,079
+everybody um but uh for the people who
+
+23
+00:00:51,559 --> 00:00:57,680
+are recordings uh listening to the
+
+24
+00:00:54,079 --> 00:00:59,320
+recordings um so anyway what is NLP
+
+25
+00:00:57,680 --> 00:01:03,120
+anyway does anybody have any ideas about
+
+26
+00:00:59,320 --> 00:01:03,120
+the definition of what NLP might
+
+27
+00:01:06,119 --> 00:01:09,119
+be
+
+28
+00:01:15,439 --> 00:01:21,759
+yes okay um it so the answer was it
+
+29
+00:01:19,240 --> 00:01:25,759
+helps machines understand language
+
+30
+00:01:21,759 --> 00:01:27,920
+better uh so to facilitate human human
+
+31
+00:01:25,759 --> 00:01:31,159
+and human machine interactions I think
+
+32
+00:01:27,920 --> 00:01:32,759
+that's very good um it's
+
+33
+00:01:31,159 --> 00:01:36,520
+uh similar to what I have written on my
+
+34
+00:01:32,759 --> 00:01:38,040
+slide here uh but natur in addition to
+
+35
+00:01:36,520 --> 00:01:41,280
+natural language understanding there's
+
+36
+00:01:38,040 --> 00:01:46,000
+one major other segment of NLP uh does
+
+37
+00:01:41,280 --> 00:01:46,000
+anyone uh have an idea what that might
+
+38
+00:01:48,719 --> 00:01:53,079
+be we often have a dichotomy between two
+
+39
+00:01:51,399 --> 00:01:55,240
+major segments natural language
+
+40
+00:01:53,079 --> 00:01:57,520
+understanding and natural language
+
+41
+00:01:55,240 --> 00:01:59,439
+generation yeah exactly so I I would say
+
+42
+00:01:57,520 --> 00:02:03,119
+that's almost perfect if you had said
+
+43
+00:01:59,439 --> 00:02:06,640
+understand and generate so very good um
+
+44
+00:02:03,119 --> 00:02:08,560
+so I I say natural technology to handle
+
+45
+00:02:06,640 --> 00:02:11,400
+human language usually text using
+
+46
+00:02:08,560 --> 00:02:13,200
+computers uh to Aid human machine
+
+47
+00:02:11,400 --> 00:02:15,480
+communication and this can include
+
+48
+00:02:13,200 --> 00:02:17,879
+things like question answering dialogue
+
+49
+00:02:15,480 --> 00:02:20,840
+or generation of code that can be
+
+50
+00:02:17,879 --> 00:02:23,239
+executed with uh
+
+51
+00:02:20,840 --> 00:02:25,080
+computers it can also Aid human human
+
+52
+00:02:23,239 --> 00:02:27,440
+communication and this can include
+
+53
+00:02:25,080 --> 00:02:30,440
+things like machine translation or spell
+
+54
+00:02:27,440 --> 00:02:32,640
+checking or assisted writing
+
+55
+00:02:30,440 --> 00:02:34,560
+and then a final uh segment that people
+
+56
+00:02:32,640 --> 00:02:37,400
+might think about a little bit less is
+
+57
+00:02:34,560 --> 00:02:39,400
+analyzing and understanding a language
+
+58
+00:02:37,400 --> 00:02:42,400
+and this includes things like syntactic
+
+59
+00:02:39,400 --> 00:02:44,959
+analysis text classification entity
+
+60
+00:02:42,400 --> 00:02:47,400
+recognition and linking and these can be
+
+61
+00:02:44,959 --> 00:02:49,159
+used for uh various reasons not
+
+62
+00:02:47,400 --> 00:02:51,000
+necessarily for direct human machine
+
+63
+00:02:49,159 --> 00:02:52,720
+communication but also for like
+
+64
+00:02:51,000 --> 00:02:54,400
+aggregating information across large
+
+65
+00:02:52,720 --> 00:02:55,760
+things for scientific studies and other
+
+66
+00:02:54,400 --> 00:02:57,519
+things like that I'll give a few
+
+67
+00:02:55,760 --> 00:03:00,920
+examples of
+
+68
+00:02:57,519 --> 00:03:04,040
+this um we now use an many times a day
+
+69
+00:03:00,920 --> 00:03:06,480
+sometimes without even knowing it so uh
+
+70
+00:03:04,040 --> 00:03:09,400
+whenever you're typing a doc in Google
+
+71
+00:03:06,480 --> 00:03:11,599
+Docs there's you know spell checking and
+
+72
+00:03:09,400 --> 00:03:13,959
+grammar checking going on behind it's
+
+73
+00:03:11,599 --> 00:03:15,920
+gotten frighten frighteningly good
+
+74
+00:03:13,959 --> 00:03:18,280
+recently that where it checks like most
+
+75
+00:03:15,920 --> 00:03:20,720
+of my mistakes and rarely Flags things
+
+76
+00:03:18,280 --> 00:03:22,799
+that are not mistakes so obviously they
+
+77
+00:03:20,720 --> 00:03:25,080
+have powerful models running behind that
+
+78
+00:03:22,799 --> 00:03:25,080
+uh
+
+79
+00:03:25,640 --> 00:03:33,080
+so and it can do things like answer
+
+80
+00:03:28,720 --> 00:03:34,599
+questions uh so I asked chat GPT who is
+
+81
+00:03:33,080 --> 00:03:37,000
+the current president of Carnegie melan
+
+82
+00:03:34,599 --> 00:03:38,920
+University and chat GPT said I did a
+
+83
+00:03:37,000 --> 00:03:40,920
+quick search for more information here
+
+84
+00:03:38,920 --> 00:03:43,439
+is what I found uh the current president
+
+85
+00:03:40,920 --> 00:03:47,120
+of car Mel University is faram Janan he
+
+86
+00:03:43,439 --> 00:03:50,040
+has been serving since July 1 etc etc so
+
+87
+00:03:47,120 --> 00:03:50,040
+as far as I can tell that's
+
+88
+00:03:50,400 --> 00:03:56,319
+correct um at the same time I asked how
+
+89
+00:03:53,799 --> 00:04:00,280
+many layers are included in the GP 3.5
+
+90
+00:03:56,319 --> 00:04:02,360
+turbo architecture and it said to me
+
+91
+00:04:00,280 --> 00:04:05,400
+GPT 3.5 turbo which is an optimized
+
+92
+00:04:02,360 --> 00:04:07,239
+version of GPT 3.5 for faster responses
+
+93
+00:04:05,400 --> 00:04:08,959
+doesn't have a specific layer art
+
+94
+00:04:07,239 --> 00:04:11,720
+structure like the traditional gpt3
+
+95
+00:04:08,959 --> 00:04:13,560
+models um and I don't know if this is
+
+96
+00:04:11,720 --> 00:04:16,600
+true or not but I'm pretty sure it's not
+
+97
+00:04:13,560 --> 00:04:18,840
+true I'm pretty sure that you know GPT
+
+98
+00:04:16,600 --> 00:04:20,560
+is a model that's much like other models
+
+99
+00:04:18,840 --> 00:04:21,560
+uh so it basically just made up the spec
+
+100
+00:04:20,560 --> 00:04:22,880
+because it didn't have any information
+
+101
+00:04:21,560 --> 00:04:26,000
+on the Internet or couldn't talk about
+
+102
+00:04:22,880 --> 00:04:26,000
+it so
+
+103
+00:04:26,120 --> 00:04:33,479
+um another thing is uh NLP can translate
+
+104
+00:04:29,639 --> 00:04:37,759
+text pretty well so I ran um Google
+
+105
+00:04:33,479 --> 00:04:39,560
+translate uh on Japanese uh this example
+
+106
+00:04:37,759 --> 00:04:41,639
+is a little bit old it's from uh you
+
+107
+00:04:39,560 --> 00:04:44,639
+know a few years ago about Co but I I
+
+108
+00:04:41,639 --> 00:04:46,240
+retranslated it a few days ago and it
+
+109
+00:04:44,639 --> 00:04:47,680
+comes up pretty good uh you can
+
+110
+00:04:46,240 --> 00:04:49,639
+basically understand what's going on
+
+111
+00:04:47,680 --> 00:04:53,520
+here it's not perfect but you can
+
+112
+00:04:49,639 --> 00:04:56,400
+understand the uh the general uh
+
+113
+00:04:53,520 --> 00:04:58,560
+gist at the same time uh if I put in a
+
+114
+00:04:56,400 --> 00:05:02,280
+relatively low resource language this is
+
+115
+00:04:58,560 --> 00:05:05,759
+Kurdish um it has a number of problems
+
+116
+00:05:02,280 --> 00:05:08,199
+when you try to understand it and just
+
+117
+00:05:05,759 --> 00:05:12,400
+to give an example this is talking about
+
+118
+00:05:08,199 --> 00:05:14,320
+uh some uh paleontology Discovery it
+
+119
+00:05:12,400 --> 00:05:15,800
+called this person a fossil scientist
+
+120
+00:05:14,320 --> 00:05:17,440
+instead of the kind of obvious English
+
+121
+00:05:15,800 --> 00:05:20,120
+term
+
+122
+00:05:17,440 --> 00:05:23,520
+paleontologist um and it's talking about
+
+123
+00:05:20,120 --> 00:05:25,240
+three different uh T-Rex species uh how
+
+124
+00:05:23,520 --> 00:05:27,039
+T-Rex should actually be split into
+
+125
+00:05:25,240 --> 00:05:29,639
+three species where T-Rex says king of
+
+126
+00:05:27,039 --> 00:05:31,560
+ferocious lizards emperator says emperor
+
+127
+00:05:29,639 --> 00:05:33,720
+of Savaged lizards and then T Regina
+
+128
+00:05:31,560 --> 00:05:35,120
+means clean of ferocious snail I'm
+
+129
+00:05:33,720 --> 00:05:37,240
+pretty sure that's not snail I'm pretty
+
+130
+00:05:35,120 --> 00:05:41,080
+sure that's lizard so uh you can see
+
+131
+00:05:37,240 --> 00:05:41,080
+that this is not uh this is not perfect
+
+132
+00:05:41,280 --> 00:05:46,680
+either some people might be thinking why
+
+133
+00:05:43,960 --> 00:05:48,400
+Google translate and why not GPD well it
+
+134
+00:05:46,680 --> 00:05:49,960
+turns out um according to one of the
+
+135
+00:05:48,400 --> 00:05:51,759
+recent studies we've done GPD is even
+
+136
+00:05:49,960 --> 00:05:55,479
+worse at these slow resource languages
+
+137
+00:05:51,759 --> 00:05:58,120
+so I use the best thing that's out
+
+138
+00:05:55,479 --> 00:06:00,440
+there um another thing is language
+
+139
+00:05:58,120 --> 00:06:02,039
+analysis can Aid scientific ific inquiry
+
+140
+00:06:00,440 --> 00:06:03,600
+so this is an example that I've been
+
+141
+00:06:02,039 --> 00:06:06,120
+using for a long time it's actually from
+
+142
+00:06:03,600 --> 00:06:09,160
+Martin sap another faculty member here
+
+143
+00:06:06,120 --> 00:06:12,440
+uh but I have been using it since uh
+
+144
+00:06:09,160 --> 00:06:14,160
+like before he joined and it uh this is
+
+145
+00:06:12,440 --> 00:06:16,039
+an example from computational social
+
+146
+00:06:14,160 --> 00:06:18,599
+science uh answering questions about
+
+147
+00:06:16,039 --> 00:06:20,240
+Society given observational data and
+
+148
+00:06:18,599 --> 00:06:22,280
+their question was do movie scripts
+
+149
+00:06:20,240 --> 00:06:24,599
+portray female or male characters with
+
+150
+00:06:22,280 --> 00:06:27,520
+more power or agency in movie script
+
+151
+00:06:24,599 --> 00:06:30,120
+films so it's asking kind of a so
+
+152
+00:06:27,520 --> 00:06:32,160
+societal question by using NLP
+
+153
+00:06:30,120 --> 00:06:35,360
+technology and the way they did it is
+
+154
+00:06:32,160 --> 00:06:36,880
+they basically analyzed text trying to
+
+155
+00:06:35,360 --> 00:06:43,080
+find
+
+156
+00:06:36,880 --> 00:06:45,280
+uh the uh agents and patients in a a
+
+157
+00:06:43,080 --> 00:06:46,479
+particular text which are the the things
+
+158
+00:06:45,280 --> 00:06:49,280
+that are doing things and the things
+
+159
+00:06:46,479 --> 00:06:52,639
+that things are being done to and you
+
+160
+00:06:49,280 --> 00:06:54,440
+can see that essentially male characters
+
+161
+00:06:52,639 --> 00:06:56,560
+in these movie scripts were given more
+
+162
+00:06:54,440 --> 00:06:58,080
+power in agency and female characters
+
+163
+00:06:56,560 --> 00:06:59,960
+were given less power in agency and they
+
+164
+00:06:58,080 --> 00:07:02,680
+were able to do this because they had
+
+165
+00:06:59,960 --> 00:07:04,840
+NLP technology that analyzed and
+
+166
+00:07:02,680 --> 00:07:08,960
+extracted useful data and made turned it
+
+167
+00:07:04,840 --> 00:07:11,520
+into a very easy form to do kind of
+
+168
+00:07:08,960 --> 00:07:15,840
+analysis of the variety that they want
+
+169
+00:07:11,520 --> 00:07:17,400
+so um I think that's a major use case of
+
+170
+00:07:15,840 --> 00:07:19,400
+NLP technology that does language
+
+171
+00:07:17,400 --> 00:07:20,919
+analysis nowadays turn it into a form
+
+172
+00:07:19,400 --> 00:07:23,960
+that allows you to very quickly do
+
+173
+00:07:20,919 --> 00:07:27,440
+aggregate queries and other things like
+
+174
+00:07:23,960 --> 00:07:30,479
+this um but at the same time uh language
+
+175
+00:07:27,440 --> 00:07:33,520
+analysis tools fail at very basic tasks
+
+176
+00:07:30,479 --> 00:07:36,000
+so these are
+
+177
+00:07:33,520 --> 00:07:38,199
+some things that I ran through a named
+
+178
+00:07:36,000 --> 00:07:41,080
+entity recognizer and these were kind of
+
+179
+00:07:38,199 --> 00:07:43,160
+very nice named entity recognizers uh
+
+180
+00:07:41,080 --> 00:07:46,240
+that a lot of people were using for
+
+181
+00:07:43,160 --> 00:07:48,039
+example Stanford core NLP and Spacey and
+
+182
+00:07:46,240 --> 00:07:50,319
+both of them I just threw in the first
+
+183
+00:07:48,039 --> 00:07:53,120
+thing that I found on the New York Times
+
+184
+00:07:50,319 --> 00:07:55,199
+at the time and it basically made at
+
+185
+00:07:53,120 --> 00:07:58,319
+least one mistake in the first sentence
+
+186
+00:07:55,199 --> 00:08:00,840
+and here it recognizes Baton Rouge as an
+
+187
+00:07:58,319 --> 00:08:04,720
+organization and here it recognized
+
+188
+00:08:00,840 --> 00:08:07,000
+hurricane EA as an organization so um
+
+189
+00:08:04,720 --> 00:08:08,879
+like even uh these things that we expect
+
+190
+00:08:07,000 --> 00:08:10,360
+should work pretty well make pretty
+
+191
+00:08:08,879 --> 00:08:13,360
+Solly
+
+192
+00:08:10,360 --> 00:08:16,199
+mistakes so in the class uh basically
+
+193
+00:08:13,360 --> 00:08:18,479
+what I want to cover is uh what goes
+
+194
+00:08:16,199 --> 00:08:20,360
+into building uh state-of-the-art NLP
+
+195
+00:08:18,479 --> 00:08:24,000
+systems that work really well on a wide
+
+196
+00:08:20,360 --> 00:08:26,240
+variety of tasks um where do current
+
+197
+00:08:24,000 --> 00:08:28,840
+systems
+
+198
+00:08:26,240 --> 00:08:30,479
+fail and how can we make appropriate
+
+199
+00:08:28,840 --> 00:08:35,000
+improvements and Achieve whatever we
+
+200
+00:08:30,479 --> 00:08:37,719
+want to do with nalp and this set of
+
+201
+00:08:35,000 --> 00:08:39,360
+questions that I'm asking here is
+
+202
+00:08:37,719 --> 00:08:40,919
+exactly the same as the set of questions
+
+203
+00:08:39,360 --> 00:08:43,519
+that I was asking two years ago before
+
+204
+00:08:40,919 --> 00:08:45,480
+chat GPT uh I still think they're
+
+205
+00:08:43,519 --> 00:08:46,920
+important questions but I think the
+
+206
+00:08:45,480 --> 00:08:48,399
+answers to these questions is very
+
+207
+00:08:46,920 --> 00:08:50,040
+different and because of that we're
+
+208
+00:08:48,399 --> 00:08:52,120
+updating the class materials to try to
+
+209
+00:08:50,040 --> 00:08:54,399
+cover you know the answers to these
+
+210
+00:08:52,120 --> 00:08:56,000
+questions and uh in kind of the era of
+
+211
+00:08:54,399 --> 00:08:58,200
+large language models and other things
+
+212
+00:08:56,000 --> 00:08:59,720
+like
+
+213
+00:08:58,200 --> 00:09:02,079
+that
+
+214
+00:08:59,720 --> 00:09:03,360
+so that's all I have for the intro maybe
+
+215
+00:09:02,079 --> 00:09:06,640
+maybe pretty straightforward are there
+
+216
+00:09:03,360 --> 00:09:08,480
+any questions or comments so far if not
+
+217
+00:09:06,640 --> 00:09:14,399
+I'll I'll just go
+
+218
+00:09:08,480 --> 00:09:17,160
+on okay great so I want to uh first go
+
+219
+00:09:14,399 --> 00:09:19,480
+into a very high Lev overview of NLP
+
+220
+00:09:17,160 --> 00:09:20,839
+system building and most of the stuff
+
+221
+00:09:19,480 --> 00:09:22,399
+that I want to do today is to set the
+
+222
+00:09:20,839 --> 00:09:24,320
+stage for what I'm going to be talking
+
+223
+00:09:22,399 --> 00:09:25,040
+about in more detail uh over the rest of
+
+224
+00:09:24,320 --> 00:09:29,200
+the
+
+225
+00:09:25,040 --> 00:09:31,720
+class and we could think of NLP syst
+
+226
+00:09:29,200 --> 00:09:34,040
+systems through this kind of General
+
+227
+00:09:31,720 --> 00:09:36,560
+framework where we want to create a
+
+228
+00:09:34,040 --> 00:09:40,600
+function to map an input X into an
+
+229
+00:09:36,560 --> 00:09:44,440
+output y uh where X and or Y involve
+
+230
+00:09:40,600 --> 00:09:47,000
+language and uh do some people have
+
+231
+00:09:44,440 --> 00:09:50,120
+favorite NLP tasks or NLP tasks that you
+
+232
+00:09:47,000 --> 00:09:52,399
+want to uh want to be handling in some
+
+233
+00:09:50,120 --> 00:09:57,000
+way or maybe what what do you think are
+
+234
+00:09:52,399 --> 00:09:57,000
+the most popular and important NLP tasks
+
+235
+00:09:58,120 --> 00:10:03,200
+nowadays
+
+236
+00:10:00,800 --> 00:10:06,120
+okay so translation is maybe easy what's
+
+237
+00:10:03,200 --> 00:10:06,120
+the input and output of
+
+238
+00:10:11,440 --> 00:10:15,720
+translation okay yeah so uh in
+
+239
+00:10:13,800 --> 00:10:17,959
+Translation inputs text in one language
+
+240
+00:10:15,720 --> 00:10:21,760
+output is text in another language and
+
+241
+00:10:17,959 --> 00:10:21,760
+then what what is a good
+
+242
+00:10:27,680 --> 00:10:32,160
+translation yeah corre or or the same is
+
+243
+00:10:30,320 --> 00:10:35,839
+the input basically yes um it also
+
+244
+00:10:32,160 --> 00:10:37,760
+should be fluent but I agree any other
+
+245
+00:10:35,839 --> 00:10:39,839
+things generation the reason why I said
+
+246
+00:10:37,760 --> 00:10:41,519
+it's tough is it's pretty broad um and
+
+247
+00:10:39,839 --> 00:10:43,360
+it's not like we could be doing
+
+248
+00:10:41,519 --> 00:10:46,360
+generation with lots of different inputs
+
+249
+00:10:43,360 --> 00:10:51,440
+but um yeah any any other things maybe a
+
+250
+00:10:46,360 --> 00:10:51,440
+little bit different yeah like
+
+251
+00:10:51,480 --> 00:10:55,959
+scenario a scenario and a multiple
+
+252
+00:10:54,000 --> 00:10:58,200
+choice question about the scenario and
+
+253
+00:10:55,959 --> 00:10:59,680
+so what would the scenario in the
+
+254
+00:10:58,200 --> 00:11:01,760
+multiple choice question are probably
+
+255
+00:10:59,680 --> 00:11:04,040
+the input and then the output
+
+256
+00:11:01,760 --> 00:11:06,480
+is an answer to the multiple choice
+
+257
+00:11:04,040 --> 00:11:07,920
+question um and then there it's kind of
+
+258
+00:11:06,480 --> 00:11:12,279
+obvious like what is good it's the
+
+259
+00:11:07,920 --> 00:11:14,880
+correct answer sure um interestingly I
+
+260
+00:11:12,279 --> 00:11:17,440
+think a lot of llm evaluation is done on
+
+261
+00:11:14,880 --> 00:11:21,160
+these multiple choice questions but I'm
+
+262
+00:11:17,440 --> 00:11:22,320
+yet to encounter an actual application
+
+263
+00:11:21,160 --> 00:11:24,880
+that cares about multiple choice
+
+264
+00:11:22,320 --> 00:11:26,880
+question answering so uh there's kind of
+
+265
+00:11:24,880 --> 00:11:30,959
+a funny disconnect there but uh yeah I
+
+266
+00:11:26,880 --> 00:11:33,519
+saw hand that think about V search comp
+
+267
+00:11:30,959 --> 00:11:36,360
+yeah Vector search uh that's very good
+
+268
+00:11:33,519 --> 00:11:36,360
+so the input
+
+269
+00:11:37,120 --> 00:11:45,000
+is can con it into or understanding and
+
+270
+00:11:42,560 --> 00:11:45,000
+it to
+
+271
+00:11:47,360 --> 00:11:53,760
+another okay yeah so I'd say the input
+
+272
+00:11:49,880 --> 00:11:56,160
+there is a query and a document base um
+
+273
+00:11:53,760 --> 00:11:57,959
+and then the output is maybe an index
+
+274
+00:11:56,160 --> 00:11:59,800
+into the document or or something else
+
+275
+00:11:57,959 --> 00:12:01,279
+like that sure um and then something
+
+276
+00:11:59,800 --> 00:12:05,040
+that's good here here's a good question
+
+277
+00:12:01,279 --> 00:12:05,040
+what what's a good result from
+
+278
+00:12:06,560 --> 00:12:10,200
+that what's a good
+
+279
+00:12:10,839 --> 00:12:19,279
+output be sort of simar the major
+
+280
+00:12:15,560 --> 00:12:21,680
+problem there I see is how you def SAR
+
+281
+00:12:19,279 --> 00:12:26,199
+and how you
+
+282
+00:12:21,680 --> 00:12:29,760
+a always like you understand
+
+283
+00:12:26,199 --> 00:12:33,000
+whether is actually
+
+284
+00:12:29,760 --> 00:12:35,079
+yeah exactly so that um just to repeat
+
+285
+00:12:33,000 --> 00:12:36,880
+it's like uh we need to have a
+
+286
+00:12:35,079 --> 00:12:38,399
+similarity a good similarity metric we
+
+287
+00:12:36,880 --> 00:12:40,120
+need to have a good threshold where we
+
+288
+00:12:38,399 --> 00:12:41,760
+get like the ones we want and we don't
+
+289
+00:12:40,120 --> 00:12:43,240
+get the ones we don't want we're going
+
+290
+00:12:41,760 --> 00:12:44,959
+to talk more about that in the retrieval
+
+291
+00:12:43,240 --> 00:12:48,440
+lecture exactly how we evaluate and
+
+292
+00:12:44,959 --> 00:12:49,920
+stuff but um yeah good so this is a good
+
+293
+00:12:48,440 --> 00:12:53,279
+uh here are some good examples I have
+
+294
+00:12:49,920 --> 00:12:55,519
+some examples of my own um the first one
+
+295
+00:12:53,279 --> 00:12:58,360
+is uh kind of the very generic one maybe
+
+296
+00:12:55,519 --> 00:13:00,800
+kind of like generation here but text in
+
+297
+00:12:58,360 --> 00:13:02,959
+continuing text uh so this is language
+
+298
+00:13:00,800 --> 00:13:04,160
+modeling so you have a text and then you
+
+299
+00:13:02,959 --> 00:13:05,440
+have the continuation you want to
+
+300
+00:13:04,160 --> 00:13:07,680
+predict the
+
+301
+00:13:05,440 --> 00:13:10,480
+continuation um text and text in another
+
+302
+00:13:07,680 --> 00:13:13,040
+language is translation uh text in a
+
+303
+00:13:10,480 --> 00:13:15,800
+label could be text classification uh
+
+304
+00:13:13,040 --> 00:13:17,760
+text in linguistic structure or uh some
+
+305
+00:13:15,800 --> 00:13:21,360
+s kind of entities or something like
+
+306
+00:13:17,760 --> 00:13:22,680
+that could be uh language analysis or um
+
+307
+00:13:21,360 --> 00:13:24,839
+information
+
+308
+00:13:22,680 --> 00:13:29,440
+extraction uh we could also have image
+
+309
+00:13:24,839 --> 00:13:31,320
+and text uh which is image captioning um
+
+310
+00:13:29,440 --> 00:13:33,560
+or speech and text which is speech
+
+311
+00:13:31,320 --> 00:13:35,240
+recognition and I take the very broad
+
+312
+00:13:33,560 --> 00:13:38,000
+view of natural language processing
+
+313
+00:13:35,240 --> 00:13:39,519
+which is if it's any variety of language
+
+314
+00:13:38,000 --> 00:13:41,519
+uh if you're handling language in some
+
+315
+00:13:39,519 --> 00:13:42,800
+way it's natural language processing it
+
+316
+00:13:41,519 --> 00:13:45,880
+doesn't necessarily have to be text
+
+317
+00:13:42,800 --> 00:13:47,480
+input text output um so that's relevant
+
+318
+00:13:45,880 --> 00:13:50,199
+for the projects that you're thinking
+
+319
+00:13:47,480 --> 00:13:52,160
+about too at the end of this course so
+
+320
+00:13:50,199 --> 00:13:55,519
+the the most common FAQ for this course
+
+321
+00:13:52,160 --> 00:13:57,839
+is does my project count and if you're
+
+322
+00:13:55,519 --> 00:13:59,360
+uncertain you should ask but usually
+
+323
+00:13:57,839 --> 00:14:01,040
+like if it has some sort of language
+
+324
+00:13:59,360 --> 00:14:05,079
+involved then I'll usually say yes it
+
+325
+00:14:01,040 --> 00:14:07,920
+does kind so um if it's like uh code to
+
+326
+00:14:05,079 --> 00:14:09,680
+code there that's not code is not
+
+327
+00:14:07,920 --> 00:14:11,480
+natural language it is language but it's
+
+328
+00:14:09,680 --> 00:14:13,000
+not natural language so that might be
+
+329
+00:14:11,480 --> 00:14:15,320
+borderline we might have to discuss
+
+330
+00:14:13,000 --> 00:14:15,320
+about
+
+331
+00:14:15,759 --> 00:14:21,800
+that cool um so next I'd like to talk
+
+332
+00:14:18,880 --> 00:14:25,240
+about methods for creating NLP systems
+
+333
+00:14:21,800 --> 00:14:27,839
+um and there's a lot of different ways
+
+334
+00:14:25,240 --> 00:14:29,720
+to create MLP systems all of these are
+
+335
+00:14:27,839 --> 00:14:32,880
+alive and well in
+
+336
+00:14:29,720 --> 00:14:35,759
+2024 uh the first one is Rule uh
+
+337
+00:14:32,880 --> 00:14:37,959
+rule-based system creation and so the
+
+338
+00:14:35,759 --> 00:14:40,399
+way this works is like let's say you
+
+339
+00:14:37,959 --> 00:14:42,480
+want to build a text classifier you just
+
+340
+00:14:40,399 --> 00:14:46,560
+write the simple python function that
+
+341
+00:14:42,480 --> 00:14:48,639
+classifies things into uh sports or
+
+342
+00:14:46,560 --> 00:14:50,240
+other and the way it classifies it into
+
+343
+00:14:48,639 --> 00:14:52,959
+sports or other is it checks whether
+
+344
+00:14:50,240 --> 00:14:55,160
+baseball soccer football and Tennis are
+
+345
+00:14:52,959 --> 00:14:59,399
+included in the document and classifies
+
+346
+00:14:55,160 --> 00:15:01,959
+it into uh Sports if so uh other if not
+
+347
+00:14:59,399 --> 00:15:05,279
+so has anyone written something like
+
+348
+00:15:01,959 --> 00:15:09,720
+this maybe not a text classifier but um
+
+349
+00:15:05,279 --> 00:15:11,880
+you know to identify entities or uh
+
+350
+00:15:09,720 --> 00:15:14,279
+split words
+
+351
+00:15:11,880 --> 00:15:16,680
+or something like
+
+352
+00:15:14,279 --> 00:15:18,399
+that has anybody not ever written
+
+353
+00:15:16,680 --> 00:15:22,800
+anything like
+
+354
+00:15:18,399 --> 00:15:24,639
+this yeah that's what I thought so um
+
+355
+00:15:22,800 --> 00:15:26,079
+rule-based systems are very convenient
+
+356
+00:15:24,639 --> 00:15:28,920
+when you don't really care about how
+
+357
+00:15:26,079 --> 00:15:30,759
+good your system is um or you're doing
+
+358
+00:15:28,920 --> 00:15:32,360
+that's really really simple and like
+
+359
+00:15:30,759 --> 00:15:35,600
+it'll be perfect even if you do the very
+
+360
+00:15:32,360 --> 00:15:37,079
+simple thing and so I I think it's worth
+
+361
+00:15:35,600 --> 00:15:39,959
+talking a little bit about them and I'll
+
+362
+00:15:37,079 --> 00:15:43,319
+talk a little bit about that uh this
+
+363
+00:15:39,959 --> 00:15:45,680
+time the second thing which like very
+
+364
+00:15:43,319 --> 00:15:47,680
+rapidly over the course of maybe three
+
+365
+00:15:45,680 --> 00:15:50,279
+years or so has become actually maybe
+
+366
+00:15:47,680 --> 00:15:52,720
+the dominant Paradigm in NLP is
+
+367
+00:15:50,279 --> 00:15:56,360
+prompting uh in prompting a language
+
+368
+00:15:52,720 --> 00:15:58,560
+model and the way this works is uh you
+
+369
+00:15:56,360 --> 00:16:00,720
+ask a language model if the following
+
+370
+00:15:58,560 --> 00:16:03,079
+sent is about sports reply Sports
+
+371
+00:16:00,720 --> 00:16:06,120
+otherwise reply other and you feed it to
+
+372
+00:16:03,079 --> 00:16:08,480
+your favorite LM uh usually that's GPT
+
+373
+00:16:06,120 --> 00:16:11,399
+something or other uh sometimes it's an
+
+374
+00:16:08,480 --> 00:16:14,440
+open source model of some variety and
+
+375
+00:16:11,399 --> 00:16:17,759
+then uh it will give you the
+
+376
+00:16:14,440 --> 00:16:20,639
+answer and then finally uh fine-tuning
+
+377
+00:16:17,759 --> 00:16:22,240
+uh so you take some paired data and you
+
+378
+00:16:20,639 --> 00:16:23,600
+do machine learning from paired data
+
+379
+00:16:22,240 --> 00:16:25,680
+where you have something like I love to
+
+380
+00:16:23,600 --> 00:16:27,440
+play baseball uh the stock price is
+
+381
+00:16:25,680 --> 00:16:29,519
+going up he got a hatrick yesterday he
+
+382
+00:16:27,440 --> 00:16:32,759
+is wearing tennis shoes and you assign
+
+383
+00:16:29,519 --> 00:16:35,319
+all these uh labels to them training a
+
+384
+00:16:32,759 --> 00:16:38,160
+model and you can even start out with a
+
+385
+00:16:35,319 --> 00:16:41,480
+prompting based model and fine-tune a a
+
+386
+00:16:38,160 --> 00:16:41,480
+language model
+
+387
+00:16:42,920 --> 00:16:49,399
+also so one major consideration when
+
+388
+00:16:47,519 --> 00:16:52,000
+you're Building Systems like this is the
+
+389
+00:16:49,399 --> 00:16:56,440
+data requirements for building such a
+
+390
+00:16:52,000 --> 00:16:59,319
+system and for rules or prompting where
+
+391
+00:16:56,440 --> 00:17:02,240
+it's just based on intuition really no
+
+392
+00:16:59,319 --> 00:17:04,640
+data is needed whatsoever it you don't
+
+393
+00:17:02,240 --> 00:17:08,240
+need a single example and you can start
+
+394
+00:17:04,640 --> 00:17:11,000
+writing rules or like just just to give
+
+395
+00:17:08,240 --> 00:17:12,640
+an example the rules and prompts I wrote
+
+396
+00:17:11,000 --> 00:17:14,679
+here I didn't look at any examples and I
+
+397
+00:17:12,640 --> 00:17:17,240
+just wrote them uh so this is something
+
+398
+00:17:14,679 --> 00:17:20,000
+that you could start out
+
+399
+00:17:17,240 --> 00:17:21,559
+with uh the problem is you also have no
+
+400
+00:17:20,000 --> 00:17:24,720
+idea how well it works if you don't have
+
+401
+00:17:21,559 --> 00:17:26,760
+any data whatsoever right so um you'll
+
+402
+00:17:24,720 --> 00:17:30,400
+you might be in trouble if you think
+
+403
+00:17:26,760 --> 00:17:30,400
+something should be working
+
+404
+00:17:30,919 --> 00:17:34,440
+so normally the next thing that people
+
+405
+00:17:32,919 --> 00:17:36,880
+move to nowadays when they're building
+
+406
+00:17:34,440 --> 00:17:39,559
+practical systems is rules are prompting
+
+407
+00:17:36,880 --> 00:17:41,240
+based on spot checks so that basically
+
+408
+00:17:39,559 --> 00:17:42,919
+means that you start out with a
+
+409
+00:17:41,240 --> 00:17:45,840
+rule-based system or a prompting based
+
+410
+00:17:42,919 --> 00:17:47,240
+system and then you go in and you run it
+
+411
+00:17:45,840 --> 00:17:48,720
+on some data that you're interested in
+
+412
+00:17:47,240 --> 00:17:50,799
+you just kind of qualitatively look at
+
+413
+00:17:48,720 --> 00:17:52,160
+the data and say oh it's messing up here
+
+414
+00:17:50,799 --> 00:17:53,440
+then you go in and fix your prompt a
+
+415
+00:17:52,160 --> 00:17:54,919
+little bit or you go in and fix your
+
+416
+00:17:53,440 --> 00:17:57,320
+rules a little bit or something like
+
+417
+00:17:54,919 --> 00:18:00,400
+that so uh this is kind of the second
+
+418
+00:17:57,320 --> 00:18:00,400
+level of difficulty
+
+419
+00:18:01,400 --> 00:18:04,640
+so the third level of difficulty would
+
+420
+00:18:03,159 --> 00:18:07,400
+be something like rules are prompting
+
+421
+00:18:04,640 --> 00:18:09,039
+with rigorous evaluation and so here you
+
+422
+00:18:07,400 --> 00:18:12,840
+would create a development set with
+
+423
+00:18:09,039 --> 00:18:14,840
+inputs and outputs uh so you uh create
+
+424
+00:18:12,840 --> 00:18:17,039
+maybe 200 to 2,000
+
+425
+00:18:14,840 --> 00:18:20,080
+examples um
+
+426
+00:18:17,039 --> 00:18:21,720
+and then evaluate your actual accuracy
+
+427
+00:18:20,080 --> 00:18:23,880
+so you need an evaluation metric you
+
+428
+00:18:21,720 --> 00:18:26,120
+need other things like this this is the
+
+429
+00:18:23,880 --> 00:18:28,400
+next level of difficulty but if you're
+
+430
+00:18:26,120 --> 00:18:30,240
+going to be a serious you know NLP
+
+431
+00:18:28,400 --> 00:18:33,000
+engineer or something like this you
+
+432
+00:18:30,240 --> 00:18:34,720
+definitely will be doing this a lot I
+
+433
+00:18:33,000 --> 00:18:37,760
+feel and
+
+434
+00:18:34,720 --> 00:18:40,360
+then so that here now you start needing
+
+435
+00:18:37,760 --> 00:18:41,960
+a depth set and a test set and then
+
+436
+00:18:40,360 --> 00:18:46,280
+finally fine-tuning you need an
+
+437
+00:18:41,960 --> 00:18:48,480
+additional training set um and uh this
+
+438
+00:18:46,280 --> 00:18:52,240
+will generally be a lot bigger than 200
+
+439
+00:18:48,480 --> 00:18:56,080
+to 2,000 examples and generally the rule
+
+440
+00:18:52,240 --> 00:18:56,080
+is that every time you
+
+441
+00:18:57,320 --> 00:19:01,080
+double
+
+442
+00:18:59,520 --> 00:19:02,400
+every time you double your training set
+
+443
+00:19:01,080 --> 00:19:07,480
+size you get about a constant
+
+444
+00:19:02,400 --> 00:19:07,480
+Improvement so if you start
+
+445
+00:19:07,799 --> 00:19:15,080
+out if you start out down here with
+
+446
+00:19:12,240 --> 00:19:17,039
+um zero shot accuracy with a language
+
+447
+00:19:15,080 --> 00:19:21,559
+model you you create a small printing
+
+448
+00:19:17,039 --> 00:19:21,559
+set and you get you know a pretty big
+
+449
+00:19:22,000 --> 00:19:29,120
+increase and then every time you double
+
+450
+00:19:26,320 --> 00:19:30,799
+it it increases by constant fact it's
+
+451
+00:19:29,120 --> 00:19:32,480
+kind of like just in general in machine
+
+452
+00:19:30,799 --> 00:19:37,360
+learning this is a trend that we tend to
+
+453
+00:19:32,480 --> 00:19:40,679
+see so um So based on this
+
+454
+00:19:37,360 --> 00:19:41,880
+uh there's kind of like you get a big
+
+455
+00:19:40,679 --> 00:19:44,200
+gain from having a little bit of
+
+456
+00:19:41,880 --> 00:19:45,760
+training data but the gains very quickly
+
+457
+00:19:44,200 --> 00:19:48,919
+drop off and you start spending a lot of
+
+458
+00:19:45,760 --> 00:19:48,919
+time annotating
+
+459
+00:19:51,000 --> 00:19:55,880
+an so um yeah this is the the general
+
+460
+00:19:54,760 --> 00:19:58,280
+overview of the different types of
+
+461
+00:19:55,880 --> 00:20:00,000
+system building uh any any question
+
+462
+00:19:58,280 --> 00:20:01,559
+questions about this or comments or
+
+463
+00:20:00,000 --> 00:20:04,000
+things like
+
+464
+00:20:01,559 --> 00:20:05,840
+this I think one thing that's changed
+
+465
+00:20:04,000 --> 00:20:08,159
+really drastically from the last time I
+
+466
+00:20:05,840 --> 00:20:09,600
+taught this class is the fact that
+
+467
+00:20:08,159 --> 00:20:11,000
+number one and number two are the things
+
+468
+00:20:09,600 --> 00:20:13,799
+that people are actually doing in
+
+469
+00:20:11,000 --> 00:20:15,360
+practice uh which was you know people
+
+470
+00:20:13,799 --> 00:20:16,679
+who actually care about systems are
+
+471
+00:20:15,360 --> 00:20:18,880
+doing number one and number two is the
+
+472
+00:20:16,679 --> 00:20:20,440
+main thing it used to be that if you
+
+473
+00:20:18,880 --> 00:20:22,679
+were actually serious about building a
+
+474
+00:20:20,440 --> 00:20:24,320
+system uh you really needed to do the
+
+475
+00:20:22,679 --> 00:20:27,080
+funing and now it's kind of like more
+
+476
+00:20:24,320 --> 00:20:27,080
+optional
+
+477
+00:20:27,159 --> 00:20:30,159
+so
+
+478
+00:20:44,039 --> 00:20:50,960
+yeah
+
+479
+00:20:46,320 --> 00:20:53,960
+so it's it's definitely an empirical
+
+480
+00:20:50,960 --> 00:20:53,960
+observation
+
+481
+00:20:54,720 --> 00:21:01,080
+um in terms of the theoretical
+
+482
+00:20:57,640 --> 00:21:03,120
+background I am not I can't immediately
+
+483
+00:21:01,080 --> 00:21:05,840
+point to a
+
+484
+00:21:03,120 --> 00:21:10,039
+particular paper that does that but I
+
+485
+00:21:05,840 --> 00:21:12,720
+think if you think about
+
+486
+00:21:10,039 --> 00:21:14,720
+the I I think I have seen that they do
+
+487
+00:21:12,720 --> 00:21:17,039
+exist in the past but I I can't think of
+
+488
+00:21:14,720 --> 00:21:19,000
+it right now I can try to uh try to come
+
+489
+00:21:17,039 --> 00:21:23,720
+up with an example of
+
+490
+00:21:19,000 --> 00:21:23,720
+that so yeah I I should take
+
+491
+00:21:26,799 --> 00:21:31,960
+notes or someone wants to share one on
+
+492
+00:21:29,360 --> 00:21:33,360
+Piaza uh if you have any ideas and want
+
+493
+00:21:31,960 --> 00:21:34,520
+to share on Patza I'm sure that would be
+
+494
+00:21:33,360 --> 00:21:35,640
+great it'd be great to have a discussion
+
+495
+00:21:34,520 --> 00:21:39,320
+on
+
+496
+00:21:35,640 --> 00:21:44,960
+Patza um Pi
+
+497
+00:21:39,320 --> 00:21:46,880
+one cool okay so next I want to try to
+
+498
+00:21:44,960 --> 00:21:48,200
+make a rule-based system and I'm going
+
+499
+00:21:46,880 --> 00:21:49,360
+to make a rule-based system for
+
+500
+00:21:48,200 --> 00:21:51,799
+sentiment
+
+501
+00:21:49,360 --> 00:21:53,480
+analysis uh and this is a bad idea I
+
+502
+00:21:51,799 --> 00:21:55,400
+would not encourage you to ever do this
+
+503
+00:21:53,480 --> 00:21:57,440
+in real life but I want to do it here to
+
+504
+00:21:55,400 --> 00:21:59,640
+show you why it's a bad idea and like
+
+505
+00:21:57,440 --> 00:22:01,200
+what are some of the hard problems that
+
+506
+00:21:59,640 --> 00:22:03,960
+you encounter when trying to create a
+
+507
+00:22:01,200 --> 00:22:06,600
+system based on rules
+
+508
+00:22:03,960 --> 00:22:08,080
+and then we'll move into building a
+
+509
+00:22:06,600 --> 00:22:12,360
+machine learning base system after we
+
+510
+00:22:08,080 --> 00:22:15,400
+finish this so if we look at the example
+
+511
+00:22:12,360 --> 00:22:18,559
+test this is review sentiment analysis
+
+512
+00:22:15,400 --> 00:22:21,799
+it's one of the most valuable uh tasks
+
+513
+00:22:18,559 --> 00:22:24,039
+uh that people do in NLP nowadays
+
+514
+00:22:21,799 --> 00:22:26,400
+because it allows people to know how
+
+515
+00:22:24,039 --> 00:22:29,200
+customers are thinking about products uh
+
+516
+00:22:26,400 --> 00:22:30,799
+improve their you know their product
+
+517
+00:22:29,200 --> 00:22:32,919
+development and other things like that
+
+518
+00:22:30,799 --> 00:22:34,799
+may monitor people's you know
+
+519
+00:22:32,919 --> 00:22:36,760
+satisfaction with their social media
+
+520
+00:22:34,799 --> 00:22:39,200
+service other things like this so
+
+521
+00:22:36,760 --> 00:22:42,720
+basically the way it works is um you
+
+522
+00:22:39,200 --> 00:22:44,400
+have uh outputs or you have sentences
+
+523
+00:22:42,720 --> 00:22:46,720
+inputs like I hate this movie I love
+
+524
+00:22:44,400 --> 00:22:48,520
+this movie I saw this movie and this
+
+525
+00:22:46,720 --> 00:22:50,600
+gets mapped into positive neutral or
+
+526
+00:22:48,520 --> 00:22:53,120
+negative so I hate this movie would be
+
+527
+00:22:50,600 --> 00:22:55,480
+negative I love this movie positive and
+
+528
+00:22:53,120 --> 00:22:59,039
+I saw this movie is
+
+529
+00:22:55,480 --> 00:23:01,200
+neutral so um
+
+530
+00:22:59,039 --> 00:23:05,200
+that that's the task input tax output
+
+531
+00:23:01,200 --> 00:23:08,880
+labels uh Kary uh sentence
+
+532
+00:23:05,200 --> 00:23:11,679
+label and in order to do this uh we
+
+533
+00:23:08,880 --> 00:23:13,120
+would like to build a model um and we're
+
+534
+00:23:11,679 --> 00:23:16,159
+going to build the model in a rule based
+
+535
+00:23:13,120 --> 00:23:19,000
+way but it we'll still call it a model
+
+536
+00:23:16,159 --> 00:23:21,600
+and the way it works is we do feature
+
+537
+00:23:19,000 --> 00:23:23,159
+extraction um so we extract the Salient
+
+538
+00:23:21,600 --> 00:23:25,279
+features for making the decision about
+
+539
+00:23:23,159 --> 00:23:27,320
+what to Output next we do score
+
+540
+00:23:25,279 --> 00:23:29,880
+calculation calculate a score for one or
+
+541
+00:23:27,320 --> 00:23:32,320
+more possib ities and we have a decision
+
+542
+00:23:29,880 --> 00:23:33,520
+function so we choose one of those
+
+543
+00:23:32,320 --> 00:23:37,679
+several
+
+544
+00:23:33,520 --> 00:23:40,120
+possibilities and so for feature
+
+545
+00:23:37,679 --> 00:23:42,200
+extraction uh formally what this looks
+
+546
+00:23:40,120 --> 00:23:44,240
+like is we have some function and it
+
+547
+00:23:42,200 --> 00:23:48,039
+extracts a feature
+
+548
+00:23:44,240 --> 00:23:51,159
+Vector for score calculation um we
+
+549
+00:23:48,039 --> 00:23:54,240
+calculate the scores based on either a
+
+550
+00:23:51,159 --> 00:23:56,279
+binary classification uh where we have a
+
+551
+00:23:54,240 --> 00:23:58,279
+a weight vector and we take the dot
+
+552
+00:23:56,279 --> 00:24:00,120
+product with our feature vector or we
+
+553
+00:23:58,279 --> 00:24:02,480
+have multi class classification where we
+
+554
+00:24:00,120 --> 00:24:04,520
+have a weight Matrix and we take the
+
+555
+00:24:02,480 --> 00:24:08,640
+product with uh the vector and that
+
+556
+00:24:04,520 --> 00:24:08,640
+gives us you know squares over multiple
+
+557
+00:24:08,919 --> 00:24:14,840
+classes and then we have a decision uh
+
+558
+00:24:11,600 --> 00:24:17,520
+rule so this decision rule tells us what
+
+559
+00:24:14,840 --> 00:24:20,080
+the output is going to be um does anyone
+
+560
+00:24:17,520 --> 00:24:22,200
+know what a typical decision rule is
+
+561
+00:24:20,080 --> 00:24:24,520
+maybe maybe so obvious that you don't
+
+562
+00:24:22,200 --> 00:24:28,760
+think about it often
+
+563
+00:24:24,520 --> 00:24:31,000
+but uh a threshold um so like for would
+
+564
+00:24:28,760 --> 00:24:34,440
+that be for binary a single binary
+
+565
+00:24:31,000 --> 00:24:37,000
+scaler score or a multiple
+
+566
+00:24:34,440 --> 00:24:38,520
+class binary yeah so and then you would
+
+567
+00:24:37,000 --> 00:24:39,960
+pick a threshold and if it's over the
+
+568
+00:24:38,520 --> 00:24:42,919
+threshold
+
+569
+00:24:39,960 --> 00:24:45,760
+you say yes and if it's under the
+
+570
+00:24:42,919 --> 00:24:50,279
+threshold you say no um another option
+
+571
+00:24:45,760 --> 00:24:51,679
+would be um you have a threshold and you
+
+572
+00:24:50,279 --> 00:24:56,080
+say
+
+573
+00:24:51,679 --> 00:24:56,080
+yes no
+
+574
+00:24:56,200 --> 00:25:00,559
+obain so you know you don't give an
+
+575
+00:24:58,360 --> 00:25:02,520
+answer and depending on how you're
+
+576
+00:25:00,559 --> 00:25:03,720
+evaluated what what is a good classifier
+
+577
+00:25:02,520 --> 00:25:07,799
+you might want to abstain some of the
+
+578
+00:25:03,720 --> 00:25:10,960
+time also um for multiclass what what's
+
+579
+00:25:07,799 --> 00:25:10,960
+a standard decision role for
+
+580
+00:25:11,120 --> 00:25:16,720
+multiclass argmax yeah exactly so um
+
+581
+00:25:14,279 --> 00:25:19,520
+basically you you find the index that
+
+582
+00:25:16,720 --> 00:25:22,000
+has the highest score in you output
+
+583
+00:25:19,520 --> 00:25:24,480
+it we're going to be talking about other
+
+584
+00:25:22,000 --> 00:25:26,559
+decision rules also um like
+
+585
+00:25:24,480 --> 00:25:29,480
+self-consistency and minimum based risk
+
+586
+00:25:26,559 --> 00:25:30,760
+later uh for text generation so you can
+
+587
+00:25:29,480 --> 00:25:33,000
+just keep that in mind and then we'll
+
+588
+00:25:30,760 --> 00:25:36,279
+forget about it for like several
+
+589
+00:25:33,000 --> 00:25:39,559
+classes um so for sentiment
+
+590
+00:25:36,279 --> 00:25:42,159
+class um I have a Cod
+
+591
+00:25:39,559 --> 00:25:45,159
+walk
+
+592
+00:25:42,159 --> 00:25:45,159
+here
+
+593
+00:25:46,240 --> 00:25:54,320
+and this is pretty simple um but if
+
+594
+00:25:50,320 --> 00:25:58,559
+you're bored uh of the class and would
+
+595
+00:25:54,320 --> 00:26:01,000
+like to um try out yourself you can
+
+596
+00:25:58,559 --> 00:26:04,480
+Challenge and try to get a better score
+
+597
+00:26:01,000 --> 00:26:06,120
+than I do um over the next few minutes
+
+598
+00:26:04,480 --> 00:26:06,880
+but we have this rule based classifier
+
+599
+00:26:06,120 --> 00:26:10,240
+in
+
+600
+00:26:06,880 --> 00:26:12,640
+here and I will open it up in my vs
+
+601
+00:26:10,240 --> 00:26:15,360
+code
+
+602
+00:26:12,640 --> 00:26:18,360
+to try to create a rule-based classifier
+
+603
+00:26:15,360 --> 00:26:18,360
+and basically the way this
+
+604
+00:26:22,799 --> 00:26:29,960
+works is
+
+605
+00:26:25,159 --> 00:26:29,960
+that we have a feature
+
+606
+00:26:31,720 --> 00:26:37,720
+extraction we have feature extraction we
+
+607
+00:26:34,120 --> 00:26:40,679
+have scoring and we have um a decision
+
+608
+00:26:37,720 --> 00:26:43,480
+rle so here for our feature extraction I
+
+609
+00:26:40,679 --> 00:26:44,720
+have created a list of good words and a
+
+610
+00:26:43,480 --> 00:26:46,720
+list of bad
+
+611
+00:26:44,720 --> 00:26:48,960
+words
+
+612
+00:26:46,720 --> 00:26:51,320
+and what we do is we just count the
+
+613
+00:26:48,960 --> 00:26:53,000
+number of good words that appeared and
+
+614
+00:26:51,320 --> 00:26:55,320
+count the number of bad words that
+
+615
+00:26:53,000 --> 00:26:57,880
+appeared then we also have a bias
+
+616
+00:26:55,320 --> 00:27:01,159
+feature so the bias feature is a feature
+
+617
+00:26:57,880 --> 00:27:03,679
+that's always one and so what that
+
+618
+00:27:01,159 --> 00:27:06,799
+results in is we have a dimension three
+
+619
+00:27:03,679 --> 00:27:08,880
+feature Vector um where this is like the
+
+620
+00:27:06,799 --> 00:27:11,320
+number of good words this is the number
+
+621
+00:27:08,880 --> 00:27:15,320
+of bad words and then you have the
+
+622
+00:27:11,320 --> 00:27:17,760
+bias and then I also Define the feature
+
+623
+00:27:15,320 --> 00:27:20,039
+weights that so for every good word we
+
+624
+00:27:17,760 --> 00:27:22,200
+add one to our score for every bad word
+
+625
+00:27:20,039 --> 00:27:25,559
+we add uh we subtract one from our score
+
+626
+00:27:22,200 --> 00:27:29,399
+and for the BIOS we absor and so we then
+
+627
+00:27:25,559 --> 00:27:30,480
+take the dot product between
+
+628
+00:27:29,399 --> 00:27:34,360
+these
+
+629
+00:27:30,480 --> 00:27:36,919
+two and we get minus
+
+630
+00:27:34,360 --> 00:27:37,640
+0.5 and that gives us uh that gives us
+
+631
+00:27:36,919 --> 00:27:41,000
+the
+
+632
+00:27:37,640 --> 00:27:46,000
+squore so let's run
+
+633
+00:27:41,000 --> 00:27:50,320
+that um and I read in some
+
+634
+00:27:46,000 --> 00:27:52,600
+data and what this data looks like is
+
+635
+00:27:50,320 --> 00:27:55,000
+basically we have a
+
+636
+00:27:52,600 --> 00:27:57,559
+review um which says the rock is
+
+637
+00:27:55,000 --> 00:27:59,480
+destined to be the 21st Century's new
+
+638
+00:27:57,559 --> 00:28:01,240
+Conan and that he's going to make a
+
+639
+00:27:59,480 --> 00:28:03,600
+splash even greater than Arnold
+
+640
+00:28:01,240 --> 00:28:07,000
+Schwarzenegger jeanclaude vanam or
+
+641
+00:28:03,600 --> 00:28:09,519
+Steven Seagal um so this seems pretty
+
+642
+00:28:07,000 --> 00:28:10,840
+positive right I like that's a pretty
+
+643
+00:28:09,519 --> 00:28:13,200
+high order to be better than Arnold
+
+644
+00:28:10,840 --> 00:28:16,080
+Schwarzenegger or John Claude vanam uh
+
+645
+00:28:13,200 --> 00:28:19,519
+if you're familiar with action movies um
+
+646
+00:28:16,080 --> 00:28:22,840
+and so of course this gets a positive
+
+647
+00:28:19,519 --> 00:28:24,120
+label and so uh we have run classifier
+
+648
+00:28:22,840 --> 00:28:25,240
+actually maybe I should call this
+
+649
+00:28:24,120 --> 00:28:27,600
+decision rule because this is
+
+650
+00:28:25,240 --> 00:28:29,120
+essentially our decision Rule and here
+
+651
+00:28:27,600 --> 00:28:32,600
+basically do the thing that I mentioned
+
+652
+00:28:29,120 --> 00:28:35,440
+here the yes no obstain or in this case
+
+653
+00:28:32,600 --> 00:28:38,360
+positive negative neutral so if the
+
+654
+00:28:35,440 --> 00:28:40,159
+score is greater than zero we uh return
+
+655
+00:28:38,360 --> 00:28:42,480
+one if the score is less than zero we
+
+656
+00:28:40,159 --> 00:28:44,679
+return negative one which is negative
+
+657
+00:28:42,480 --> 00:28:47,240
+and otherwise we returns
+
+658
+00:28:44,679 --> 00:28:48,760
+zero um we have an accuracy calculation
+
+659
+00:28:47,240 --> 00:28:51,519
+function just calculating the outputs
+
+660
+00:28:48,760 --> 00:28:55,840
+are good and
+
+661
+00:28:51,519 --> 00:28:57,440
+um this is uh the overall label count in
+
+662
+00:28:55,840 --> 00:28:59,919
+the in the output so we can see there
+
+663
+00:28:57,440 --> 00:29:03,120
+slightly more positives than there are
+
+664
+00:28:59,919 --> 00:29:06,080
+negatives and then we can run this and
+
+665
+00:29:03,120 --> 00:29:10,200
+we get a a score of
+
+666
+00:29:06,080 --> 00:29:14,760
+43 and so one one thing that I have
+
+667
+00:29:10,200 --> 00:29:19,279
+found um is I I do a lot of kind
+
+668
+00:29:14,760 --> 00:29:21,240
+of research on how to make NLP systems
+
+669
+00:29:19,279 --> 00:29:23,600
+better and one of the things I found
+
+670
+00:29:21,240 --> 00:29:26,679
+really invaluable
+
+671
+00:29:23,600 --> 00:29:27,840
+is if you're in a situation where you
+
+672
+00:29:26,679 --> 00:29:29,720
+have a
+
+673
+00:29:27,840 --> 00:29:31,760
+set task and you just want to make the
+
+674
+00:29:29,720 --> 00:29:33,760
+system better on the set task doing
+
+675
+00:29:31,760 --> 00:29:35,159
+comprehensive error analysis and
+
+676
+00:29:33,760 --> 00:29:37,320
+understanding where your system is
+
+677
+00:29:35,159 --> 00:29:39,880
+failing is one of the best ways to do
+
+678
+00:29:37,320 --> 00:29:42,200
+that and I would like to do a very
+
+679
+00:29:39,880 --> 00:29:43,640
+rudimentary version of this here and
+
+680
+00:29:42,200 --> 00:29:46,519
+what I'm doing essentially is I'm just
+
+681
+00:29:43,640 --> 00:29:47,480
+randomly picking uh several examples
+
+682
+00:29:46,519 --> 00:29:49,320
+that were
+
+683
+00:29:47,480 --> 00:29:52,000
+correct
+
+684
+00:29:49,320 --> 00:29:54,840
+um and so like let let's look at the
+
+685
+00:29:52,000 --> 00:29:58,200
+examples here um here the true label is
+
+686
+00:29:54,840 --> 00:30:00,760
+zero um in this predicted one um it may
+
+687
+00:29:58,200 --> 00:30:03,440
+not be as cutting as Woody or as true as
+
+688
+00:30:00,760 --> 00:30:05,039
+back in the Glory Days of uh weekend and
+
+689
+00:30:03,440 --> 00:30:07,440
+two or three things that I know about
+
+690
+00:30:05,039 --> 00:30:09,640
+her but who else engaged in film Mak
+
+691
+00:30:07,440 --> 00:30:12,679
+today is so cognizant of the cultural
+
+692
+00:30:09,640 --> 00:30:14,480
+and moral issues involved in the process
+
+693
+00:30:12,679 --> 00:30:17,600
+so what words in here are a good
+
+694
+00:30:14,480 --> 00:30:20,840
+indication that this is a neutral
+
+695
+00:30:17,600 --> 00:30:20,840
+sentence any
+
+696
+00:30:23,760 --> 00:30:28,399
+ideas little bit tough
+
+697
+00:30:26,240 --> 00:30:30,919
+huh starting to think maybe we should be
+
+698
+00:30:28,399 --> 00:30:30,919
+using machine
+
+699
+00:30:31,480 --> 00:30:37,440
+learning
+
+700
+00:30:34,080 --> 00:30:40,320
+um even by the intentionally low
+
+701
+00:30:37,440 --> 00:30:41,559
+standards of fratboy humor sority boys
+
+702
+00:30:40,320 --> 00:30:43,840
+is a
+
+703
+00:30:41,559 --> 00:30:46,080
+Bowser I think frat boy is maybe
+
+704
+00:30:43,840 --> 00:30:47,360
+negative sentiment if you're familiar
+
+705
+00:30:46,080 --> 00:30:50,360
+with
+
+706
+00:30:47,360 --> 00:30:51,960
+us us I don't have any negative
+
+707
+00:30:50,360 --> 00:30:54,519
+sentiment but the people who say it that
+
+708
+00:30:51,960 --> 00:30:55,960
+way have negative senent maybe so if we
+
+709
+00:30:54,519 --> 00:31:01,080
+wanted to go in and do that we could
+
+710
+00:30:55,960 --> 00:31:01,080
+maybe I won't save this but
+
+711
+00:31:01,519 --> 00:31:08,919
+uh
+
+712
+00:31:04,240 --> 00:31:11,840
+um oh whoops I'll go back and fix it uh
+
+713
+00:31:08,919 --> 00:31:14,840
+crass crass is pretty obviously negative
+
+714
+00:31:11,840 --> 00:31:14,840
+right so I can add
+
+715
+00:31:17,039 --> 00:31:21,080
+crass actually let me just add
+
+716
+00:31:21,760 --> 00:31:29,159
+CR and then um I'll go back and have our
+
+717
+00:31:26,559 --> 00:31:29,159
+train accurate
+
+718
+00:31:32,159 --> 00:31:36,240
+wa maybe maybe I need to run the whole
+
+719
+00:31:33,960 --> 00:31:36,240
+thing
+
+720
+00:31:36,960 --> 00:31:39,960
+again
+
+721
+00:31:40,960 --> 00:31:45,880
+and that budg the training accuracy a
+
+722
+00:31:43,679 --> 00:31:50,360
+little um the dev test accuracy not very
+
+723
+00:31:45,880 --> 00:31:53,919
+much so I could go through and do this
+
+724
+00:31:50,360 --> 00:31:53,919
+um let me add
+
+725
+00:31:54,000 --> 00:31:58,320
+unengaging so I could go through and do
+
+726
+00:31:56,000 --> 00:32:01,720
+this all day and you probably be very
+
+727
+00:31:58,320 --> 00:32:01,720
+bored on
+
+728
+00:32:04,240 --> 00:32:08,360
+engage but I won't do that uh because we
+
+729
+00:32:06,919 --> 00:32:10,679
+have much more important things to be
+
+730
+00:32:08,360 --> 00:32:14,679
+doing
+
+731
+00:32:10,679 --> 00:32:16,440
+um and uh so anyway we um we could go
+
+732
+00:32:14,679 --> 00:32:18,919
+through and design all the features here
+
+733
+00:32:16,440 --> 00:32:21,279
+but like why is this complicated like
+
+734
+00:32:18,919 --> 00:32:22,600
+the the reason why it was complicated
+
+735
+00:32:21,279 --> 00:32:25,840
+became pretty
+
+736
+00:32:22,600 --> 00:32:27,840
+clear from the uh from the very
+
+737
+00:32:25,840 --> 00:32:29,639
+beginning uh the very first example I
+
+738
+00:32:27,840 --> 00:32:32,200
+showed you which was that was a really
+
+739
+00:32:29,639 --> 00:32:34,720
+complicated sentence like all of us
+
+740
+00:32:32,200 --> 00:32:36,240
+could see that it wasn't like really
+
+741
+00:32:34,720 --> 00:32:38,679
+strongly positive it wasn't really
+
+742
+00:32:36,240 --> 00:32:40,519
+strongly negative it was kind of like in
+
+743
+00:32:38,679 --> 00:32:42,919
+the middle but it was in the middle and
+
+744
+00:32:40,519 --> 00:32:44,600
+it said it in a very long way uh you
+
+745
+00:32:42,919 --> 00:32:46,120
+know not using any clearly positive
+
+746
+00:32:44,600 --> 00:32:47,639
+sentiment words not using any clearly
+
+747
+00:32:46,120 --> 00:32:49,760
+negative sentiment
+
+748
+00:32:47,639 --> 00:32:53,760
+words
+
+749
+00:32:49,760 --> 00:32:56,519
+um so yeah basically I I
+
+750
+00:32:53,760 --> 00:33:00,559
+improved um but what are the difficult
+
+751
+00:32:56,519 --> 00:33:03,720
+cases uh that we saw here so the first
+
+752
+00:33:00,559 --> 00:33:07,639
+one is low frequency
+
+753
+00:33:03,720 --> 00:33:09,760
+words so um here's an example the action
+
+754
+00:33:07,639 --> 00:33:11,519
+switches between past and present but
+
+755
+00:33:09,760 --> 00:33:13,120
+the material link is too tenuous to
+
+756
+00:33:11,519 --> 00:33:16,840
+Anchor the emotional connections at
+
+757
+00:33:13,120 --> 00:33:19,519
+purport to span a 125 year divide so
+
+758
+00:33:16,840 --> 00:33:21,080
+this is negative um tenuous is kind of a
+
+759
+00:33:19,519 --> 00:33:22,799
+negative word purport is kind of a
+
+760
+00:33:21,080 --> 00:33:24,760
+negative word but it doesn't appear very
+
+761
+00:33:22,799 --> 00:33:26,159
+frequently so I would need to spend all
+
+762
+00:33:24,760 --> 00:33:29,720
+my time looking for these words and
+
+763
+00:33:26,159 --> 00:33:32,480
+trying to them in um here's yet another
+
+764
+00:33:29,720 --> 00:33:34,240
+horse franchise mucking up its storyline
+
+765
+00:33:32,480 --> 00:33:36,639
+with glitches casual fans could correct
+
+766
+00:33:34,240 --> 00:33:40,159
+in their sleep negative
+
+767
+00:33:36,639 --> 00:33:42,600
+again um so the solutions here are keep
+
+768
+00:33:40,159 --> 00:33:46,880
+working until we get all of them which
+
+769
+00:33:42,600 --> 00:33:49,159
+is maybe not super fun um or incorporate
+
+770
+00:33:46,880 --> 00:33:51,639
+external resources such as sentiment
+
+771
+00:33:49,159 --> 00:33:52,880
+dictionaries that people created uh we
+
+772
+00:33:51,639 --> 00:33:55,960
+could do that but that's a lot of
+
+773
+00:33:52,880 --> 00:33:57,480
+engineering effort to make something
+
+774
+00:33:55,960 --> 00:34:00,639
+work
+
+775
+00:33:57,480 --> 00:34:03,720
+um another one is conjugation so we saw
+
+776
+00:34:00,639 --> 00:34:06,600
+unengaging I guess that's an example of
+
+777
+00:34:03,720 --> 00:34:08,359
+conjugation uh some other ones are
+
+778
+00:34:06,600 --> 00:34:10,520
+operatic sprawling picture that's
+
+779
+00:34:08,359 --> 00:34:12,040
+entertainingly acted magnificently shot
+
+780
+00:34:10,520 --> 00:34:15,480
+and gripping enough to sustain most of
+
+781
+00:34:12,040 --> 00:34:17,399
+its 170 minute length so here we have
+
+782
+00:34:15,480 --> 00:34:19,079
+magnificently so even if I added
+
+783
+00:34:17,399 --> 00:34:20,480
+magnificent this wouldn't have been
+
+784
+00:34:19,079 --> 00:34:23,800
+clocked
+
+785
+00:34:20,480 --> 00:34:26,599
+right um it's basically an overlong
+
+786
+00:34:23,800 --> 00:34:28,839
+episode of tales from the cryp so that's
+
+787
+00:34:26,599 --> 00:34:31,480
+maybe another
+
+788
+00:34:28,839 --> 00:34:33,040
+example um so some things that we could
+
+789
+00:34:31,480 --> 00:34:35,320
+do or what we would have done before the
+
+790
+00:34:33,040 --> 00:34:37,720
+modern Paradigm of machine learning is
+
+791
+00:34:35,320 --> 00:34:40,079
+we would run some sort of normalizer
+
+792
+00:34:37,720 --> 00:34:42,800
+like a stemmer or other things like this
+
+793
+00:34:40,079 --> 00:34:45,240
+in order to convert this into uh the
+
+794
+00:34:42,800 --> 00:34:48,599
+root wordss that we already have seen
+
+795
+00:34:45,240 --> 00:34:52,040
+somewhere in our data or have already
+
+796
+00:34:48,599 --> 00:34:54,040
+handed so that requires um conjugation
+
+797
+00:34:52,040 --> 00:34:55,879
+analysis or morphological analysis as we
+
+798
+00:34:54,040 --> 00:34:57,400
+say it in
+
+799
+00:34:55,879 --> 00:35:00,680
+technicals
+
+800
+00:34:57,400 --> 00:35:03,960
+negation this is a tricky one so this
+
+801
+00:35:00,680 --> 00:35:06,760
+one's not nearly as Dreadful as expected
+
+802
+00:35:03,960 --> 00:35:08,800
+so Dreadful is a pretty bad word right
+
+803
+00:35:06,760 --> 00:35:13,000
+but not nearly as Dreadful as expected
+
+804
+00:35:08,800 --> 00:35:14,440
+is like a solidly neutral um you know or
+
+805
+00:35:13,000 --> 00:35:16,359
+maybe even
+
+806
+00:35:14,440 --> 00:35:18,920
+positive I would I would say that's
+
+807
+00:35:16,359 --> 00:35:20,640
+neutral but you know uh neutral or
+
+808
+00:35:18,920 --> 00:35:23,800
+positive it's definitely not
+
+809
+00:35:20,640 --> 00:35:26,359
+negative um serving s doesn't serve up a
+
+810
+00:35:23,800 --> 00:35:29,480
+whole lot of laughs so laughs is
+
+811
+00:35:26,359 --> 00:35:31,880
+obviously positive but not serving UPS
+
+812
+00:35:29,480 --> 00:35:34,440
+is obviously
+
+813
+00:35:31,880 --> 00:35:36,839
+negative so if negation modifies the
+
+814
+00:35:34,440 --> 00:35:38,240
+word disregard it now we would probably
+
+815
+00:35:36,839 --> 00:35:41,440
+need to do some sort of syntactic
+
+816
+00:35:38,240 --> 00:35:45,599
+analysis or semantic analysis of
+
+817
+00:35:41,440 --> 00:35:47,520
+some metaphor an analogy so puts a human
+
+818
+00:35:45,599 --> 00:35:50,640
+face on a land most westerners are
+
+819
+00:35:47,520 --> 00:35:52,880
+unfamiliar though uh this is
+
+820
+00:35:50,640 --> 00:35:54,960
+positive green might want to hang on to
+
+821
+00:35:52,880 --> 00:35:58,800
+that ski mask as robbery may be the only
+
+822
+00:35:54,960 --> 00:35:58,800
+way to pay for this next project
+
+823
+00:35:58,839 --> 00:36:03,640
+so this this is saying that the movie
+
+824
+00:36:01,960 --> 00:36:05,560
+was so bad that the director will have
+
+825
+00:36:03,640 --> 00:36:08,359
+to rob people in order to get money for
+
+826
+00:36:05,560 --> 00:36:11,000
+the next project so that's kind of bad I
+
+827
+00:36:08,359 --> 00:36:12,880
+guess um has all the depth of a waiting
+
+828
+00:36:11,000 --> 00:36:14,520
+pool this is kind of my favorite one
+
+829
+00:36:12,880 --> 00:36:15,880
+because it's really short and sweet but
+
+830
+00:36:14,520 --> 00:36:18,800
+you know you need to know how deep a
+
+831
+00:36:15,880 --> 00:36:21,440
+waiting pool is um so that's
+
+832
+00:36:18,800 --> 00:36:22,960
+negative so the solution here I don't
+
+833
+00:36:21,440 --> 00:36:24,680
+really even know how to handle this with
+
+834
+00:36:22,960 --> 00:36:26,880
+a rule based system I have no idea how
+
+835
+00:36:24,680 --> 00:36:30,040
+we would possibly do this yeah machine
+
+836
+00:36:26,880 --> 00:36:32,400
+learning based models seem to be pretty
+
+837
+00:36:30,040 --> 00:36:37,000
+adaptive okay and then I start doing
+
+838
+00:36:32,400 --> 00:36:37,000
+these ones um anyone have a good
+
+839
+00:36:38,160 --> 00:36:46,800
+idea any any other friends who know
+
+840
+00:36:42,520 --> 00:36:50,040
+Japanese no okay um so yeah that's
+
+841
+00:36:46,800 --> 00:36:52,839
+positive um that one's negative uh and
+
+842
+00:36:50,040 --> 00:36:54,920
+the solution here is learn Japanese I
+
+843
+00:36:52,839 --> 00:36:56,800
+guess or whatever other language you
+
+844
+00:36:54,920 --> 00:37:00,040
+want to process so like obviously
+
+845
+00:36:56,800 --> 00:37:03,720
+rule-based systems don't scale very
+
+846
+00:37:00,040 --> 00:37:05,119
+well so um we've moved but like rule
+
+847
+00:37:03,720 --> 00:37:06,319
+based systems don't scale very well
+
+848
+00:37:05,119 --> 00:37:08,160
+we're not going to be using them for
+
+849
+00:37:06,319 --> 00:37:11,400
+most of the things we do in this class
+
+850
+00:37:08,160 --> 00:37:14,240
+but I do think it's sometimes useful to
+
+851
+00:37:11,400 --> 00:37:15,640
+try to create one for your task maybe
+
+852
+00:37:14,240 --> 00:37:16,680
+right at the very beginning of a project
+
+853
+00:37:15,640 --> 00:37:18,560
+because it gives you an idea about
+
+854
+00:37:16,680 --> 00:37:21,160
+what's really hard about the task in
+
+855
+00:37:18,560 --> 00:37:22,480
+some cases so um yeah I wouldn't
+
+856
+00:37:21,160 --> 00:37:25,599
+entirely discount them I'm not
+
+857
+00:37:22,480 --> 00:37:27,400
+introducing them for no reason
+
+858
+00:37:25,599 --> 00:37:29,880
+whatsoever
+
+859
+00:37:27,400 --> 00:37:34,160
+so next is machine learning based anal
+
+860
+00:37:29,880 --> 00:37:35,400
+and machine learning uh in general uh I
+
+861
+00:37:34,160 --> 00:37:36,640
+here actually when I say machine
+
+862
+00:37:35,400 --> 00:37:38,160
+learning I'm going to be talking about
+
+863
+00:37:36,640 --> 00:37:39,560
+the traditional fine-tuning approach
+
+864
+00:37:38,160 --> 00:37:43,520
+where we have a training set Dev set
+
+865
+00:37:39,560 --> 00:37:46,359
+test set and so we take our training set
+
+866
+00:37:43,520 --> 00:37:49,680
+we run some learning algorithm over it
+
+867
+00:37:46,359 --> 00:37:52,319
+we have a learned feature extractor F A
+
+868
+00:37:49,680 --> 00:37:55,839
+possibly learned feature extractor F
+
+869
+00:37:52,319 --> 00:37:57,880
+possibly learned scoring function W and
+
+870
+00:37:55,839 --> 00:38:00,800
+uh then we apply our inference algorithm
+
+871
+00:37:57,880 --> 00:38:02,839
+our decision Rule and make decisions
+
+872
+00:38:00,800 --> 00:38:04,200
+when I say possibly learned actually the
+
+873
+00:38:02,839 --> 00:38:06,119
+first example I'm going to give of a
+
+874
+00:38:04,200 --> 00:38:07,760
+machine learning based technique is uh
+
+875
+00:38:06,119 --> 00:38:10,079
+doesn't have a learned feature extractor
+
+876
+00:38:07,760 --> 00:38:12,800
+but most things that we use nowadays do
+
+877
+00:38:10,079 --> 00:38:12,800
+have learned feature
+
+878
+00:38:13,200 --> 00:38:18,040
+extractors so our first attempt is going
+
+879
+00:38:15,640 --> 00:38:21,760
+to be a bag of words model uh and the
+
+880
+00:38:18,040 --> 00:38:27,119
+way a bag of wordss model works is uh
+
+881
+00:38:21,760 --> 00:38:30,160
+essentially we start out by looking up a
+
+882
+00:38:27,119 --> 00:38:33,240
+Vector where one element in the vector
+
+883
+00:38:30,160 --> 00:38:36,240
+is uh is one and all the other elements
+
+884
+00:38:33,240 --> 00:38:38,040
+in the vector are zero and so if the
+
+885
+00:38:36,240 --> 00:38:40,319
+word is different the position in the
+
+886
+00:38:38,040 --> 00:38:42,839
+vector that's one will be different we
+
+887
+00:38:40,319 --> 00:38:46,280
+add all of these together and this gives
+
+888
+00:38:42,839 --> 00:38:48,200
+us a vector where each element is the
+
+889
+00:38:46,280 --> 00:38:50,359
+frequency of that word in the vector and
+
+890
+00:38:48,200 --> 00:38:52,520
+then we multiply that by weights and we
+
+891
+00:38:50,359 --> 00:38:55,520
+get a
+
+892
+00:38:52,520 --> 00:38:57,160
+score and um here as I said this is not
+
+893
+00:38:55,520 --> 00:39:00,359
+a learned feature
+
+894
+00:38:57,160 --> 00:39:02,079
+uh Vector this is basically uh sorry not
+
+895
+00:39:00,359 --> 00:39:04,359
+a learn feature extractor this is
+
+896
+00:39:02,079 --> 00:39:06,200
+basically a fixed feature extractor but
+
+897
+00:39:04,359 --> 00:39:09,839
+the weights themselves are
+
+898
+00:39:06,200 --> 00:39:11,640
+learned um so my my question is I
+
+899
+00:39:09,839 --> 00:39:14,599
+mentioned a whole lot of problems before
+
+900
+00:39:11,640 --> 00:39:17,480
+I mentioned infrequent words I mentioned
+
+901
+00:39:14,599 --> 00:39:20,760
+conjugation I mentioned uh different
+
+902
+00:39:17,480 --> 00:39:22,880
+languages I mentioned syntax and
+
+903
+00:39:20,760 --> 00:39:24,599
+metaphor so which of these do we think
+
+904
+00:39:22,880 --> 00:39:25,440
+would be fixed by this sort of learning
+
+905
+00:39:24,599 --> 00:39:27,400
+based
+
+906
+00:39:25,440 --> 00:39:29,640
+approach
+
+907
+00:39:27,400 --> 00:39:29,640
+any
+
+908
+00:39:29,920 --> 00:39:35,200
+ideas maybe not fixed maybe made
+
+909
+00:39:32,520 --> 00:39:35,200
+significantly
+
+910
+00:39:36,880 --> 00:39:41,560
+better any Brave uh brave
+
+911
+00:39:44,880 --> 00:39:48,440
+people maybe maybe
+
+912
+00:39:53,720 --> 00:39:58,400
+negation okay so maybe doesn't when it
+
+913
+00:39:55,760 --> 00:39:58,400
+have a negative qu
+
+914
+00:40:02,960 --> 00:40:07,560
+yeah yeah so for the conjugation if we
+
+915
+00:40:05,520 --> 00:40:09,200
+had the conjugations of the stems mapped
+
+916
+00:40:07,560 --> 00:40:11,119
+in the same position that might fix a
+
+917
+00:40:09,200 --> 00:40:12,920
+conjugation problem but I would say if
+
+918
+00:40:11,119 --> 00:40:15,200
+you don't do that then this kind of
+
+919
+00:40:12,920 --> 00:40:18,160
+fixes conjugation a little bit but maybe
+
+920
+00:40:15,200 --> 00:40:21,319
+not not really yeah kind of fix
+
+921
+00:40:18,160 --> 00:40:24,079
+conjugation because like they're using
+
+922
+00:40:21,319 --> 00:40:26,760
+the same there
+
+923
+00:40:24,079 --> 00:40:28,400
+probably different variations so we
+
+924
+00:40:26,760 --> 00:40:31,359
+learn how to
+
+925
+00:40:28,400 --> 00:40:33,400
+classify surrounding
+
+926
+00:40:31,359 --> 00:40:35,000
+structure yeah if it's a big enough
+
+927
+00:40:33,400 --> 00:40:36,760
+training set you might have covered the
+
+928
+00:40:35,000 --> 00:40:37,880
+various conjugations but if you haven't
+
+929
+00:40:36,760 --> 00:40:43,000
+and you don't have any rule-based
+
+930
+00:40:37,880 --> 00:40:43,000
+processing it it might still be problems
+
+931
+00:40:45,400 --> 00:40:50,359
+yeah yeah so in frequent words if you
+
+932
+00:40:48,280 --> 00:40:52,560
+have a large enough training set yeah
+
+933
+00:40:50,359 --> 00:40:54,599
+you'll be able to fix it to some extent
+
+934
+00:40:52,560 --> 00:40:56,480
+so none of the problems are entirely
+
+935
+00:40:54,599 --> 00:40:57,880
+fixed but a lot of them are made better
+
+936
+00:40:56,480 --> 00:40:58,960
+different languages is also made better
+
+937
+00:40:57,880 --> 00:41:00,119
+if you have training data in that
+
+938
+00:40:58,960 --> 00:41:04,599
+language but if you don't then you're
+
+939
+00:41:00,119 --> 00:41:06,240
+out of BL so um so now what I'd like to
+
+940
+00:41:04,599 --> 00:41:10,800
+do is I'd look to like to look at what
+
+941
+00:41:06,240 --> 00:41:15,079
+our vectors represent so basically um in
+
+942
+00:41:10,800 --> 00:41:16,880
+uh in binary classification each word um
+
+943
+00:41:15,079 --> 00:41:19,119
+sorry so the vectors themselves
+
+944
+00:41:16,880 --> 00:41:21,880
+represent the counts of the words here
+
+945
+00:41:19,119 --> 00:41:25,319
+I'm talking about what the weight uh
+
+946
+00:41:21,880 --> 00:41:28,520
+vectors or matrices correspond to and
+
+947
+00:41:25,319 --> 00:41:31,640
+the weight uh Vector here will be
+
+948
+00:41:28,520 --> 00:41:33,680
+positive if the word it tends to be
+
+949
+00:41:31,640 --> 00:41:36,680
+positive if in a binary classification
+
+950
+00:41:33,680 --> 00:41:38,400
+case in a multiclass classification case
+
+951
+00:41:36,680 --> 00:41:42,480
+we'll actually have a matrix that looks
+
+952
+00:41:38,400 --> 00:41:45,480
+like this where um each column or row uh
+
+953
+00:41:42,480 --> 00:41:47,079
+corresponds to the word and each row or
+
+954
+00:41:45,480 --> 00:41:49,319
+column corresponds to a label and it
+
+955
+00:41:47,079 --> 00:41:51,960
+will be higher if that row tends to uh
+
+956
+00:41:49,319 --> 00:41:54,800
+correlate with that uh that word tends
+
+957
+00:41:51,960 --> 00:41:56,920
+to correlate that little
+
+958
+00:41:54,800 --> 00:41:59,240
+bit so
+
+959
+00:41:56,920 --> 00:42:04,079
+this um training of the bag of words
+
+960
+00:41:59,240 --> 00:42:07,720
+model is can be done uh so simply that
+
+961
+00:42:04,079 --> 00:42:10,200
+we uh can put it in a single slide so
+
+962
+00:42:07,720 --> 00:42:11,599
+basically here uh what we do is we start
+
+963
+00:42:10,200 --> 00:42:14,760
+out with the feature
+
+964
+00:42:11,599 --> 00:42:18,880
+weights and for each example in our data
+
+965
+00:42:14,760 --> 00:42:20,800
+set we extract features um the exact way
+
+966
+00:42:18,880 --> 00:42:23,920
+I'm extracting features is basically
+
+967
+00:42:20,800 --> 00:42:25,720
+splitting uh splitting the words using
+
+968
+00:42:23,920 --> 00:42:28,000
+the python split function and then uh
+
+969
+00:42:25,720 --> 00:42:31,319
+Counting number of times each word
+
+970
+00:42:28,000 --> 00:42:33,160
+exists uh we then run the classifier so
+
+971
+00:42:31,319 --> 00:42:36,280
+actually running the classifier is
+
+972
+00:42:33,160 --> 00:42:38,200
+exactly the same as what we did for the
+
+973
+00:42:36,280 --> 00:42:42,640
+uh the rule based system it's just that
+
+974
+00:42:38,200 --> 00:42:47,359
+we have feature vectors instead and
+
+975
+00:42:42,640 --> 00:42:51,559
+then if the predicted value is
+
+976
+00:42:47,359 --> 00:42:55,160
+not value then for each of the
+
+977
+00:42:51,559 --> 00:42:56,680
+features uh in the feature space we
+
+978
+00:42:55,160 --> 00:43:02,200
+upweight
+
+979
+00:42:56,680 --> 00:43:03,599
+the um we upweight The Weight by the
+
+980
+00:43:02,200 --> 00:43:06,000
+vector
+
+981
+00:43:03,599 --> 00:43:09,920
+size by or by the amount of the vector
+
+982
+00:43:06,000 --> 00:43:13,240
+if Y is positive and we downweight the
+
+983
+00:43:09,920 --> 00:43:16,240
+vector uh by the size of the vector if Y
+
+984
+00:43:13,240 --> 00:43:18,520
+is negative so this is really really
+
+985
+00:43:16,240 --> 00:43:20,559
+simple it's uh probably the simplest
+
+986
+00:43:18,520 --> 00:43:25,079
+possible algorithm for training one of
+
+987
+00:43:20,559 --> 00:43:27,559
+these models um but I have an
+
+988
+00:43:25,079 --> 00:43:30,040
+example in this that you can also take a
+
+989
+00:43:27,559 --> 00:43:31,960
+look at here's a trained bag of words
+
+990
+00:43:30,040 --> 00:43:33,680
+classifier and we could step through
+
+991
+00:43:31,960 --> 00:43:34,960
+this is on exactly the same data set as
+
+992
+00:43:33,680 --> 00:43:37,240
+I did before we're training on the
+
+993
+00:43:34,960 --> 00:43:42,359
+training set
+
+994
+00:43:37,240 --> 00:43:43,640
+um and uh evaluating on the dev set um I
+
+995
+00:43:42,359 --> 00:43:45,880
+also have some extra stuff like I'm
+
+996
+00:43:43,640 --> 00:43:47,079
+Shuffling the order of the data IDs
+
+997
+00:43:45,880 --> 00:43:49,440
+which is really important if you're
+
+998
+00:43:47,079 --> 00:43:53,160
+doing this sort of incremental algorithm
+
+999
+00:43:49,440 --> 00:43:54,960
+uh because uh what if what if your
+
+1000
+00:43:53,160 --> 00:43:57,400
+creating data set was ordered in this
+
+1001
+00:43:54,960 --> 00:44:00,040
+way where you have all of the positive
+
+1002
+00:43:57,400 --> 00:44:00,040
+labels on
+
+1003
+00:44:00,359 --> 00:44:04,520
+top and then you have all of the
+
+1004
+00:44:02,280 --> 00:44:06,680
+negative labels on the
+
+1005
+00:44:04,520 --> 00:44:08,200
+bottom if you do something like this it
+
+1006
+00:44:06,680 --> 00:44:10,200
+would see only negative labels at the
+
+1007
+00:44:08,200 --> 00:44:11,800
+end of training and you might have
+
+1008
+00:44:10,200 --> 00:44:14,400
+problems because your model would only
+
+1009
+00:44:11,800 --> 00:44:17,440
+predict negatives so we also Shuffle
+
+1010
+00:44:14,400 --> 00:44:20,319
+data um and then step through we run the
+
+1011
+00:44:17,440 --> 00:44:22,559
+classifier and I'm going to run uh five
+
+1012
+00:44:20,319 --> 00:44:23,640
+epochs of training through the data set
+
+1013
+00:44:22,559 --> 00:44:27,160
+uh very
+
+1014
+00:44:23,640 --> 00:44:29,599
+fast and calculate our accuracy
+
+1015
+00:44:27,160 --> 00:44:33,280
+and this got 75% accuracy on the
+
+1016
+00:44:29,599 --> 00:44:36,160
+training data set and uh 56% accuracy on
+
+1017
+00:44:33,280 --> 00:44:40,000
+the Deb data set so uh if you remember
+
+1018
+00:44:36,160 --> 00:44:41,520
+our rule-based classifier had 42 uh 42
+
+1019
+00:44:40,000 --> 00:44:43,880
+accuracy and now our training based
+
+1020
+00:44:41,520 --> 00:44:45,760
+classifier has 56 accuracy but it's
+
+1021
+00:44:43,880 --> 00:44:49,359
+overfitting heavily to the training side
+
+1022
+00:44:45,760 --> 00:44:50,880
+so um basically this is a pretty strong
+
+1023
+00:44:49,359 --> 00:44:53,480
+advertisement for why we should be using
+
+1024
+00:44:50,880 --> 00:44:54,960
+machine learning you know I the amount
+
+1025
+00:44:53,480 --> 00:44:57,800
+of code that we had for this machine
+
+1026
+00:44:54,960 --> 00:44:59,720
+learning model is basically very similar
+
+1027
+00:44:57,800 --> 00:45:02,680
+um it's not using any external libraries
+
+1028
+00:44:59,720 --> 00:45:02,680
+but we're getting better at
+
+1029
+00:45:03,599 --> 00:45:08,800
+this
+
+1030
+00:45:05,800 --> 00:45:08,800
+cool
+
+1031
+00:45:09,559 --> 00:45:16,000
+so cool any any questions
+
+1032
+00:45:13,520 --> 00:45:18,240
+here and so I'm going to talk about the
+
+1033
+00:45:16,000 --> 00:45:20,760
+connection to between this algorithm and
+
+1034
+00:45:18,240 --> 00:45:22,839
+neural networks in the next class um
+
+1035
+00:45:20,760 --> 00:45:24,200
+because this actually is using a very
+
+1036
+00:45:22,839 --> 00:45:26,319
+similar training algorithm to what we
+
+1037
+00:45:24,200 --> 00:45:27,480
+use in neural networks with some uh
+
+1038
+00:45:26,319 --> 00:45:30,079
+particular
+
+1039
+00:45:27,480 --> 00:45:32,839
+assumptions cool um so what's missing in
+
+1040
+00:45:30,079 --> 00:45:34,800
+bag of words um still handling of
+
+1041
+00:45:32,839 --> 00:45:36,880
+conjugation or compound words is not
+
+1042
+00:45:34,800 --> 00:45:39,160
+perfect it we can do it to some extent
+
+1043
+00:45:36,880 --> 00:45:41,079
+to the point where we can uh memorize
+
+1044
+00:45:39,160 --> 00:45:44,079
+things so I love this movie I love this
+
+1045
+00:45:41,079 --> 00:45:46,920
+movie another thing is handling word Ser
+
+1046
+00:45:44,079 --> 00:45:49,240
+uh similarities so I love this movie and
+
+1047
+00:45:46,920 --> 00:45:50,720
+I adore this movie uh these basically
+
+1048
+00:45:49,240 --> 00:45:52,119
+mean the same thing as humans we know
+
+1049
+00:45:50,720 --> 00:45:54,200
+they mean the same thing so we should be
+
+1050
+00:45:52,119 --> 00:45:56,079
+able to take advantage of that fact to
+
+1051
+00:45:54,200 --> 00:45:57,839
+learn better models but we're not doing
+
+1052
+00:45:56,079 --> 00:46:02,760
+that in this model at the moment because
+
+1053
+00:45:57,839 --> 00:46:05,440
+each unit is uh treated as a atomic unit
+
+1054
+00:46:02,760 --> 00:46:08,040
+and there's no idea of
+
+1055
+00:46:05,440 --> 00:46:11,040
+similarity also handling of combination
+
+1056
+00:46:08,040 --> 00:46:12,760
+features so um I love this movie and I
+
+1057
+00:46:11,040 --> 00:46:14,920
+don't love this movie I hate this movie
+
+1058
+00:46:12,760 --> 00:46:17,079
+and I don't hate this movie actually
+
+1059
+00:46:14,920 --> 00:46:20,400
+this is a little bit tricky because
+
+1060
+00:46:17,079 --> 00:46:23,240
+negative words are slightly indicative
+
+1061
+00:46:20,400 --> 00:46:25,280
+of it being negative but actually what
+
+1062
+00:46:23,240 --> 00:46:28,119
+they do is they negate the other things
+
+1063
+00:46:25,280 --> 00:46:28,119
+that you're saying in the
+
+1064
+00:46:28,240 --> 00:46:36,559
+sentence
+
+1065
+00:46:30,720 --> 00:46:40,480
+so um like love is positive hate is
+
+1066
+00:46:36,559 --> 00:46:40,480
+negative but like don't
+
+1067
+00:46:50,359 --> 00:46:56,079
+love it's actually kind of like this
+
+1068
+00:46:52,839 --> 00:46:59,359
+right like um Love is very positive POS
+
+1069
+00:46:56,079 --> 00:47:01,760
+hate is very negative but don't love is
+
+1070
+00:46:59,359 --> 00:47:04,680
+like slightly less positive than don't
+
+1071
+00:47:01,760 --> 00:47:06,160
+hate right so um It's actually kind of
+
+1072
+00:47:04,680 --> 00:47:07,559
+tricky because you need to combine them
+
+1073
+00:47:06,160 --> 00:47:10,720
+together and figure out what's going on
+
+1074
+00:47:07,559 --> 00:47:12,280
+based on that another example that a lot
+
+1075
+00:47:10,720 --> 00:47:14,160
+of people might not think of immediately
+
+1076
+00:47:12,280 --> 00:47:17,880
+but is super super common in sentiment
+
+1077
+00:47:14,160 --> 00:47:20,160
+analysis or any other thing is butt so
+
+1078
+00:47:17,880 --> 00:47:22,599
+basically what but does is it throws
+
+1079
+00:47:20,160 --> 00:47:24,160
+away all the stuff that you said before
+
+1080
+00:47:22,599 --> 00:47:26,119
+um and you can just pay attention to the
+
+1081
+00:47:24,160 --> 00:47:29,000
+stuff that you saw beforehand so like we
+
+1082
+00:47:26,119 --> 00:47:30,440
+could even add this to our um like if
+
+1083
+00:47:29,000 --> 00:47:31,760
+you want to add this to your rule based
+
+1084
+00:47:30,440 --> 00:47:33,240
+classifier you can do that you just
+
+1085
+00:47:31,760 --> 00:47:34,640
+search for butt and delete everything
+
+1086
+00:47:33,240 --> 00:47:37,240
+before it and see if that inputs your
+
+1087
+00:47:34,640 --> 00:47:39,240
+accuracy might be might be a fun very
+
+1088
+00:47:37,240 --> 00:47:43,480
+quick thing
+
+1089
+00:47:39,240 --> 00:47:44,880
+to cool so the better solution which is
+
+1090
+00:47:43,480 --> 00:47:46,800
+what we're going to talk about for every
+
+1091
+00:47:44,880 --> 00:47:49,480
+other class other than uh other than
+
+1092
+00:47:46,800 --> 00:47:52,160
+this one is neural network models and
+
+1093
+00:47:49,480 --> 00:47:55,800
+basically uh what they do is they do a
+
+1094
+00:47:52,160 --> 00:47:59,400
+lookup of uh dense word embeddings so
+
+1095
+00:47:55,800 --> 00:48:02,520
+instead of looking up uh individual uh
+
+1096
+00:47:59,400 --> 00:48:04,640
+sparse uh vectors individual one hot
+
+1097
+00:48:02,520 --> 00:48:06,920
+vectors they look up dense word
+
+1098
+00:48:04,640 --> 00:48:09,680
+embeddings and then throw them into some
+
+1099
+00:48:06,920 --> 00:48:11,880
+complicated function to extract features
+
+1100
+00:48:09,680 --> 00:48:16,359
+and based on the features uh multiply by
+
+1101
+00:48:11,880 --> 00:48:18,280
+weights and get a score um and if you're
+
+1102
+00:48:16,359 --> 00:48:20,359
+doing text classification in the
+
+1103
+00:48:18,280 --> 00:48:22,520
+traditional way this is normally what
+
+1104
+00:48:20,359 --> 00:48:23,760
+you do um if you're doing text
+
+1105
+00:48:22,520 --> 00:48:25,960
+classification with something like
+
+1106
+00:48:23,760 --> 00:48:27,280
+prompting you're still actually doing
+
+1107
+00:48:25,960 --> 00:48:29,960
+this because you're calculating the
+
+1108
+00:48:27,280 --> 00:48:32,960
+score of the next word to predict and
+
+1109
+00:48:29,960 --> 00:48:34,720
+that's done in exactly the same way so
+
+1110
+00:48:32,960 --> 00:48:37,760
+uh even if you're using a large language
+
+1111
+00:48:34,720 --> 00:48:39,359
+model like GPT this is still probably
+
+1112
+00:48:37,760 --> 00:48:41,800
+happening under the hood unless open the
+
+1113
+00:48:39,359 --> 00:48:43,400
+eye invented something that very
+
+1114
+00:48:41,800 --> 00:48:45,559
+different in Alien than anything else
+
+1115
+00:48:43,400 --> 00:48:48,440
+that we know of but I I'm guessing that
+
+1116
+00:48:45,559 --> 00:48:48,440
+that propably hasn't
+
+1117
+00:48:48,480 --> 00:48:52,880
+happen um one nice thing about neural
+
+1118
+00:48:50,880 --> 00:48:54,480
+networks is neural networks
+
+1119
+00:48:52,880 --> 00:48:57,559
+theoretically are powerful enough to
+
+1120
+00:48:54,480 --> 00:49:00,000
+solve any task if you make them uh deep
+
+1121
+00:48:57,559 --> 00:49:01,160
+enough or wide enough uh like if you
+
+1122
+00:49:00,000 --> 00:49:04,520
+make them wide enough and then if you
+
+1123
+00:49:01,160 --> 00:49:06,799
+make them deep it also helps further so
+
+1124
+00:49:04,520 --> 00:49:08,079
+anytime somebody says well you can't
+
+1125
+00:49:06,799 --> 00:49:11,119
+just solve that problem with neural
+
+1126
+00:49:08,079 --> 00:49:13,240
+networks you know that they're lying
+
+1127
+00:49:11,119 --> 00:49:15,720
+basically because they theoretically can
+
+1128
+00:49:13,240 --> 00:49:17,359
+solve every problem uh but you have you
+
+1129
+00:49:15,720 --> 00:49:19,799
+have issues of data you have issues of
+
+1130
+00:49:17,359 --> 00:49:23,079
+other things like that so you know they
+
+1131
+00:49:19,799 --> 00:49:23,079
+don't just necessarily work
+
+1132
+00:49:23,119 --> 00:49:28,040
+outs cool um so the final thing I'd like
+
+1133
+00:49:26,400 --> 00:49:29,319
+to talk about is the road map going
+
+1134
+00:49:28,040 --> 00:49:31,319
+forward some of the things I'm going to
+
+1135
+00:49:29,319 --> 00:49:32,799
+cover in the class and some of the
+
+1136
+00:49:31,319 --> 00:49:35,200
+logistics
+
+1137
+00:49:32,799 --> 00:49:36,799
+issues so um the first thing I'm going
+
+1138
+00:49:35,200 --> 00:49:38,240
+to talk about in the class is language
+
+1139
+00:49:36,799 --> 00:49:40,559
+modeling fun
+
+1140
+00:49:38,240 --> 00:49:42,720
+fundamentals and uh so this could
+
+1141
+00:49:40,559 --> 00:49:44,240
+include language models uh that just
+
+1142
+00:49:42,720 --> 00:49:46,559
+predict the next words it could include
+
+1143
+00:49:44,240 --> 00:49:50,559
+language models that predict the output
+
+1144
+00:49:46,559 --> 00:49:51,599
+given the uh the input or the prompt um
+
+1145
+00:49:50,559 --> 00:49:54,559
+I'm going to be talking about
+
+1146
+00:49:51,599 --> 00:49:56,520
+representing words uh how how we get
+
+1147
+00:49:54,559 --> 00:49:59,319
+word representation subword models other
+
+1148
+00:49:56,520 --> 00:50:01,440
+things like that uh then go kind of
+
+1149
+00:49:59,319 --> 00:50:04,200
+deeper into language modeling uh how do
+
+1150
+00:50:01,440 --> 00:50:07,799
+we do it how do we evaluate it other
+
+1151
+00:50:04,200 --> 00:50:10,920
+things um sequence encoding uh and this
+
+1152
+00:50:07,799 --> 00:50:13,240
+is going to cover things like uh
+
+1153
+00:50:10,920 --> 00:50:16,280
+Transformers uh self attention modals
+
+1154
+00:50:13,240 --> 00:50:18,559
+but also very quickly cnns and rnns
+
+1155
+00:50:16,280 --> 00:50:20,880
+which are useful in some
+
+1156
+00:50:18,559 --> 00:50:22,200
+cases um and then we're going to
+
+1157
+00:50:20,880 --> 00:50:24,040
+specifically go very deep into the
+
+1158
+00:50:22,200 --> 00:50:25,960
+Transformer architecture and also talk a
+
+1159
+00:50:24,040 --> 00:50:27,280
+little bit about some of the modern uh
+
+1160
+00:50:25,960 --> 00:50:30,240
+improvements to the Transformer
+
+1161
+00:50:27,280 --> 00:50:31,839
+architecture so the Transformer we're
+
+1162
+00:50:30,240 --> 00:50:33,839
+using nowadays is very different than
+
+1163
+00:50:31,839 --> 00:50:36,200
+the Transformer that was invented in
+
+1164
+00:50:33,839 --> 00:50:37,240
+2017 uh so we're going to talk well I
+
+1165
+00:50:36,200 --> 00:50:38,760
+wouldn't say very different but
+
+1166
+00:50:37,240 --> 00:50:41,359
+different enough that it's important so
+
+1167
+00:50:38,760 --> 00:50:43,280
+we're going to talk about some of those
+
+1168
+00:50:41,359 --> 00:50:45,079
+things second thing I'd like to talk
+
+1169
+00:50:43,280 --> 00:50:47,000
+about is training and inference methods
+
+1170
+00:50:45,079 --> 00:50:48,839
+so this includes uh generation
+
+1171
+00:50:47,000 --> 00:50:52,119
+algorithms uh so we're going to have a
+
+1172
+00:50:48,839 --> 00:50:55,520
+whole class on how we generate text uh
+
+1173
+00:50:52,119 --> 00:50:58,319
+in different ways uh prompting how uh we
+
+1174
+00:50:55,520 --> 00:50:59,720
+can prompt things I hear uh world class
+
+1175
+00:50:58,319 --> 00:51:01,799
+prompt engineers make a lot of money
+
+1176
+00:50:59,720 --> 00:51:05,480
+nowadays so uh you'll want to pay
+
+1177
+00:51:01,799 --> 00:51:08,760
+attention to that one um and instruction
+
+1178
+00:51:05,480 --> 00:51:11,520
+tuning uh so how do we train models to
+
+1179
+00:51:08,760 --> 00:51:13,720
+handle a lot of different tasks and
+
+1180
+00:51:11,520 --> 00:51:15,839
+reinforcement learning so how do we uh
+
+1181
+00:51:13,720 --> 00:51:18,520
+you know like actually generate outputs
+
+1182
+00:51:15,839 --> 00:51:19,839
+uh kind of Judge them and then learn
+
+1183
+00:51:18,520 --> 00:51:22,599
+from
+
+1184
+00:51:19,839 --> 00:51:25,880
+there also experimental design and
+
+1185
+00:51:22,599 --> 00:51:28,079
+evaluation so experimental design uh so
+
+1186
+00:51:25,880 --> 00:51:30,480
+how do we design an experiment well uh
+
+1187
+00:51:28,079 --> 00:51:32,000
+so that it backs up what we want to be
+
+1188
+00:51:30,480 --> 00:51:34,559
+uh our conclusions that we want to be
+
+1189
+00:51:32,000 --> 00:51:37,000
+backing up how do we do human annotation
+
+1190
+00:51:34,559 --> 00:51:38,880
+of data in a reliable way this is
+
+1191
+00:51:37,000 --> 00:51:41,160
+getting harder and harder as models get
+
+1192
+00:51:38,880 --> 00:51:43,359
+better and better because uh getting
+
+1193
+00:51:41,160 --> 00:51:45,000
+humans who don't care very much about
+
+1194
+00:51:43,359 --> 00:51:48,559
+The annotation task they might do worse
+
+1195
+00:51:45,000 --> 00:51:51,119
+than gp4 so um you need to be careful of
+
+1196
+00:51:48,559 --> 00:51:52,240
+that also debugging and interpretation
+
+1197
+00:51:51,119 --> 00:51:53,960
+technique so what are some of the
+
+1198
+00:51:52,240 --> 00:51:55,160
+automatic techniques that you can do to
+
+1199
+00:51:53,960 --> 00:51:57,720
+quickly figure out what's going wrong
+
+1200
+00:51:55,160 --> 00:52:00,040
+with your models and improve
+
+1201
+00:51:57,720 --> 00:52:01,599
+them and uh bias and fairness
+
+1202
+00:52:00,040 --> 00:52:04,200
+considerations so it's really really
+
+1203
+00:52:01,599 --> 00:52:05,799
+important nowadays uh that models are
+
+1204
+00:52:04,200 --> 00:52:07,880
+being deployed to real people in the
+
+1205
+00:52:05,799 --> 00:52:09,880
+real world and like actually causing
+
+1206
+00:52:07,880 --> 00:52:11,760
+harm to people in some cases that we
+
+1207
+00:52:09,880 --> 00:52:15,160
+need to be worried about
+
+1208
+00:52:11,760 --> 00:52:17,000
+that Advanced Training in architectures
+
+1209
+00:52:15,160 --> 00:52:19,280
+so we're going to talk about distill
+
+1210
+00:52:17,000 --> 00:52:21,400
+distillation and quantization how can we
+
+1211
+00:52:19,280 --> 00:52:23,520
+make small language models uh that
+
+1212
+00:52:21,400 --> 00:52:24,880
+actually still work well like not large
+
+1213
+00:52:23,520 --> 00:52:27,559
+you can run them on your phone you can
+
+1214
+00:52:24,880 --> 00:52:29,920
+run them on your local
+
+1215
+00:52:27,559 --> 00:52:31,640
+laptop um ensembling and mixtures of
+
+1216
+00:52:29,920 --> 00:52:33,480
+experts how can we combine together
+
+1217
+00:52:31,640 --> 00:52:34,760
+multiple models in order to create
+
+1218
+00:52:33,480 --> 00:52:35,880
+models that are better than the sum of
+
+1219
+00:52:34,760 --> 00:52:38,799
+their
+
+1220
+00:52:35,880 --> 00:52:40,720
+parts and um retrieval and retrieval
+
+1221
+00:52:38,799 --> 00:52:43,920
+augmented
+
+1222
+00:52:40,720 --> 00:52:45,480
+generation long sequence models uh so
+
+1223
+00:52:43,920 --> 00:52:49,920
+how do we handle long
+
+1224
+00:52:45,480 --> 00:52:52,240
+outputs um and uh we're going to talk
+
+1225
+00:52:49,920 --> 00:52:55,760
+about applications to complex reasoning
+
+1226
+00:52:52,240 --> 00:52:57,760
+tasks code generation language agents
+
+1227
+00:52:55,760 --> 00:52:59,920
+and knowledge-based QA and information
+
+1228
+00:52:57,760 --> 00:53:04,160
+extraction I picked
+
+1229
+00:52:59,920 --> 00:53:06,760
+these because they seem to be maybe the
+
+1230
+00:53:04,160 --> 00:53:09,880
+most important at least in research
+
+1231
+00:53:06,760 --> 00:53:11,440
+nowadays and also they cover uh the
+
+1232
+00:53:09,880 --> 00:53:13,640
+things that when I talk to people in
+
+1233
+00:53:11,440 --> 00:53:15,280
+Industry are kind of most interested in
+
+1234
+00:53:13,640 --> 00:53:17,559
+so hopefully it'll be useful regardless
+
+1235
+00:53:15,280 --> 00:53:19,799
+of uh whether you plan on doing research
+
+1236
+00:53:17,559 --> 00:53:22,839
+or or plan on doing industry related
+
+1237
+00:53:19,799 --> 00:53:24,160
+things uh by by the way the two things
+
+1238
+00:53:22,839 --> 00:53:25,920
+that when I talk to people in Industry
+
+1239
+00:53:24,160 --> 00:53:29,599
+they're most interested in are Rag and
+
+1240
+00:53:25,920 --> 00:53:31,079
+code generation at the moment for now um
+
+1241
+00:53:29,599 --> 00:53:32,319
+so those are ones that you'll want to
+
+1242
+00:53:31,079 --> 00:53:34,680
+pay attention
+
+1243
+00:53:32,319 --> 00:53:36,599
+to and then finally we have a few
+
+1244
+00:53:34,680 --> 00:53:40,079
+lectures on Linguistics and
+
+1245
+00:53:36,599 --> 00:53:42,720
+multilinguality um I love Linguistics
+
+1246
+00:53:40,079 --> 00:53:44,839
+but uh to be honest at the moment most
+
+1247
+00:53:42,720 --> 00:53:47,760
+of our Cutting Edge models don't
+
+1248
+00:53:44,839 --> 00:53:49,240
+explicitly use linguistic structure um
+
+1249
+00:53:47,760 --> 00:53:50,799
+but I still think it's useful to know
+
+1250
+00:53:49,240 --> 00:53:52,760
+about it especially if you're working on
+
+1251
+00:53:50,799 --> 00:53:54,880
+multilingual things especially if you're
+
+1252
+00:53:52,760 --> 00:53:57,040
+interested in very robust generalization
+
+1253
+00:53:54,880 --> 00:53:58,920
+to new models so we're going to talk a
+
+1254
+00:53:57,040 --> 00:54:02,599
+little bit about that and also
+
+1255
+00:53:58,920 --> 00:54:06,079
+multilingual LP I'm going to have
+
+1256
+00:54:02,599 --> 00:54:09,119
+fure so also if you have any suggestions
+
+1257
+00:54:06,079 --> 00:54:11,400
+um we have two guest lecture slots still
+
+1258
+00:54:09,119 --> 00:54:12,799
+open uh that I'm trying to fill so if
+
+1259
+00:54:11,400 --> 00:54:15,440
+you have any things that you really want
+
+1260
+00:54:12,799 --> 00:54:16,440
+to hear about um I could either add them
+
+1261
+00:54:15,440 --> 00:54:19,319
+to the
+
+1262
+00:54:16,440 --> 00:54:21,079
+existing you know content or I could
+
+1263
+00:54:19,319 --> 00:54:23,240
+invite a guest lecturer who's working on
+
+1264
+00:54:21,079 --> 00:54:24,079
+that topic so you know please feel free
+
+1265
+00:54:23,240 --> 00:54:26,760
+to tell
+
+1266
+00:54:24,079 --> 00:54:29,160
+me um then the class format and
+
+1267
+00:54:26,760 --> 00:54:32,280
+structure uh the class
+
+1268
+00:54:29,160 --> 00:54:34,000
+content my goal is to learn in detail
+
+1269
+00:54:32,280 --> 00:54:36,640
+about building NLP systems from a
+
+1270
+00:54:34,000 --> 00:54:40,520
+research perspective so this is a 700
+
+1271
+00:54:36,640 --> 00:54:43,599
+level course so it's aiming to be for
+
+1272
+00:54:40,520 --> 00:54:46,960
+people who really want to try new and
+
+1273
+00:54:43,599 --> 00:54:49,280
+Innovative things in uh kind of natural
+
+1274
+00:54:46,960 --> 00:54:51,359
+language processing it's not going to
+
+1275
+00:54:49,280 --> 00:54:52,760
+focus solely on reimplementing things
+
+1276
+00:54:51,359 --> 00:54:54,319
+that have been done before including in
+
+1277
+00:54:52,760 --> 00:54:55,280
+the project I'm going to be expecting
+
+1278
+00:54:54,319 --> 00:54:58,480
+everybody to do something something
+
+1279
+00:54:55,280 --> 00:54:59,920
+that's kind of new whether it's coming
+
+1280
+00:54:58,480 --> 00:55:01,359
+up with a new method or applying
+
+1281
+00:54:59,920 --> 00:55:03,559
+existing methods to a place where they
+
+1282
+00:55:01,359 --> 00:55:05,079
+haven't been used before or building out
+
+1283
+00:55:03,559 --> 00:55:06,640
+things for a new language or something
+
+1284
+00:55:05,079 --> 00:55:08,359
+like that so that's kind of one of the
+
+1285
+00:55:06,640 --> 00:55:11,480
+major goals of this
+
+1286
+00:55:08,359 --> 00:55:13,000
+class um learn basic and advanced topics
+
+1287
+00:55:11,480 --> 00:55:15,559
+in machine learning approaches to NLP
+
+1288
+00:55:13,000 --> 00:55:18,359
+and language models learn some basic
+
+1289
+00:55:15,559 --> 00:55:21,480
+linguistic knowledge useful in NLP uh
+
+1290
+00:55:18,359 --> 00:55:23,200
+see case studies of NLP applications and
+
+1291
+00:55:21,480 --> 00:55:25,680
+learn how to identify unique problems
+
+1292
+00:55:23,200 --> 00:55:29,039
+for each um one thing i' like to point
+
+1293
+00:55:25,680 --> 00:55:31,160
+out is I'm not going to cover every NLP
+
+1294
+00:55:29,039 --> 00:55:32,920
+application ever because that would be
+
+1295
+00:55:31,160 --> 00:55:35,520
+absolutely impossible NLP is being used
+
+1296
+00:55:32,920 --> 00:55:37,079
+in so many different areas nowadays but
+
+1297
+00:55:35,520 --> 00:55:38,960
+what I want people to pay attention to
+
+1298
+00:55:37,079 --> 00:55:41,280
+like even if you're not super interested
+
+1299
+00:55:38,960 --> 00:55:42,400
+in code generation for example what you
+
+1300
+00:55:41,280 --> 00:55:44,200
+can do is you can look at code
+
+1301
+00:55:42,400 --> 00:55:46,160
+generation look at how people identify
+
+1302
+00:55:44,200 --> 00:55:47,680
+problems look at the methods that people
+
+1303
+00:55:46,160 --> 00:55:50,880
+have proposed to solve those unique
+
+1304
+00:55:47,680 --> 00:55:53,039
+problems and then kind of map that try
+
+1305
+00:55:50,880 --> 00:55:54,799
+to do some generalization onto your own
+
+1306
+00:55:53,039 --> 00:55:57,799
+problems of Interest so uh that's kind
+
+1307
+00:55:54,799 --> 00:56:00,280
+of the goal of the NLP
+
+1308
+00:55:57,799 --> 00:56:02,440
+applications finally uh learning how to
+
+1309
+00:56:00,280 --> 00:56:05,160
+debug when and where NLP systems fail
+
+1310
+00:56:02,440 --> 00:56:08,200
+and build improvements based on this so
+
+1311
+00:56:05,160 --> 00:56:10,200
+um ever since I was a graduate student
+
+1312
+00:56:08,200 --> 00:56:12,720
+this has been like one of the really
+
+1313
+00:56:10,200 --> 00:56:15,920
+important things that I feel like I've
+
+1314
+00:56:12,720 --> 00:56:17,440
+done well or done better than some other
+
+1315
+00:56:15,920 --> 00:56:19,280
+people and I I feel like it's a really
+
+1316
+00:56:17,440 --> 00:56:21,119
+good way to like even if you're only
+
+1317
+00:56:19,280 --> 00:56:22,680
+interested in improving accuracy knowing
+
+1318
+00:56:21,119 --> 00:56:25,039
+why your system's failing still is the
+
+1319
+00:56:22,680 --> 00:56:27,599
+best way to do that I so I'm going to
+
+1320
+00:56:25,039 --> 00:56:30,559
+put a lot of emphasis on
+
+1321
+00:56:27,599 --> 00:56:32,559
+that in terms of the class format um
+
+1322
+00:56:30,559 --> 00:56:36,280
+before class for some classes there are
+
+1323
+00:56:32,559 --> 00:56:37,880
+recommended reading uh this can be
+
+1324
+00:56:36,280 --> 00:56:39,559
+helpful to read I'm never going to
+
+1325
+00:56:37,880 --> 00:56:41,119
+expect you to definitely have read it
+
+1326
+00:56:39,559 --> 00:56:42,480
+before the class but I would suggest
+
+1327
+00:56:41,119 --> 00:56:45,160
+that maybe you'll get more out of the
+
+1328
+00:56:42,480 --> 00:56:47,319
+class if you do that um during class
+
+1329
+00:56:45,160 --> 00:56:48,079
+we'll have the lecture um in discussion
+
+1330
+00:56:47,319 --> 00:56:50,559
+with
+
+1331
+00:56:48,079 --> 00:56:52,359
+everybody um sometimes we'll have a code
+
+1332
+00:56:50,559 --> 00:56:55,839
+or data walk
+
+1333
+00:56:52,359 --> 00:56:58,760
+um actually this is a a little bit old I
+
+1334
+00:56:55,839 --> 00:57:01,880
+I have this slide we're this year we're
+
+1335
+00:56:58,760 --> 00:57:04,160
+going to be adding more uh code and data
+
+1336
+00:57:01,880 --> 00:57:07,400
+walks during office hours and the way it
+
+1337
+00:57:04,160 --> 00:57:09,400
+will work is one of the Tas we have
+
+1338
+00:57:07,400 --> 00:57:11,160
+seven Tas who I'm going to introduce
+
+1339
+00:57:09,400 --> 00:57:15,000
+very soon but one of the Tas will be
+
+1340
+00:57:11,160 --> 00:57:16,839
+doing this kind of recitation where you
+
+1341
+00:57:15,000 --> 00:57:18,200
+um where we go over a library so if
+
+1342
+00:57:16,839 --> 00:57:19,480
+you're not familiar with the library and
+
+1343
+00:57:18,200 --> 00:57:21,960
+you want to be more familiar with the
+
+1344
+00:57:19,480 --> 00:57:23,720
+library you can join this and uh then
+
+1345
+00:57:21,960 --> 00:57:25,400
+we'll be able to do this and this will
+
+1346
+00:57:23,720 --> 00:57:28,240
+cover things like
+
+1347
+00:57:25,400 --> 00:57:31,039
+um pie torch and sentence piece uh we're
+
+1348
+00:57:28,240 --> 00:57:33,280
+going to start out with hugging face um
+
+1349
+00:57:31,039 --> 00:57:36,559
+inference stuff like
+
+1350
+00:57:33,280 --> 00:57:41,520
+VM uh debugging software like
+
+1351
+00:57:36,559 --> 00:57:41,520
+Xeno um what were the other
+
+1352
+00:57:41,960 --> 00:57:47,200
+ones oh the open AI API and light llm
+
+1353
+00:57:45,680 --> 00:57:50,520
+other stuff like that so we we have lots
+
+1354
+00:57:47,200 --> 00:57:53,599
+of them planned we'll uh uh we'll update
+
+1355
+00:57:50,520 --> 00:57:54,839
+that um and then after class after
+
+1356
+00:57:53,599 --> 00:57:58,079
+almost every class we'll have a question
+
+1357
+00:57:54,839 --> 00:58:00,079
+quiz um and the quiz is intended to just
+
+1358
+00:57:58,079 --> 00:58:02,000
+you know make sure that you uh paid
+
+1359
+00:58:00,079 --> 00:58:04,480
+attention to the material and are able
+
+1360
+00:58:02,000 --> 00:58:07,520
+to answer questions about it we will aim
+
+1361
+00:58:04,480 --> 00:58:09,559
+to release it on the day of the course
+
+1362
+00:58:07,520 --> 00:58:11,599
+the day of the actual lecture and it
+
+1363
+00:58:09,559 --> 00:58:14,559
+will be due at the end of the following
+
+1364
+00:58:11,599 --> 00:58:15,960
+day of the lecture so um it will be
+
+1365
+00:58:14,559 --> 00:58:18,920
+three questions it probably shouldn't
+
+1366
+00:58:15,960 --> 00:58:20,680
+take a whole lot of time but um uh yeah
+
+1367
+00:58:18,920 --> 00:58:23,400
+so we'll H
+
+1368
+00:58:20,680 --> 00:58:26,319
+that in terms of assignments assignment
+
+1369
+00:58:23,400 --> 00:58:28,640
+one is going to be build your own llama
+
+1370
+00:58:26,319 --> 00:58:30,200
+and so what this is going to look like
+
+1371
+00:58:28,640 --> 00:58:32,680
+is we're going to give you a partial
+
+1372
+00:58:30,200 --> 00:58:34,319
+implementation of llama which is kind of
+
+1373
+00:58:32,680 --> 00:58:37,960
+the most popular open source language
+
+1374
+00:58:34,319 --> 00:58:40,160
+model nowadays and ask you to fill in um
+
+1375
+00:58:37,960 --> 00:58:42,839
+ask you to fill in the parts we're going
+
+1376
+00:58:40,160 --> 00:58:45,920
+to train a very small version of llama
+
+1377
+00:58:42,839 --> 00:58:47,319
+on a small data set and get it to work
+
+1378
+00:58:45,920 --> 00:58:48,880
+and the reason why it's very small is
+
+1379
+00:58:47,319 --> 00:58:50,480
+because the smallest actual version of
+
+1380
+00:58:48,880 --> 00:58:53,039
+llama is 7 billion
+
+1381
+00:58:50,480 --> 00:58:55,359
+parameters um and that might be a little
+
+1382
+00:58:53,039 --> 00:58:58,400
+bit difficult to train with
+
+1383
+00:58:55,359 --> 00:59:00,680
+resources um for assignment two we're
+
+1384
+00:58:58,400 --> 00:59:04,559
+going to try to do an NLP task from
+
+1385
+00:59:00,680 --> 00:59:06,920
+scratch and so the way this will work is
+
+1386
+00:59:04,559 --> 00:59:08,520
+we're going to give you an assignment
+
+1387
+00:59:06,920 --> 00:59:10,880
+which we're not going to give you an
+
+1388
+00:59:08,520 --> 00:59:13,400
+actual data set and instead we're going
+
+1389
+00:59:10,880 --> 00:59:15,760
+to ask you to uh perform data creation
+
+1390
+00:59:13,400 --> 00:59:19,359
+modeling and evaluation for a specified
+
+1391
+00:59:15,760 --> 00:59:20,640
+task and so we're going to tell you uh
+
+1392
+00:59:19,359 --> 00:59:22,599
+what to do but we're not going to tell
+
+1393
+00:59:20,640 --> 00:59:26,400
+you exactly how to do it but we're going
+
+1394
+00:59:22,599 --> 00:59:29,680
+to try to give as conrete directions as
+
+1395
+00:59:26,400 --> 00:59:32,359
+we can um
+
+1396
+00:59:29,680 --> 00:59:34,160
+yeah will you be given a parameter limit
+
+1397
+00:59:32,359 --> 00:59:36,559
+on the model so that's a good question
+
+1398
+00:59:34,160 --> 00:59:39,119
+or like a expense limit or something
+
+1399
+00:59:36,559 --> 00:59:40,440
+like that um I maybe actually I should
+
+1400
+00:59:39,119 --> 00:59:44,240
+take a break from the assignments and
+
+1401
+00:59:40,440 --> 00:59:46,520
+talk about compute so right now um for
+
+1402
+00:59:44,240 --> 00:59:49,319
+assignment one we're planning on having
+
+1403
+00:59:46,520 --> 00:59:51,599
+this be able to be done either on a Mac
+
+1404
+00:59:49,319 --> 00:59:53,520
+laptop with an M1 or M2 processor which
+
+1405
+00:59:51,599 --> 00:59:57,079
+I think a lot of people have or Google
+
+1406
+00:59:53,520 --> 00:59:59,839
+collab um so it should be like
+
+1407
+00:59:57,079 --> 01:00:02,160
+sufficient to use free computational
+
+1408
+00:59:59,839 --> 01:00:03,640
+resources that you have for number two
+
+1409
+01:00:02,160 --> 01:00:06,079
+we'll think about that I think that's
+
+1410
+01:00:03,640 --> 01:00:08,280
+important we do have Google cloud
+
+1411
+01:00:06,079 --> 01:00:11,520
+credits for $50 for everybody and I'm
+
+1412
+01:00:08,280 --> 01:00:13,440
+working to get AWS credits for more um
+
+1413
+01:00:11,520 --> 01:00:18,160
+but the cloud providers nowadays are
+
+1414
+01:00:13,440 --> 01:00:19,680
+being very stingy so um so it's uh been
+
+1415
+01:00:18,160 --> 01:00:22,160
+a little bit of a fight to get uh
+
+1416
+01:00:19,680 --> 01:00:23,680
+credits but I I it is very important so
+
+1417
+01:00:22,160 --> 01:00:28,480
+I'm going to try to get as as many as we
+
+1418
+01:00:23,680 --> 01:00:31,119
+can um and so yeah I I think basically
+
+1419
+01:00:28,480 --> 01:00:32,280
+uh there will be some sort of like limit
+
+1420
+01:00:31,119 --> 01:00:34,480
+on the amount of things you can
+
+1421
+01:00:32,280 --> 01:00:36,240
+practically do and so because of that
+
+1422
+01:00:34,480 --> 01:00:39,920
+I'm hoping that people will rely very
+
+1423
+01:00:36,240 --> 01:00:43,359
+heavily on pre-trained models um or uh
+
+1424
+01:00:39,920 --> 01:00:46,079
+yeah pre-trained models
+
+1425
+01:00:43,359 --> 01:00:49,599
+and yeah so that that's the the short
+
+1426
+01:00:46,079 --> 01:00:52,799
+story B um the second thing uh the
+
+1427
+01:00:49,599 --> 01:00:54,720
+assignment three is to do a survey of
+
+1428
+01:00:52,799 --> 01:00:57,920
+some sort of state-ofthe-art research
+
+1429
+01:00:54,720 --> 01:01:00,760
+resarch and do a reimplementation of
+
+1430
+01:00:57,920 --> 01:01:02,000
+this and in doing this again you will
+
+1431
+01:01:00,760 --> 01:01:03,440
+have to think about something that's
+
+1432
+01:01:02,000 --> 01:01:06,359
+feasible within computational
+
+1433
+01:01:03,440 --> 01:01:08,680
+constraints um and so you can discuss
+
+1434
+01:01:06,359 --> 01:01:11,839
+with your Tas about uh about the best
+
+1435
+01:01:08,680 --> 01:01:13,920
+way to do this um and then the final
+
+1436
+01:01:11,839 --> 01:01:15,400
+project is to perform a unique project
+
+1437
+01:01:13,920 --> 01:01:17,559
+that either improves on the state-of-the
+
+1438
+01:01:15,400 --> 01:01:21,000
+art with respect to whatever you would
+
+1439
+01:01:17,559 --> 01:01:23,440
+like to improve with this could be uh
+
+1440
+01:01:21,000 --> 01:01:25,280
+accuracy for sure this could be
+
+1441
+01:01:23,440 --> 01:01:27,760
+efficiency
+
+1442
+01:01:25,280 --> 01:01:29,599
+it could be some sense of
+
+1443
+01:01:27,760 --> 01:01:31,520
+interpretability but if it's going to be
+
+1444
+01:01:29,599 --> 01:01:33,599
+something like interpretability you'll
+
+1445
+01:01:31,520 --> 01:01:35,440
+have to discuss with us what that means
+
+1446
+01:01:33,599 --> 01:01:37,240
+like how we measure that how we can like
+
+1447
+01:01:35,440 --> 01:01:40,839
+actually say that you did a good job
+
+1448
+01:01:37,240 --> 01:01:42,839
+with improving that um another thing
+
+1449
+01:01:40,839 --> 01:01:44,680
+that you can do is take whatever you
+
+1450
+01:01:42,839 --> 01:01:47,280
+implemented for assignment 3 and apply
+
+1451
+01:01:44,680 --> 01:01:49,039
+it to a new task or apply it to a new
+
+1452
+01:01:47,280 --> 01:01:50,760
+language that has never been examined
+
+1453
+01:01:49,039 --> 01:01:53,119
+before so these are also acceptable
+
+1454
+01:01:50,760 --> 01:01:54,240
+final projects but basically the idea is
+
+1455
+01:01:53,119 --> 01:01:55,559
+for the final project you need to do
+
+1456
+01:01:54,240 --> 01:01:57,480
+something something new that hasn't been
+
+1457
+01:01:55,559 --> 01:01:59,880
+done before and create new knowledge
+
+1458
+01:01:57,480 --> 01:02:04,520
+with the respect
+
+1459
+01:01:59,880 --> 01:02:07,640
+toy um so for this the instructor is me
+
+1460
+01:02:04,520 --> 01:02:09,920
+um I'm uh looking forward to you know
+
+1461
+01:02:07,640 --> 01:02:13,599
+discussing and working with all of you
+
+1462
+01:02:09,920 --> 01:02:16,119
+um for TAS we have seven Tas uh two of
+
+1463
+01:02:13,599 --> 01:02:18,319
+them are in transit so they're not here
+
+1464
+01:02:16,119 --> 01:02:22,279
+today um the other ones uh Tas would you
+
+1465
+01:02:18,319 --> 01:02:22,279
+mind coming up uh to introduce
+
+1466
+01:02:23,359 --> 01:02:26,359
+yourself
+
+1467
+01:02:28,400 --> 01:02:32,839
+so um yeah nhir and akshai couldn't be
+
+1468
+01:02:31,599 --> 01:02:34,039
+here today because they're traveling
+
+1469
+01:02:32,839 --> 01:02:37,119
+I'll introduce them later because
+
+1470
+01:02:34,039 --> 01:02:37,119
+they're coming uh next
+
+1471
+01:02:40,359 --> 01:02:46,480
+time cool and what I'd like everybody to
+
+1472
+01:02:43,000 --> 01:02:48,680
+do is say um like you know what your
+
+1473
+01:02:46,480 --> 01:02:53,079
+name is uh what
+
+1474
+01:02:48,680 --> 01:02:55,799
+your like maybe what you're interested
+
+1475
+01:02:53,079 --> 01:02:57,319
+in um and the reason the goal of this is
+
+1476
+01:02:55,799 --> 01:02:59,200
+number one for everybody to know who you
+
+1477
+01:02:57,319 --> 01:03:00,720
+are and number two for everybody to know
+
+1478
+01:02:59,200 --> 01:03:03,440
+who the best person to talk to is if
+
+1479
+01:03:00,720 --> 01:03:03,440
+they're interested in
+
+1480
+01:03:04,200 --> 01:03:09,079
+particular hi uh I'm
+
+1481
+01:03:07,000 --> 01:03:15,520
+Aila second
+
+1482
+01:03:09,079 --> 01:03:15,520
+year I work on language and social
+
+1483
+01:03:16,200 --> 01:03:24,559
+and I'm I'm a second this year PhD
+
+1484
+01:03:21,160 --> 01:03:26,799
+student Grand and Shar with you I search
+
+1485
+01:03:24,559 --> 01:03:28,480
+is like started in the border of MP and
+
+1486
+01:03:26,799 --> 01:03:31,000
+computer interaction with a lot of work
+
+1487
+01:03:28,480 --> 01:03:32,640
+on automating parts of the developer
+
+1488
+01:03:31,000 --> 01:03:35,319
+experience to make it easier for anyone
+
+1489
+01:03:32,640 --> 01:03:35,319
+to
+
+1490
+01:03:39,090 --> 01:03:42,179
+[Music]
+
+1491
+01:03:47,520 --> 01:03:53,279
+orif
+
+1492
+01:03:50,079 --> 01:03:54,680
+everyone first
+
+1493
+01:03:53,279 --> 01:03:57,119
+year
+
+1494
+01:03:54,680 --> 01:04:00,119
+[Music]
+
+1495
+01:03:57,119 --> 01:04:03,559
+I don't like updating primar models I
+
+1496
+01:04:00,119 --> 01:04:03,559
+hope to not update Prim
+
+1497
+01:04:14,599 --> 01:04:19,400
+modelm yeah thanks a lot everyone and
+
+1498
+01:04:17,200 --> 01:04:19,400
+yeah
+
+1499
+01:04:20,839 --> 01:04:29,400
+than and so we will um we'll have people
+
+1500
+01:04:25,640 --> 01:04:30,799
+uh kind of have office hours uh every ta
+
+1501
+01:04:29,400 --> 01:04:32,880
+has office hours at a regular time
+
+1502
+01:04:30,799 --> 01:04:34,480
+during the week uh please feel free to
+
+1503
+01:04:32,880 --> 01:04:38,400
+come to their office hours or my office
+
+1504
+01:04:34,480 --> 01:04:41,960
+hours um I think they are visha are they
+
+1505
+01:04:38,400 --> 01:04:43,880
+posted on the site or okay yeah they
+
+1506
+01:04:41,960 --> 01:04:47,240
+they either are or will be posted on the
+
+1507
+01:04:43,880 --> 01:04:49,720
+site very soon um and come by to talk
+
+1508
+01:04:47,240 --> 01:04:51,480
+about anything uh if there's nobody in
+
+1509
+01:04:49,720 --> 01:04:53,079
+my office hours I'm happy to talk about
+
+1510
+01:04:51,480 --> 01:04:54,599
+things that are unrelated but if there's
+
+1511
+01:04:53,079 --> 01:04:58,039
+lots of people waiting outside or I
+
+1512
+01:04:54,599 --> 01:05:00,319
+might limit it to uh like um just things
+
+1513
+01:04:58,039 --> 01:05:02,480
+about the class so cool and we have
+
+1514
+01:05:00,319 --> 01:05:04,760
+Patza we'll be checking that regularly
+
+1515
+01:05:02,480 --> 01:05:06,839
+uh striving to get you an answer in 24
+
+1516
+01:05:04,760 --> 01:05:12,240
+hours on weekdays over weekends we might
+
+1517
+01:05:06,839 --> 01:05:16,000
+not so um yeah so that's all for today
+
+1518
+01:05:12,240 --> 01:05:16,000
+are there any questions
\ No newline at end of file