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sides no one understands how models work they re black boxes the best preparation we can provide is a general attitude of caveat emptor this is an understandable response because things moved too quickly in the past four years we were still struggling to absorb the basic principles of statistical machine learning when statistical ml was displaced by a new generation of tools that seemed even more mysterious journalists more or less gave up on explaining things but there are principles that undergird machine learning statistical learning is really at bottom a theory of learning it tries to describe mathematically what it means to generalize about examples the concept of a bias variance tradeoff for instance allows us to reason more precisely about the intuitive insight that there is some optimal level of abstraction for our models of the world illustration borrowed from https upscfever com upsc fever en data deeplearning2 2 html deep learning admittedly makes things more complex than the illustration above implies in fact understanding the nature of the generalization performed by llms is still an exciting challenge see the first few minutes of this recent talk by ilya sutskever for an example of reflection on the topic but if students are going to be fine tuning their own models they will definitely need at least a foundation in concepts like variance and overfitting a basic course on statistical learning should be part of the core curriculum i might go a little further and suggest that professors in every department are going to want reflect on principles and applications of machine learning so we can give students the background they need to keep thinking critically about our domain of expertise in a world where some not all aspects of reading and analysis may be automated is this a lot of work yes are we already overburdened and should we have more support yes and yes professors have already spent much of their lives mastering one field of expertise asking them to pick up the basics of another field on the fly while doing their original jobs is a lot so adapting to ai will happen slowly and it will be imperfect and we should all cut each other slack but to look on the bright side none of this is boring it s not just a technical hassle to fend off there are fundamental intellectual challenges here and if we make it through these rapids in one piece we re likely to see some new things categories deep learning reproducibility and replication social effects of machine learning we can save what matters about writing at a price post author by tedunderwood post date july 31 2023 9 comments on we can save what matters about writing at a price it s beginning to sink in that generative ai is going to force professors to change their writing assignments this fall corey robin s recent blog post is a model of candor on the topic a few months ago he expected it would be hard for students to answer his assignments using ai at least it would require so much work that students would effectively have to learn everything he wanted to teach then he asked his 15 year old daughter to red team his assignments m y daughter started refining her inputs putting in more parameters and prompts the essays got better more specific more pointed perhaps not every 15 year old would get the same result but still robin is planning to go with in class exams until a better option comes along it s a good short term solution in this post i d like to reflect on the better options we may need over the long term if we want students to do more thinking than can fit into one exam period if you want an immediate pragmatic fix there is good advice out there already about adjusting writing assignments institutions have not been asleep at the wheel my own university has posted a practical guide and the modern language association and conference on college composition and communication have to their credit quickly drafted a working paper on the topic that avoids panic and makes a number of wise suggestions a recurring theme in many of these documents is the value of process focused instruction working paper 10 why focus on process a cynical way to think about it is that documenting the writing process makes it harder for students to cheat there are lots of polished 5 page essays out there to imitate but fewer templates that trace the evolution of an idea from an initial insight through second thoughts to dialectical final draft making it harder to cheat is not a bad idea but the mla cccc task force doesn t dwell on this cynical angle instead they suggest that we should foreground process knowledge and metacognition because those things were always the point of writing instruction this is much the same thesis corey robin explores at the end of his post when he compares writing to psychotherapy only on the couch have i been led to externalize myself to throw my thoughts and feelings onto a screen and to look at them to see them as something other coldly and from a distance the way i do when i write midjourney a hand writing with a quill reflected in a mirror by mc escher in the style of meta representation ar 3 2 weird 50 robin s spin on this insight is elegiac in losing take home essays we might lose an opportunity to teach self critique the task force spins it more optimistically suggesting that we can find ways to preserve metacognition and even ways to use llms large language models to help students think about the writing process i prefer their optimistic spin but of course one can imagine an even more elegiac riposte to the task force report won t ai eventually find ways to simulate critical metacognition itself writing the fake process reflection along with the final essay yes that could happen so this is where we reach the slightly edgier spin i feel we need to put on teach the process which is that over the long run we can only save what matters about writing if we re willing to learn something ourselves it isn t a good long term strategy for us to approach these questions with the attitude that we professors have a fixed repository of wisdom and the only thing ai should ever force us to discuss is how to convey that wisdom effectively to students if we take that approach then yes the game is over as soon as a model learns what we know it will become possible to cheat by simulating learning but if the goal of education is actually to learn new things and we re learning those things along with our students then simulating the process is not something to fear consider assignments that take the form of an experiment for instance experiments can be faked but you don t get very far doing so because fake experiments don t replicate if a simulated experiment does reliably replicate in the real world we don t call that cheating but in silico research that taught us something new if humanists and social scientists can find cognitive processes analogous to experiment processes where a well documented simulation of learning is the same thing as learning we will be in the enviable position robin originally thought he occupied students who can simulate the process of doing an assignment will effectively have completed the assignment i don t think most take home essays actually occupy that safe position yet because in reality our assignments often ask students to reinvent a wheel or rehearse a debate that has already been worked through by some earlier generation a number of valid if perhaps conflicting answers to our question are already on record the verb rehearse may sound dismissive but i don t mean this dismissively it can have real value to walk in the shoes of past generations sometimes ontogeny does need to recapitulate phylogeny and we should keep asking students to do that occasionally even if they have to do it with pencil on paper but we will also need to devise new kinds of questions for advanced students questions that are hard to answer even with ai assistance because no one knows what the answer is yet one approach is to ask students to gather and interpret fresh evidence by doing ethnography interviewing people digging into archival boxes organizing corpora for text analysis etc these are assignments of a more demanding kind than we have typically handed undergrads but that s the point some things are actually easier now and colleges may have to stretch students further in order to challenge them gathering fresh evidence puts the emphasis on empirical data and effectively preserves the take home essay by turning it into an experiment what about other parts of humanistic education interpretive reflection theory critique normative debate i think all of those matter too i can t say yet how we ll preserve them it s not the sort of problem one person could solve but i am willing to venture that the meta answer is we ll preserve these aspects of education by learning from the challenge and adapting these assignments so they can t be fulfilled merely by rehearsing received ideas maybe for instance language models can help writers reflect explicitly on the wheels they re reinventing and recognize that their normative argument requires another twist before it will genuinely break new ground if so that s not just a patch for writing assignments but an advance for our whole intellectual project i understand that this is an annoying thesis if you strip away the gentle framing i m saying that we professors will have to change the way we think in order to respond to generative ai that s a presumptuous thing to say about disciplines that have been around for hundreds of years pursuing aims that remained relatively constant while new technologies came and went however that annoying thesis is what i believe machine learning is not just another technology and patching pedagogy is not going to be a sufficient response as marc watkins has recently noted patching pedagogy with surveillance is a cure worse than the disease this time we can only save what matters about our disciplines if we re willing to learn something in the process the best i can do to make that claim less irritating is to add that i think we re up for the challenge i don t feel like a voice crying in the wilderness on this i see a lot of recent signs from the admirable work of the mla and cccc to books like the ends of knowledge eds scarborough and rudy that professors are thinking creatively about a wide range of recent challenges and are capable of responding in ways that are at once critical and self critical learning is our job we ve got this references center for innovation in teaching and learning uiuc artificial intelligence implications in teaching and learning champaign il 2023 mla cccc joint task force on writing and ai mla cccc joint task force on writing and ai working paper overview of the issues statement of principles and recommendations july 2023 robin corey how chatgpt changed my plans for the fall july 30 2023 rudy seth and rachel scarborough king the ends of knowledge outcomes and endpoints across the arts and sciences london bloomsbury 2023 watkins marc will 2024 look like 1984 july 31 2023 tags writing instruction categories deep learning fiction using gpt 4 to measure the passage of time in fiction post author by tedunderwood post date march 19 2023 41 comments on using gpt 4 to measure the passage of time in fiction language models have been compared to parrots but the bigger danger is that they turn people into parrots a student who asks for a paper about middlemarch for instance will get a pastiche loosely based on many things in the model s training set this may not count as plagiarism but it won t produce anything new but there are ways to use language models actively and creatively we can select evidence to be analyzed put it in a prompt and specify the questions to be asked used this way language models can create new knowledge that didn t exist when they were trained there are many ways to do this and people may eventually get quite creative but let s start with a familiar task so we can evaluate the results and decide whether language models really help an obvious place to start is content analysis a research method that analyzes hundreds or thousands of documents by posing questions about specific themes below i work through a simple example of content analysis using the openai api to measure the passage of time in fiction see this github repo for code to spoil the suspense i find that for this task language models add something valuable not just because they re more accurate than older ways of doing this at scale but because they explain themselves better why measure time in fiction researchers already have several good ways to automate content analysis named entity extraction addresses certain kinds of questions topic modeling addresses others but there are also tricky questions that remain hard to answer by counting words in 2017 for instance i started to wonder how much time passes on average across a page of a novel literary critical tradition suggested that there had been a pretty stable balance between scene minute by minute description and summary which may cover weeks or years until modernists started to leave out the summary and make every page breathlessly immediate 1 but when i sat down with two graduate students sabrina lee and jessica mercado to manually characterize a thousand passages from fiction we found instead a long trend the average length of time represented in 250 words of fiction had been getting steadily shorter since the early eighteenth century there was a trend toward immediacy in other words but modernism didn t begin the trend 2 the average length of time described in 250 words of narration y axis is logarithmic the shaded ribbon represents a 95 confidence interval for the dashed curve which is itself calculated by loess regression from underwood why literary time is measured in minutes p 352 how well do bag of words methods estimate time we decided to characterize passages manually because references to time in fiction can t always be taken literally if a character thinks or says wow it s been thirty years but feels like yesterday you don t want to conclude that thirty years have passed on the page so word counting seemed risky even as human readers we often found it hard to decide how much time was passing but when a passage was read by two different people our estimates agreed with each other well enough to conclude that fictive time was a meaningful construct if not a precise one r 74 on log transformed durations a year after we published our paper greg yauney showed that word counting methods can do an acceptable job of estimating time 3 he trained a bag of words model on the passages we had labeled and applied it to a new set of passages labeled by new readers the model predicted durations correlated with human estimates at r 35 while this is much lower than inter human agreement the model was stable enough to precisely measure a t...
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