[HN Gopher] Cynthia Rudin wins the 2021 AAAI Squirrel AI Award
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Cynthia Rudin wins the 2021 AAAI Squirrel AI Award
Author : mcenedella
Score : 147 points
Date : 2021-10-12 10:07 UTC (12 hours ago)
(HTM) web link (pratt.duke.edu)
(TXT) w3m dump (pratt.duke.edu)
| [deleted]
| ChemSpider wrote:
| Is this a respected prize? I never heard of it before.
|
| That said, no doubt that explainable ML/AI is important.
| nothrowaways wrote:
| Yeah, this is their second year i assume.
| JustFinishedBSG wrote:
| I mean, respected or not I would not say no to a free meal and
| USD$1M.
| ndr wrote:
| Is $1 Million respectable?
| wongarsu wrote:
| Can money buy respectability?
|
| If I awarded $1 Million to a random person every year,
| receiving that award wouldn't make that person more
| accomplished, and the award wouldn't be mentioned outside the
| local newspaper. On the other hand an award that gives no
| money but consistently awards the best researcher in the
| field can be very noteworthy.
|
| What the $1 million does accomplish is make people pay
| attention, so everyone will much more quickly reach a verdict
| whether this is a price worth paying attention to. But two
| years is a bit quick for that verdict.
| melony wrote:
| Just tie it to a stochastic metric like random acts of
| kindness and make a big overblown press release about it.
| The Nobel peace prize speaks for itself on how useless
| metrics empower the well connected in creating prestige.
| Igelau wrote:
| If you give me $1 million, I promise I'll respect you.
| newsbinator wrote:
| > Can money buy respectability?
|
| Definitely!
| adrian_mrd wrote:
| Hello Golden Globes and the Hollywood Foreign Press
| Association!
| [deleted]
| dagw wrote:
| _Is this a respected prize?_
|
| It's a very new prize (this is only the second winner), so it's
| still too early to tell. But is backed by a reasonably
| respectable organization.
| enriquto wrote:
| > is backed by a reasonably respectable organization.
|
| Respectability of the prize will arise mostly from the people
| who receive the prizes, not from the organization itself.
| Would you like to receive the same prize that got all these
| other geniuses?
|
| The Nobel is respectable because so many great scientists got
| it. The composition of the Nobel committee is irrelevant, as
| long as they keep giving the prize to the best.
| nothrowaways wrote:
| Congratulations! I think Interpretable AI mentioned in the
| article is what is commonly called explainable AI.
| qPM9l3XJrF wrote:
| Cynthia has a great paper where she distinguishes between
| "interpretable" and "explainable"
|
| https://arxiv.org/pdf/1811.10154.pdf
| nothrowaways wrote:
| Oh thanks for the pointers
| randcraw wrote:
| "Explainable" just means you didn't build your app/service
| using any technology that isn't interpretable. Her argument
| is that the strategy of reverse engineering a method to
| convert it from inexplicable to explicable is inherently less
| effective than maintaining explicability at all times in the
| app's genesis -- from the design phase through
| implementation.
|
| But Rudin's Premise is philosophical more than practical. If
| the problem at hand is better solved using a black box (in
| terms of accuracy, precision, robustness, etc), her premise
| says simply, don't do it. Unfortunately in the cutthroat
| world of capitalism, that strategy can't compete with the
| cutting edge.
|
| Where Rudin's Premise is more suitable is in writing
| regulations to address AI app problems where social
| unfairness is unchecked (like the COMPAS app that advises
| legal authorities on meting out parole decisions without
| explaining its reasoning). There are many such (ab)uses for
| AI today in social services or policing which merit
| rethinking since AI-based injustice so offer bedevils the
| proprietary lack of transparency in such apps.
|
| Another excellent discussion of problems like these is Cathy
| O'Neil's book "Weapons of Math Destruction". Too bad she
| couldn't share the Squirrel prize.
| https://www.amazon.com/Weapons-Math-Destruction-Increases-
| In...
| pjmorris wrote:
| From the linked paper, linked again [0] below, I _think_
| this represents Rudins philosophical view, and why it could
| be practical: Here is the Rashomon set
| argument: Consider that the data permit a large set of
| reasonably accurate predictive models to exist. Because
| this set of accurate models is large, it often contains at
| least one model that is interpretable. This model is thus
| both interpretable and accurate. Unpacking this
| argument slightly, for a given data set, we define the
| Rashomon set as the set of reasonably accurate predictive
| models (say within a given accuracy from the best model
| accuracy of boosted decision trees). Because the data are
| finite, the data could admit many close-to-optimal models
| that predict differently from each other: a large Rashomon
| set. I suspect this happens often in practice because
| sometimes many different machine learning algorithms
| perform similarly on the same dataset, despite having
| different functional forms (e.g., random forests, neural
| networks, support vector machines). As long as the Rashomon
| set contains a large enough set of models with diverse
| predictions, it probably contains functions that can be
| approximated well by simpler functions, and so the Rashomon
| set can also contain these simpler functions. Said another
| way, uncertainty arising from the data leads to a Rashomon
| set, a larger Rashomon set probably contains interpretable
| models, thus interpretable accurate models often exist.
| If this theory holds, we should expect to see interpretable
| models exist across domains. These interpretable models may
| be hard to find through optimization, but at least there is
| a reason we might expect that such models
|
| exist.
|
| [0] https://arxiv.org/pdf/1811.10154.pdf
| qPM9l3XJrF wrote:
| >If the problem at hand is better solved using a black box
| (in terms of accuracy, precision, robustness, etc)
|
| It's been a while since I read her work, but IIRC one of
| the positions she argues for, which I find plausible, is
| that interpretable models can be performance competitive.
| For example, it could be that the only reason black box
| methods outperform is because they've been more heavily
| researched, and if we were to put more research into
| interpretable methods, we could achieve parity. I also
| mentioned a few reasons why we might expect interpretable
| models to perform better _a priori_ in this comment
| https://news.ycombinator.com/item?id=28838321
| randcraw wrote:
| I'd find Rudin's argument a lot more convincing if she
| offered an existence proof, like using the same number of
| examples to train an equally discriminative SVM or random
| forest (or hybrid) that can equal the performance of
| AlexNet in 2012 on the ImageNet ILSVRC (or in another
| domain where DNNs are SOTA).
|
| Until that can be done, I think few outside academia will
| invest time or money in alternative non-DNN methods in
| the hope of competing with today's even superior DNN
| variants. There's a decade of evidence now that DNNs are
| incontestable discriminators in numerous domains,
| relative to pre-2012 ML technology anyway.
| pjmorris wrote:
| Explanatory quote from the linked paper (and thanks for the
| link, qPM9l3XJrF!):
|
| "Rather than trying to create models that are inherently
| interpretable, there has been a recent explosion of work on
| "Explainable ML," where a second (posthoc) model is created to
| explain the first black box model. This is problematic.
| Explanations are often not reliable, and can be misleading, as
| we discuss below. If we instead use models that are inherently
| interpretable, they provide their own explanations, which are
| faithful to what the model actually computes"
| Stephen6252 wrote:
| Thanks for sharing, I am very impressed with your post.
| https://www.mybalancenow.work/
| thedrake wrote:
| Several great insights from a person that truly cares about not
| only the outcome of models but what is causing the outcome. Her
| talks about parole guidelines being taken over by ai are great.
| Bostonian wrote:
| Interesting article, but I think this sentence was unfair to
| other AI scholars, who also want AI to help society.
|
| "While many scholars in the developing field of machine learning
| were focused on improving algorithms, Rudin instead wanted to use
| AI's power to help society."
| qPM9l3XJrF wrote:
| Most AI papers I see aren't directly focused on using AI to
| help society. It's unclear how a small performance increase on
| ImageNet helps society, for instance.
| jhgb wrote:
| I would imagine that it helps by saving computational
| resources, making the results cheaper to obtain.
| lallysingh wrote:
| Many AI researchers do focus on algorithms, no?
| JustFinishedBSG wrote:
| Plenty focus on theory.
| pjmorris wrote:
| A Peter Norvig quote from yesterday's article about his
| transition to Stanford...
|
| "In the past, the interesting questions were around what
| algorithm is best for doing this optimization. Now that we have
| a great set of algorithms and tools, the more pressing
| questions are human-centered: Exactly what do you want to
| optimize? Whose interests are you serving? Are you being fair
| to everyone? Is anyone being left out? Is the data you
| collected inclusive, or is it biased?"
|
| [0] https://news.ycombinator.com/item?id=28833933
| jhgb wrote:
| That makes the sentence quoted by GP sound like a category
| error to me. Developing better algorithms and developing more
| useful models to run on those algorithms are not an
| "either/or" situation.
| tremon wrote:
| I think this is meant as a distinction between theoretical and
| applied science, phrased to a low common denominator (which, as
| a consequence, causes miscommunication due to not being
| specific enough).
| ur-whale wrote:
| Any link to her actual work?
| walnut_eater wrote:
| Her most cited paper is https://arxiv.org/abs/1811.10154 and it
| references a lot of her other work. It provides a good
| representation of what she does and what she is well known for.
| qPM9l3XJrF wrote:
| Of the "representative publications" on her homepage here
| https://ece.duke.edu/faculty/cynthia-rudin the one that seems
| most relevant to the topic of the article is this paper on
| interpretable financial lending:
| https://arxiv.org/pdf/2106.02605.pdf
|
| "The machine learning model is a two-layer additive risk model,
| which resembles a two-layer neural network, but is decomposable
| into subscales. In this model, each node in the first (hidden)
| layer represents a meaningful subscale model, and all of the
| nonlinearities are transparent. Our online visualization tool
| allows exploration of this model, showing precisely how it came
| to its conclusion. We provide three types of explanations that
| are simpler than, but consistent with, the global model: case-
| based reasoning explanations that use neighboring past cases, a
| set of features that were the most important for the model's
| prediction, and summary-explanations that provide a customized
| sparse explanation for any particular lending decision made by
| the model."
|
| I was curious about the customized sparse explanation. It looks
| like there is an illustrative example from later in the paper:
|
| "For all 700 (7.1%) people where:
|
| * ExternalRiskEstimate <= 63 , and
|
| * NetFractionRevolvingBurden >= 73,
|
| the global model predicts a high risk of default."
|
| "A rule returned by OptConsistentRule is globally-consistent,
| in the sense that there exists no previous case that satisfies
| the conditions in the rule but is predicted differently, by the
| global model, from what is stated in the rule. In contrast,
| explanations (from other methods) that are not consistent may
| hold for one customer but not for another, which could
| eventually jeopardize trust (e.g., "That other person also
| satisfied the rule but he wasn't denied a loan, like I was!")"
|
| You can see the online visualization tool her team built here:
| http://dukedatasciencefico.cs.duke.edu/models/
|
| In retrospect, it's not all that surprising to me that a model
| such as this is able to outperform a black box like a neural
| network. For example, one of the things this model does which
| black box models don't do is enforce "monotonicity constraints"
| which ensure that as risk factors increase, the estimated risk
| should also increase. It makes sense that this would be a
| useful inductive bias which improves generalization performance
| -- if a black box model found that an increase in risk factors
| _decreased_ estimated risk, it seems likely that this would be
| a result of overfitting (or multicollinearity gone haywire).
|
| Of course another reason to expect simple/interpretable models
| to generalize better is Occam's Razor.
|
| My big question about this sort of approach would be whether
| it's able to extend to the sort of unstructured data problems
| that deep learning has done really well on. It looks like some
| of her recent papers on Google Scholar address this:
| https://scholar.google.com/citations?hl=en&user=mezKJyoAAAAJ...
| (specifically thinking of the BacHMMachine paper and the
| Interpretable Mammographic Image Classification paper). Maybe
| someone else can summarize them.
| thendrill wrote:
| Ahh... Works of those is quite private. Just like women in
| tech. Usually behind closed doors, and not uploaded to
| ofans....
| gwf wrote:
| I was also one of Cynthia's Ph.D. advisors when she was a
| graduate student at Princeton, some twenty years ago. It was
| obvious to me then that she would go on to do great things, so
| it's delightful to read this news this morning.
|
| My fondest memory of Cynthia, however, has nothing to do with
| science, and everything to do with just being a kind person. We
| were at the NEC Research Institute's company picnic where they
| had an inflatable dragon for the kids to jump around within its
| interior. Me, Cynthia, and my wife went inside without any kids
| and jumped around like idiots for a while. Cynthia and my wife
| got bored, so I stayed behind for One More Big Bounce. With the
| epic bounce, I also succeeded in cracking a vertebra, nearly
| passing out on the spot from the pain. Eventually, I would crawl
| out, an ambulance was called, and I was brought to the Princeton
| ER.
|
| I would have a full recovery, but I was in the ER for several
| hours that night. Cynthia came with us to the ER, and when she
| saw how uncomfortable I was on the gurney, she went back to her
| dorm to retrieve her favorite blanket, so that I would have even
| a small comfort. I am not sure how long she stayed, but I know
| that she was there with me longer than anyone else except my
| wife.
|
| Anyhow, she's a lovely human being and I am honored and proud to
| have known her and witnessed the origins of her career.
| typest wrote:
| In my senior year at Duke, I took her ML class (her first
| semester at Duke). She was an excellent professor, one of the
| absolute best I had while there. She focused heavily on both
| implementation and theory, which I found to be rare.
|
| Her class became so popular within the add/drop period that
| Duke added a second section and also doubled the attendance for
| each section. I'm pretty sure she went from being supposed to
| teach about 70 students to teaching 300. Nevertheless, her
| teaching was top notch, and I learned more there than pretty
| much any other CS class, and still rely on this knowledge
| today!
|
| I too am really glad she won this award.
| adrian_mrd wrote:
| Thanks for sharing. It's frequently the little acts of everyday
| kindness that go a long way in this world, like the blanket
| example you cite.
| srean wrote:
| Hearty congratulations. I am very happy for her.
|
| I am more familiar with her older work on ranking and boosting. I
| do not have any technical commentary to add, just a personal
| anecdote that she is one of the nicest, warmest person that I
| have met. I wish her well with utmost sincerity.
| varispeed wrote:
| I wish one day I'll read that Computer Scientist _earns_ $1
| Million.
|
| Most engineers here get like PS60k salary (PS3600 a month after
| PAYE tax), while companies they work for make billions out of
| their work. Not only that, but they also don't contribute back
| into the local communities, because they use aggressive tax
| avoidance strategies. Corporations need to start sharing their
| profits with the workers and pay taxes otherwise it will
| eventually spark another revolution.
| typest wrote:
| It is quite common for engineers with Cynthia Rudin's ability
| to earn > 1 million. If you have that level of ML skill you can
| lead teams at many companies for over that amount.
| Someone wrote:
| IMO, the salaries are (ballpark) fine for the effort and risk
| involved, flexibility of working hours, stress levels, etc. I
| don't see a good reason why engineers should make more than,
| say, teachers or nurses, just because the company they work for
| makes billions.
|
| What's wrong is that these companies make billions, most of it
| because they happened to get to the top of the food chain.
| JustFinishedBSG wrote:
| > I wish one day I'll read that Computer Scientist _earns_ $1
| Million.
|
| They do.
|
| Not that I personally believe they deserve it. For the exact
| same reasons I don't think a CEO is not "worth" thousands of
| engineers, I don't think that just because you happened to
| graduate in ML you are worth tens/hundred times more that the
| others.
|
| Or more accurately maybe they _are_ worth that much, but the
| general population is severely underpaid.
| randcraw wrote:
| Reportedly, Ilya Sutskever was offered upward of $2
| million/year to remain at Google as he departed for OpenAI. By
| many accounts, he's not alone at earning over $1 million/year
| as an independent contributor.
|
| When Hinton, Krishevsky, and Sutskever sold DNNresearch
| (incorporated only days before) to Google, their $44 million
| crossed that line too, since the company had no products or IP
| that was independent of UToronto, AFAIK. The three were
| effectively "hired" as indep contributors.
| twen_ty wrote:
| I assume you're from the UK? Where? I ask because salaries in
| London are definitely higher. A senior engineer should get 80k
| and a principle engineer/architect should be on 90-120k.
| dr_dshiv wrote:
| What's with "Squirrel AI"? Did that seem slightly out of place?
| paranoidroid wrote:
| Squirrel AI is a Chinese online education company. It is the
| first large scale AI-powered adaptive education provider in
| China [...] https://en.wikipedia.org/wiki/Squirrel_AI Thousand
| Talents Obfuscation Initiative?
| paranoidroid wrote:
| The benefit for humanity has award has only been a thing
| since 2021, Mrs. Rudin received the 2022 version.
|
| So lets say its been running for 2 years.
|
| And the only other comparable scientific awards of such
| monetary value are Turing and Nobel?
|
| Wow very generous people.
|
| 1. https://www.aaai.org/Awards/squirrel-ai-award.php
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