
Yann LeCun quits Twitter amid acrimonious exchanges on AI bias - Yuqing7
https://syncedreview.com/2020/06/30/yann-lecun-quits-twitter-amid-acrimonious-exchanges-on-ai-bias/
======
supernova87a
This is a problem with the recent social movements -- and you can even listen
to Dave Chappelle offer this criticism: the people who may be right may be
entitled to be offended, but the way they're going about it is not going to
help the cause.

It is not good, when anyone who offers even a little bit of truth or
reasonableness during a discussion is struck down as the enemy and attacked
instantly by the vehemence of someone's offense.

If the successful chief scientist of a huge company can be taken down by
forces such as this, while speaking truthful statements, what is the
likelihood if I were a lowly junior researcher, I would dare voice an opinion
or participate in the discussion? Or after a while, seeing what happens to
such people, want to help?

Fear and exclusionary self-righteousness are not the way to win a social
struggle. History shows that success comes from making more and more people
feel they are a part of the solution -- not making them fear to open their
mouths. Especially if, in the end, we each still stand alone in the voting
booth and pull the lever based on how we've been made to feel.

~~~
ponker
I agree with your gist but let's not pretend that Yann LeCun has been "taken
down." He is still tremendously wealthy, respected, influential, etc... it
looks like the only change is that he's not on Twitter.

~~~
randomsearch
If an individual retains their personal wealth and lifestyle, but is unable to
speak publicly - at least on one major platform, perhaps on many or all - then
I don’t think it’s a justification of mob culture to say “well, he has a shiny
car.” It doesn’t make the mob any less hurtful or hateful, and it doesn’t
somehow redress the harm that’s being done to debate and free speech, which in
the long run will probably have very damaging consequences not so much for him
as for the mob.

~~~
fastasucan
How is he unable, other by his own choice?

~~~
chillacy
Calling this his choice is an odd word selection, if someone is under
extortion or blackmail do they give in by choice? If option A is horrible and
one has to chose option B, is it fine and dandy because it was made by choice?

------
emtel
Can anyone provide a simple explanation or example that illustrates why Yann
was wrong? I’m highly predisposed to take his side in this, but I wonder if I
could be missing something.

~~~
Kapura
This is fundamentally a forest/trees type of situation. LeCun sees the issue
with this one model, says, "If this had been trained on a different sample it
wouldn't have this issue," and stops his train of reasoning there. The problem
he is seeing is the mis-trained model, and nothing more.

But the problem is larger than this single model, because this issue (or
similar ones) are pervasive in the fields in which AI are being employed. If a
neural net is helping a court hand down sentences, it is going to be trained
on historical sentencing data, and will in turn reflect the biases present in
that data. If you are still only seeing the one tree, you say "well we must
correct for the historical bias," and absolve yourself of thinking of the
larger problem. That forest problem is that we will always be feeding these
algorithms biased inputs, unless we do the work to understand social biases
and attempt to rectify them.

~~~
Wolfenstein98k
It's not the job of the AI researcher to solve "social biases" in every field,
it's their job to build the AI. LeCun is right to focus on his task and
correct it, not just start talking about structural issues to which he cannot
have insight anyway.

PS "Do the work" is a creepy phrase that is popping up everywhere in SocJus. I
recommend describing the "work" that needs "doing" instead of just saying "the
work" need be "done".

~~~
staplers
This is like saying it's not a civil engineers job to solve the geography's
faults. If you can't build a safe and balanced bridge, then don't.

It has real world consequences that can drastically damage communities.

~~~
jollofricepeas
Another example...

The current nightmare that is cyber security is caused by developers who do
not understand that with great power comes great responsibility.

The culture of software “engineering” and development is fundamentally broken
and based entirely on “its not my problem if someone else gets hurt, I just
build things.”

There’s literally not a single other industry where this level of greed and
willful neglect is acceptable.

Of course, this would be reflected in the AI community as well.

~~~
Nasrudith
That shows a fundamental lack of understanding of the fields of software
engineering, cyber security and the fields. Both terms are individually
overloaded such that many issues aren't even within their domain let alone
actually handled by developers! No matter how good the lock is it does no good
when the key is left on /top of tbe doormat/ by the user.

Blaming greed of engineers for security problems? Seriously?! That is like
railing against EMTs as being responsible for the Coronavirus because they
wanted to get rich without working hard. It conflates so many different areas
and roles that it isn't even coherent logic and is nonsensical.

~~~
jollofricepeas
This is exactly the attitude to which I’m referring.

If you don’t understand that engineering is the fundamental bedrock of IT and
the lack of security application during the engineering SDLC is a consistent
failure then I don’t know what to tell you except maybe to gain more
experience in software engineering and read more about data breaches.

Cyber security:
[https://en.m.wikipedia.org/wiki/Computer_security](https://en.m.wikipedia.org/wiki/Computer_security)

But for arguments sake let’s just stick to network, application, mobile and
IOT security.

\- Why is MFA not on by default?

\- How many devs have prod credentials published to Github.com right now?

\- How many unsecured IOT devices are there?

\- Why is email security such a dumpster fire?

\- How many companies have an SSDLC?

\- How many companies require separation of duties and approvals before a dev
publishes another AWS bucket or some other unprotected data store to the web?

Go ahead and blame business but they don’t know jack about software
engineering. We determine which corners to cut as opposed to stating to
business that security is just a part of doing engineering. We’re the one’s
who decide and thus it’s our responsibility despite denials of people like
yourself.

------
KerrickStaley
I think one issue here is that Twitter makes it hard to have good
conversations because it motivates people to optimize for pithiness and
likeability/retweetability over substance.

That said, I'm disappointed at the lack of professionalism shown by Timnit
Gebru in this conversation. I understand that she's frustrated that mainstream
AI/ML research doesn't have a good understanding of how models can reflect and
reinforce systemic racism. But her response to Yann doesn't attempt to find
common ground, doesn't add any meaningful facts to the conversation, and is
ad-hominem ("why are YOU too dumb to understand this?"). Timnit is a Research
Scientist at Google who leads a group that works on Ethical AI. In that role
I'd expect that her mission would be to elevate the collective understanding
of ethical problems in AI, and solutions to those problems. Her communication
here doesn't achieve that.

This is important to me because I'm also a (fledgling) member of the AI/ML
community, and I want us to be able to have civil and constructive
conversations.

~~~
excerionsforte
"why are YOU too dumb to understand this?"

Citation needed. I don't like this sensationalism.

"civil and constructive conversations."

What was uncivil nor constructive? One person blamed a dataset for bias, but
said researchers should not be on the hook for consequences stemming from the
output that could be put into a deployed product. The other disagreed with the
notion and stating that this was brought up in the past showing clear
frustration. Uncivil tends to be a dogwhistle indicating how a person SHOULD
act as if there are strict set of parameters for how to show frustration.

~~~
jml7c5
You are correct about the 'quote', as it does not seem Gebru ever wrote that.
I am surprised none of these sibling comments have affirmed that. While
personally I presume that parent poster meant the 'quote' only as an
illustrative pharaphrase, it is clearly very easy to misinterpret. (Of course,
if one assigns less charitable intent it is intentionally misleading, but I
generally assume the best of people.) Either way, it feels intellectually
dishonest that no one else has conceded this.

As an aside: I wish English had some more elegant method for forming compound
words than just-concatenate-words-with-hyphens. Compounding of phrases and use
of imagined quotes as compound words is one of those common flourishes of
verbal communication that is difficult to express in written English.

As a note: I believe it is primarily the "dogwhistle" reference is what is
earning you down-votes, as it subtly implies — whether intended or not — that
parent poster is somehow racist. I presume the best intentions and up-voted
you, but keep in mind how it can be interpreted.

~~~
KerrickStaley
"why are YOU too dumb to understand this?" is not a quote and was not meant to
be; it's a paraphrase/interpretation of what she wrote. I think this is fairly
obvious in the context of my comment.

------
ponker
The critic here, Timnit Gebru, is not doing a good job of articulating a well-
reasoned argument about why dataset bias is not the only problem with ML.
Allow me to attempt to do her job:

Machine learning approaches are powerful because they can take a “small” set
of labeled data and use that to make billions of new inferences at low cost.
The problem is that even when these data sets are fully representative of the
“real world,” the “real world” is rife with biases and unfairness and machine
learning takes these unfairnesses and gives them a huge Tensorflow-powered
megaphone.

For example, let’s say you wanted to use machine learning to create a system
for employers to make sure that they aren’t hiring likely drug dealers. If you
get a data set of every single American drug dealer’s mug shot, you’d get a
disproportionately black population because white kids selling ecstasy to
their high school classmates rarely get arrested and booked. The data set is
not “biased” —- it is complete —- but the enforcement of the law is biased.
But now, rather than using the development of this software as a moment to
_remove_ unfair bias, a machine learning model will amplify it, causing lots
of black men to be denied legitimate employment opportunities, which will
actually increase the chance that they sell illegal drugs to support
themselves.

This machine learning doesn’t just amplify the biases in the data set, it
amplifies all of the biases and other imperfections in all of the events that
fed into the creation of that data set.

~~~
azepoi
I feel like this is really unhelpful nitpicking on the definition of dataset
bias. In your example the dataset you want is the one of drug dealers so the
dataset from the convictions IS biased and uncomplete.

~~~
ponker
Well this is a contrived example. But the fact is that all datasets contain
the full biases of the world that created them. So there is no "unbiased"
dataset... meaning that powerful AI amplifies the world as it is, rather than
a designed point of view of how the world should be. That's fine in some cases
(e.g. tumor detection in X Rays -- the combined judgments of thousands of
surgeons might truly reflect how we want to treat a patient) but may not be
better in other cases (the combined judgments of thousands of police officers
may NOT be how we want to treat a suspect).

~~~
azepoi
And imo LeCun would agree with this, they were talking past each other and I
don't get the behavior of people lashing out and forcibly getting everyone to
bend to their opinions.

------
KKKKkkkk1
I guess Yann is making a statement about Twitter being a toxic cesspool or
whatever. The thing is, in other public forums where he will make himself
available for open conversation, he will experience the same thing. When you
are Chief AI Scientist at Facebook, you are the face of Facebook, whether you
like it or not.

~~~
TAForObvReasons
The criticisms leveled at LeCun has nothing to do with Facebook or his
employment, but rather with his commentary.

To summarize (in case you didn't read the article):

> LeCun responded, “ML systems are biased when data is biased. This face
> upsampling system makes everyone look white because the network was
> pretrained on FlickFaceHQ, which mainly contains white people pics.

> “The consequences of bias are considerably more dire in a deployed product
> than in an academic paper,” continued LeCun in a lengthy thread of tweets
> suggesting it’s not ML researchers that need to be more careful selecting
> data but engineers.

Other AI scientists, most notably Google's Timnit Gebru, disagreed with the
framing of the situation strictly in terms of dataset bias:

> Research scientist, co-founder of the “Black in AI” group, and technical co-
> lead of the Ethical Artificial Intelligence Team at Google Timnit Gebru
> tweeted in response, “I’m sick of this framing. Tired of it. Many people
> have tried to explain, many scholars. Listen to us. You can’t just reduce
> harms caused by ML to dataset bias.”

It's an especially sensitive time now when AI is being used to power many
judgments with real-life consequences.

~~~
cottonseed
Not sure why you're being downvoted. I haven't been following this and I
appreciated the summary.

------
creato
I can't help but shake my head at this whole story.

Someone (who by the way describes themselves more as an artist than anything
else in public bios) builds a ML "bullshit artist" (literally) that
hallucinates portraits from ridiculously undersampled data. Someone else runs
a low resolution image of Obama through a hallucinating computer program, and
gets a hallucination.

The story should end here at "these hallucinated portraits have basically
nothing to do with the input data, they're meaningless". I would bet that
input images that are this low resolution are difficult or impossible to
determine the race of, no matter how diverse the training data for this model
could have been. Note that the skin color of the low resolution Obama and the
random "white" guy it generated are basically the same!? I doubt this pipeline
will ever be capable of not confusing the race of its input and output.

What is on display here is the uselessness of this ML pipeline, not its racist
nature. I have seen probably a dozen of examples of this ML pipeline failing
hilariously in ways that have nothing to do with race. Yet this failure is
suddenly fascinating to the entire tech journalism space as an example of ML
algorithms being racist?!

And somehow, despite the absurdity of this situation, even ML "luminaries" get
drawn into a destructive whirlpool of social justice debate about a
hallucinating computer program that doesn't even work sometimes? What the fuck
are we all doing here?

~~~
karpierz
If you look at this single instance, independent of the world around it, it
looks ridiculous.

You need to look at it in the context of a long[1] list[2] of works[3]
detailing this exact problem, and the frustration that an leading expert in
the field still doesn't take it seriously, despite the real-world consequences
of that attitude.

1)
[https://www.pnas.org/content/117/14/7684](https://www.pnas.org/content/117/14/7684)

2)
[http://proceedings.mlr.press/v81/buolamwini18a.html?mod=arti...](http://proceedings.mlr.press/v81/buolamwini18a.html?mod=article_inline)

3)
[https://www.liebertpub.com/doi/full/10.1089/big.2016.0047](https://www.liebertpub.com/doi/full/10.1089/big.2016.0047)

~~~
free_rms
Why not pick all those other hills to die on, then?

If you want to talk about statistical methods being a horrible idea for
policing, I'm with you. Probably most machine learning experts would be, too,
just like most software engineers don't trust electronic voting. Instead,
people chose "dataset bias affects machine learning models", the most basic
Machine Learning 101 fact ever, to try and debate against.

It's like the woke people seek out the weakest possible ground because it
generates in-group opposition that they can fight with and out-woke. Stronger
ground could have _national_ consensus, way beyond bay area consensus.

~~~
cxw
Actually, that's Gebru's point. Gebru was pointing out frustration that LeCun
was focusing on this narrow question of statistical bias, rather than the
broader social issues you're getting at.

~~~
saym
Your comment, among the sea of others, got through and made some sense to me.
My follow-up questions are:

Does that negate the truthfulness of LeCun's response? What would Gebru rather
he do or say?

I feel like I can't tell what the desired form of action is from the article,
and I'm unsure what good it does for removing bias from ML to attack prominent
figures (on twitter let alone anywhere public).

------
megiddo
Whatever. I'm sure LeCun will be dying for another job.

Gebru undermines her own cause by being a shitty ambassador. While asking for
deep empathy in a radically marginal area of inter-sectional study, she seems
to lack any of her own. I'm sure berating an adult for a technical assertion -
one that is prima facie true - is an excellent tactic that has worked to
change minds and open up insightful public discourse.

Gebru's commentary smacks of narcissism. Want a little air time and some
notoriety? Call a famous person a bigot and see what happens. She has little
to risk and much to gain.

~~~
eric_b
Agreed, it bothers me that this tactic is so successful. I could not find an
instance in this "discussion" where Gebru outlined any concrete steps to solve
for, or lessen bias, in ML. It seems she mainly wants to complain, rather than
_fix_ the problem. I have been googling about ideas to lessen ML bias, and the
best I've found is "diverse training datasets" which Gebru herself says "is
not enough".

Well what is then? Outrage without a solution is useless.

~~~
aiaiai
Hire more minorities in ML. One of the few industries where forced diversity
is a good thing since your models will be better (wider range of opinions and
sources).

See, it's not actually hard. Gebru is just correct in saying you are not
listening.

~~~
tinyhouse
You give too much credit to the people who build those systems. Having more
minorities in ML is a good thing, but I don't think it's gonna change anything
regarding bias in ML systems.

------
curiousgal
I still don't get what solution they are proposing to the problem. I have also
always assumed that any bias stems from data since the system uses it to
learn. What's the alternative?

~~~
aiaiai
Hire minorities to work on your machine learning models. At the very least it
ensures you won't 'forget' to train the model with black faces.

~~~
MaximumYComb
I'm not from the US but there is a link between socioeconomic status and
education. I would assume that with the large percentage of minorities,
especially black Americans, raised in poverty that there would be a lack of
suitable candidates. Just one of the ways that history of racism affects the
modern world.

Do they have any practical suggestions on where to find a huge number of
suitable qualified people? The people in prestigious academic positions often
come from not just non-poverty backgrounds but wealthy backgrounds.

~~~
aiaiai
Facebook is rich enough to invest in black people's education and essentially
train data scientists from the ground up. A black scholarship for data science
would be a great gesture from them. Plenty of other industries do this for
their fields as well.

------
etaioinshrdlu
Also, consider that most of us here believe in the freedom to tinker, the
freedom to use general purpose computing, and the freedom to freely share
results.

These freedoms are also at stake here. "Banning bad AI research" is just an
awful idea.

------
dekhn
I recommend to all scientists that they disengage from Twitter. I know some
think it's an effective way to communicate, but the vast majority of
communication on the site seems to devalue rational thought. Ultimately,
tweeting places your career at risk for reasons that are not well-aligned with
reasonable principles.

One of the problems today is that you can be totally technically correct about
something, but if you appear insensitive, you can be criticized to the point
of being silenced. I am very troubled by the idea that rational, polite people
who are trying to be helpful are being silenced because their point of view is
not 100% consistent with that of the popular mob.

------
dnautics
While I sympathize with this view: "You can’t just reduce harms caused by ML
to dataset bias" (there's clearly other stuff like operator bias), I think
there's a problem that if you don't focus first on dataset bias, you'll never
reduce the harms caused by ML.

------
mlthoughts2018
This whole episode unfortunately reminds me of this Robin Hanson post.

[http://www.overcomingbias.com/2013/08/inequality-is-about-
gr...](http://www.overcomingbias.com/2013/08/inequality-is-about-
grabbing.html)

The state of AI fairness research is really poor, as in the focus is on
politics and sincere effort to develop useful algorithmic notions of fairness
aren’t even attempted.

It creates a way to take notoriety and clout within the AI field by doing work
that is “about” fairness without actually solving fairness problems.

This article itself is a good example. Comparing Timnit Gebru’s body of work
or authority in the field even remotely with Yann LeCun is preposterous, and
Gebru happening to focus on the impacts of bias in the field doesn’t change
that. Yet the article is totally loaded to highlight Gebru and undercut LeCun.

I think AI fairness is tremendously important for the world, but I don’t see
anyone remotely tackling the problem in a rigorous way at all. Instead I see
people trying to hype up fluff work about fairness as a hot topic into fame &
clout so they can be de facto leaders in the overall field of AI despite not
actually contributing to it (not even in fairness research).

The whole thing politicizes it in a really ugly way. It reminds me of the
Feynman anecdote where people try to get him to settle a dispute about the
physics of electricity having an implication for observers of the Sabbath.

“ It really was a disappointment. Here they are, slowly coming to life, only
to better interpret the Talmud. Imagine! In modern times like this, guys are
studying to go into society and do something--to be a rabbi--and the only way
they think that science might be interesting is because their ancient,
provincial, medieval problems are being confounded slightly by some new
phenomena.”

------
nicolapede
Maybe someone already said it, but I just don't see Twitter as a public space
-- it is not Hyde Park Corner, it is a large courtyard, very large indeed, but
privately owned. From this perspective, I am really not bothered of who is on
it and who is not (I am not on it).

The topic discussed in the Twitter thread, on the other hand, is much more
interesting. I am a layman in ML (although I have implemented a NN myself, but
I am not a data scientist) so I offer my view hoping to get some feedback and
be corrected. My view is that the original post was correct and LeCun was not.
I just can't see what sort of objective function you can use to solve every
potential problem arising in the future about similar issues. You want a
"totally unbiased" model? Then, you use total diversity in your training set?
Can't it happen that the model will then enforce diversity in a non-diverse
picture? Is that your ideal result?

That might be a silly example and maybe not 100% correct for the example
discussed, but, in general, my layman feeling is just that ML cannot help in
cases where moral judgement is required. It can provide a good tool in any
other case where, for example, a quadratic error is a reliable error measure,
but that seems a hard limitation.

------
RcouF1uZ4gsC
At this point, I think it would be a net win for society if all the tech
companies got together and just ended Twitter. Revoke their DNS registration,
shut down their cloud hosting, etc. Just completely end it. Twitter is tearing
us apart. And I think it is intrinsically that way. Twitter is the meth lab of
the of the Internet.

~~~
ornxka
I don't really understand sentiments like this - Twitter is just a means for
people to communicate. If the apparent result of this communication is
"tearing us apart", then isn't that the fault of the end to which people
communicate, and not the means by which they communicate?

~~~
kaibee
> I don't really understand sentiments like this - Twitter is just a means for
> people to communicate. If the apparent result of this communication is
> "tearing us apart", then isn't that the fault of the end to which people
> communicate, and not the means by which they communicate?

No, tooling and the incentives around them matter. Twitter makes it super easy
to find the worst examples of the other side/take them out of context, and
circulate what they said among 'your people' to get massive retweets. Twitter
does this because it creates a more engaging experience. Yes, if people were
perfect robots, Twitter could just be a means for people to communicate. But
the incentive structure of social media, ie: what goes viral, selects for
outrage. The medium is the message.

~~~
blondin
> Twitter makes it super easy to find the worst examples of the other
> side/take them out of context, and circulate what they said among 'your
> people' to get massive retweets. Twitter does this...

i mean, i can give plenty others that do exactly what you said. twitter is not
unique and certainly not the problem.

i read his final(?) tweets and the reasons why he decided to leave twitter.
and i don't think or remember him blaming the medium.

~~~
AlexCoventry
The pressure to keep one's communication brief due to the 240 character limit
makes nuanced communication much more laborious, and that results in more
conflict.

------
thecleaner
How can such things have cults ? This was a fierece debate of ideas which
should have been allowed to continue instead of people jumping in and spoiling
the debate with personal attacks. Had this debate continued based purely on
facts and best practices we would have had better ideas about bias for free. I
just don't understand this behaviour. People are Yann LeCun "fans", people are
PyTorch "fans" (such people also sent Francois Chollet hate mail), elon musk
"fans" (if you bet against you are a bad person, wth). Just to be clear "fans"
are not very smart people and just because someone likes one technology or one
idea over another doesn't make them Yann LeCun or a great technologist or a
pioneer. It just makes one an idiot. Great ideas should win or lose regardless
of who said what.

------
jl2718
What I’m reading is that researchers are getting mobbed for reporting their
results. What are they supposed to do? Not report results? Report false
results?

Then they go on to say this it can’t be solved by changing the dataset. Okay,
exactly what are the researchers supposed to do, aside from give up and go
into sociology?

------
nec4b
In China it is enough not to speak against the CP, to be left alone and do
your research. In the USA you actively have to signal your allegiance to the
cause. And not only that, the cause can change overnight and what you said
couple of years ago, may not be PC today.

------
blackbear_
There are two fundamentally distinct research questions at play:

1\. Can a computer learn X _at all_ (and how)?

2\. Can a computer learn X _in an unbiased way_ (and how)?

Where (1) is clearly a prerequisite for (2). Most of the ML community is
currently focusing on (1), and Yann is right in saying that unbiasedness
should not be a concern for those focusing on it. And Timnit is right in
insisting that more focus should be spent on (2), which is a perfectly valid
research question in its own right and not something to be left only to ML
engineers.

With that said, not being familiar with Timnit's work and seeing her explode
like that did not leave a good impression of her and her cause.

Just my 2c.

------
supergirl
what I get from the tweets is that Yann offers a more or less technical
explanation for a specific issue. then he gets screamed at with straw-man
arguments and personal attacks by people who seem desperate for recognition in
an emerging field (ethics of AI) (e.g. spamming about some single paper that
they brought to "Yann's house", meaning an ML conference, as if it's the new
Bible).

tbh Yann should've known what he gets into. everyone that knows anything about
AI would already know the explanation. writing it on twitter would just
attract an angry mob of pseudo-scientists.

------
auggierose
I read with horror once here that in some places in the US, AI is used to
grade pupils exams in the form of long-form essays.

AI in its current form should not be used ANYWHERE as the default arbiter
where human accountability is needed. Like in grading an exam, or in dishing
out a court sentence. That is a pretty obvious thing, but most people are
immune to understanding obvious things, especially if it doesn't fit their
agenda.

------
mD5pPxMcS6fVWKE
In fact, bias in statistical models has existed long before AI. For example,
credit scores are biased, they will on average give lower score to a black
person. So what is the politically correct solution here, to multiply credit
scores by a certain factor based on race so average scores of all races are
the same?

~~~
quonn
That‘s not a solution a bank will buy or pay for, because the bank would do
worse on average.

The solution is to still have humans in the loop who can make reasonable
decisions.

~~~
mD5pPxMcS6fVWKE
Apartment managers see credit score below X and reject an applicant. If they
were to make their own decisions, it would be a legal liability. If they
reject based on credit score and other non-protected criteria, they can't be
sued.

------
AzzieElbab
Twitter is like Linkedin for self-destruction these days. It is not going to
end well for the platform and its users. On the positive side, at least we
know these ridiculous debates are happening, as opposed to someone simply
posing a job for unspecified reasons

------
bsaul
It may be a bit off-topic, but i'm still amazed people still consider twitter
a good platform for expressing political views, or actually any views at all.

Instantaneous, short messages is clearly not the preferred medium for
intelligent conversations.

------
AndrewKemendo
I think my favorite take on this was from Joscha Bach:

[https://twitter.com/Plinz/status/1277509345756852224](https://twitter.com/Plinz/status/1277509345756852224)

~~~
jobu
> _Twitter is not the ideal forum for controversies between people who
> specialize on what 's true and people who specialize on what's right"_

Basically all of social media.

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seesawtron
I still see his account on Twitter so I am confused what does it mean to
"quit" twitter? Can you not hide your account like you can in Facebook? Not
familiar with the social media platforms so much.

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zozbot234
See also
[https://news.ycombinator.com/item?id=23672809](https://news.ycombinator.com/item?id=23672809)

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guerrilla
I think part of the problem here is that she's concerned that the data is
biased because the people training it are biased. The data isn't racist,
researchers choosing data are racist.

Pointing out the data is biased is just pointing at a secondary cause rather
than the primary cause. The humans are the location in the chain of causality
(i.e. a learning human) where permanent change is possible.

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oarabbus_
I liked Bengio's take on this (plus much more) on Reddit:
[https://old.reddit.com/r/MachineLearning/comments/hiv3vf/d_t...](https://old.reddit.com/r/MachineLearning/comments/hiv3vf/d_the_machine_learning_community_has_a_toxicity/)

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YeGoblynQueenne
Note that the poster of that thread is not Yoshua Bengio, it's someone with
the reddit handle "yusuf-bengio"; a very misleading username.

~~~
KKKKkkkk1
It's a pun username. I don't think it's a misleading username. The poster
actually refers to Bengio in the third person. And he makes some very good
points.

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AlexCoventry
It's misleading to refer to them as "Bengio," on HN, in this context. It
definitely created a false impression for me.

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naveen99
i wonder if the program should be ethical use of intelligence, not just
artificial intelligence to prevent the reckless use of logic under an
influence.

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nec4b
AI research will simple move more to countries where there is less witch hunt
and researchers don't have to defend themselves from SWJ crowd.

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max_
I thought LeCun was at Facebook?

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jml7c5
He is. Read "quits Twitter" as "has decided to stop using Twitter".

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JoeMayoBot
From the the perspective of a technical person, such as a software developer
or AI scientist, it's easy to think about what we do as working with data and
algorithms. While we might step back and look at a system as a whole, a lot of
technical people can't communicate the birds eye view of what they're
creating. They might have the capacity, but easier for them to rely on the
technical description - yeah we can just make sure the data is good and the
problem is fixed. However, that doesn't communicate fully and allow another
person to understand the larger picture or that you even recognize it
yourself.

As an example, consider the architectural view of a system where you need
availability, performance, reliability, and scalability scalability. Over time
we've learned to add Security. In recent years we've begun to learn how
important how to include accessibility and privacy - we're learning and don't
always get it right. All these things are often implied and we expect people
to believe that we already know to include them. Now, lets look at more
dimensions that are affecting what we build in the area of equality,
diversity, and inclusion, which are part of our discussion, especially when
we're talking about AI where we lack explainability.

So, it's true that we have to look at the data and that's part of the data
science associated with AI work. When we are doing data science, it's more
than just munging data to try to get a validation set match. We have to do the
same thing we do with all other software and look at the domain of the
problem, what it's purpose is, who are the users, and what are we trying to
accomplish. If we examine the data set these things will help inform us on the
appropriateness of the data being used, which takes analysis, just like other
software problems.

It's simple to say, let's just make sure we have an equal amount of data from
each represented group. While that isn't bad, it isn't enough. Imagine hiring
or loan application program. We have demographics like name, location,
occupation, sex, race, etc. Some obvious discriminants are sex and race.
However, think about things like location - is it possible for someone to be
denied a loan because they live in a part of town considered high-risk? Maybe
a human wouldn't make that connection, but a machine learning algorithm with
historical data that discriminates will also use that data to automate the
discrimination.

We have to look at the history, current status, and goals of what we want to
do to ensure we're thinking the problem through, rather than some mechanical
input-process-output steps. That's why simplifying the discussion with "just
change the data" doesn't communicate the source of the problem or how it
should be fixed. It would have been nice for both people to ask "BTW, what did
you mean by ...?" to open the conversation so we all could learn.

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keith___talent
Maybe we shouldn’t use Ai for anything in critical situations. Perhaps there
is no perfect model for AI training. Nuance is valuable and perhaps current AI
systems are not valid answers to the problems we face. Money will say
otherwise but it’s hard to support these kinds of solutions. Better trained
people are a solution that is unpopular currently.

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kimsant
Yann LeCun is an excellent engineer, and he is making pedagogy about AI. His
point is totally valid, but yet, he gets political replys that lose him.

Yan LeCun would be comfortable talking about why back propagation in convNets
does better with white tones and shadows, but is not a politician, seller,
politically correct personality, able to answer some "You can't reduce the
society bias of ML to some kinda data bias ALWAYS #somehashtag".

See my point?

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TrackerFF
Yeah well no shit, if your dataset is _exttemely_ imbalanced, you're gonna end
up with predictions that lean towards the majority class, so to speak.

Doesn't mean that the model is broken or prejudice. It _does_ what it has
learned to do.

If you want to fix the model by taking into account the said imbalanced data,
that's one thing - but the dataset still remains the same.

Prejudice or biad would be to build a dataset by improperly cherry-picking
your data, or other sampling errors.

Don't need to be a Ph.D to see or understand this.

