Hacker News Reader

Comment by dealforager | Hacker News Reader

No. Every time someone makes a big stink about someone getting fired at one of the top tech companies, it is promptly followed by an article like this. A trillion dollar company that hires thousands of researchers and consistently produces some of the highest quality research with real results is not going to implode from one person being gone. Another pattern I've seen is someone leaving a company, followed by writing an article about how the company is doomed. Of course, the doom never arrives and the companies do even better. All of us are replaceable, from Bill Gates to Jeff Bezos to this researcher. This doesn't mean I agree with the firing, just saying that there is no implosion incoming.

View on HN

Of general interest, Jeff Dean’s comment on why the Gebru paper didn’t meet Google’s publication guidelines:

“Highlighting risks without pointing out methods for researchers and developers to understand and mitigate those risks misses the mark on helping with these problems”

from https://docs.google.com/document/d/1f2kYWDXwhzYnq8ebVtuk9CqQ...

Isn't that asking too much. How would a researcher also know how mitigate the problems they identified.

The problems they identified have been known for years and there are lots of papers exploring how to mitigate.

The whole paper was a nothing burger wrapped in social justice language with asides about how global warming is Actually Racism because of disparate impact (interesting, not an ML topic).

If the problems aren't novel and you're proposing zero solutions, it shouldn't be a paper.

I wonder what would drive someone to do this. Anger? Loneliness? Work pressure? Imposter syndrome? Her prior work seemed more observational than theoretical, and the she got thrown into a top-level leadership position in a theoretical research organization with almost zero experience. Is there a single person that would have defended decision if that person was a straight able christian white or north asian male? (Note: please do not respond by speaking for others that do not share your own views.)

I wonder when the “performative wokeness” bubble will burst.

I don't know where you get these ridiculous ideas about imposter syndrome. Take one look at her career. She's written signal processing algorithms at Apple, won awards at conferences, got a PhD and worked for Microsoft in their AI ethics lab. She was more than qualified for her position and the research paper she was publishing passed peer review.

You're right, this wouldn't have happened if she were a straight white male - if she were a straight white male, there wouldn't be comments like yours making asinine assumptions about how good she was at her job.

An impressive career makes this paper's quality and the subsequent meltdown over it more puzzling, not less.

Non-sequitur. I invite you to disabuse me of any of my exact statements.

> You're right, this wouldn't have happened if she were a straight white male

If anything, straight white males are trashed harder around here.

That's the only problem I've ever had with having a woman on the team: the fear of saying something that offends someone, her or other.

This is #4 (Pollution of Agency) and #8 (Anomalousness) from Joanna Russ's How to Suppress Women's Writing (https://en.wikipedia.org/wiki/How_to_Suppress_Women%27s_Writ...).

Poorly written papers are a regular occurrence in any field - that doesn't justify attributing bad faith or character issues to Dr. Gebru.

survey papers, reproducibility papers, and the like are still valid contributions to science

No, it is not too much to ask in the specific case of Gebru’s bad paper. Several of the arguments are specious, like comparing the total energy consumption for training GPT with car trips, or demanding that NLP researchers have to keep up with rapidly changing activist “woke” vocabulary and ensure their models are respecting it.

These are ridiculous claims, and it’s fair to respond to them by saying, “well, what exactly do you imagine a solution or mitigation looks like?”

Essentially, by the nature of how specious Gebru’s stated problems are, they demand clarity over what an “ethical solution” even is, conceptually, and why everyone would have to agree.

For example, you could discuss economies of scale or train-once-finetune-everywhere approaches with GPT that reduce total energy needs. Or you could discuss how researchers can register the corpus they use and the snapshot of time it was grabbed, with an open understanding that as long as the methods and data are reproducible, there is no research ethical issue with studying that corpus, no matter how much bias or lack of woke vocab a given person believes it has. (And also, nobody is required to just accept activist language as important or valid.)

Gebru did none of this. The article could literally be summed up by Gebru saying, “I think <supposedly shocking evidence> is bad, therefore its connection to something in ML is bad.”

E.g. “I think, subjectively, that the raw energy use to train GPT is bad. Here are some shocking comparisons. Therefore GPT is bad.”

It’s incredibly unrigorous and juvenile. Dean’s comments that it needs to clearly state mitigations is actually a super generous, polite way of saying the paper is just subjective amateur hour.

Isn't that what the "directions for future research" section is for?

Sure if no methods/work exists. In this case there already has been some work done, to mitigate these issues. So either mention them in related work and/or highlight why theses methods arent enough.

Of course. Especially to expect all that to be in each individual published paper.

wasn't the real issue that the standards they cited were quoted later in the process than normal

To be fair, the article is not about the firing. In any case, the two researchers who got fired did more harm to Google than good. Internally no one cares they left. AI etichs is an esoteric academic research field.

I'm in the research community and I think you're significantly underetsimating the effect of firing Gebru and Mitchell. Machine learning is the hottest research area in computer science and ethics of ML is possibly its hottest subfield. And people pay attention to employers' actions. I think Microsoft Research is still feeling reputation effects from closing down its Silicon Valley lab 10 years ago with no warning. It sent a message to everyone who worked there that they had no job security, and plenty left for academia. The research community is not going to forget about Google's actions here nor, for the most part, will it view Google very favorably.

I think you're very wrong. Yes, there's hype around ML etichs. Trends in ML come and go. Do you see any VC investing in etichs in ML startups? As I said, it's an esoteric academic field.

How do you even compare between the two? MSR closed an entire lab out of the blue of some great researchers who didn't do anything wrong. Here you have two employees going against their company and shitting on it publicly. The only researchers who will not want to work at Google after this saga are the ones Google better off without.

> Do you see any VC investing in etichs in ML startups?

Perhaps you should pay less attention to VCs and more attention to governments and academic institutions, who for example in Canada are investing 10s of millions of dollars into AI ethics/FATE/AI for good research.

Sometimes, the point isn't just to make money, it's to actually improve humanity.

The point is not just making money. The point is solving real world problems. AI ethics focus on esoteric problems that don't solve any real world problems. AI ethics research has very little impact if at all on real issues.

> AI ethics focus on esoteric problems that don't solve any real world problems

I'm not sure now what you think AI ethics research is. Do you think systemic discrimination is not a real world problem?

Systemic discrimination is indeed a real world problem. That's exactly the problem. AI ethics doesn't help solving systemic discrimination for the simple reason that AI is not causing systemic discrimination.

AI systems are trained on data. There's an abundance of English data which is why systems are often biased to work better on English. Similarly, an image recognition system might be biased if you don't provide it with data representing all demographics. There's nothing new about this and you don't need AI ethics research to solve these issue.

Focusing on AI ethics thinking it has impact on systemic discrimination, instead of focusing on real issues that cause systemic discrimination, is my main issue with all of this.

> you don't need AI ethics research to solve these issue.

what are you talking about? This is exactly the kind of research that's classified as AI ethics. "Solving these issues".

> instead of focusing on real issues that cause systemic discrimination

Identifying which ML models _actually running in production_ cause systemic discrimination (e.g. as you mentioned poor image recognition, bail predictions, etc.) is exactly focusing on real issues that... cause systemic discrimination.

> AI is not causing systemic discrimination

This is simply not true. Bad ML models have an impact on systemic discrimination right now, in that they amplify it.

> instead of focusing on real issues that cause systemic discrimination

It's a fallacy to think we can't do both, there's enough humans. Both making better AI and making better societal systems.

> Identifying which ML models _actually running in production_ cause systemic discrimination (e.g. as you mentioned poor image recognition, bail predictions, etc.) is exactly focusing on real issues that... cause systemic discrimination.

There's nothing systemic about these issues. I already mentioned it's a data problem. Nothing new. It's very easy to build a fair image recognition system by representing all demographics. And even then AI systems will continue to make mistakes. Some AI ethics researchers cherry pick on those mistakes to justify their entire research.

> It's very easy to build a fair image recognition system by representing all demographics.

I wish it was easy. Unfortunately, reality is more complicated, as it tends to be [1,2,3,4].

[1] https://arxiv.org/abs/2010.03058

[2] https://arxiv.org/abs/1911.05248

[3] https://arxiv.org/abs/2008.11600

[4] https://arxiv.org/abs/1905.12101

> Some AI ethics researchers cherry pick on those mistakes to justify their entire research.

This is a weird statement. This is like saying police cherry pick on criminals to justify their existence.

Do you not believe in harm reduction? Don't you think some part of AI research should be dedicated to minimizing how many "AI systems will continue to make mistakes"?

Thanks for the references. I will check them out once I get a chance. I do know one of these papers and from my understanding the modeling bias is on underrepresented features or the long tail, which again can be thought as a data problem that can be solved with better data collection.

I do agree that in the real world datasets are often biased because they represent the real world... and there are indeed modeling approaches to address such issues. (e.g., designing a loss function to up/down weight of certain types of examples). There's nothing new about this, it's been known in ML for decades.

why on earth should i care any more or less about systemic racism just because some charlie tells me it's unethical? Inventing ethics for machines helps cure exactly nothing. Only that i may be deemed by a machine to be an inferior human and less worthy than a "superior" being. Pushing technology that enforces your ideology is a horrible idea.

> Pushing technology that enforces your ideology is a horrible idea.

But that's what we're all doing. By being "against AI ethics", you are effectively pushing technology that enforces _your_ ideology.

Your ideology seems to happen to be different than mine, but you would be naive to think that the status quo is somehow "ideology-free".

Do you still think it's a horrible idea? Try being a bit less deontological and a bit more consequentialist. Doing nothing has consequences too.

>Your ideology seems to happen to be different than mine.

if you're saying i'm a racist, i'm not. Is everything that some people say are racist actually racist? No. Is racism a problem? absolutely!

The status quo, however, should be changed by people,not people with machines. Doing nothing, that involves not using obscure algorithms to force people to think a certain way is better, in my opinion. You can't call something ethical just because you think it should be; it must be argued out. Using AI to shut out some more of that argument will only create a universal standard, not necessarily the correct one.

> if you're saying i'm a racist

I wasn't, sorry if that's what you interpreted.

> The status quo however should be changed by people,not people with machines

I'm not sure I understand what this means. People/companies own machines (and ML models) and use them. So shouldn't we make sure that the machines' decisions align with what people/companies _want_ them to do? (i.e. that the people's ethics align with the ML model's ethical consequences; I'm 100% sure that people who deploy "racist models" don't do it on purpose or out of malice)

> You can't call something ethical just because you think it should be; it must be argued out

On one hand this sounds like a strawman. No one thinks that something is ethical because someone randomly declared it so.

On the other hand... ethics are a human construct, and will continue to evolve as our culture evolves over decades and centuries. Shouldn't we construct ML models which are flexible in that they can align themselves with the ethics we collectively decide? We don't know how to do that yet!

> Using AI to shut out some more of that argument will only create a universal standard, not necessarily the correct one.

You seem to be under the impression that the fields of AI ethics is dedicated to brainwashing people into some particular unpopular moral philosophy. This is simply untrue. Within the field of AI ethics there is a lot of diversity of thought and disagreement on how human morals should be "encoded" so that AI can "align" with these morals. And I'm using the plural of morals because obviously there will never be a humanity-wide consensus on ethics, and if AI is to be deployed in the world it needs to reflect this diversity.

Here's an example of AI people disagreeing if you don't believe me: https://jacobbuckman.com/2021-02-15-fair-ml-tools-require-pr...

Ethics and morals don't exist. Full stop.

Codifying ethics perpetuates falsehood. Every single generation in history believed that the "had it" only to be denigrated as hopelessly misguided by the next generation. We are making the same mistake, only less people are killed right now, so it looks like we are more successful. Remember the Pacifism of the inter-war years? it bred fascism. The pendulum swings.

AI ethics cannot hope to remain in style for long, while they will almost certainly exist for far too long. Accepted standards of 2 years ago, are already out of date.

I'm lost for a solution.

I do think that the less AI is claimed to be ethical, the less it will be trusted, which is the best cure i can think of. Honesty is the basis of the whole of scientific inquiry, and is probably scarcer in google's ethics research department than anywhere else in the building. (programs don't run if the math's wrong, economics as well)

No, you're wrong. Ethics and morals do exist. Money exists. Ideas exist in our brain, functionally.

Are all these things _ideas_? Human creations? Sure. The universe is absolutely indifferent to us. But these _ideas_ have real-world impact, and I'm not indifferent to my own suffering.

Societies function at the scale they do right now because there is enough overlap in how I perceive the world and how another random human perceives the world so that even though we don't know each other, we can still cooperate [see e.g. 1 for great discussions on this] e.g. exchange money for goods.

[1] https://www.preposterousuniverse.com/podcast/2021/02/01/132-...

> AI ethics cannot hope to remain in style for long

Again, you seem to be conflating "AI ethics" with a particular ethical stance, let's call it woke humanism, and you seem to think that the people who work on AI ethics work to enforce this belief on others. This is wrong. We're perfectly aware that humans have a variety of ethical preferences, see my previous post. Lots of people who work in "AI ethics" are definitely not woke humanists.

> Accepted standards of 2 years ago, are already out of date.

I'm not sure what you're trying to say here. Um, sure we keep finding better algorithms... no one ever, ever, ever, has claimed that their paper is the ultimate algorithm and no no one will find better. But 2 years ago, killing a random person in the street was wrong. It's still wrong today, it was wrong 2000 years ago, and it's going to stay this way for the foreseeable future.

> I'm lost for a solution

The research field of AI ethics exists because we don't know what the solution is!!! Come join us if you're so concerned.

> Honesty is the basis of the whole of scientific inquiry

If you value honesty, then you should value research that tries to make ML models "honest", by revealing how they make the predictions they do and where that fails. I don't understand your antagonism towards ML FATE (fairness accountability transparency and ethics) research

my points still stand. History has shown us that defining ethics and writing them down merely spreads falsehood. I don't think we are actually better off morally now then 50 years ago. In some areas yes, in others we have regressed. (look at addiction rates now) Now to categorically state that ai ethics is as diverse as "traditional" ethics can't be true by definition. (mine, if you ask)

As for killing random people on the street never being socially acceptable... ...Just look at europe 80 years ago.

AI Ethical research can never be an accurate representation of the majority of humanity - or even the majority of its users! If it can it is not sustainable for any long period of time.

What about far more stable principles, such as murder and racism, you ask?

They are prone to being overplayed or downplayed, are state executions murder, or justice? What if the victim/ hangman happens to be black? Why should it matter? Just ignore some issues? That's misleading by omission.

It would be better to just admit "yes, we at Giant Tech know our ethics are bs, but we had to put something down or our machines won't work. Maybe we are not ready for advanced ai. Maybe there's a limit on what programmers can do. Yea. We know. turns out computers DO have limits. We'll have to find other ways to make money."

"But why should we say that!" cry all the executives when this speech is proposed to them.

"because it's true, and if we don't act now the company is screwed. And our clients will also get screwed" is the answer of the timid executive who first suggested this.

"how will the truth help us?" respond all the execs in unision.

"if we can keep up the lie for long enough, we shall all long be millionaires and retired before it implodes! We shall long be out of danger! who cares if some people lose money?"

"Yes, but don't you feel bad for all the shareholders? And how can we possibly fool people for decades to come that our ai isn't bs?" Responds the poor executive weakly.

"By creating a fake team and telling everyone they are ethics researchers" they say. "Really they are just pawns to help us earn more money. Fish get eaten by bigger fish you know?"

"can you just help me change a few lines on my press statement?" Asks the first executive.

I think you're missing the point I'm trying to make, which is that developing "fair" algorithms, is not about developing algs that are e.g. pro-white-black equality, it is about developing algs that have option of equality built-in. It is then up to the user of the algorithm (you, Google, whoever), to "input" what or who should be equal.

It just so happens that currently the "input" is racial and gender equality. That's a societal choice, and one that is likely to change if e.g. racial equality is achieved and some new inequality arises. Maybe eye-color-based discrimination, who knows.

More generally than "equality", AI Ethics research gives us tools to analyze current methods and see where they fail to meet our ethical standards.

> History has shown us that defining ethics and writing them down merely spreads falsehood

Humans have been trying to improve their own condition for as long as there have been humans. Collectively defining acceptable behaviors is a never-ending task. Does that mean we should not undertake it? Absolutely not!

Writing down ethics isn't about spreading falsehoods, it's about cooperation. Cooperation involves compromise:

> AI Ethical research can never be an accurate representation of the majority of humanity - or even the majority of its users! If it can it is not sustainable for any long period of time.

Laws can never be an accurate representation of the majority of humanity - or even the majority of its users! If it can it is not sustainable for any long period of time.

Culture can never be an accurate representation of the majority of humanity - or even the majority of its users! If it can it is not sustainable for any long period of time.

Morals can never be an accurate representation of the majority of humanity - or even the majority of its users! If it can it is not sustainable for any long period of time.

Do you see the pattern? Things change, that's normal. We still have laws, and culture and morals, but we adapt them to our needs. Are you suggesting we should simply reject anything that changes? You won't be left with much.

I'm still very much interested in improving my own condition. That includes pushing people to behave in ways which I think would do that. People have different interests and their condition is often at odds with other people's condition. This is the foundational difficulty of living in a society of more than 1 individual. Yet we 8 billion humans still manage to be fairly successful at it. I wonder why?

Cultures and morals change. Does that make the morals of the past falsehoods? Of course not. They're just different perspectives on the human condition, probably best suited to the material conditions of the past.

Calling someone today is often seen as rude when a text would suffice. This is due to our material conditions, the ubiquity of cellphones.

> It would be better to just admit ...

You're suggesting we should admit defeat? Give up and let Google maximize profit? AI is a wonderful tool that could improve the material conditions of most of humanity if used correctly. It could also be devastating. I'd rather it not be devastating, so I'm going to continue supporting people who try to do research into aligning AI with whatever ethics we collectively agree on.

> I don't think we are actually better off morally now then 50 years ago

This is pretty sad.

Don't confuse your own cynicism vis-à-vis big tech with some nihilistic historical inevitability. The global improvement of the material conditions of people in the last 50 years have enabled us to start asking for ourselves what morals we actually want on a global scale, rather than this exercise being left solely to a self-interested elite.

Regardless of how better off morally _you_ think we are or aren't now, the space of collective possibilities is now immensely larger, whether you like it or not. That, is wonderful.

So, could you tell me for example, what this relatively often cited paper solves or where it's actually applied to solve a real world problem? https://openreview.net/forum?id=Sy2fzU9gl

I don't know this paper but it doesn't matter. I didn't say only ethics in AI research doesn't solve real world problems. It's a research field mostly for academia not industry IMHO. Obv many ethics researchers would disagree with me on this and that's OK. Partly because they would rather have the option to work at Google with FAANG salary vs a middle tier university in the middle of nowhere.

You seem to have an already strongly formed opinion on the topic, and it seems it would be very hard for people to have you even acknowledge that they may have valuable diverging views.

Starting from that point, what do you expect from a discussion? What kind of information would lead you to think again about the situation?

I'm happy to change my view if someone shows real world issues AI ethics research is helping in solving.

I'm much more scared about the singularity (AI safety) in the ethics field than models having bias, while the models are improving accuracy over time.

People say that AGI is still far away, but I haven't seen any results of being able to contain the harm AGI can do to us humans.

What these researchers are doing is the easy part of AI ethics.

Why would a VC invest in an ethics startup?

How much money something can make is not a good arbiter of how important it is

e.g. the hippocratic oath

In terms of attracting AI researchers, think of this:

Gebru has very publicly got into fights with Yann LeCunn and now with Jeff Dean. If you are building AI, who would you rather build your team around, Dean/LeCunn or Gebru? If you are an AI researcher, do you want a join a team where one of the team members is in the habit of aggressively accusing other researchers of racism? Would you be worried that your research might fall within their crosshairs for some reason or another? For example, if you are working on natural language research, and your model ends up doing better with Indo-European languages versus those from other families, do you want to be accused of propagating racist power structures on Twitter?

I don't think that's the right question. It's just about if Google can attract top researchers (or needs to).

> ethics of ML is possibly its hottest subfield.

Is this really true? I don't see ethics in ML papers getting the same attention in major conferences as theoretical or experimental breakthroughs in deep / reinforcement learning.

Don't get me wrong, ethics could be hot outside the ML academia, but I very much doubt it's something majority of grad students in ML are dying to get into.

Not sure, depends who you ask. But almost every deep learning and RL innovation opens a can of ethics worms...

i guess you are right that potential employees will consider this behaviour in their calculations. for the most part by adjusting their salary demands with an additional "risk adjustment bonus". as the FAANG can easily swallow that additional cost and are still incredibly attractive i doubt there will be a big effect besides loosing some value-oriented people. i doubt this will make a difference numbers-wise. nonetheless i applaud employees sharing their view of a companies inner workings for us others to have more information to make an informed decision themselves - yeah transparency

More importantly it has no connection to the bottom line, which is why Google management doesn't seem particularly concerned with disquiet in that research group, as long as it doesn't spread to the rest of the company.

Google and other companies should regard rigorous research and discussion about AI ethics as long-term protection of their bottom lines. If they start launching products and selling services that are found to unfairly favor or disfavor certain groups of people, they will be vulnerable to lawsuits, government regulation, and damage to their reputations.

One of the core issues in AI ethics, really the core issue currently, is that any product you launch or service you run will be found by some subset of the population to unfairly favor certain groups of people. No amount of research will allow Google to build a model so neutral everyone has to agree with it, because people want different things and have different ideas and assumptions about what's fair. As they found in 2019 with their AI ethics board, even basic ideas like "let's listen to everyone" are subject to this dilemma, because some groups feel that it's unfair to listen to other groups.

I think it is important to shift AI ethics to become more of an investment but that requires more tooling to evaluate AI ethics problems and the business risks.

This may not change end of year results, but this kind of research is what gives Google a credible voice when it comes to shaping public discourse and influencing legislative process, for instance.

AI etichs is an esoteric academic research field.

I would argue it’s a Public Relations field.

i agree. Ethics means nothing. It's just an emotional security blanket. They are no more qualified than a 5 year old to invent ethical standards.

>AI etichs is an esoteric academic research field.

I don't agree in general but I do think these two researchers, and this whole saga have just hurt the AI ethics field.

But when you write something like this, do you also understand that their actual research is widely considered to be of a high quality and very important? So if you agree that ethics is important, would you leave them off a top 10 list (and who would you put on)?

I speculate this article is because of the firing.

> All of us are replaceable

That's a management illusion. Try to replace e.g. someone like Fabrice Bellard, Mike Pall or Claude Shannon. Of course such things happen in big companies, but mostly because management is too limited to properly assess the true value of certain individuals. But the article is actually about a different topic.

That's an ego illusion. It hurts to admit that we're not replaceable, but we are. The job might not get done as well, or done in a different way than we'd do it, but it'll still get done.

It's certainly true for most of us but not the names the op mentioned.

I would argue the real ego resistance is in not accepting such people exist.

Both are true: there are supernaturally talented people and also an incredibly wide world.

If you take an intellect so impressive that they are one in ten million, there will be still be almost eight hundred of those people in the world.

We are also reasoning from the POV of our own reality. We see the people we did get, but it could be the case that we missed some brilliant minds that do exist in some alternative universe, but came ahead anyway. There are so many factors in play.

> If you take an intellect so impressive that they are one in ten million, there will be still be almost eight hundred of those people in the world.

Intellects aren’t fungible. Even if there are 800 Fabrice Ballard-level minds out there, I doubt most of them have honed their brain on the exact problems he’s worked on. You can’t just find another one-in-ten-million mind and put them to work on the problems of another 1/1e7 mind and expect comparable results.

Essentially it's a clash between the Great Man conception of history and the process version. The Great Man version is easier to understand. You can look at a specific individual and easily conclude that their actions had an enormous impact. For people such as Mao who had sway over billions, it is certainly a conclusion that seems to withstand quite a bit of scrutiny. But any person is a product of their context and we have to deal with multi-factored forces that might be impossible for a single human mind to model or grasp given the quantity of data. This is particularly relevant for scientific pursuits as opposed to political decisions. Newton and Leibniz sound irreplaceable if you read their biographies, but they came up with calculus separately around the same time. The same goes for Darwin and Wallace. If the conditions are ripe, individuals matter less. Technology isn't a predefined ladder like in the civ games, but every civ is at a juncture where so and so technology has a probability of being discovered. It's not unrealistic to assume that if certain lab conditions exist, it's only a matter of time until someone stumbles on to penicillin even if from a historical and emotional perspective it seems like a freak accident.

I can't draw a conclusive answer to these questions following the logical consequence of my own arguments, but at least we have to come at the problem with the knowledge that our own minds are drawn to simple narratives and to individual achievements. Hence assuming replaceability in the absence of very strong evidence to the contrary

There's a whole set of problems that people can work on. There's solutions for most of them. Some of those solutions aren't very good, but they're the best we have.

Fabrice Bellard has worked on a subset of the problems we have. He's created good solutions for them. But if he hadn't, we would have some other, lesser, solution for those problems. Like we do for the problems he hasn't worked on.

No, you can't expect comparable results. But you can expect some results.

> We would have some other, lesser, solution for those problems. [...] No, you can't expect comparable results. But you can expect some results

Which is exactly my point: if you can’t expect comparable results, the person is not replaceable.

I think we're violently agreeing then, as I said "the job might not get done as well, or done in a different way than we'd do it, but it'll still get done"

Replaceability is a vain concept.

Simile: saying “your brain is replaceable”. Beyond the fact that the most likely context is a threat, it is a poor argument: while technically true, what would remain of me would not be meaningfully me. And the surgery is work that would be hard-pressed to generate the expected value, such that the only reason to do it, is either out of anger or as a consequence of irremediable damage.

Companies are stories. The decisions are made internally, but their meaning is narrated externally. If you change the protagonists, the story changes. The case of Uber’s self-driving car division is quite an example of that.

Does the change in Google’s story converge to a positive or a negative light?

> Does the change in Google’s story converge to a positive or a negative light?

Like all stories, the meaning and message of the story is formed in the mind of the reader, not the mind of the writer.

Every reader will make their own meaning, and fit that into their own story.

It's impossible to say whether this change results in a positive or negative effect: it will be positive for some, negative for some.

>> It hurts to admit that we're not replaceable, but we are

The more people, the less the individual is valued. But that does not make the individual less valuable. Unfortunately, for a few years now, respect for the performance and qualifications of others has been declining more and more. This increases the illusion that everyone could be replaceable. Just ask your family if they see it that way in relation to you; the illusion of replaceability definitely ends here.

  The job might not get done as well, or done in a different way than we'd do it, but it'll still get done.

If the job isn't done as well, then no one isn't as replaceable as you put it.

Excellence can't be replaced as easily. Maybe for certain kinds of jobs yes, but for all jobs? No. If that were the case then we'd be inundated with Einsteins, etc. And we aren't.

How many people have the opportunity to be Einstein?

How many people have the right brain, and the right interest, and write the right paper at the right time?

How many are starving in an underdeveloped country and no access to education, for that matter.

Einstein wasn't necessarily a unique genius standing at the pinnacle of an intellectual mountain. He was a beneficiary of survivor bias. We don't know how many other "Einsteins" there have been, or could have been, because we only tell success stories.

  How many people have the opportunity to be Einstein?

Everyone who has access to (public) education, probably. And of those whomever has a relentless will for achievement. And/or is, by nature, curious about stuff. There's a reason why the lines between genius and mental illness get blurred sometimes. Remember John Nash, Jr.?

  How many people have the right brain, and the right interest, and write the right paper at the right time?

  How many are starving in an underdeveloped country and no access to education, for that matter.

I wouldn't know but I'd estimate millions.

  Einstein wasn't necessarily a unique genius standing at the pinnacle of an intellectual mountain.

Whether you like it or not, he was a genius, and unique in his own way (like everyone else is - even you), along with various other well-known peers of his time and lots of other people before them.

Now, obviously, they, as well as any "proper" scientist, are well aware that none of their work would mean anything if they didn't stand on the shoulders of giants. Science is a branching tree of giant people.

  He was a beneficiary of survivor bias. We don't know how many other "Einsteins" there have been, or could have been, because we only tell success stories.

Following your train of thought then no one's achievements - even those who you claim don't have the "right brain," "right interest," don't "write the right paper at the right time," are "starving in an underdeveloped country" and "without access to education" - would mean anything.

So, to get back to the subject: replaceability depends on the kind of job. It may be simpler to replace a fast food worker, but a Richard Feynman? an Albert Einstein? or <a name of a scientist whose name isn't publicly known but has made a difference in their field>? I doubt it. Those people made a difference in their respective fields and no one can take that from them. And I'd say the same if it were someone else from other countries, ethnicities, etc.

That's maybe true for YOUR job. The more a job has a well-defined description, the truer your statement is.

My job title is "technical chap". I have no job description apart from "make all the things work". I could be replaced very easily ;)

People are somewhere on the scale of greatness. At some point it becomes harder and harder to find replacements that will be able to get that job done. People are very capable to steer projects into failure.

It's not, at least not in ML for a lab as prestigious as Google AI. They probably have several hundred researchers with excellent publications that would be willing to drop everything and get a FANG salary.

Alright. 99.999% of us are replaceable.

Also this is a management illusion. There is no evidence for this assumption. You don't even know the probability distribution. There is no reason to assume that the percentage is equally distributed across all firms or countries. And anyway, the article is about something else.

No, it's an axiom. It defines a way to make collective/collaborative entities hopefully bigger than the sum of their parts. I think of these things (corporations, groups, movements) as aggregate people, and that's very much what Google is about.

Google deals almost entirely with aggregate people: statistics, algorithms, collective behaviors, machine learning, implementation that's never about individuals but is about larger population trends. Aggregates, not special unique snowflakes.

As such this is not an illusion but an axiom. Google and entities like it (themselves humongous aggregate 'people') MAKE individuals replaceable, the better to be dealing with other entities like themselves. This is only going to accelerate the more they get to bring AI and machine learning into the mix… which by now is long established, nowhere more than at Google.

> it's an axiom

An axiom which only applies to a certain percentage of cases?

Maybe an axiom as in being something we assume (because we can't/won't figure out whether it is actually true) this and base our decisions on this axiom.

Then what is your distribution then?

Those exceptional individuals are incredibly rare, like one or two in a generation. So you need to be Shannon-likes to be not replaced by some middle manager in a big corporate? Emm, if someone were this accomplished, why would they care about one employment? That is the wrong question to ask.

Truth is, if Google thought they were not replaceable, it would not fire them this easily.

Much more people than you expect have at least one exceptional skill; from my observations I would say at least 20 to 30%; the more extraordinary skills per person, the rarer of course. And if indeed the human workforce were really such a generic, easily replaceable commodity, why do most companies, including Google, go to such great lengths in recruiting, with assessment centers and so on? And why are there so many unemployed IT specialists, for example in Germany, when at the same time the industry associations claim that jobs cannot be filled?

Brings to mind De Gaulle saying "the graveyards are filled with indispensable men."

And yet so many people consider themselves important enough e.g. to post comments here. It just seems that it is always the others who are dispensable. For people who make it into management, this tendency even seems to intensify (or it was the prerequisite why they wanted to be in management).

Okay, approximately all of us are replaceable. We can agree there is an epsilon of people who are clearly beyond others. However for almost all the work that has to get done, the actual bar is "can you write decent Python?", not "can you design and implement a novel algorithm for computing Pi?"

> approximately all of us are replaceable

I guess it depends on the purpose for which we are all supposed to be replaceable. Nature probably doesn't care which individuals reproduce or are eaten, as long as the numbers are right. Human society with its elaborate specializations and long training periods has added a few more dimensions.

Shannon built on Hartley, as much as Einstein built on Lorentz.

That's not to say these weren't great minds, but the concepts where in the air and the race to formalize them was on; most of the "second places" are today forgotten or their contribution diminished from the modern "winner takes all" mentality, but none of them existed in a vacuum.

The history of science is fraught with independent discoveries, from calculus to the the telephone, up and including mass energy relation and the basis that later became quantum mechanics.

If A and B made the same discovery independently, that is evidence that A was replaceable, but that C built on D is not evidence that C was replaceable.

Did you read the article? Gebru's firing isn't the focus of it.

Yes, I stopped reading when it mentioned the last good paper was from 2017. This is simply not true. I don't have time to go through all of their papers right now, but as someone else mentioned the protein folding one was a real breakthrough. They also have lots of great stuff in the NLP space (something similar to gpt-3 like 2 years earlier). Also tons of stuff on the actual training architecture/methods.

Edit: I want to add that saying the title has nothing to do with the article is not helping the case. I finished reading the article in case I was being unfair, but I still stand with my original comment.

The title, which does not mention anyone's firing, has everything to do with the article...

Anyway, you stopped reading in the first sentence? That's essentially the same as not reading it.

It says the high point is 2017, not the last good paper. There are of course other good papers coming out go Google. But the novelty is dropping and the angst is increasing.

But this is false as well. Bert was a great breakthrough in NLP in late 2018. IMO bigger breakthrough than AlphaGo, but less media friendly. It has freaking 16 000 citations and it's used all across industry.

Lots of people think protein folding is a bigger deal than beating the go champion.

Journalists were asking people from DeepMind why winning in Go is important. They said because it may lead to breakthrough in e.g. medicine. Well it didn't, at least not yet and not directly.

But we still got AlphaFold. And AlphaFold is the type of breakthroughs DeepMind is meant to make. Not playing games.

I think judging the decline of a rapidly evolving field by literally one of the biggest breakthroughs in its entire history is not good. I also don't like clickbait and I think the audience here generally doesn't either, even if you justify it at the end.

To be fair, you wouldn't know that from the article's subtitle:

> What does Timnit Gebru’s firing and the recent papers coming out of Google tell us about the state of research at the world’s biggest AI research department.

I read the article and I don't get what's the focus of it. It seems a disconnected rambling about vague deep learning issues with Gebru's name interspersed several time in the text as to suggest her relevance.

Hear hear.

These clickbaity article titles are tiresome.

Thanks for saying it like it is.

Please read the article. I address some of the issues raised. My point is that Gebru's firing is symptomatic of some deep problems Google are experiencing. Thank you.

>. I don’t want to downplay the deep instutionalised sexism and racism that is at play in Gebru’s firing — that is there for all to see.

This is a very badly written and uniformed article, and sentences like these essentially illustrate the thinking here (It's imploding because I don't like it).

Here is an alternative reading: Google is cleaning house of toxic activists who are not interested in serious ethics research but use it as a vehicle for their ultra-progressive political agendas.

but isn't ethics inextricably linked to the worldview generally? We have some common ground nowadays (no killing in so called "civilized" countries), but to me it seems like you are just proposing the usual neo-xyz-argument: things can't be changed (which - originally developed in the thinktanks of cold-war-USA under heavy fascist influence - now has been the main global narrative without any institutionalized counter for 30 years)

I read the article and i'm not following the argument delivered at all.

There is no real proof to this.

I'm following and reading research @ google (stuff like this https://ai.googleblog.com/ and other sources) for ages now and NOTHING indicates an 'implosion'.

It is strong research with real and constant results.

I have no idea why the autor would even consider using the word 'implode'.

Its not rocket science that data is biased and it just will continue be researched and a solution will be found. For the single reason that biased systems in certain areas will not deliver the results you need to use it properly.

> For the single reason that biased systems in certain areas will not deliver the results you need to use it properly.

well, they produce happy numbers for papers and depending on the brainwash-level of a population, they might also sufficiently often do the "right thing" towards minorities that noone cares too much about, independent of whether it would stand a chance against objective evaluation.

It is research; It doesn't need to be perfect.

And the research they do is, even with this bias, ground breaking.

80% might be to get this thing running, 20% might be to finetune it for minorities.

When google started the ML stuff for translation, they did start with english, now they support much more languages than before.

Nothing symptomatic about firing toxic employees. The only thing imploding is AI etichs research. There are good people doing quality research that will now have much harder time finding a job in industry because of the bad rep these Google employees "contributed" to the field.

I read the article. Doesn't change the fact that your chosen title is clickbaity. Even if you disclaim it in the article itself.

It is a valid criticism that the headline is clickbaity.

>All of us are replaceable

This sounds more like a reason to unionize than a reason to celebrate a firing.

I believe you, but just for the sake of it: What AI research has had business value from Google in the last years that you could sell?

WaveNet is one example:

"Google Assistant adds 9 new AI-generated voices": https://venturebeat.com/2019/09/18/google-assistant-gains-9-...

Data center cooling is another one:

"How Google is Using AI for Data Center Cooling": https://www.bmc.com/blogs/data-center-cooling/

AlphaFold may bring millions if not billions to DeepMind, Google and Alphabet. Figuring out structure of a single protein may cost up to 100 000 dollars.

Bert as applied to search from late 2019: https://www.google.com/amp/s/blog.google/products/search/sea...

Time will show; winning the competition was just the first step.

Indeed, that is why I'm saying "it may", but people are crazy if they expect that basic research will make billions of profits a year after.

It normally takes some 10 to 20 years for basic research to produce profits.

mRNA vaccines are technology from 1990 and are first time used commercially on mass scale on humans 30 years later.

BERT has been powering all Google's search traffic at this moment:

https://searchengineland.com/google-bert-used-on-almost-ever...

It is THE biggest change that Google's search algorithm had ever been through, I would assume. And to push such a fundamentally different model to ALL their English traffic is pretty telling itself that how much an improvement Google had been seeing.

This is easily billions of ROI for Google, if not tens of billions.

Are you kidding me? Everything they do pretty much. Google search results, Gmail Compose, Google Translate, etc etc.

Alphafold is transformational for life sciences. I find it hard to articulate how much it's worth - maybe the sum of the top three life sciences companies today?

Honestly - the discussion is over, the AI folks won.

Unfortunately, they’ve got the data... that alone will attract talent and money.

  highest quality research with real results

Did you mean "highest quality research with real [risotto]"?

You must not have read the article, which isn't about Gebru's (and now Mitchell's) firing. There would be a lot to say about the ongoing credibility of any of Google's statements or research concerning ethical AI at this point, and lots of folks have said those things.

This article is an analysis of the extreme weaknesses in the current seemingly-productive approach to ML language model research. The intro anecdote about the failure of game-playing models to handle games with representational elements or indirect rewards is extremely important. But the failure of the Big AI community to recognize those same failures in its approach to building language models is the pending crisis that the article's title refers to. Gebru's firing is not the cause of the impending implosion of Google's AI research, but rather a leading indicator and warning sign.

The article discusses some concrete challenges in AI research, but the author's only argument for why Google won't be able to tackle these problems is that they fired Gebru. As he mentions in the article, it's not that the Big AI community has failed to recognize these issues; "40 Google researchers, from throughout the organisation" discussed some of them just a few weeks before the Gebru controversy.

Perhaps equally importantly, the author made a very serious accusation that Google has "deep instutionalised sexism and racism". Are we really intended to just gloss over that, treat it as unimportant filler?

Reply on news.ycombinator.com