Is Google’s AI research about to implode?

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.

Set aside the ethics issues for the moment, the criticism that Google is neglecting alternatives to neural networks is probably reasonably fair.

But on the other hand, research labs or not, Google is still a commercial entity driven by a shorter term horizon than your average academic.

Google Research has probably subconsciously drifted towards areas (or at least applications) that can tangibly benefit Google in the near future. Neural networks might not be The Answer (TM), but they can deliver results today, and there's still untapped potential. Speech recognition, search, speech synthesis - all core Google products - have all benefited from neural networks, not to mention the broader applications like protein folding for DeepMind.

I'm hesitant to find fault in a corporate group that's doubling down on the stuff that's actually delivering results, even if it's probably not the path to AGI.

I probably wouldn't be so lenient with OpenAI, because they are (were) purely a research outfit, openly committed to AGI.

> 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.

Really, where is that to see. You weaken your whole case with this kind of casual reference to "oh and she was also a black women" so racism and sexism apply.

Its a type of crying wolf that loses you more people in what is the potentially important issue at hand, her work at google.

I really wish there would be repurcusions for this kind of casual libel, as it can be thrown in today's climate without a second thought.

The implosion argument in this article is solely ~based~ triggered by Google's botched handling of AI Ethics researchers. To argue that this will implode the complete AI organization seems like wild stretch. On a normal day, all the arguments padding the article would fall on deaf ears.

So, the answer to the question is no. Instead, AI Ethics research will probably implode. AI research, probably no.

For AI research to implode, major AI research leadership need to be seen leaving. And I am not talking about the corporate leadership but the research thought leadership whose sole purpose is to shield the junior/senior researchers from corporate BS (and who are themselves well-known researchers).

Edit: It is perhaps be more appropriate to have said that the botched AI Ethics researchers handling solely triggered the article.

Feels like the author has an axe to grind.

Research isn't about knowing all the answers or always being successful. Negative results are just as important as positive ones. Author does not even mention the recent protein folding result.

As I have said before, whenever someone talks about ethics and AI, I ask the question "Whose ethics? Ethics according to who?"

It's a fantastic chance to export your viewpoint on the entire world when this ethical AI begins to underlay various services. For example, "Members of Group X are inferior/should die" being unethical while "Members of Group Y are inferior/should die" getting past the AI ethics is a great way to slide this past a ton of people. No need for argument, it just comes pre-censored on the commenting system.

AI, as a field, is going to have to face a painful question: what if we get answers we do not like? I'm Irish enough by extraction, so I'll use it as a self-denigrating point ... what if AI found that the Irish, just by genetics, did actually tend toward alcoholism and drunkenness? Would we accept that, or would we say "No, that's wrong. Go back to the drawing board until we get the answers we want"?

My guess is that we are going to look for the latter. AI is going to give us the answers we want, because that's how we are going to build it. AI won't be constrained by ethics so much as it will by the discovered truths we are unwilling to accept, whatever they may be. And that means there's a space for people with a thousand tiny axes to grind to be employed. It will be a chance to shape what "truth" in the Orwellian sense will mean.

As the author admits, the headline is a clickbait question.

Indeed, the article highlights some issues with AI research, in particular those which can lead to ethical problems when AI methods are implemented in consumer products. These are by now well-known and important issues and people should find a way to resolve them.

Then, in its final paragraph, the article suddenly claims to have answered its title question in the affirmative! Am I alone in thinking that the issues, valid as they could be, do not obviously spell doom for the entire program?

This article is correct about underspecification, but completely dodges the argument on language and understanding, drawing the hackneyed, shallow distinction between "pattern matching" and "understanding".

How do you define understand? People just use the word "understand" to fill in for the magic stuff that they say humans can do but machines can't. They also sometimes use "symbolic reasoning" that way, but the best work on machine understanding and symbolic reason is done by DeepMind:

https://arxiv.org/pdf/2102.03406.pdf

The examples in the original article are about truth, not understanding, mainly because we don't have anything approaching a formal definition of 'understanding'. But, if anyone does, it is probably the deep learning community, where conferences in the last 3-4 years have had hundreds of papers working carefully to examine what is the nature and structure of the knowledge encoded in these kind of systems.

Yeah, tech and AI is pretty unpopular and they'll probably have a hard time finding people who want to work there since the entire planet is now independently wealthy

May be "AI Ethics" research will be affected and I believe this is good thing. After reading David Graeber's Bullshit Jobs, He claims that administrative jobs in universities and managerial, duct tape IT jobs in industry as examples of it. Now it occurred to me that this AI ethics research is just one more example of proliferating bullshit job. Just recycle same idea and conclusions thousand times and you have years of research and hundreds of papers generated out of it.

And a mutual admiration society of university departments, twitter fans and these type of researchers in tech companies keep everyone in high esteem as world leading authorities.

Every time critics try to lump conspiracy theories, LGBT representation, and fake news together just to make a general point, it directly puts me off, I think that's mostly a cheap shot and lazy.

I'm out of the loop on the whole Gebru situation, from what I know she was researching ethics wrt to AI, so I don't really get the whole "novel idea/work" part in the last paragraph. I often get the impression that such critics never see the rapid developments in AI as progress as long as they don't cover the topics they would like to see focussed. It will never be good enough and always a concern because of X.

Nice try, but still a miss. Building assumptions into NN won’t work for anything complex like ladders and keys which according to this author are built in to human brains? Please. Yes, NN are still in their infancy but they are clearly foundational to general intelligence. They will probably require many additional discoveries about how they need to be connected and maybe even a few updates to the model of the neuron but they aren’t going away. Secondly, of course a language model is going to parrot back the data it is trained on, that’s a major goal ie given a giant dataset answer these questions. It’s also how a lot of casual human conversation works, we generally just parrot back things we’ve read or heard. The interesting parts are the new syntheses that human brains come up with but even those are standing on the shoulders of the dataset so to speak. You’ll never get a truly neutral view of the data, I don’t even know what such a thing would look like maybe just pure ignorance of the data could be considered neutral? Biases aka heuristics are a a core part of learned intelligence, they can of course be flawed or entirely incorrect for a given environment but they serve a purpose and you can’t do away with them or the ability to form them based on observed data. You can optimize them for specific goals but you can’t get by with just one.

The article is a good overview, but I don't think saying that Gebru was fired is accurate. This fact is commonly repeated, but I believe she resigned.

It reminds me of the recent Paul Graham essay where he talked about discovering, in graduate school, that the "knowledge based" A.I. (e.g. SHRDLU) was a "fraud."

Specifically: we could make small worlds in which syntax and semantics lined up (like the Zork parser) to seem like it understood natural language, but there was a point you couldn't go further.

I think some graduate students now will be disillusioned by the claims being made for deep learning today for the same reason that those systems will quickly master the things they are good at and leave a residue of even more difficult problems for the 2050s and beyond.

> Is Google's AI research about to implode?

No. (For reference see https://en.wikipedia.org/wiki/Betteridge%27s_law_of_headline...)

Seriously though - even if the points in the article were true, none of them imply any kind of implosion or significant disruption in Google's research.

> Maybe one day we will see the transition from Hinton (ways of representing complex data in a neural network) to Gebru (accountable representation of those data sets) in the same way as we see the transition from Newton to Einstein.

This is so loaded and gives Gebru so much undeserved credit that it undermines that author's credibility.

Interesting thesis. Is it me or does the author compare Gebru to Einstein?

Sounds like the criticism is basically that Google's screwdriver can't be used to hammer in nails.

> For these types of games, the algorithms can’t learn because they require an understanding of the concept of ladders, ropes and keys.

The underlying problem of AI is that we've taken something we don't really understand, and applied it. When something like that does not work - what can you really do?

> is that the technical challenges lie in finding ways to organise and construct language and image datasets in a way that is as ‘neutral’ as possible

I think part of the problem is that 'neutral' models dont exist. And framing it that way already loses half the battle. There is no such thing as a neutral perspective.

It's like when you learn a language, people always say they want to lose their accent as if it's a process of reduction to some perfect median. When in truth the way to 'lose your accent' is to pick a model speaker you like and mimic theirs.

Of course I do agree we should probably replace the current biased ones with better ones, but the 'neutral' goal irks me. When it is picking one bias for another.

As a casual observer, I get the impression that Google's corporate attitude toward research on Ethical AI is fundamentally analogous to Exxon's view of climate research in the 80s (and since). Namely, that Google, correctly, views research in the area as potentially undermining key revenue sources in the short and mid term. It is sad, but unsurprising. Government funded research and regulators will most likely be the force driving AI to be more ethical -- if that's going to happen. That's what we see whenever corporate and public interests collide head on.

I'm not sure I get the logic of the article. Google AI Research is going to implode because we know what the current models can't do? I thought we do research precisely because we wanted to expand our boundary of out knowledge. That is, shouldn't a research institution implode if there is nothing to research?

And why is Gebru relevant in this article? Does she do quality research, and the research results have to do with the claimed implode?

This article makes no sense to me. If the premise was will Google AI Ethics research implode, then yes. Removing key memembers of the team has affected the team morale and created a toxic environment for the remaining researchers. However, the post seems to imply that stifling the Stochastic Parrots paper is somehow proof that Google AI stifles innovative research that is critical to AI progress. This is a quite a weak claim even before looking at logic of the claims that get to that point.

Google AI, Google Brain, and Deepmind are all different groups at Google with different mandates and research goals. While what's happening in the Ethical AI team is troubling, it's rather a large and unfounded leap that it'll affect research productivity for the other teams.

Digging deeper the article is confusing and sometimes plain wrong on its assessment of AI research. Broadly the deep learning and RL approach to AI has been critiqued for its lack of semantic and symbolic understanding. These are not Google specific and the articles examples of this are terrible.

The first example of limitations of AlphaZero on Montezuma's revenge is a bad example. The author implies that RL failed because it didn't understand ladders. But later approaches still solved this using by using stochastic exploration strategies and not introducing conceptual knowledge to the model as the article implies is the key limitation.

On the language modelling, its weird the article cites GPT3 as problematic given that GPT3 was developed at OpenAI's research and not Google. Also GPT3 is pretrained using next word prediction which only consider left context and is far more limited that BERT which considers bi-directional context and produces richer word level embeddings. That being said the Stochastic Parrots paper does specially critique BERT.

But it's not a new critique. Emily Bender, the other major co-author, is a computational linguist who has always been critical of deep learning approaches to NLP. Bender along with Gary Marcus and many others have called for AI that considers symbolic and linguistic knowledge and have been critical of purely data-driven deep learning approaches. Stochastic Parrots is not new in its critique of large language models, it just provides newer evidence specific to the current state of language model research.

So I'm not sure how any of this is a signal that Google AI is imploding. The broader trends in AI is not just throw more compute and make bigger models. It just happens that large models works well for OpenAI and Google for specific problems. Google also has one of the largest knowledge graphs and there is open line of research that combines symbolic knowledge from KG with deep learning methods. There is also active research both at Google and elsewhere that aims to add more make current deep learning approach more "intelligent" by using linguistic and symbolic knowledge.

I'm confused as to how Google AI research is imploding. Google PR attempting to censor Stochastic Parrots (which was still published) because of bad PR optics has nothing to do with active research questions elsewhere at Google Brain, Deepmind, and Google AI.

The problem is that AI etichs research is an esetoric academic field that has little real value in indistry. I will explain.

Take the famous paper the article mentions. I read the paper. It's full of nonsense. It talks about environmental costs of training huge models. Give me a break. Then the problem with the biased datasets. Yes, everyone knows if you train on racist text you gonna get a racist LM. AI researchers are not stupid. They don't need etichs research to figure this out. The paper even gives an example of an Arab guy who got arrested because the Israeli police used an MT system that made a mistake in translation. The problem is not AI, the problem is the stupidity of the Israeli police who didn't verify the translation before arresting someone (he got released of course). Image recognition is another issue AI etichs like to bring up. "A black man was falsy identified!!". But when Yann Lecun said it's a training data problem the AI etichs researchers exploded. "No! AI models are racist!!". Etc.

Now, if you have to work with a biased dataset, then ethics/fairness research might have some value in guiding you on how to build more fair models. But in industry and esp companies like Google, cannot take chances and almost always resort to data as the solution.

Google AI would only implode when Google, or the corporate world finding that AI can't deliver the financial promise that was hyped.

And it is happening regardless of the drama, from the perspective of job market.

Wait a minute. The conspiracy answers given by AI sounds like a confirmation of facts! (Text written by my own AI model)

I would say the QAnon example demonstrates how useful the model actually is. It has been fed some [false] knowledge and answered in perfect accordance with what it has been taught. Doesn't this mean it is can give useful answers to real questions if taught real (scientifically proven) facts?

That GPT-3 excerpt is just hilarious.

Nope

the stupid clickbait title take away all credibility from the article

No its not.

Great that we taked about it :-)

“ Is there hope for Google Brain? One glimmer can be found in the fact that most of the articles I cite here, criticising Google’s overall approach, are written by their own researchers. But the fact that they fired Gebru — the author of at least three of the most insightful articles ever produced by their research department and in modern AI as a whole — is deeply worrying. If the leaders who claim to be representing the majority of ‘Googlers’ can’t accept even the mildest of critiques from co-workers, then it does not bode well for the company’s innovation. The risk is that the ‘assumption free’ neural network proponents will double down on their own mistakes.”