None of this has anything to do with AI. It’s just a barely tangential critique of... society? Capitalism? Either way it has nothing to do with the OP.

Man you didn't spend much time in academia. The number of psychopaths I encountered in academia quite easily exceeded those in Business, and I've been in business a lot longer. Maybe I was just unlucky/lucky; don't know.

Anyway, "AI" is an actual subject, but as used in the marketing and press releases, and at this point on HN, is a sort of grandiose malapropism meaning "statistics and machine learning."

The ideology spelled out by the author is ill presented, but fairly real.

1) "AI" as presented is a continuation of the program of the technocratic managerial "elite." The social class of people who more or less started in the time of Herbert Hoover, and while having some mild successes (the creation of the highway system, public health initiatives, WW-2 production), have mostly discredited themselves for decades (aka the shitty roads, a nation of fat diabetics, deindustrialization because muh free market reasons). Similar social classes also failed spectacularly in the Soviet Union.

2) Data collection is mostly useless. In intelligence work, in marketing, political work: most of it is completely useless, and collecting it and acting on it is a sort of cargo cult for technocratic managerial nerds, economists and other such human refuse. Oh, it pays off sometimes: it doesn't matter though; collecting it and performing actions based on it becomes is its own reward. There's an entire social class of "muh science" nerds who think it a sort of moral imperative to collect and act on data even if it is obviously useless. The very concept that their KPIs and databases might be filled with the sheerest gorp .... or that you might not be able to achieve marketing uplift no matter what you do... doesn't compute for some people. But it's real. I encounter bullshit like this every day.

3) "AI" as labor replacement is a wish, rather than a fact. They really mean your job will be replaced by an Alien or Immigrant, not a computer. As the article points out; the human proctored stuff on the web is the valuable stuff. They didn't point out that "AI" produces a ton of web (and email) spam content as well: and it's almost entirely shit. Something like a search engine is somewhat a somewhat valuable linear algebra way of organizing data in an automated fashion, which ding-dongs continue to conflate with "AI." Human proctored data is almost infinitely more valuable. I don't like Wikipedia, but if I had to pick between it and existing search engines; I'll take Wikipedia all day.

We are already using techniques fro AI (CI/ML) in several problems. I am not sure why you assume that no value was created.

I think a lot of it is ANN based now, but it worked pretty well when it was just KNN. Calling KNN "AI" is pretty cope; it's just a database query. Of course, the large ANN networks are also more or less just a database query refactored as a function transformer, but nobody likes to talk about that.

Most of this is a discussion about semantics, which is silly.

In 99% of industry, AI is used as a synonym for Machine Learning. The remaining 1% refers to Artificial General Intelligence, which is a long term vision for what might be achieved using, again, Machine Learning.

The state of the art is using transformers I believe, which are a special kind of ANN

If you don't consider machine learning AI, then there are no existing instances of AI. It's all either ML or vaporware.

Recommendation systems are probably one of the most significant advance, YouTube for the à user that actually care about discovering wonderful content/music is a marvel thanks to machine learning. I also believe that Google search use ML to some extent. Text to speech and speech to text give a voice to the disabled Translation make knowledge and communication accessible But the most important technologies like dependency parsing are enabler of a future AGI that could transcend human intelligence in some revolutionary metrics. Just look at this non exhaustive list of what AI can do and how well: https://paperswithcode.com/sota

"the holy grail of AI, it's ultimate goal, is AGI"

I don't think it is an explicit goal for many people - most people seem to be focusing on using 'AI' techniques to solve narrowly defined practical problems that were difficult or impossible with other techniques.

Edit: AGI was perhaps more of an explicit goal during the phase of "good old fashioned" symbolic AI the peaked in the 1980s and early 1990s.

Full AGI has little economic use aside from a brief resurgence of slavery. Its real value lies in creating an alien, terrestrial, intelligence to which we can compare and learn about ourselves.

Philosophically AGI is ancient and has always been about humans.

Reason for the nitpicking is, that for me - as a trained mathematician - and seemingly some other commenters, the gap between linearity and non-linearity is yet inside mathematics quite substantial. This is true also in general. Non-linearity might be not sufficient, but it is certainly necessary for any definition and/or serious discussion of AGI.

Just insert the words "these days" and "mostly", and you're pretty much covered. Also a honorable mention for calculus perhaps. Even decision trees use gradient boosting very commonly.

AI isn't intelligent, its something created by intelligence.

It’s weird that you compare AI to the human brain. I think the goal is here in the long term is to surpass the mere human brain. For that reason it’s stupid to try and model it at all. Maybe some future neural architecture will make the human brain look like a computer built using vacuum tubes.

The difference is that we have neither the scientific knowledge nor the computational power to make an exact model of the human brain

So AI is the part of computing we don't know how to do yet as in the joke :) ?

I actually don't get what your point is.

I do, there is a difference between an universal approximator and intelligence.

But I share the objection. And there are more parts, like graph theory, calculus, statistics or more advanced stochastic approaches.

Yes but what's worrisome is that AI models are ideological otimizations of cognitive drones. That can be weaponized in a psy-war.

I wouldn't say modeling is horse shit. It's really marketing surrounding modeling that's horse shit.

Modeling is actually very very useful but is too often oversold, which drives me bananas. It's especially bad in the world of data driven or data intensive models because that's trendy and sells software, hardware, and services. The problem is that various forms of modeling are complex and therefor expensive and convincing people to invest in furthering the domain results in these poor public misrepresentations and overselling.

Modeling gives us ways to abstract reality and attempt to predict the future or find patterns we might otherwise miss. Sometimes it works, a lot of times it doesn't, but when it does it's great. Everytime--its expensive and people need to feel they didnt waste their money.

And the opening:

As a field, artificial intelligence has always been on the border of respectability, and therefore on the border of crackpottery.

This was written in 1976.

I don't know the paper yet, but I love the title

I think ML is a bit too complex (different approaches trees, DNNs,...) to oversimplify it like that. One could say, it's just math - but even experts can't really tell some of the why it works behind back propagation in DNNs. A lot of research is empirical, like switching to ReLU...

ML is a commodity now, even popular with artists to create art. Being a commodity means you dont have to understand the inner workings to benefit from it. I don't see anything wrong with that, but a lack of proof for biases/backdoors/bugs in models is an issue that I'm worried about when its applied too enthusiastically in critical areas.

Article is weak though, and hard to follow conclusion.

I was thinking about the second one you linked, even though it's not exactly what I'm saying, one of the points being made is similar. Also the first half of this: https://www.cs.utexas.edu/users/EWD/transcriptions/EWD08xx/E... is related.

Not arguing, just suggesting an alternative phrasing that might better convey the intent. When people say thinks in this sense they mean determines to a certain level of confidence, rather than modelling consciousness.

I don't think that is the case.

No one except Descartes: "I think therefore I am."

But all this does is underline that natural language is a vague mesh of concept categories anchored to words with multiple overloaded meanings.

One of the reasons philosophy exists is because sometimes it attempts to untangle the overloading.

The argument about AI is no different, but with the complication that the marketing concept mesh has different requirements to the research concept mesh, and both are different to "Make this work by next Tuesday" mesh.

Google used to have an ancestor of word2vec which tried - very crudely - to map some of these meshes. Predictably it was much better at mapping word meshes than concept meshes, but it was still an interesting approach.

The underlying problem is that we literally don't have a clear representation system for these concept meshes. Words are overloaded, code and math are too specific in their different ways. The various shades of ML are still too concrete.

So we're going to keep having these debates until someone invents a representation system for natural language concept maps that isn't based on verbal approximations.

I haven't read that book and I'm not going to pretend that I have. I'll take that as a reading suggestion.

There's more to AI than Machine Learning; even if you only use Machine Learning you need some sort of system to embed it.

Unless you are doing academic research.

You may like the article "Troubling Trends in Machine Learning Scholarship", Lipton & Steinhardt, 2018 https://arxiv.org/abs/1807.03341

Especially the trend of "misuse of language, e.g., by choosing terms of art with colloquial connotations or by overloading established technical terms"

Yeah, this is an interesting take. Broadly speaking I agree, but you have to be pragmatic about that. There exist some accepted conventions it's just senseless to break just to make a point, for example, if you are defining a ring, making + distributive over \* rather than the opposite as is usual is not inherently wrong, but I would classify that as bad notation.

Similarly in OOP, you could technically name getters in any way you want, but why would you not conform to the accepted "getValue()" standard?

Of course fooling yourself into thinking you have achieved things you actually haven't by clever usage of natural language is the opposite end of the spectrum, and it's exactly what I was ranting against.

I guess "skepticism" is the correct word, indeed.

It is. Even in models that are actually linear (e.g. linear regression), calculus is used to characterise the function used to minimise the errors.

I'd say the authors have impressive resumes, though the arguments about economics and society are too far out there for me. So I didn't read very far into that stuff to see what they have gained from the position.

But I see it more as using accurate descriptions rather than just alternatives, which does have value in not misleading people.

I'm afraid I don't understand. In what way does contextualizing and centering human agency and humanity change how these questions are asked and considered in ways unavailable to us today?

Because today, Google’s search algorithm is assumed to have maximum value among the pieces, and Google gets to capture far more value than the people who generate the training data for search.

Wouldn't we need to consider the humanity of the person making the query, who may not want what the human who controls a given website that was scraped by the humans at Google wants?

Oh, absolutely! The point is that would further lower the weight/importance/power we ought to allocate to Google’s algorithms. Again, prioritizing the human roles & needs, rather than fetishizing the “AI”.

Because of the humans -- the content consumers, like me and you.

There were a number of websites which relied on humans (like old yahoo catalog and opendmoz). They still exist (I think), but humans did not choose them.

There is a whole bunch of other search engines other than Google, and I am sure some of them give way more control to creators. Humans did not choose them either -- likely because other humans filled them with spam and SEO.

It is all about humans -- they are the real decisive force because they get to choose if AI lives or dies.

(That said, as a part of this "decisive force", I feel modern Google products have way too many ads, and that current developments are against my interest. I guess this does show that as long as your software is great, you can charge great price, in $$$ or in attention to ads)

They didn't make the point very well but I think they may have been trying to argue that algorithms while pretending to be objective have biases of their creators built in. The ideology they are critical of being that algorithmic decision making is the best or most of objective form of decision making.

I strongly suspect that the author of this article does not have much background or interest in AI, and is merely part of a push to politicize it.

I have spoken to people who are adamant that AI is racist and needs to be forced to change, but seem at a loss to go into even layman levels of technical details.

So I suspect that they are just parroting a received opinion.

To be clear, of course I know that there are all sorts of systematic forms of unfairness. But I think that AI is just an implementation of systems.

The AI umbrella extends as far as what are essentially spreadsheets. Are spreadsheets unfair, or are the policies unfair? Does it make sense to lobby Microsoft to do something about predatory lenders using Excel?

- there is no explanation of what the "AI philosophy" or "AI way of thinking" is according to the authors

"The core of the ideology is that a suite of technologies, designed by a small technical elite, can and should become autonomous from and eventually replace, rather than complement, not just individual humans but much of humanity."

That's not what I meant. I mean that overusing the term "AI" is kinda bad and annoying but I think it's not doing the heavy lifting in the rapid adoption of these technologies and the buzz it creates.

Even if we just talked about large-scale high-performance statistical data processing, or "algorithmic automation" (AA) it would still be used everywhere, it would just be more difficult for laypeople to come to grips with it.

It's a bit as if people around 2000 were saying it's stupid to talk about "cyberspace", it's not some new space out there, it's just people sitting behind their computers connected by wires! But that term was not the reason for the hype.

Now, sure the label AI does contribute its share to the hype, but this stuff is so good that whatever way you call it (as long as it's not too many syllables), it will become a buzzword just for that reason. It's like euphemism treadmills. You can rename the concept, but it will re-acquire the same meanings. I mean "convolutional" managed to become quite a buzzword itself (even though it's a well defined technical term), as it appeared so often alongside really sexy stories.

According to the authors this undefined philosophy has caused economical and social damage

The authors propose "focusing on the human" as an alternative but the two aren't mutually exclusive at all

Yes, it's rather vague.

What seems to be annoying the original author is simple. Most of the areas where machine learning is currently deployed are somewhat obnoxious. Ad targeting. Face recognition. Behavior recognition. Those are areas where some error is expected, so mediocre ML performance is acceptable. ML isn't yet good enough to drive, for example. That has a lower tolerance for errors.

All this is "focusing on the human". In the sense of "Big Brother is Watching You". Be careful of what you ask for.

On explanation of AI as a philosophy:

"AI" is best understood as a political and social ideology rather than as a basket of algorithms. The core of the ideology is that a suite of technologies, designed by a small technical elite, can and should become autonomous from and eventually replace, rather than complement, not just individual humans but much of humanity.

On economical and social damage: The article mentions that less than 10 percent of the US workforce is counted as employed by the technology sector, that essential contributions of others aren't counted as "work" or compensated, that this has "hollowed out" the economy and contributed to the concentration of wealth in an elite, and that this has contributed to concentrating power, as well.

On mutual exclusivity: The article proposes paying people for contributions, rather than writing them off as non-work. It also mentions humans with "AI resources" outperforming AI alone.

They explain it here in this paragraph:

“AI” is best understood as a political and social ideology rather than as a basket of algorithms. The core of the ideology is that a suite of technologies, designed by a small technical elite, can and should become autonomous from and eventually replace, rather than complement, not just individual humans but much of humanity. Given that any such replacement is a mirage, this ideology has strong resonances with other historical ideologies, such as technocracy and central-planning-based forms of socialism, which viewed as desirable or inevitable the replacement of most human judgement/agency with systems created by a small technical elite. It is thus not all that surprising that the Chinese Communist Party would find AI to be a welcome technological formulation of its own ideology.

It's not obvious on the page itself but the URL classifies this article as "opinion". Do your opinions equal journalism?

Not yet it isn't. Nobody trusts it enough to not require a trained oncologist to look at the samples. Not to mention the machines or software are unavailable for end user doctors. It's in research.

How about Oncology? It appears to be making a big difference there

It doesn't really. I'm not aware of any oncology work that has yet been replacement by a machine, a few hype articles aside every year or so.

Plus there was AlphaGo, which was thought to be impossible, up until the very moment it actually worked, right?

Also no. After the AI winter people were more conservative about when computers would be able to compete in Go, and people generally thought it was five more years away or so. But nobody believed playing go was impossible or even out of reach, why would anyone have thought that

You missed voice recognition, machine translation and copyright violation detection. (Where it creates problems for content creators.)

And some automated blood morphology analysis and MRI/CT/scintigraphy/X-ray analysis, but that's in research so far.

There are plenty of indirect applications of AI in COVID but the core problem is not learning, it's searching and testing solutions. So a simulator would serve better. Learning comes after you already have the training data and want a model to solve similar cases.

That kind of ai is actual ai. No doubt THAT ai has progressed a lot. But the hype ai is just a wet dream for wannabe grahams.

Voice recognition, text translation, OCR, image compression? "Analysing stats about data" alone is something that can be applied to everything from antiviruses to climate models.

Machine Learning algorithms and Artificial Neural Networks are still mostly adequate terms for what's going on in the industry thus abusing of term "AI" (artificial intelligence) makes it just a marketing BS.

It's true that the terms ML and ANN predate the current hype but they were introduced/kept in use for the same reason: talking in mathematical terms does not excite research grant decision makers or business customers. Neural is a buzzword, it's easy to interpret for laypeople. If you talk about Latent Dirichlet Allocation, Support Vector Regression, Projection Pursuit, Principal Component Analysis, Reproducing Kernel Hilbert Spaces, Reverse-Mode Automatic Differentiation etc etc, then people yawn.

Why do we call linear programming "programming"? It has nothing to do with machine instructions. The answer: the name was made up for the hype and for securing research grants, when math funding was dry, but CS research was popular.

Why call "dynamic programming" like that? What's dynamic about it? Because the inventor wanted a name nobody can object to and sounds buzzy enough.

People try to name things in sexy ways to gain an edge.

I have no clue what "actively engaged" data actually means, since the author doesn't provide any links to the studies asserting its superiority. GPT-3 was trained on 45TB of text, little of which was written with AI training in mind.

I think “actively engaged” here means “curated”, where a human had to provide labels or guidance.

Obviously the rise of big data and deep models that can induce probabilities is the source of pretty much all advancement in AI since heavy reliance on curation and logic were abandoned. As such, AI really is as much a technology now as it has ever been. It seems that the central revelation in the field since the demise of symbolic AI (aka GOFAI) has been that so much useful pattern matching can be achieved by 1) the inducing of sufficient probability from large data to enable high levels of (narrow) intelligent behavior and 2) doing it using so little human curation.

Perhaps the author is suggesting that recent advances in AI are not really based in intelligence or cognition, and thus is only a trivially small subset of the greater endeavor of building machines that can think. If so, then the misnomer she objects to isn't “AI”, it's “intelligence”.

I think the point is that software automation tools (automatic testing, nice IDEs) generally don't lead to companies hiring fewer devs, it leads to companies being able to do more with the same number of devs and consequently hiring more. Same thing in construction or any globally competitive industry

Point being that software developers automate away their own manual tasks (and even entire jobs) faster than they automate anything else.

It's a fallacy to assume that the consequences of future automation is going to be just like past automation. There's no reason to assume that there will be new jobs to replace old jobs next time. Because this time it's going to descend on us a lot faster.

This is not a "simple" perspective, and if I'm permitted to be equally offensive, I suggest yours is a naive perspective. After all, this is already beginning to play out in our economies today.

It used to be that being successful meant that you owned the most successful store in your town, and maybe also the three other closest towns. Now, success is judged on a national or even international level. Success is achieved by fewer people at bigger scales. Success then looked like Wal-Mart. And now it looks like Amazon.

Success used to look like a dozen taxi medallions in NYC. Now it looks like Uber.

Do you really think that the "American Dream" will withstand a large fraction of truck drivers, taxi drivers, manufacturing line workers and clerical workers being made forcefully unemployed? You're not seriously suggesting that the answer is to teach truck drivers to code?

A calculator cannot write a novel or learn to do a new task

There is a fundamental difference between an AGI that can perform any human task and a tool that can only do one task.

So far there has always been something only humans can do, so they get hired. But when the true AGI can do anything, humans are obsolete.

Companies can use automation to grow their output while keeping their workforce intact by taking on new projects that were not previously possible.

This idea does not scale. It assumes that there is no upper limit for demand/production, and assumes that a business can suddenly gain the expertise to enter new markets to sell new products. It also assumes that investors would prefer risky reinvestment strategies to cutting costs by reducing labor and increasing short term profits.

If automation were that dangerous, then being a software developer should be the worst job because it automates itself away

Automation goes after the easiest targets first. IT is still growing because it is an immature profession, and a platform for automation itself. I remember IT in the late 90s - basically anyone who could operate a computer could get a job doing installs. In fact my first job was adding Trumpet Winsock to machines that didn't have any built-in way to access the internet. A few years later you could still make decent money installing and configuring operating systems. Those jobs are long gone and replaced by SCCM and other similar tools. They have been replaced by jobs that require much more technical skill. Entire teams are replaced every day by outsourcing to automation platforms like AWS, because those platforms can provide the same services with fewer employees.

A better example is farming or manufacturing. Take a look at productivity versus employment since 1900.[1]

This anti-automation rhetoric is like children hating school while their parents force them to attend - preferring short term ease that comes with a much bigger long term penalty (temporal discounting at work).

No, it's a pretty straightforward recognition that retraining even a few percent of any given workforce every year is going to lead to massive inequality and social problems, especially when there is no infrastructure to provide for living costs while a worker gets retrained. A manufacturing worker can be retrained to a better job, but how long do you think it would take to educate that person so they could design and maintain the machines that replace them? How much would that cost? Who is going to pay for it? And if demand for the product is flat, does it make any sense?

It's also a recognition that we are nearing peak production of practically everything. Developed nations are leveling off and declining in population. Around a third of our food is discarded. Ride-sharing is reducing demand for cars. E-commerce is able to offer discounts because it uses less labor. We are now at the point where major technology vendors have resorted to designing addictive experiences to compete for attention. What's left after that is saturated? I'm almost afraid to ask.

[1] https://www.ncci.com/Articles/Pages/II_Insights_QEB_Impact-A...

“If anything, that would seem to suggest that these impose a bound on how much we can automate.”

Why would this be?

All automation is now built on top of already automated processes.

The level of automation we can achieve isn’t obviously bounded by human cognitive limits.

The level of human participation certainly is.

I agree.

In fact, automation has been the hallmark of the western world, and in the US in particular.

Labor costs have always been high in the US and automation has been used since the beginning - for hundreds of years.

For a long time, "AI" meant "unsolved hard problem" to hackers like us. Speech synthesis and recognition was AI. Text recognition was AI. Some compiler optimization was AI. Search was AI. Now, those things are, well, those things. They work.

Things are a little different now. The feasibility of neural networks trained up on vast data sets means we have non-human systems with hunches. That is, we have AIs capable of delivering results without explanations.

Take a look at Neal Stephenson's recent "Fall" for a social network that uses AI to generate "news" stories where the training metric is engagement. The consequence is the construction of an alternate social universe, and the kind of dystopia only Stephenson can dream up. https://www.worldcat.org/title/fall-or-dodge-in-hell/oclc/11...

Human hunches are often accompanied by a sense of ethics. The agricultural savant who sexes hatchling baby chickens knows the success of the farm depends on a low error rate. The judge sentencing a culprit knows the consequence of making mistakes.

I wonder how hard it would be to add a sense of ethics to neural network results? Maybe it's just a matter of managing the error rates. But this article suggests otherwise.

The article is confusing.

There are private intelligence agencies like google and facebook that make money from extracting all the information they can of individuals in the society.

There is a huge interest on their part on ending privacy restrictions, of course. There is lots of money too, and people in charge in governments that are interested in using private security agencies like public security agencies because the private sector is usually way more efficient than the public one. That also applies to spies.

This is prone for abuses, and society should react to protect themselves. You can easily create a "dictatorship in practice" from a democracy with it.

Now, AI is just the technology that makes global surveillance possible, machines are cheap and easy to control. The alternative humans are expensive and prone to whistle blowers denouncing your abuses.

But AI is not an ideology, it is just a technique set, and you can use it for very good things, like counting blood cells, driving cars or watch your house while you are away.

It is a good thing that exist, only that it must be controlled by the users and not be the controller itself.

Maybe not necessarily, if long term the end result is fewer people making a living and instead relying on a centralized system that props them up economically or otherwise “provides” for them. In that scenario agency is removed from people. Historical or modern slaves also lack some aspects of agency. In that sense you can see that to allow AI you have to be okay with people having less agency. To fib that it’s easier when you can ignore their natural agency.

AI is a buzzword. A fresh label slapped on old technology that became much more usable in the last years and allows us to tell apart cats and dogs with 98.6% accuracy.

This is a full reversion of where we were at the start of the century. Back then, the AI paradox was in full effect - if no reliable implementation had been found, it was AI. If you could just A* your way through a maze, it wasn't intelligent routefinding anymore, it was just a simple algorithm.

The change is mostly in terms of money. The promise of AI has become a draw for investment rather than a repellant. Additionally, there is an interest in turning the mountains of surveillance data that exist now from a situationally useful tool to a tradable commodity.

Broadly understood this thesis could be properly developed, AI approaches are based on aggregated data not the particular user, and will in a conflict of interests always go against the particular user. There is a lot of ideology present in the way platforms operate, but mostly its to embellish that they are profit driven.

well, no matter if you agree or disagree with JL, I think it's great WIRED can still occasionally publish great articles like that

The hostility here to this article is interesting. To me, a reasonable interpretation is that it criticizes the way we understand/conceptualize some areas of modern technology. Indeed, if for some legal or practical reason research labs wouldn't have access to the data generated by the public (i.e. humans), many breakthroughs wouldn't happen. This is the other side to emphasizing the progress in algorithms (which is of course hard to deny).

You can't fully extricate technology from the societies where it exists, and things like naming, branding, institutions, and of course ideologies. There is a tendency to treat some areas of technology like some kinds of ancient gods or idols that have their own "needs" and "mandates" to force on their environment no matter what.

I happen to like[1] some their political proposals, like forcing the barter "data for services" to some fully disclosed monetary form. Also, coming up with some method of using these technologies that is compatible with individual freedom is a big concern. Even from purely practical standpoint, people trying and doing what they want has obvious value compared to everything having to be accepted by some (always to some extent self-interested and narrow-minded) authority. Shifting the language to talking about how we can enable and shape "AIs" as humans and societies seems reasonable: emphasizes that we have natural agency in all this.

[1] Liking doesn't necessarily mean supporting yet.

I personnaly think AI more as a computer assisted culturals productions. The contents of theses productions are numerical, quantitaives stuff, so it look like the result of a pure intelligence. But if you take some distance with what is actually done with AI you can realize that AI is mostly the development of our current cultural environment. So AI is not ideology, but our current ideology let us see "AI" as a pure intelligence rather than a automated cultural production. What have to be questioned is why does AI produced stuff are seen as pure intelligence in our cultural context. What is called AI is "just" a cultural fact (but a fascinating one!).

This was written in 2018 on this topic: https://www.forbes.com/sites/cognitiveworld/2018/11/01/artif...

AI as “ideology” may be a stretch, but it tends there with all the mythos behind a word like “artificial intelligence”.

Naming things matters.

While the head of Apple’s ML/AI strategy likes “machine intelligence” [1], I prefer “machine learning”.

“Intelligence” is a loaded word. I think (hope) that “learning” can be understood as narrowly and crudely as the field actually requires.

A more sober term would help the entire world manage this better on average, IMO, than if we use the unnecessarily scary and confusing term “artificial intelligence”.

[1] “For this reason and others, many AI experts (Giannandrea included) have suggested alternative terms like "machine intelligence" that don't draw parallels to human intelligence.”

https://arstechnica.com/gadgets/2020/08/apple-explains-how-i...

"AI" is neither ideology nor technology.

It's a term.

In a nutshell, the term "AI" refers to any situation when software is contrived to massage data in some complicated way that produces useful answers, without any clear, rational explanation why.

(The associated "ideology" consists mainly of the position that this is just terrific, and we should be doing more of it.)

I'm going to guess Poland? Since I'm pretty much a regular on r/europe :D

But yeah, it's a standard tactic of dehumanizing people. Except in this case, it's applied to AI. Which we don't really have yet. It's rather strange.

AI is a blanket term or a bucket term. It can mean Artificial General Intelligence - AGI or Artificial Narrow Intelligence - ANI. In my experience and opinion, as soon as something is well defined it becomes ANI - Vision systems, Natural Language Processing, Machine Learning, Neural Networks - it loses the name "AI" and gets its own specific name.

How AI is talked about depends on the audience. Nowadays, no one really thinks of rule based engines or scripts as AI, neither as ANI or AGI, but back in the day people thought maybe you could just have enough rules to replicate intelligence. So old ANI is just "an algorithm"

To researchers, as soon as an approach is well defined it becomes ANI; at least so far. In future there may be an approach to AI that becomes AGI, but ANI is not considered AI to most researchers.

For industry, AI generally denotes a handful of ANI approaches - Machine Learning and Neural Nets. If you say "AI" in a corporate environment, this is what people will think of.

this article makes many valid points but I would have loved to read a version that was written without use of the terms "AI" or "artificial intelligence", as I think it would have been clearer and more interesting.

Every technology that hasn't materialized yet is an idealogy.

He does. Perhaps not in this article but its position he's had in previous talks and articles.

Here's the talk I attended:https://www.youtube.com/watch?v=8qpB9v-OrAU

There is something fundamentally different about an AI, though. Perhaps the ability to produce output at scale. Perhaps that its creator/owner may not have the ability it trains the AI to perform.

I'm not sure. But I feel that comparing AI to humans here is disingenuous or naive.

That zizek reference made me chuckle!

Derivative works are allowed under copyright law. Though the interpretation is steadily being constrained especially on site like Youtube.

His claim is more extreme. Essentially any individual involved in producing the data (annotators, aggregators, that grad student, etc) should be compensated for any downstream profit produced by any model that consumed that data. It's an interesting interpretation of Marxist critiques that workers are separated from the products they produce and alienated from the value produced by the fruits of their labor. A car factory workers don't get any more compensated for the sale, resale, and any other subsequent profit made the use of a vehicles they helped produce.

While it sounds great in theory I have no idea how you'd even begin to implement that. Ironically the best means we have available to developing that solution is the technology that he critiques as anti-human.

Are you saying that DeepMind should have to pay royalties to the Go players whose games were used as training material for AlphaGo? Should the same apply to human players who study others' games as training material?

(Obviously, it's a moot point for AlphaZero which was trained solely via self-play, and kind of puts a spanner in the works for Jaron Lanier's views particularly if unsupervised machine learning displaces supervised machine learning as the dominant tool)

Instagram and YouTube are examples of algorithms optimizing the wrong thing. Whatever you like, it gives you more of it. More moreness, to the point people find themselves in dark echo chambers. Eventually you stop seeing contrary opinions or viewpoints. You stop growing.

I had the opportunity to talk to him after his talk in Boston, he was pretty pessimistic and struggled to define his conception of humanity without a negative referent

I thought the video you shared had a great 10-15 minute segment starting around ~50:00.

Sure define AI research whatever way you want. That's not particularly helpful though to change the outcome he fears.

JL’s point is that it makes a huge sociological difference, because it (the contemporary mythology/theology of “AI”) massively changes how much we’re willing to question the status quo, rather than accepting it as “AI”.

In a world where we have infinite resources and everything, what do you do [...] I think of those around me, my time with my loved ones and friends. Humanity and meaning becomes for me the ability to enjoy and witness the lives of those around me, the relationships I cultivate and shared experience I have.

There’s a subtle but crucial difference between infinite resources (not biasing us towards certain modes of being) versus a landscape promoting infinite (ever-increasing) consumption. Eg: imagine Google having every video on earth, but you have to search/ask, rather than having auto-recommendations.

This is not unique to AI. The digital divide existed before AI.

Yes, but the aura of “AI” gives it a new legitimacy and authority in our ambient narrative.

—

PS: if you wish to continue this discussion, feel free to hit me up via email :-)

It is at best fundamentally nihilist [...] I believe we define meaning for ourselves.

Is the latter ("we define meaning for ourselves") not a fundamentally nihilist stance?

Also in AI the real innovation seems mostly in the west - Google, Facebook, Open AI, Tesla, DeepMind etc. We're not going to get AGI by having lots of people label images.

I do like how his proposed solution does not even remotely solve what he thinks the problem is.

Say you pay image labelers $100k a year. Unfortunately, as soon as they produce a finished model, that algorithm replaces them, permanently.

If a model is profitable to create, it produces structural unemployment. If it's not profitable, then those jobs continue to exist. The only way his proposal functions is if it's a stealth ban on AI. There's no sustainable way for an image labeler to have anything like a middle class income for more than a few months.

And his proposal has to apply... globally? How much does Tencent pay its image labelers? I can't image the PRC version of mturk is any kinder and gentler.

This article is status signaling, not critique.

Pieces like this remind me of soviet propaganda when they literally claimed that “cybernetics is capitalists’ whore”. Some people are just incapable of seeing past their political beliefs (or unwilling to)

Duh.

One hint is in the suffix -ology meaning "study of". With study comes frames, schools of thought, agendas, dogma.

My intro to technology as ideology was Neil Postman's Technopoly [1992]. https://en.wikipedia.org/wiki/Technopoly Challenging ideas for a young technophile.

FWIW, when referring to specifics, I'd be more comfortable with "techniques" instead of "technology". So a book title could be "Weaponizing AI Techniques to Maximize Dopamine Addiction via Social Mediums" instead of "Recognize Kitten Pictures With Awesome AI Technology for the LOLs".

Of course Jaron Lannier is coauthor. Frequent go-to mildly provocative but still hip contrarian for Wired, the historically full throated Big Tech apologist, booster, and primary paid placement press release conduit.

Glen Weyl is Founder and Chair of the RadicalxChange Foundation and Microsoft’s Office of the Chief Technology Officer Political Economist and Social Technologist (OCTOPEST). Jaron Lanier is the author of Ten Arguments for Deleting Your Social Media Accounts Right Now and Dawn of the New Everything. He (and Glen) are researchers at Microsoft but do not speak for the company.

The statement that Lanier and Weyl "do not speak for the company" includes an unacknowledged assumption that their position is not tacitly influenced by Microsoft. I don't think this assumption is demonstrably true for these two, nor for anyone who works for a company. Rather, the general maxim applies: "It's difficult to make a man understand something when his sustenance depends on his not understanding it."

That said, what are Lanier and Weyl incentivized not to understand?

Let's look deeper at (what I believe is) the central thesis of the article. There is a problem:

When people provide data, behavioral examples, and even active problem solving online, it is not considered “work” but is instead treated as part of an off-the-books barter for certain free internet services.

And the solution:

Active engagement is possible only if, unlike in the usual AI attitude, all contributors, not just elite engineers, are considered crucial role players and are financially compensated.

AI is presented as a one-sided type of commerce where "contributors" (the vast majority of people) are unwittingly exploited by "elite engineers" -- Google, Facebook, Twitter, Amazon, TikTok, etc. M$ plays in this space, but they lack a competitive advantage and do probably would prefer the whole idea disappear. I'll call these two groups "AI losers" and "AI winners" for clarity.

Here's how I interpret this thesis:

The AI losers consume, engage with, and create content, cluelessly leaving behind a trail of golden breadcrumbs ("data") for the AI winners to profit from. To change this, AI losers just need to wake up to the fact that they're all pooping out gold and start selling these nuggets to AI winners on some kind of open market.

The question looming above all of this is, "Why haven't they already done so?" No one wants to ask this question, much less answer it.

Why is that? In the mindset of Lanier, Weyl and the entire "humanist" camp (including Tristan Harris and his team), the AI losers aren't just being exploited, but they are too stupid to realize they're being exploited. Hence, the injustice will only stop if brilliant ethicists like themselves step in to intervene. At present, they are addressing the general public, but inevitably their crusade will take them to Washington, where either they will team up with the same people they're crusading against or fizzle out.

What they are incentivized not to understand is that the AI losers are not stupid. I don't even agree that they're necessarily being exploited. They're getting world-class online services from giant tech companies without paying a cent. They're also engaging in collective bargaining with these giants to get their way (via the social lever of brand safety, which is flawed, but that's another issue). This is an equation that actually works in their favor; if the equation changes, you can't assume that their behavior will remain the same.

Lanier and Weyl don't consider that perhaps the AI losers pay $0.00 these services not because they de-value their own privacy, but because these services are actually worth $0.00 to them. They don't see usage of technology as a choice on the part of consumers, but a compulsion that must be satisfied at any price. But they don't know how many users a $5/year Google would have, or a $5/month Facebook, if any at all.

Another question that remains to be seen is what the legal environment is going to look like for these companies in the post-Snowden, post-Techlash environment. Is this "data" a trail of golden breadcrumbs (like all big tech spokespeople including Lanier and Weyl believe it is -- and in fact, their salary depends on that being so), or a trail of nuclear waste? Only time will tell, really.

I agree that there is a form of commerce going on, but the commerce is three-sided, not two-. It's actually between the AI winners, AI losers, and the government, and it's much more complicated than the authors lead on. I submit that the AI "winners" are in a more tenuous position (financially and legally) than we are being led to believe.

the adornment and “deepfake” transformation of the human face, now common on social media platforms like Snapchat and Instagram, was introduced in a startup sold to Google by one of the authors;

Uhh excuse me but deepfakes were not introduced at this company. It was a mobile qr and image recognition technology and that is all. I should know because I was the lead tester of this technology at Neven Vision. They must be talking about DeepDream a google project by the company founder many years later.

There isn't as much difference between the West and East as one might imagine. Westerners claim they want privacy, but see no problem giving it away for shinier toys. If you want privacy, it can be had, both in the East and West, you simply have to pay for it. Or learn how technology works and prioritize it. I moved off of Windows decades ago, OSX years ago, and am slowly tightening up all aspects of my digital life, for power, convenience, and yes, privacy.

Privacy is a luxury good like everything else humans want. Available for the masses only if you're willing to spend the time and energy learning how to DIY, or can just be bought if you're rich.

It was a weird experience today watching Apple's announcement video. Everything there is designed to keep you locked into their platform, and it was amusing when I saw how machine learning on your phone was given such significant space. They didn't even try to claim that this would help out ordinary people, jumping immediately to reducing costs of very very deep-pocketed customers, medical device manufacturers. Oh but an AR bird will know where your hand is maybe a bit better too. :rolleyes:

I mean, that's Apple's market, along with "pro" users, with the HomePod tech being a sad afterthought, a bone tossed to the less-than-extremely-well-heeled users as an alternative to mass-market Amazon devices.

America has a cult of the big and flashy, and the engine runs on our data so companies can predict and influence the behavior of the masses. Predictable revenue flows are the name of the game. China's government just has an added incentive to bake it all right into the social fabric. That's all.

We may end up with machines that are not technology.

AI is best defined as whatever computers can’t quite do yet but we think they can do soon. It’s a moving target. Lots of things used to be “AI” that are commonplace now.