We do get ice and snow in Portland, along with flooding and landslides. No, it's not the same as Midwest, but we do get a few days every other year or so that you just don't drive out in. The black ice around a couple curvy sections of i-5 are notoriously bad at night in winters. (Terwilliger)
I have lived in the midwest, as well as Portland. It is good that Portland only occasionally gets ice, because in like-for-like conditions it is way more dangerous than the midwest. Primarily because of hills. I found driving in snow & ice in the midwest to be mostly a non-event, even on inadequate tires.
I really hope we're able to get them without the city council messing things up. The way they reacted to the news at first, you'd think Minneapolis was the first city to ever have autonomous vehicles. That, mixed with a heavy dose of "What about the buggy whip makers??"
It's funny to me that the average American is Islamophobic, but the US government sharply divides middle eastern countries based on alliances (and how rich they are). Qatari Emir? You're a friend of the US government. Poor Pakistani? Enemy. Lebanese farmer? We'll think about it.
It makes sense given a few things, although it's not as bad as you're saying:
1. The median American lives in a city and has exposure to Muslims and is most likely not Islamophobic.
2. Due to the voting structure of the U.S., people who don't live in cities and don't get exposure to Muslims get outsized voting rights.
3. Most American electeds are much more well travelled than Americans who don't live in cities.
So basically, elites have to necessarily balance (and exploit) the biases of over-represented minorities with their own largely metropolitan beliefs.
All of this is made more ironic in that the moral structures of the Abrahamic religions, including Islam, are all influences on and in line with, traditional American values, which American elites don't follow (see Epstein) but Americans who don't live in cities largely do.
> The median American lives in a city and has exposure to Muslims and is most likely not Islamophobic
Most Islamophobic people I know live in cities. Is there really that much of a change related to urbanism for Islamophobia, one you adjust for political alignment and religiousness?
For reference, this is the America I see every day:
Over the past 17 years I've lived in three houses (in the suburbs of two different cities in two different states- one East Coast, one land-locked) and an apartment in NYC (obviously also East Coast). In all of the East Coast spots (urban and suburban) there was a mosque closer than the nearest McDonald's. For the land-locked state suburb the mosque was 2 miles away and the nearest McDonald's was 0.75 miles away.
I'm not selecting these houses to be convenient to the Mosque- I've never been in any of those Mosques. It's just an artifact of living in the sort of neighborhoods that I like. I tend to agree that it isn't urban/rural per se, as much as it's Openness of the Big Five personality traits. Which, at least in the US, tends to be correlated with a lot of other things (college education, density of living, etc.).
IRT the "college education", Collin County is statistically higher educated than most of the country demographically-speaking. >56% achieved bachelors or higher compared to NYC at ~42%. For reference, Santa Clara County in California is also at 56%, so about as educated as the area with Apple, Google, and Facebook at least as far as that statistic analyzes.
Oh this is very real. As someone who lives in Ottawa, Shopify employees are a unique brand of people who think they are tuned into the SV trends but are just huffing their own farts. They're all acting out a small-scale replica of taking peptides and trying to found Uber for Polycules but in a sleepy capital city full of government employees.
10+ years ago companies were hoovering up data for ML - trying to find correlations in high-dimensionality data. Mostly the results were garbage but occasionally you hit on a real, unexpected phenomenon.
Nowadays you just throw all the data into a black box and believe whatever it says blindly.
They don't have time preference because they don't have intent or reasoning. They can't be "reincarnated" because they're not sentient, they're a series of weights for probable next tokens.
No. They don't have time preference like us, because (wall clock) time doesn't exist for them. An LLM only "exists" when it is actively processing a prompt or generating tokens. After it is done, it stops existing as an "entity".
A real world second doesn't mean anything to the LLM from its own perspective. A second is only relevant to them as it pertains to us.
Time for LLMs is measured in tokens. That's what ticks their clock forward.
I suppose you could make time relevant for an LLM by making the LLM run in a loop that constantly polls for information. Or maybe you can keep feeding it input so much that it's constantly running and has to start filtering some of it out to function.
That would still be time as it pertains to us. Even if I put time stamps into the chat all the LLM knows that it's some amount of time later - it can't actually do anything in the time between two prompts.
Can we maybe make it "don't anthropoCENTRIZE the LLMs" .
The inverse of anthropomorphism isn't any more sane, you see. By analogy: just because a drone is not an airplane, doesn't mean it can't fly!
Instead, just look at what the thing is doing.
LLMs absolutely have some form of intent (their current task) and some form of reasoning (what else is step-by-step doing?) . Call it simulated intent and simulated reasoning if you must.
Meanwhile they also have the property where if they have the ability to destroy all your data, they absolutely will find a way. (Or: "the probability of catastrophic action approaches certainty if the capability exists" but people can get tired of talking like that).
> LLMs absolutely have intent (their current task)
That's like saying a 2000cc 4-Cylinder Engine "has the intent to move backward". Even with a very generous definition of "intent", the component is not the system, and we're operating in context where the distinction matters. The LLM's intent is to supply "good" appended text.
If it had that kind of intent, we wouldn't be able to make it jump the rails so easily with prompt injection.
> and reasoning (what else is step-by-step doing?) .
Oh, that's easy: "Reasoning" models are just tweaking the document style so that characters engage in film noir-style internal monologues, latent text that is not usually acted-out towards the real human user.
Each iteration leaves more co-generated clues for the next iteration to pick up, reducing weird jumps and bolstering the illusion that the ephemeral character has a consistent "mind."
> That's like saying a 2000cc 4-Cylinder Engine "has the intent to move backward". Even with a very generous definition of "intent", the component is not the system, and we're operating in context where the distinction matters. The LLM's intent is to supply "good" appended text.
Fair, but typically you use a 2000cc engine in a car. Without the gearbox, drive train, wheels, chassis, etc attached, the engine sits there and makes noise. When used in practice, it does in fact make the car go forward and backward.
Strictly the model itself doesn't have intent, ofc. But in practice you add a context, memory system, some form of prompting requiring "make a plan", and especially <Skills> . In practice there's definitely -well- a very strong directionality to the whole thing.
> and bolstering the illusion that the ephemeral character has a consistent "mind."
And here I thought it allowed a next token predictor to cycle back to the beginning of the process, so that now you can use tokens that were previously "in the future". Compare eg. multi pass assemblers which use the same trick.
> LLMs absolutely have some form of intent (their current task)
They have momentum, not intent. They don’t think, build a plan internally, and then start creating tokens to achieve the plan. Echoing tokens is all there is. It’s like an avalanche or a pachinko machine, not an animal.
> some form of reasoning (what else is step-by-step doing?)
I think they reflect the reasoning that is baked into language, but go no deeper. “I am a <noun>” is much more likely than “I am a <gibberish>”. I think reasoning is more involved than this advanced game of mad libs.
Apologies, I tend to use web chats and agent harnesses a lot more than raw LLMs.
Strictly for raw models, most now do train on chain-of-thought, but the planning step may need to be prompted in the harness or your own prompt. Since the model is autoregressive, once it generates a thing that looks like a plan it will then proceed to follow said plan, since now the best predicted next tokens are tokens that adhere to it.
Or, in plain english, it's fairly easy to have an AI with something that is the practical functional equivalent of intent, and many real world applications now do.
You realize the generation of the "Chain-of-thought" is also autoregressive, right?
It's not a real reasoning step, it's a sequence of steps, carried out in English (not in the same "internal space" as human thought - every time the model outputs a token the entire internal state vector and all the possibilities it represents is reduced down to a concrete token output) that looks like reasoning. But it is still, as you say, autoregressive.
And thus - in plain english - it is determined entirely by the prompt and the random initial seed. I don't know what that is but I know it's not intent.
So I already rewrote and deleted this more times than I can count, and the daystar is coming up. I realize I got caught up in the weeds, and my core argument was left wanting. Sorry about that. Regrouping then ...
Anthropomorphism and Anthropodenial are two different forms of Anthropocentrism.
But the really interesting story to me is when you look at the LLM in its own right, to see what it's actually doing.
I'm not disputing the autoregressive framing. I fully admit I started it myself!
But once we're there, what I really wanted to say (just like Turing and Dijkstra did), is that the really interesting question isn't "is it really thinking?" , but what this kind of process is doing, is it useful, what can I do or play with it, and -relevant to this particular story- what can go (catastrophically) wrong.
I don't know if they have intent. I know it's fairly straightforward to build a harness to cause a sequence of outputs that can often satisfy a user's intent, but that's pretty different. The bones of that were doable with GPT-3.5 over three years ago, even: just ask the model to produce text that includes plans or suggests additional steps, vs just asking for direct answers. And you can train a model to more-directly generate output that effectively "simulates" that harness, but it's likewise hard for me to call that intent.
I think it’s helpful to try to use words that more precisely describe how the LLM works. For instance, “intent” ascribes a will to the process. Instead I’d say an LLM has an “orientation”, in that through prompting you point it in a particular direction in which it’s most likely to continue.
That is a silly point. We very clearly are not "a series of weights for probable next tokens", as we can reason based on prior data points. LLMs cannot.
Unless you're using some mystical conception of "reason", nothing about being able to "reason based on prior data points" translates to "we very clearly are not a series of weights for probable next tokens".
And in fact LLMs can very well "reason based on prior data points". That's what a chat session is. It's just that this is transient for cost reasons.
We are much more than weights which output probable next tokens.
You are a fool if you think otherwise. Are we conscious beings? Who knows, but we’re more than a neural network outputting tokens.
Firstly, and most obviously, we aren’t LLMs, for Pete’s sake.
There are parts of our brains which are understood (kinda) and there are parts which aren’t. Some parts are neural networks, yes. Are all? I don’t know, but the training humans get is coupled with the pain and embarrassment of mistakes, the ability to learn while training (since we never stop training, really), and our own desires to reach our own goals for our own reasons.
I’m not spiritual in any way, and I view all living beings as biological machines, so don’t assume that I am coming from some “higher purpose” point of view.
>We are much more than weights which output probable next tokens.
You are a fool if you think otherwise. Are we conscious beings? Who knows, but we’re more than a neural network outputting tokens.
That's just stating a claim though. Why is that so?
Mine is reffering to the "brain as prediction machine" establised theory. Plus on all we know for the brain's operation (neurons, connections, firings, etc).
>There are parts of our brains which are understood (kinda) and there are parts which aren’t. Some parts are neural networks, yes. Are all?
What parts aren't? Can those parts still be algorithmically described and modelled as some information exchange/processing?
>but the training humans get is coupled with the pain and embarrassment of mistakes
Those are versions of negative feedback. We can do similar things to neural networks (including human preference feedback, penalties, and low scores).
>the ability to learn while training (since we never stop training, really)
I already covered that: "The main difference is the training part and that it's always-on."
We do have NNs that are continuously training and updating weights (even in production).
For big LLMs it's impractical because of the cost, otherwise totally doable. In fact, a chat session kind of does that too, but it's transient.
They're not artificial intelligence neural networks.
They're biological neural networks. Brains are made of neurons (which Do The Thing... mysteriously, somehow. Papers are inconclusive!) , Glia Cells (which support the neurons), and also several other tissues for (obvious?) things like blood vessels, which you need to power the whole thing, and other such management hardware.
Bioneurons are a bit more powerful than what artificial intelligence folks call 'neurons' these days. They have built in computation and learning capabilities. For some of them, you need hundreds of AI neurons to simulate their function even partially. And there's still bits people don't quite get about them.
But weights and prediction? That's the next emergence level up, we're not talking about hardware there. That said, the biological mechanisms aren't fully elucidated, so I bet there's still some surprises there.
If you claim something might "very well" be something you state you need some better proof. Otherwise we might also "very well" be living in the matrix.
People always say this kind of thing. Human minds are not Turing machines or able to be simulated by Turing machines. When you go about your day doing your tasks, do you require terajoules of energy? I believe it is pretty clear human thinking is not at all like a computer as we know them.
>People always say this kind of thing. Human minds are not Turing machines or able to be simulated by Turing machines
That's just a claim. Why so? Who said that's the case?
>When you go about your day doing your tasks, do you require terajoules of energy?
That's the definition of irrelevant. ENIAC needed 150 kW to do about 5,000 additions per second. A modern high-end GPU uses about 450 W to do around 80 trillion floating-point operations per second. That’s roughly 16 billion times the operation rate at about 1/333 the power, or around 5 trillion times better energy efficiency per operation.
Given such increase being possible, one can expect a future computer being able to run our mental tasks level of calculation, with similar or better efficiency than us.
Furthermore, "turing machine" is an abstraction. Modern CPUs/GPUs aren't turing machines either, in a pragmatic sense, they have a totally different architecture. And our brains have yet another architecture (more efficient at the kind of calculations they need).
What's important is computational expressiveness, and nothing you wrote proves that the brains architecture can't me modelled algorithmically and run in an equally efficient machine.
Even equally efficient is a red herring. If it's 1/10000 less efficient would it matter for whether the brain can be modelled or not? No, it would just speak to the effectiveness of our architecture.
We very obviously are not just a series of weights for probable next tokens. Like seriously, you can even ask an LLM and it will tell you our brains work differently to it, and that’s not even including the possibility that we have a soul or any other spiritual substrait.
>We very obviously are not just a series of weights for probable next tokens.
How exactly? Except via handwaving? I refer to the "brain as prediction machine theory" which is the dominant one atm.
>you can even ask an LLM and it will tell you our brains work differently to it
It will just tell me platitudes based on weights of the millions of books and articles and such on its training. Kind of like what a human would tell me.
>and that’s not even including the possibility that we have a soul or any other spiritual substrait.
That's good, because I wasn't including it either.
"brain as prediction machine theory" is dominant among whom, exactly? Is it for the same reason that the "watchmaker analogy" was 'dominant' when clockwork was the most advanced technology commonly available?
Its really just a matter of degrees. There are 1 million, 1 million, 1 trillion parameter LLMs... and you keep scaling those parameters and you eventually get to humans. But it's still probable next tokens (decisions) based on previous tokens (experience).
> Its really just a matter of degrees. There are 1 million, 1 million, 1 trillion parameter LLMs... and you keep scaling those parameters and you eventually get to humans.
It isn’t because humans and current LLMs have radically different architectures
LLMs: training and inference are two separate processes; weights are modifiable during training, static/fixed/read-only at runtime
Humans: training and inference are integrated and run together; weights are dynamic, continuously updated in response to new experiences
You can scale current LLM architectures as far as you want, it will never compete with humans because it architecturally lacks their dynamism
Actually scaling to humans is going to require fundamentally new architectures-which some people are working on, but it isn’t clear if any of them have succeeded yet
> LLMs: training and inference are two separate processes
True, but we have RAG to offset that.
> it architecturally lacks their dynamism
We'll get there eventually. Keep in mind that the brain is now about 300k years into fine-tuning itself as this species classified as homo sapiens. LLMs haven't even been around for 5 years yet.
In practice that doesn’t always work… I’ve seen cases where (a) the answer is in the RAG but the model can’t find it because it didn’t use the right search terms-embeddings and vector search reduces the incidence of that but cannot eliminate it; (b) the model decided not to use the search tool because it thought the answer was so obvious that tool use was unnecessary; (c) model doubts, rejects, or forgets the tool call results because they contradict the weights; (d) contradictions between data in weights and data in RAG produce contradictory or ineloquent output; (e) the data in the RAG is overly diffuse and the tool fails to surface enough of it to produce the kind of synthesis of it all which you’d get if the same info was in the weights
This is especially the case when the facts have changed radically since the model was trained, e.g. “who is the Supreme Leader of Iran?”
> We'll get there eventually. Keep in mind that the brain is now about 300k years into fine-tuning itself as this species classified as homo sapiens. LLMs haven't even been around for 5 years yet.
We probably will eventually-but I doubt we’ll get there purely by scaling existing approaches-more likely, novel ideas nobody has even thought of yet will prove essential, and a human-level AI model will have radical architectural differences from the current generation
LOL. Oook.. No i dont think so. The human experience and the mechanisms behind it have a lot of unknowns and im pretty sure that trying to confine the human experience into the amount of parameters there are is short sighted.
Still many unknowns, but we do know some key fundamentals, such as that the brain is "just" trillions of neurons organized in various ways that keep firing (going from high to low electric potential) at different rates. Pretty similar to how the fundamental operation of today's digital computers is the manipulation of 0s and 1s.
They’re both neural networks, but the architectures built using those neural connections, and the way they are trained and operate are completely different. There are many different artificial neural network architectures. They’re not all LLMs.
AlphaZero isn’t a LLM. There are Feed Forward networks, recurrent networks, convolutional networks, transformer networks, generative adversarial networks.
Brains have many different regions each with different architectures. None of them work like LLMs. Not even our language centres are structured or trained anything like LLMs.
I'd argue that regardless of the architecture, the more sophisticated brain is still a (massive) language model. If you really think about it, language is the construct that allows brains to go beyond raw instinct and actually create concepts that're useful for "intelligently" planning for the future. The real difference is that brains are trained with raw sensory data (nerve impulses) while today's LLMs are trained with human-generated data (text, images, etc).
It's not at all a language model in the way that LLMs are. At this point we might as well just say that both process information, that's about the level of similarity they have except for the implementation detail of neurons.
Language came after conceptual modeling of the world around us. We're surrounded by social species with theory of mind and even the ability to recognise themselves and communicate with each other, but none of them have language. Even the communications faculties they have operate in completely different parts of their brains than ours with completely different structure. Actually we still have those parts of the brain too.
Conceptual representation and modeling came first, then language came along to communicate those concepts. LLMs are the other way around, linguistic tokens come first and they just stream out more of them.
This is why Noam Chomsky was adamant that what LLMs are actually doing in terms of architecture and function has nothing to do with language. At first I thought he must be wrong, he mustn't know how these things work, but the more I dug into it the more I realised he was right. He did know, and he was analysing this as a linguist with a deep understanding of the cognitive processes of language.
To say that brains are language models you have to ditch completely what the term language model actually means in AI research.
That's a different statement, yes brains and LLMs are both neural networks.
An LLM is a specific neural architectural structure and training process. Brains are also neural networks, but they are otherwise nothing at all like LLMs and don't function the ways LLMs do architecturally other than being neural networks.
Plus, brain structure and physiology changes thoughout the interweaved processes of learning, aging, acting, emoting, recalling, what have you. It's not an "architecture" that we can technologically recreate, as so much of it emerges from a vastly higher level of complexity and dynamism.
Our brains work differently, yes. What evidence do you have that our brains are not functionally equivalent to a series of weights being used to predict the next token?
I'm not claiming that to be the case, merely pointing out that you don't appear to have a reasonable claim to the contrary.
> not even including the possibility that we have a soul or any other spiritual substrait.
If we're going to veer off into mysticism then the LLM discussion is also going to get a lot weirder. Perhaps we ought to stick to a materialist scientific approach?
You are setting the bar in a way that makes “functional equivalence” unfalsifiable.
If by “functionally equivalent” you mean “can produce similar linguistic outputs in some domains,” then sure we’re already there in some narrow cases. But that’s a very thin slice of what brains do, and thus not functionally equivalent at all.
There are a few non-mystical, testable differences that matter:
- Online learning vs. frozen inference: brains update continuously from tiny amounts of data, LLMs do not
- Grounding: human cognition is tied to perception, action, and feedback from the world. LLMs operate over symbol sequences divorced from direct experience.
- Memory: humans have persistent, multi-scale memory (episodic, procedural, etc.) that integrates over a lifetime. LLM “memory” is either weights (static) or context (ephemeral).
- Agency: brains are part of systems that generate their own goals and act on the world. LLMs optimize a fixed objective (next-token prediction) and don’t have endogenous drives.
I did not claim the ability of current LLMs to be on par with that of humans (equivalently human brains). I objected that you have not presented evidence refuting the claim that the core functionality of human brains can be accomplished by predicting the next token (or something substantially similar to that). None of the things you listed support a claim on the matter in either direction.
I don't follow. If you provide criteria I can most likely provide evidence, unless your criteria is "vaguely cylindrical and vaguely squishy" in which case I obviously won't be able to.
The person I replied to made a definite claim (that we are "very obviously not ...") for which no evidence has been presented and which I posit humanity is currently unable to definitively answer in one direction or the other.
When two things are obviously radically different (a squishy mass of trillions of interconnected carbon based blobs fed by some sort of continuous oxygen based chemical reaction, and a series of distributed transitors on silicon wafers) then the burden of proof shifts to the other guy to provide the clear and convincing evidence that they should be considered functionally the same thing.
But I made no such claim. I was explicit that my position is "humanity is currently unable to definitively answer in one direction or the other".
Two things being physically different does not exclude their also having functional similarities. The argument presented amounts to A and B have large physical differences, A does X, therefore B does not do X. That doesn't follow.
Pacing is a big part of endurance sport. If you're in the lead you know intellectually you want to pace for sub-2 hours, but if you're watching someone beat you maybe it gives you the extra edge?
It does sound like the course and the weather made it more likely to happen. And technical advances in shoe composition.
That's not a description of how the pacing for this race actually happened.
> The leading men went through halfway in 60 minutes and 29 seconds: fast but not exceptionally so. But it turned out that Sawe was merely warming up.
Between 30 and 35 kilometres, Sawe and Kejelcha ran a stunning 13:54 for 5km to see off Kiplimo. Yet, staggeringly, more was to come as the pair covered kilometres 35 to 40 in 13:42. To put this into context, that time is two seconds faster than the 5km parkrun world record, set by the Irish international Nick Griggs.
It was only after a 24th mile, run in 4:12, that Kejelcha wilted. But still Sawe kept going. Astonishingly, he crossed the line having run the second half in just over 59 minutes.
“Before 41 kilometres, I’m enjoying, I’m relaxed,” said Kejelcha, who had won silver over 10,000m at last year’s world championships.
“My body is all great. At exactly 41 kilometres, my body stopped. I tried to push, but my legs were done.
Sawe, though, powered on to set the fastest official marathon time in history. For good measure, it was also 10 seconds faster than Eliud Kipchoge’s unofficial 26.2 mile best, set in Vienna in 2019.
My team has also adopted this - it's much easier to add another layer than to refine or simplify what exists. We have AI skills to help us debug microservices that call microservices that have circular dependencies.
This was possible before but someone would maybe notice the insane spaghetti. Now it's just "we'll fix it with another layer of noodles".
That's so interesting because where I work, the push was to "add one more API" to existing services, turning them into near monoliths for the sake of deployment and access. Still a mess of util and helper functions recursively calling each other, but at least it's one binary in one container.
Unfortunately I saw this pre-AI with microservices, where while empowering developers with their beloved microservices, we create intense complexity and deployment headaches. AI will fix the slop with an obscuring layer of complexity on top.
Oh the "micro services" are all coupled. To test anything you have to deploy a constellation of interdependent services with redundant DBs, each generating new IDs for the same underlying resource.
> Any data/sources on which this might be based? The pandemic was 6 years ago; do these "Agile" (the tech term) companies really carry many unproductive lines-of-business for so long?
Do big tech companies like FB and Google even pretend to be "agile" anymore? I think they mostly sell themselves on institutional stability and monopolist market positions rather than speed of execution
Did they ever pretend, and anyone ever believed that? "Agile" organization is even more of a bullshit concept than "Agile" in the team.
Excepting for trivial-size, freshly formed startups, companies cannot be "Agile", because finance and legal and HR and even marketing have constrains setting the tempo - you cannot just drive them with a sprint as if it was a clock signal.
> "Agile" organization is even more of a bullshit concept than "Agile" in the team.
> Excepting for trivial-size, freshly formed startups, companies cannot be "Agile", because finance and legal and HR and even marketing have constrains setting the tempo - you cannot just drive them with a sprint as if it was a clock signal.
Implementations of Agile at different companies can be an issue, yes. But that is to be expected in any large organization, simply because of scale. It doesn't change the fact that the on-the-ground teams at agile orgs work to a different cadence and approach than historically traditionally structured companies.
There are a few different ways to manage interfacing with parts of the org that need to march to a different beat. That always creates friction, and has to be managed properly. Any large org can suffer from hubris, middling management skills and capacity, wasted effort. Problems of scale, I guess.
Enterprise methodologies like Scaled Agile Framework (SAFe) are explicitly designed around managing that friction. Developers may complain about the additional toil and process overhead it imposes on them, but for the organization as a whole this is sometimes the least bad option.
None of the important projects or FANGs build with frameworks like that, sorry. Project management frameworks are all larp and mostly used by slow moving firms from the past.
Right, those frameworks aren't a good fit for sophisticated tech companies. They're targeted towards enterprises where software is just one component of a larger strategy. Sometimes moving slower is a better way to create shareholder value.
Yes, they do. In fact, they have several competing committees to market the concepts internally through armies of PMs and TPMs and KPIs and efficiency metrics tracking. Also your AI token use of course.
I was in two different Google orgs and neither one used any sort of agile methodology at all, and in fact I almost never heard agile even discussed. Some teams arranged their bugs "kanban"-style but that was up to the individual team; it wasn't a company-wide decision.
> Do big tech companies like FB and Google even pretend to be "agile" anymore?
Folks from those companies will have to speak up, but my understanding is that yes, internally these large tech orgs use the Agile Methodology, as opposed to the 'traditional' 'Waterfall' development methods.
Technically the images look great, very impressive. Production-wise I can also see how this could be useful for low-budget interior dialogue scenes where you don't want the set dressing to distract. It really draws focus to the actors and lets the director paint a more impressionistic backdrop.
The exterior shots I've got more mixed feelings about. I think these shallow lenses work best when you have a very controlled backdrop that can be deliberately staged. Using it in a wide outdoor shot feels like a real risk unless you're doing some Kubrickian blocking to make sure everyone is arranged just-so. Or you're making them stand stock-still.
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