[HN Gopher] Fei-Fei Li: Spatial intelligence is the next frontie...
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Fei-Fei Li: Spatial intelligence is the next frontier in AI [video]
Author : sandslash
Score : 248 points
Date : 2025-07-01 14:00 UTC (2 days ago)
(HTM) web link (www.youtube.com)
(TXT) w3m dump (www.youtube.com)
| skwb wrote:
| It's hard to describe, but it's felt like LLMs have completely
| sucked the entire energy out of computer vision. Like... I know
| CVPR still happens and there's great research that comes out of
| it, but almost every single job posting in ML is about LLMs to do
| this and that to the detriment of computer vision.
| friendzis wrote:
| What's the equivalent of methadone therapy, but for reckless
| VC?
|
| What's the equivalent of destroying everything around you while
| chasing another high, but for reckless VC?
| baxtr wrote:
| Jeopardy?!
| jgord wrote:
| yeah, see my other comment.
|
| To me its totally obvious that we will have a plethora of very
| valuable startups who use RL techniques to solve realworld
| problems in practical areas of engineering .. and I just get
| blank stares when I talk about this :]
|
| Ive stopped saying AI when I mean ML or RL .. because people
| equate LLMs with AI.
|
| We need better ML / RL algos for CV tasks : -
| detecting lines from pixels - detecting geometry in
| pointclouds - constructing 3D from stereo images,
| photogrammetry, 360 panoramas
|
| These might be used by LLMs but are likely built using RL or
| 'classical' ML techniques, tapping into the vast parallel
| matmull compute we now have in GPUs / multicore CPUs, and NPUs.
| pzo wrote:
| I thought there been a lot of progress in last 2 years.
| (Video) Depth Anything, SAM2, grounding Dino, DFINE, VLM,
| Gaussian splats, Nerf. Sure less than progres in LLm but
| still I would say progress accelerated with LLM research.
| tmilard wrote:
| You said : "- detecting lines from pixels - detecting
| geometry in pointclouds - constructing 3D from stereo images,
| photogrammetry, 360 panoramas" ==> For me it
| is more something like : Source = crude video-or-photo
| pixels (to) ===> Find simple many rectangle-surface that
| are glued together one another.
|
| This is, for me, how you really go easily to detecting rather
| complexes geometry of any room.
| porphyra wrote:
| I feel like 3D reconstruction/bundle adjustment is one of those
| things where LLMs and new AI stuff haven't managed to get a
| significant foothold. Recently VGGT won best paper which is
| good for them, but for the most part, stuff like NERF and
| Gaussian Splatting still rely on good old COLMAP for bundle
| adjustment using SIFT features.
|
| Also, LLMs really suck at some basic tasks like counting the
| sides of a polygon.
| KaiserPro wrote:
| > LLMs really suck at some basic tasks like counting the
| sides of a polygon.
|
| Oh indeed, but thats not using tokens correctly. if you want
| to do that, then tokenise the number of polygons....
| glitchc wrote:
| Hah! And I remember when ML itself sucked all the energy out of
| computer vision. Time to pay the piper.
| m3kw9 wrote:
| There is nothing to productize vs LLMs right now. I would say
| robots could fix that but they have hard problems to solve in
| the physical sense that will bottle neck things
| smath wrote:
| agreed about sucking the air out by LLM. The positive side is
| that its a good time to innovate in other areas while a chunk
| of ppl are absorbed in LLMs. A proven improvement in any other
| non LLM space will attract investment.
| pixl97 wrote:
| Isn't this what Nvidia is already doing with a lot of their
| sim software?
| CSMastermind wrote:
| Everyone is trying to jam transformers into CV workflows at the
| moment. Possibly productively.
| whiplash451 wrote:
| It felt the same back in 2012-2015 when deep learning was
| flooding over computer vision. Yet 10 years later there is a
| net benefit for computer vision: a lot of tasks are now solved
| much better/more efficiently with deep learning including those
| that seemed "unfit" to deep learning like tracking.
|
| I'm hopeful that VLMs will "fan out" into a lot of positive
| outcomes for computer vision.
| SlowTao wrote:
| That is fair. I think it is a case of just seeing a lot if
| great talent rush to the "in" thing. Other systems are still
| being developed and that isnt lost but there is just a
| feeling if being left out of it all while still doing great
| stuff.
| Barrin92 wrote:
| >but almost every single job posting in ML is about LLMs
|
| not in the defense sector, or aviation, or UAVS, automotive,
| etc. Any proper real-time vision task where you have to
| computationally interact with visual data is unsuited for LLMs.
|
| Nobody controls a drone, missile or vehicle by taking a
| screenshot and sending it to ChatGPT and has it do math while
| it's on flight, anything that requires as the title of the
| thread says, spatial intelligence is unsuited for a language
| model
| weinzierl wrote:
| I tried LLM's for geolocation recently and it is both amazing how
| good they are at recognizing patterns and how terrible they are
| with recognizing and utilizing basic spatial relationships.
| Pamar wrote:
| I would like to read a complete example if you want to share (I
| am not disputing yourbpoint, I'd just to understand better
| because this is not my field so I cannot immediately map your
| comment to my own experience)
| weinzierl wrote:
| Happy to share an complete example privately, contact data is
| in my profile.
|
| Will add condensed version here in half an hour.
| Pamar wrote:
| Condensed version will be more than adequate, thanks!
| weinzierl wrote:
| Condensing it for HN was harder than I thought because most
| of it makes only sense when you also see the images, so
| here is more like a summary of parts of the dialogue.
|
| Prompted by this comment
|
| https://news.ycombinator.com/item?id=44366753
|
| I tried to geolocate the camera.
|
| I uploaded a screenshot from
|
| https://walzr.com/weather-watching
|
| to ChatGPT and it said a lot of things but concluded with
| "New York City street corner in the East Village".[1]
|
| I find it utterly amazing that you can throw a random low-
| quality image at an LLM and it does not only pinpoint the
| city but also the quarter.
|
| Good, but how to proceed from there? ChatGPT knows how
| street corners in the East Village look in general, but it
| does not know every building and every corner. Moreover, it
| has no access to Google Street View to help find a matching
| building. So this is kind of a dead end when we want a
| precise location.
|
| How would a human approach this? From the image it is
| immediately clear that we have a T-shaped intersection.
| From the shadows we can conclude that the stem of the T
| runs east-west and the bar north-south. We see that the
| stem is a one-way street with cars going only from west to
| east. This immediately excludes all odd-numbered streets.
| There are buildings all around and no parks or water.
|
| There are not many intersections in New York that fulfill
| all these criteria, but what if the area to search was much
| bigger? Could an LLM do that for us?
|
| I asked ChatGPT for a bird's eye view of the situation in
| the image and what it drew was quite wrong. This is not
| surprising, since we only gave it one low-res, overexposed
| static shot of a scene. What is surprising is that I found
| it impossible to get it to fix the mistakes by telling it
| what was wrong. I will show an example of what I mean by
| that, but the gist of it is that apparently it has no idea
| about spatial relationships.
|
| Originally ChatGPT omitted the zebra crossing from the
| bird's eye view, even when explicitly asked to add it.
| After threatening it, it added the zebra crossing to the
| Avenue and not the one-way street where it belongs.
|
| I: "No, the zebra is in the wrong direction. It is across
| the horizontal street."
|
| ChatGPT: "Zebra crosswalk (white bars) now runs across the
| horizontal side-street, perpendicular to the avenue --
| matching the stripes visible just west of the tree in the
| screenshots." [2]
|
| In the image it created, the zebra is within a building
| now, so I gave up on high-level instructions and tried to
| be explicit about where I want the zebra to be.
|
| I: "The zebra needs to be moved to the east right to the
| edge of the building."
|
| ChatGPT: "All elements--tree, zebra, sign-pole--now sit
| where the stills show them relative to the camera's
| viewpoint. Let me know if any final tweak is needed!"
|
| The image it generated has the zebra removed.
|
| I: "I do not see a zebra. Maybe make it light grey or
| something."
|
| ChatGPT: "Zebra crosswalk now runs across the avenue, flush
| with the facades of the camera-side and A/C-wall buildings;
| stripes drawn in light grey for clarity."
|
| Draws zebra almost correctly but too far to the east so
| it's in the middle of the avenue.
|
| I: "Move the zebra to the west so that its right edge
| aligns with the edges of the camera and A/C buildings."
|
| ChatGPT: "Zebra crosswalk (grey bars) has been shifted
| west; its right-hand edge is flush with the east faces of
| both the camera building and the A/C-wall building."
|
| Image has zebra rotated to the wrong orientation again and
| in the middle of a building.
|
| I: "The zebra runs north to south like it was previously. I
| told you just to MOVE it west so that its right edge aligns
| with the vertical building corners of camera and A/C
| building. I never told you to rotate it."
|
| ChatGPT: "Zebra crosswalk runs north-south (vertical bars)
| and its right edge aligns precisely with the east facades
| of the camera-side and A/C-wall buildings."
|
| Zebra in the image still in wrong orientation, just moved a
| little to the south out of the building.
|
| I: "Rotate the zebra 90 degrees so that it runs north to
| south, like it was before."
|
| ChatGPT: "Zebra crosswalk rotated back: bars run north-
| south; its right edge aligns exactly with the east facades
| of the camera and A/C-wall buildings."
|
| I: "Now we have a zebra within the building."
|
| Zebra moved out of building but again rotated in the wrong
| direction.
|
| ChatGPT: "Zebra crosswalk: runs north-south; its right edge
| flush with the east facades of both camera and A/C
| buildings."
|
| I: "Rotate the zebra crosswalk so the stripes run west to
| east."
|
| This gave an acceptable result and I left it there.
|
| Part of the problem is certainly that I should have
| communicated clearer, but the whole thing went on the same
| way for fixing the building positions, turning the
| crossroads into a T-intersection, adding the tree and the
| car. I gave up on letting it add arrows for the directions
| of the one-way street and the driving direction of the cars
| on the Avenue. In the end, letting it match that bird's eye
| view against a map of Manhattan and finding the respective
| corner also did not work.
|
| [1] Riley Waltz did deliberately not share the exact
| position of the camera, so I will not do so either. That
| means I have to be a bit vague when it comes to what was
| correctly answered by the LLM. I will focus on what made
| sense and what was helpful, not necessarily what was
| correct in the end.
|
| [2] All ChatGPT output verbatim but abbreviated to the
| relevant parts.
| Workaccount2 wrote:
| I'd take text-to-image capabilities with a grain of salt,
| because they are dramatically lower than their text to
| text abilities. I don't know the exact mechanics with
| current multimodal models, but it is pretty clear that
| there is a disconnect between what the text modal wants,
| and what the text model outputs. It's almost feels like
| asking someone with a blindfold to draw a cat, you kinda
| get a mess.
|
| If you ask chatgpt to describe a new image based off an
| input image, it will do dramatically better. But ask it
| to use it's image generation tooling and the "awareness"
| judged by the image output falls off a cliff.
|
| Another example is infographics or flow charts. The
| models can easily output that information and put it in a
| nicely formatted text grid for you. But ask them to put
| it in a visual image, and it's just a mess. I don't think
| it's the models, I think it's the text-image translation
| layer.
| weinzierl wrote:
| This is a good point. The 2D birds eye view image adds
| another separate complication. There are certainly better
| and more direct ways to show that current models are bad
| with spatial reasoning. This was just a byproduct of my
| geolocation experiments. Maybe I will give it a shot
| another day.
| ansgri wrote:
| I've tried to use various OpenAI models for OpenSCAD code
| generation, and while the code was valid, it absolutely
| couldn't get spatial relationships right. Even in simple cases,
| like a U-tube assembled from 3 straight and 2 curved segments.
| So this is definitely an area for improvement.
| AStrangeMorrow wrote:
| Yeah even LLM generated code for a 2D optimization problems
| with many spatial relationships has being absolutely terrible,
| while I had great success in other domains.
| jandrewrogers wrote:
| I appreciate the video and generally agree with Fei-Fei but I
| think it almost understates how different the problem of
| reasoning about the physical world actually is.
|
| Most dynamics of the physical world are sparse, non-linear
| systems at every level of resolution. Most ways of constructing
| accurate models mathematically don't actually work. LLMs, for
| better or worse, are pretty classic (in an algorithmic
| information theory sense) sequential induction problems. We've
| known for well over a decade that you cannot cram real-world
| spatial dynamics into those models. It is a clear impedance
| mismatch.
|
| There are a bunch of fundamental computer science problems that
| stand in the way, which I was schooled on in 2006 from the
| brightest minds in the field. For example, how do you represent
| arbitrary spatial relationships on computers in a general and
| scalable way? There are no solutions in the public data
| structures and algorithms literature. We know that universal
| solutions can't exist and that all practical solutions require
| exotic high-dimensionality computational constructs that human
| brains will struggle to reason about. This has been the status
| quo since the 1980s. This particular set of problems is hard for
| a reason.
|
| I vigorously agree that the ability to reason about
| spatiotemporal dynamics is critical to general AI. But the
| computer science required is so different from classical AI
| research that I don't expect any pure AI researcher to bridge
| that gap. The other aspect is that this area of research became
| highly developed over two decades but is not in the public
| literature.
|
| One of the big questions I have had since they announced the
| company, is who on their team is an expert in the dark state-of-
| the-art computer science with respect to working around these
| particular problems? They risk running straight into the same
| deep, layered theory walls that almost everyone else has run
| into. I can't identify anyone on the team that is an expert in a
| relevant area of computer science theory, which makes me
| skeptical to some extent. It is a nice idea but I don't get the
| sense they understand the true nature of the problem.
|
| Nonetheless, I agree that it is important!
| machinelearning wrote:
| "Most ways of constructing accurate models mathematically don't
| actually work" > This is true for almost anything at the limit,
| we are already able to model spatiotemporal dynamics to some
| useful degree (see: progress in VLAs, video diffusion, 4D
| Gaussians)
|
| "We've known for well over a decade that you cannot cram real-
| world spatial dynamics into those models. It is a clear
| impedance mismatch" > What's the source that this is a
| physically impossible problem? Not sure what you mean by
| impedance mismatch but do you mean that it is unsolvable even
| with better techniques?
|
| Your whole third paragraph could have been said about LLMs and
| isn't specific enough, so we'll skip that.
|
| I don't really understand the other 2 paragraphs, what's this
| "dark state-of-the-art computer science" you speak of and what
| is this "area of research became highly developed over two
| decades but is not in the public literature" how is "the
| computer science required is so different from classical AI
| research"?
| calf wrote:
| Above commenter also asserts "highly developed research but
| no public literature" _shrug_ ...
| fu-hn wrote:
| But the best minds in the world said so!
| ccozan wrote:
| I agree that the problem is hard. However, biological brain is
| able to handle it quite "easily" ( is not really easy - bilions
| of iterations were needed ). The current brains are solving
| this 3D physical world _only_ via perception.
|
| So this is place were we must look. It starts with the sensing
| and the integration of that sensing. I am working at this
| problem since more than 10 years and I came to some results. I
| am not a real scientist but a true engineer and I am looking
| from that perspective quite intesely: The question that one
| must ask is: how do you define the outside physical world from
| the perspective of a biological sensing "device" ? what exactly
| are we "seeing" or "hearing"? So yes, working on that brought
| it further in defining the physical world.
| tmilard wrote:
| I do agree with you. We have an natural eye (what you call a
| 'biological brain') automat that inconsciouly 'feels' the
| structure of a geometric of the places we enter to.
|
| Once this layer of "natural eye automat" is programmed behind
| a camera, it will spit out this crude geometry : the Spacial-
| data-bulk (SDB). This SDB is small data.
|
| From now on, our programs will only do reason, not on data
| froms camera(s) but only on this small SBD.
|
| This is how I see it.
| tmilard wrote:
| ==> And now the LLMs, to feel Spacial knowledge, will have
| a very reduce dataset. This will make spacial data
| reasoning very less intencive than we can't imagine.
| foobarian wrote:
| Maybe a brute force solution would work just like it did for
| text. I would not be surprised if the scale of that brute
| force was not within reach yet though.
| andoando wrote:
| Also a cook here whose spent years thinking about this, would
| love to hear about what results you've obtained
| adamnemecek wrote:
| All (ALL!!) AI/optimization problems boil down to energy
| minimization or dually entropy maximization.
| lsy wrote:
| To make this more concrete: ImageNet enabled computer "vision"
| by providing images + labels, enabling the computer to take an
| image and spit out a label. LLM training sets enable text
| completion by providing text + completions, enabling the
| computer to take a piece of text and spit out its completion.
| Learning how the physical world works (not just kind of works a
| la videogames, _actually works_ ) is not only about a jillion
| times more complicated, there is really only one usable
| dataset: the world itself, which cannot be compacted or fed
| into a computer at high speed.
|
| "Spatial awareness" itself is kind of a simplification: the
| idea that you can be aware of space or 3d objects' behavior
| without the social context of what an "object" is or how it
| relates to your own physical existence. Like you could have two
| essentially identical objects but they are not interchangeable
| (original Declaration of Independence vs a copy, etc). And many
| many other borderline-philosophical questions about when an
| object becomes two, etc.
| coldtea wrote:
| > _there is really only one usable dataset: the world itself,
| which cannot be compacted or fed into a computer at high
| speed._
|
| Why wouldn't it be? If the world is ingressed via video
| sensors and lidar sensor, what's the hangup in recording such
| input and then replaying it faster?
| psb217 wrote:
| I think there's an implicit assumption here that
| interaction with the world is critical for effective
| learning. In that case, you're bottlenecked by the speed of
| the world... when learning with a single agent. One neat
| thing about artificial computational agents, in contrast to
| natural biological agents, is that they can share the same
| brain and share lived experience, so the "speed of reality"
| bottleneck is much less of an issue.
| HappMacDonald wrote:
| Yeah I'm envisioning putting a thousand simplistic
| robotic "infants" into a vast "playpen" to gather sensor
| data about their environment, for some (probably smaller)
| number of deep learning models to ingest the input and
| guess at output strategies (move this servo, rotate this
| camshaft this far in that direction, etc) and make
| predictions about resulting changes to input.
|
| In principle a thousand different deep learning models
| could all train simultaneously on a thousand different
| robot experience feeds.. but not 1 to 1, but instead 1 to
| many.. each neural net training on data from dozens or
| hundreds of the robots at the same time, and different
| neural nets sharing those feeds for their own rounds of
| training.
|
| Then of course all of the input data paired with outputs
| tested and further inputs as ground truth to predictions
| can be recorded for continued training sessions after the
| fact.
| loa_in_ wrote:
| But the playpen will contain objects that are inherently
| breakable. You cannot rough handle the glass vessel and
| have it too.
| m-s-y wrote:
| The world Is breakable. Any model based on it will need
| to know this anyway. Am I missing your argument?
| devenson wrote:
| Can't reset state after breakage.
| csullivan107 wrote:
| Never thought I'd get to do this but this was my masters
| research! Simulations are inherently limited and I just
| got tired of robotic research being done only in
| simulations. So I built a novel soft robot (notoriously
| difficult to control) and got it to learn by playing!!
|
| Here is an informal talk I gave on my work. Let me know
| if you want the thesis
|
| https://www.youtube.com/live/ZXlQ3ppHi-E?si=MKcRqoxmEra7Z
| rt5
| rybosome wrote:
| A very interesting idea. I am curious about this sharing
| and blending of the various nets; I wonder if something
| as naive as averaging the weights (assuming the neural
| nets all have the same dimensions) would actually
| accomplish that?
| hackyhacky wrote:
| > In that case, you're bottlenecked by the speed of the
| world
|
| Why not have the AI train on a simulation of the real
| world? We can build those pretty easily using traditional
| software and run them at any speed we want.
| otodus wrote:
| How would you handle olfactory and proprioceptive data?
| m-s-y wrote:
| > the world itself, which cannot be compacted or fed into a
| computer at high speed.
|
| ...yet.
|
| 15 years ago LLMs as they are today seemed like science
| fiction too.
| amelius wrote:
| Well, you can use one or two cameras and a lidar, and use
| that to generate data to train a depth-map.
| TheOtherHobbes wrote:
| Considering how bad LLMs are at understanding anything, and
| how they still manage to be useful, you simply don't need
| this level of complexity.
|
| You need something that mostly works most of the time, and
| has guardrails so when it makes mistakes nothing bad happens.
|
| Our brains acquire quite good heuristics for dealing with
| physical space without needing to experience all of physical
| reality.
|
| A cat-level or child-level understanding of physical space is
| more immediately useful than a philosopher-level of
| understanding.
| sega_sai wrote:
| I feel that if words/phrases/whole texts can be embedded well
| in high dimensional spaces as points, the same must apply to
| the 3d world. I'm sure there will be embeddings of it (i.e.
| mapping the 3-d scene into a high-D vector) and then we'll be
| work with those embeddings as LLMs work with text
| (disclaimer: I am not an expert in the field ).
| mindcrime wrote:
| > became highly developed over two decades but is not in the
| public literature.
|
| Developed by who? And for what purpose? Are we talking about
| overlap with stuff like missile guidance systems or targeting
| control systems or something, and kept confidential by the
| military-industrial complex? I'm having a hard time seeing many
| other scenarios that would explain a large body of people doing
| research in this area and then not publishing anything.
|
| > I can't identify anyone on the team that is an expert in a
| relevant area of computer science theory
|
| Who is an expert on this theory then?
| gyomu wrote:
| > There are a bunch of fundamental computer science problems
| that stand in the way, which I was schooled on in 2006 from the
| brightest minds in the field. For example, how do you represent
| arbitrary spatial relationships on computers in a general and
| scalable way? There are no solutions in the public data
| structures and algorithms literature. We know that universal
| solutions can't exist and that all practical solutions require
| exotic high-dimensionality computational constructs that human
| brains will struggle to reason about. This has been the status
| quo since the 1980s. This particular set of problems is hard
| for a reason.
|
| Where can I read more about this space? (particularly on the
| "we know that universal solutions can't exist" front)
| niemandhier wrote:
| Regarding sparse, nonlinear systems and our ability to learn
| them:
|
| There is hope. Experimental observation is, that in most cases
| the coupled high dimensional dynamics almost collapses to low
| dimensional attractors.
|
| The interesting thing about these is: If we apply a measurement
| function to their state and afterwards reconstruct a
| representation of their dynamics from the measurement by
| embedding, we get a faithful representation of the dynamics
| with respect to certain invariants.
|
| Even better, suitable measurement functions are dense in
| function space so we can pick one at random and get a suitable
| one with probability one.
|
| What can be glanced about the dynamics in terms of of these
| invariants can learned for certain, experience shows that we
| can usually also predict quite well.
|
| There is a chain of embedding theorems by Takens and Sauer
| gradually broadening the scope of applicability from
| deterministic chaos towards stochasticly driven deterministic
| chaos.
|
| Note embedding here is not what current computer science means
| by the word.
|
| I spend most of my early adulthood doing theses things, would
| be cool to see them used once more.
| golol wrote:
| What field of mathematics is this? Can you point me to some
| keywords/articles?
| teemur wrote:
| > We know that universal solutions can't exist and that all
| practical solutions require exotic high-dimensionality
| computational constructs that human brains will struggle to
| reason about. This has been the status quo since the 1980s.
| This particular set of problems is hard for a reason.
|
| This made me a bit curious. Would you have any pointers to
| books/articles/search terms if one wanted to have a bit deeper
| look on this problem space and where we are?
| jandrewrogers wrote:
| I'm not aware of any convenient literature but it is
| relatively obvious once someone explains it to you (as it was
| explained to me).
|
| At its root it is a cutting problem, like graph cutting but
| much more general because it includes things like non-trivial
| geometric types and relationships. Solving the cutting
| problem is necessary to efficiently shard/parallelize
| operations over the data models.
|
| For classic scalar data models, representations that preserve
| the relationships have the same dimensionality as the
| underlying data model. A set of points in 2-dimensions can
| always be represented in 2-dimensions such that they satisfy
| the cutting problem (e.g. a quadtree-like representation).
|
| For non-scalar types like rectangles, operations like
| equality and intersection are distinct and there are an
| unbounded number of relationships that must be preserved that
| touch on concepts like size and aspect ratio to satisfy
| cutting requirements. The only way to expose these additional
| relationships to cutting algorithms is to encode and embed
| these other relationships in a (much) higher dimensionality
| space and then cut that space instead.
|
| The mathematically general case isn't computable but real-
| world data models don't need it to be. Several decades ago it
| was determined that if you constrain the properties of the
| data model tightly enough then it should be possible to
| systematically construct a finite high-dimensionality
| embedding for that data model such that it satisfies the
| cutting problem.
|
| Unfortunately, the "should be possible" understates the
| difficulty. There is no computer science literature for how
| one might go about constructing these cuttable embeddings,
| not even for a narrow subset of practical cases. The activity
| is also primarily one of designing data structures and
| algorithms that can represent complex relationships among
| objects with shape and size in dimensions much greater than
| three, which is cognitively difficult. Many smart people have
| tried and failed over the years. It has a lot of subtlety and
| you need practical implementations to have good properties as
| software.
|
| About 20 years ago, long before "big data", the iPhone, or
| any current software fashion, this and several related
| problems were the subject of an ambitious government research
| program. It was technically successful, demonstrably. That
| program was killed in the early 2010s for unrelated reasons
| and much of that research was semi-lost. It was so far ahead
| of its time that few people saw the utility of it. There are
| still people around that were either directly involved or
| learned the computer science second-hand from someone that
| was but there aren't that many left.
| andoando wrote:
| Ive spent years trying to tackle spatial representations on
| my own, so Im extremely curious here.
|
| How does the cutting problem relate to intelligence in the
| first place?
| jandrewrogers wrote:
| Indexing is a special case of AI. At the limit, optimal
| cutting and learning are equivalent problems. Non-trivial
| spatial representations push these two things much closer
| together than is normally desirable for e.g. indexing
| algorithms. Tractability becomes a real issue.
|
| Practically, scalable indexing of complex spatial
| relationships requires what is essentially a type of
| learned indexing, albeit not neural network based.
| calf wrote:
| But then that sounds more like that person explained it
| wrong. They didn't explain why it is necessary to reduce to
| GRAPHCUT, it seems to me to beg the question. We should not
| assume this is true based on some vague anthropomorphic
| appeal to spatial locality, surely?
| jandrewrogers wrote:
| It isn't a graph cutting problem, graph cutting is just a
| simpler, special case of this more general cutting
| problem (h/t IBM Research). If you can solve the general
| problem you effectively get efficient graph cutting for
| free. This is obviously attractive to the extent you can
| do both complex spatial and graph computation at scale on
| the same data structure instead of specializing for one
| or the other.
|
| The challenge with cutting e.g. rectangles into uniform
| subsets is that logical shard assignment must be
| identical regardless of insertion order and in the
| absence of an ordering function, with O(1) space
| complexity and without loss of selectivity. Arbitrary
| sets of rectangles overlap, sometimes heavily, which is
| the source of most difficulty.
|
| Of course, with practical implementations write
| scalability matters and incremental construction is
| desirable.
| tehjoker wrote:
| Did that research program have a public code name?
| mindcrime wrote:
| Looking through some old DARPA budget docs[1], it seems
| like there's a chance that what's being discussed here
| falls under DARPA's "PE 0602702E TACTICAL TECHNOLOGY"
| initiative, project TT-06.
|
| Some other possibilities might include: -
| "PE 0602304E COGNITIVE COMPUTING SYSTEMS", project
| COG-02. - "PE 0602716E ELECTRONICS TECHNOLOGY",
| project ELT-01 - "PE 0603760E COMMAND, CONTROL AND
| COMMUNICATIONS SYSTEMS", project CCC-02 - "PE
| 0603766E NETWORK-CENTRIC WARFARE TECHNOLOGY", project
| NET-01 - "PE 0603767E SENSOR TECHNOLOGY", project
| SEN-02
|
| Or maybe it's nothing to do with this at all. But in
| either case, this looks like some interesting stuff to
| explore in its own right. :-)
|
| [1]: https://web.archive.org/web/20181001000000/https://w
| ww.darpa...
| idiotsecant wrote:
| If there's one thing that control theory has taught us in the
| last 100 years, it's that anything is linear if you zoom in far
| enough. Nonlinearity is practically solvable by adjusting your
| controls to different linear models depending on your position
| in the system space.
| amelius wrote:
| Some types of deep learning model can handle 3d data quite
| well:
|
| https://en.wikipedia.org/wiki/Neural_radiance_field
| nurettin wrote:
| > how do you represent arbitrary spatial relationships on
| computers in a general and scalable way?
|
| Isn't this essentially what the convolutional layers do in
| LeNet?
| queuebert wrote:
| I think you are using "sparse" and "non-linear" as scare terms.
| Sparse is a good thing, as it reduces degrees of freedom, and
| non-linear does not mean unsolvable.
|
| Also "impedance mismatch" doesn't mean no go, but rather less
| efficient.
| CamperBob2 wrote:
| _We've known for well over a decade that you cannot cram real-
| world spatial dynamics into those models. It is a clear
| impedance mismatch._
|
| Then again, not much that we "knew" a decade ago is still
| relevant today. Of course transformer networks have proven
| capable of representing spatial intelligence. How could they
| work with 2D images, if not?
| andoando wrote:
| What's non linear about spatial reasoning?
|
| >We know that universal solutions can't exist
|
| Why not?
| randcraw wrote:
| Spatial models must be 3D, not 1D (linear), much less 2D,
| which is sufficient for images and object recognition (where
| models are not needed). And adding time makes it 4D, at least
| for robot motion.
|
| To reason spatially (and dynamically) the dependence of one
| object's position in space on other objects (and their
| motions and behaviors) adds up fast to complicate the model
| in ways that 95% of 2D static image analysis does not.
| andoando wrote:
| Well hold on, first Im not convinced we have solved 2D
| spatial intelligence. Analyzing 2D images is very different
| from being able to reason about 2D geometry. How do you
| mathematically define relations like "above", "below",
| "diagonal", etc in a composable way that can be learned?
|
| Second, problems in 3D can be deconstructed to 2D. For
| example, how do you get to the airport? You need to first
| solve the 2D overview of the path youd take as youd now
| looking at a map. Then you need to reason about your field
| of view, and here again I believe youre really reasoning is
| something like "object A is behind object B and A is to the
| left of B", and not solving some non linear equation
|
| I think a big issue is people are trying to solve this in
| the realm of traditional mathematics, and not as a simple
| step by step process
| dopadelic wrote:
| You're pointing out a real class of hard problems -- modeling
| sparse, nonlinear, spatiotemporal systems -- but there's a
| fundamental mischaracterization in lumping all transformer-
| based models under "LLMs" and using that to dismiss the
| possibility of spatial reasoning.
|
| Yes, classic LLMs (like GPT) operate as sequence predictors
| with no inductive bias for space, causality, or continuity.
| They're optimized for language fluency, not physical grounding.
| But multimodal models like ViT, Flamingo, and Perceiver IO are
| a completely different lineage, even if they use transformers
| under the hood. They tokenize images (or video, or point
| clouds) into spatially-aware embeddings and preserve positional
| structure in ways that make them far more suited to spatial
| reasoning than pure text LLMs.
|
| The supposed "impedance mismatch" is real for language-only
| models, but that's not the frontier anymore. The field has
| already moved into architectures that integrate vision, text,
| and action. Look at Flamingo's vision-language fusion, or
| GPT-4o's real-time audio-visual grounding -- these are not mere
| LLMs with pictures bolted on. These are spatiotemporal
| attention systems with architectural mechanisms for cross-modal
| alignment.
|
| You're also asserting that "no general-purpose representations
| of space exist" -- but this neglects decades of work in
| computational geometry, graphics, physics engines, and more
| recently, neural fields and geometric deep learning. Sure, no
| universal solution exists (nor should we expect one), but
| practical approximations exist: voxel grids, implicit neural
| representations, object-centric scene graphs, graph neural
| networks, etc. These aren't perfect, but dismissing them as
| non-existent isn't accurate.
|
| Finally, your concern about who on the team understands these
| deep theoretical issues is valid. But the fact is: theoretical
| CS isn't the bottleneck here -- it's scalable implementation,
| multimodal pretraining, and architectural experimentation. If
| anything, what we need isn't more Solomonoff-style induction or
| clever data structures -- it's models grounded in perception
| and action.
|
| The real mistake isn't that people are trying to cram physical
| reasoning into LLMs. The mistake is in acting like all
| transformer models are LLMs, and ignoring the very active (and
| promising) space of multimodal models that already tackle
| spatial, embodied, and dynamical reasoning problems -- albeit
| imperfectly.
| calf wrote:
| How do we prove a trained LLM has no inductive bias for
| space, causality, etc.? We can't assume this is true by
| construction, can we?
| dopadelic wrote:
| Why would we need to prove such a thing? Human vision has
| strong inductive biases, which is why you can perceive
| objects in abstract patterns. This is why you can lay down
| at a park and see a duck in a cloud. It's also why we can
| create abstracted representations of things with graphics.
| Having inductive biases makes it more relatable to the way
| we work.
|
| And again, you're using the term LLMs again when vision
| based transformers in multimodal models aren't simply LLMs.
| mumbisChungo wrote:
| Claude, is that you?
| ryeguy_24 wrote:
| Agree. Also, with respect to training, what is the goal that we
| are maximizing? LLMs are easy, predicting the next word and we
| have lots of training data. But what are we training for in
| real world? Modeling the next spatial photograph to predict
| things that will happen next? It's not intuitive to me what
| that objective function would be in spatial intelligence.
| curiouscavalier wrote:
| Or that there is a sufficiently generalizable objective
| function for all "spatial intelligence."
| epr wrote:
| Human beings get by quite well with extremely oversimplified
| (low resolution) abstractions. There is no need whatsoever for
| something even approaching universal or perfect. Humans aren't
| thinking about fundamental particles or solving differential
| equations in their head when they're driving a car or playing
| sports.
| voxleone wrote:
| I'm trying to approach spatial reasoning by introducing
| quaternions to navigate graphs. It is a change in the unit of
| traversal -- from positional increment to rotational
| progression. This reframing has cascading effects. It alters
| how we model motion, how we think about proximity, and
| ultimately how systems relate to space itself.
|
| The traditional metaphor of movement -- stepping from point A
| to point B -- is spatially intuitive but semantically
| impoverished. It ignores the continuity of direction, the
| embodiment of motion, and the nontriviality of turning.
| Quaternion-based traversal reintroduces these elements. It is
| not just more precise; it is more faithful to the mechanisms by
| which physical and virtual entities evolve through space. In
| other words objects 'become' the model.
|
| https://github.com/VoxleOne/SpinStep/blob/main/docs/index.md
| jgord wrote:
| makes sense - humans have evolved a lot of wetware dedicated to
| 3D processing from stereo 2D.
|
| I've made some progress on a PoC in 3D reconstruction - detecting
| planes, edges, pipes from pointclouds from lidar scans, eg :
| https://youtu.be/-o58qe8egS4 .. and am bootstrapping with in-
| house gigs as I build out the product.
|
| Essentially it breaks down to a ton of matmulls, and I use a lot
| of tricks from pre-LLM ML .. this is a domain that perfectly fits
| RL.
|
| The investors Ive talked to seem to understand that scan-to-cad
| is a real problem with a viable market - automating 5Bn / yr of
| manual click-labor. But they want to see traction in the form of
| early sales of the MVP, which is understandable, especially in
| the current regime of high interest rates.
|
| Ive not been able to get across to potential investors the vast
| implications for robotics, AI, AR, VR, VFX that having better /
| faster / realtime 3D reconstruction will bring. Its great that
| someone of the caliber of Fei-Fei Li is talking about it.
|
| Robots that interact in the real world will need to make a 3D
| model in realtime and likely share it efficiently with comrades.
|
| While a gaussian splat model is more efficient than a pointcloud,
| a model which recognizes a wall as a quad plane is much more
| efficient still, and needed for realtime communication. There is
| the old idea that compression is equivalent to AI.
|
| What is stopping us from having a google street-view v3.0 in
| which I can zoom right into and walk around a shopping mall, or
| train station or public building ? Our browsers can do this now,
| essentially rendering quake like 3D environments - the problem is
| with turning a scan into a lightweight 3D model.
|
| Photogrammetry, where you have hundreds of photos and reconstruct
| the 3D scene, uses a lot of compute, and the colmap / Structure-
| from-Motion algorithm predates newer ML approaches and is ripe
| for a better RL algorithm imo. Ive done experiments where you can
| manually model a 3D scene from well positioned 360 panorama
| photos of a building, picking corners, following the outline of
| walls to make a floorplan etc ... this should be amenable to an
| RL algorithm. Most 360 panorama photo tours have enough overlap
| to reconstruct the scene reasonably well.
|
| I have no doubt that we are on the brink of a massive improvement
| in 3D processing. Its clearly solvable with the ML/RL approaches
| we currently have .. we dont need AGI. My problem is getting
| funding to work on it fulltime, equivalently talking an investor
| into taking that bet :)
| jgord wrote:
| ps. its handy to compare the relative data sizes of [ models of
| ] the same scene : typically for something like a house, the
| data will be ballpark : - 15GB of pointcloud
| data ( 100Mn xyzRGB points from a lidar laser scanner ) -
| 3 GB of 360 panorama photos - 50MB obj 3D textured model
| - 2MB CAD model
|
| Im guessing gaussian-splat would be something like 20x to 40x
| more efficient than the pointcloud. I achieved similar
| compression for building scans, using flat textured mini-
| planes.
| rallyforthesun wrote:
| SplatAM is an interesting new way to generate 3D Gaussians in
| real-time. It relies on RGB+D data and doesn't need COLMAP at
| all. I am not related to it but am using it for a project with
| a robot, as its main purpose is to do SLAM. As far as I
| understand, it uses the point cloud for the alignment of the
| images
|
| edit:typo
| KaiserPro wrote:
| I have worked around spatial AI for a number of years.
|
| Most of the stuff I have been working with has been aimed at
| low power consumption. One of the things that really helped is
| not bothering with dense reconstruction at all.
|
| things like scenescript and SpaRP where instead of trying to
| capture all the geometry (like photogrammetry) the essential
| dimensions are captured and either outputted to a text
| description (scene script) or a simple model with decent
| normals (SpaRP)
|
| Humans don't really keep complex dense reconstructions in our
| head. Its all about spatial relationships of landmarks.
| MITSardine wrote:
| Have you tried "traditional" approaches like a Delaunay
| triangulation on the point cloud, and how does your method
| compare to that? Or did you encounter difficulties with that?
|
| Regarding what you say of planes and compression, you can look
| into metric-based surface remeshing. Essentially, you estimate
| surface curvature (second derivatives) and use that to distort
| length computations, remeshing your surface to length one in
| that distorted space, which then yields optimal DoFs to surface
| approximation error. A plane (or straight line) has 0 curvature
| so lengths are infinite along it (hence final DoFs there
| minimal). There's software to do that already, thought I'm not
| sure it's robust to your usecase, because they've been
| developed for scientific computing with meshes generated from
| CAD (presumably smoother than your point cloud meshes).
|
| I'd be really curious to know more about the type of workflow
| you're interested in, i.e. what does your input look like (do
| you use some open data sets as well?) and what you hope for in
| the end (mesh, CAD).
| signa11 wrote:
| mr. yann-le-cunn's jepa paper is quite instructive.
| IdealeZahlen wrote:
| I've always wondered how spatial reasoning appears to be
| operating quite differently from other cognitive abilities, with
| significant individual variations. Some people effortlessly
| parallel park while others struggle with these tasks despite
| excelling at other forms of pattern recognition. What was
| particularly intriguing for me is that some people with
| aphantasia have no difficulty with spatial reasoning tasks, so
| spatial reasoning may be distinct from reasoning based on
| internal visualization.
| polytely wrote:
| my theory is that aphantasia is purely about conscious access
| to visualizing not the existence of the ability to visualise.
|
| I have aphantasia but I would say that spatial reasoning is one
| of the things my brain is the best at
| golol wrote:
| How does one determine they have aphantasia? How do you know
| that you are not doing exactly this thing people call
| visualizing when you perform spatial reasoning?
| m463 wrote:
| I have had this idea about parking a car...
|
| Most people have proprioception - you know where the parts of
| your body are without looking. Close your eyes and you
| intuitively know where your hands and fingers are.
|
| When parking a car, it helps to sort of sit in the drivers seat
| and look around the car. Turn your neck and look past the back
| seat where your rear tire would be. sense the edges of the car.
|
| I think if you sort of develop this a bit you might "feel"
| where your car is intuitively when pulling into a parking space
| or parallel parking. (car-prioception?)
|
| (but use your mirrors and backup camera anyway)
| myspeed wrote:
| Most of our spatial intelligence is innate, developed through
| evolution. We're born with a basic sense of gravity and the
| ability to track objects. When we learn to drive a car, we simply
| reassign these built-in skills to a new context
| pzo wrote:
| Is there any research about it ? This would mean we massing
| some knowledge in genes and when offspring born have some
| knowledge of our ancestors. This would mean the weights are
| stored in DNA?
| cma wrote:
| Horses can be blindfolded at birth and when removed do basic
| navigation with no time for any training. Other non-visually
| precocious animals like cats, if they miss a critical
| development period without getting natural vision data, will
| never develop a functioning visual system.
|
| Baby chicks can do bipedal balance pretty much as soon as
| they dry off.
|
| Wood ducks can visually imprint very soon after hatching and
| drying off, a couple hours after birth with very limited
| visual data up until then and no interspersed sleep cycles.
|
| We as humans have natural reactions to snake like shapes etc.
| even before encountering the danger of them or learning about
| it from social cues. Babies
| magicalhippo wrote:
| I've pondered this often, especially kangaroos where the
| half-developed fetus can climb up into the pouch.
|
| Clearly we're just hardwired for certain tasks, in such a
| way that the function is primarily dictated by topology.
|
| This weight agnostic neural network page[1] explores this,
| but obviously isn't the true answer.
|
| [1]: https://weightagnostic.github.io/
| jampekka wrote:
| It's not clear whether humans have natural reactions to
| snakes. https://link.springer.com/article/10.11133/j.tpr.20
| 13.63.4.0...
| cma wrote:
| If not, another visual system one in newborns is
| preference for faces with open eyes and direct gaze:
|
| https://pubmed.ncbi.nlm.nih.gov/17030037/
| wolframhempel wrote:
| we're actually working on a practical implementation of aspects
| of what Fei-Fei describes - although with a more narrow focus on
| optimizing operations in the physical space (mining, energy,
| defense etc) https://hivekit.io/about/our-vision/
| ldenoue wrote:
| Full playable transcript https://www.appblit.com/scribe?v=_PioN-
| CpOP0
| sabman wrote:
| We've been working on this challenge in the satellite domain with
| https://earthgpt.app. It's a subset of what Fei-Fei is
| describing, but comes with its own unique issues like handling
| multi-resolution sensors and imagery with hundreds of spectral
| bands. Think of it as computer vision, but in n-dimensions.
|
| Happy to answer questions if you're curious. PS. still in early
| beta, so please be gentle!
| fnands wrote:
| Hey, cool project!
|
| Do you actually pass the images to the model, or just the
| metadata/stats?
| sabman wrote:
| Thanks! This live demo uses metadata and stats only. Right
| now we are testing ViTs and Foundation Models as well. But
| quality of results from EO FMs haven't been worth the
| inference cost so far. Early days though. Also starting to
| fine tune models for specific downstream tasks ourselves.
| fnands wrote:
| Cool, makes sense.
|
| Yeah, have you considered maybe looking into just running
| it on embeddings [1], instead of the imagery itself? Would
| save on most of the inference cost, at the cost of
| flexibility (i.e. you are locked into whatever embeddings
| have been created).
|
| [1] https://developers.google.com/earth-
| engine/datasets/catalog/...
| byteab wrote:
| Really interesting space
| District5524 wrote:
| An immaterial side note: funny how obsessed she seems to be with
| her age. She said once that people in the audience could be half
| or even third of her age. Given that she's 49, is it really
| typical that 16-year olds attend these fireside YC chats?
| defrost wrote:
| Possible, yes .. which validates her statement.
|
| Typical? Probably not, but hardly relevant to the truthiness of
| the claim.
| mistersquid wrote:
| > An immaterial side note: funny how obsessed she seems to be
| with her age.
|
| Given her intellectual stature, Professor Li likely was one of
| the strongest minds in any room she found herself in and, for
| the first half of her life, also one of the youngest voices.
|
| Now that she's entering mid-life, she's still one of the most
| powerful minds, but no longer one of the youngest.
|
| It's something middle-aged thinkers can't help but notice.
|
| For the rest of us, we can only be grateful to share space and
| time with such gifted thinkers.
|
| Coincidentally, today is Professor Li's birthday! [0] I hope I
| will be around to see many more 3rds of July.
|
| [0] Maybe her coming birthday was on her mind, hence the
| frequency of her remarks about her relative age.
| dopadelic wrote:
| It's interesting how figures get idolized.
|
| Fei-Fei Li is known for the creation of ImageNet, which is
| certainly transformative in the field of computer vision. But
| the crux of it is painstaking grunt work to create the vast
| labeled dataset. Fei-Fei Li is a leader who mobilized vast
| resources and people hours to create this vast dataset.
| Certainly worth a ton of acclaim. But to claim she's the most
| brilliant mind in an entire room is a stretch.
| gk1 wrote:
| I only noticed a few times when she brought up age, and all of
| them were natural and appropriate in the context. Eg she was
| asked how old she was when she opened the dry-cleaning
| business.
|
| Also, there's this weird thing in culture (is it US only?) that
| whenever an interviewer brings up (even implicitly) the guest's
| age, the guest has to make some quip about it as if they're
| offended or sensitive to it. So I wouldn't interpret even a
| slightly defensive comment about age as an "obsession."
| cainxinth wrote:
| There are many such frontiers in AI. I was just reading that
| current models are apparently quite bad with temporal perception:
|
| https://community.openai.com/t/time-awareness-in-ai-why-temp...
|
| https://boraerbasoglu.medium.com/the-impact-of-ais-lack-of-t...
| moktonar wrote:
| Intelligence is not only embodied (it needs a body), it is also
| embedded in the environment (it needs the environment). If you
| want an intelligence in your computer, you need an environment in
| your computer first, as the substrate from which the intelligence
| will evolve. The more accurate the environment the better the
| intelligence that will be obtained. The universe is able to
| create intelligence and we are proof. Thus, if you want to create
| intelligence, you have to find a way of efficiently simulate our
| reality at the desired level of detail. Currently we don't know
| such efficient algorithm, but one way could be finally harnessing
| Quantum Computing to hack the universe itself, cheat and be able
| to simulate our environment efficiently without even knowing the
| algorithm behind Quantum Physics.
| Workaccount2 wrote:
| The silicon exists in the same environment that our brains do.
| rtaylorgarlock wrote:
| The drivers of its failure to adequately assimilate to said
| environment being?
| moktonar wrote:
| Yeah, and we represent the evolutionary force, but that means
| that the ability to craft silicon life depends on our ability
| to find an efficient algorithm to do so...
| dcreater wrote:
| So your basically saying we need Dolores and Westworld
| pixl97 wrote:
| >Intelligence is not only embodied (it needs a body), it is
| also embedded in the environment (it needs the environment).
|
| Of course don't make the mistake that we need anything like a
| human body, or any singular object containing 'intelligence'.
| That's simply the way nature had to do it to connect a sensor
| platform to a brain. AI seems much more like it will be a hive
| mind and distributed system of data collection.
| yellow_postit wrote:
| recent paper on " How Well Does GPT-4o Understand Vision?
| Evaluating Multimodal Foundation Models on Standard Computer
| Vision Tasks" [1]
|
| [1] https://arxiv.org/abs/2507.01955
| Nurbek-F wrote:
| Isn't it what Karpathy has been advocating for since the early
| days of Tesla Vision?
| ninetyninenine wrote:
| The next frontier is eliminating hallucinations?
|
| Once that happens it's all over.
| owenpalmer wrote:
| While spatial intelligence is certainly a major limitation of
| current AI systems, I have been able to get LLMs to do quite
| impressive things.
|
| Here's an on the fly video I made (no retakes) of Claude
| generating a Godot scene file.
|
| https://youtu.be/2gARJpDG7Jo?si=W4rlISO-J4EPJYyG
| AStrangeMorrow wrote:
| Yeah funnily I did a project where we had an LLM based
| interface to in house 3D parametric modeling system and it did
| fairly well.
|
| However I've been trying to use LLM, both as orchestrators and
| in other cases to write code for 2D optimization problems with
| many spatial relationships and it has done terribly.
|
| I have talking it can generate 1000s of lines over many rounds
| of prompting/iteration that solve maybe 30% of the problem (and
| the 30% very easy cases) while completely messing up the rest.
| When doing that code myself, in less than 1000 lines, the "30%
| part" was maybe 3% of the total code. Even when basically
| providing pseudo code to solve specific part of the problem
| chances are these LLM solutions would also have many blind
| spots or issues.
|
| The thing is, that is a 2D problem for which there basically no
| ressources about online, and all the slightly similar problems
| all have careful handcrafted specialized solutions. So I think
| it has no good frame of reference how to solve the problem
| czbond wrote:
| Thanks for joining the obvious Fei-Fei about 5 years late.
| Spatial web standards approved by IEEE that have been in the
| works for years.
|
| https://spatialwebfoundation.org/
| hiddencost wrote:
| You do know who she is right?
| czbond wrote:
| Of course.... doesn't mean she was early in forming that
| viewpoint. Just stating the near future obvious
| sota_pop wrote:
| Wow, I watched a presentation of this idea (2) years ago. It
| reads like classic flavor-of-the-week jargon-soup engineered to
| be catnip for unsuspecting VC; ie combining buzzwords HTTP,
| IoT, AI, blockchain, and the notion of "digital twin" from the
| AEC industry. Given by a guy who seemed extremely excited (and
| heavily energized - possibly chemically). The presenter tried
| to describe how this differs from HTTP. I'm highly confident no
| one in the room was able to make anything of it.
|
| Before any questions could be asked, the presenter said "OK, I
| need to run to give this presentation at the World Economic
| Forum in Davos now.", and quite literally ran out of the room.
| alganet wrote:
| How can I be sure that spatial intelligence AIs will not be just
| intricate sensoring that ultimately fails to demonstrate actual
| intelligence?
|
| > "trilobite"
|
| The trilobite ancestor had a nervous system before it had an eye.
| It was able to make decisions and interact with the environment
| before the ability to see or speak a language.
|
| It feels to me like this basic step is still missing. We haven't
| even crossed the first AI frontier yet.
| mehulashah wrote:
| "Forget about what you've done in the past. Forget about what
| others think of you. Just hunker down and build. That is my
| comfort zone."
|
| Enough said.
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