The state of Computer Vision and AI: we are really, really far (2012)
karpathy.github.io
karpathy.github.io
The brain in reality is quite slow. We know neurons require at least 5 ms to do any computation. But the real power lies in the shear parallelism.
Essentially biology set a limit of 5 ms but evolution worked around it by creating billions of neurons. Even If they are slower, because there are so many of them, they can do more computation in that 5 ms gap than all computers in the world combined ! Its truly marvellous when you sit back and think about that.
When you think about how that gets done just using that information, the modern computer architecture works completely differently , What that means is we do not even have the adequate hardware to start working on this problem.
We can push the current limit of computation to solve sub-problems and it seems under that constrain we have done very well. But its a slow evolution. GPUs gave us a clue, now we have FPGAs and soon we will have better hardware to create "better" intelligent machines. The machinery that is the brain is vastly complex and beautiful, but not well understood. Its a slow and incremental process but we will get there sometime this or the next century.
2 + 2 can be viewed as a computation that happens in nature and our brain all the time.
But the real magic doesn't lie in the pure computation. Our brain understands what that 2 means in the context of all the knowledge of the entire Universe. 2 cows, 2 sheeps, 2 planets ?
Understanding context is what we are really good at, as said in the article "prior" knowledge. So its not surprising that a cheap calculator can outsmart me in computation when it doesn't actually compute the context of its computation, which is sort if cheating.
There's also some evidence that the "efficiency" of the more powerful parts of animal brains (including humans) simply comes from a sort of n-dimensional conditional railroading, which would work like this if my understanding is correct: if you map (B/W) visual inputs to XY axes of a slice of neurons, then when a line is formed, this fires up a line of neurons, which allows a direct connection between the Z±1 neurons of the first and last neurons on the mapped line, so this connection becomes the line. These Z±1 neurons have pathways to various other nearby other potential Z±1 neurons; a connection lighting up between Z±1 neurons that don't have a straightline connection over the XY map could detect different shapes. Then the Z±1 layers and their connections could be creating highway maps between both Z±2 neurons and diagonally-offset neurons that don't "fit in the grid" we're imagining here (remember, we're in 3D and neurons don't form a homogeneous grid, so this is all abstraction anyway).
These second-level connections, arguably identifying something like shapes, have connections in such a way that the next level up detects something else, etc. (insert magic we don't understand), until the detection results in recognizing a specific object, while the side-channel connections we haven't mentioned yet but kept occurring at every level connect to a different side-channel ZY map for, say, the position of the object (with its own layers of processing that eventually connect to the object concept neuron so that the "position" is associated with the recognized object), and/or perhaps other side-channel maps or parallel XY mappings for various other things. And of course, a lot of these things connect to completely different parts that we ignore here, and a lot of the information indirectly makes its way to the conscious mind in this way; the brain doesn't compile a complete vision report and submit it to the conscious mind in one download, it's all using the same hardware.
I think that will take us 500 years, give or take a few.
You could say the same about every tool that we use, which we don't totally understand. It just needs to work.
Then there's also the problem of your estimate. 500 years is a rather exaggerated timeframe. Twenty years ago I could've had the most prominent field experts tell me humans would never in the next three hundred years figure out biology even for the simplest of living organisms, because they were simply too irreducibly complex for that. And then a few years ago we simulated an entire worm's nervous system and gave it enough of a body for it to use, move around, and receive input from its entirely-fictional environment. Now we've got people working on doing the same thing with a cat brain. We're nearing breakthroughs in creating fully-synthetic, fully-functional animal organs that you can essentially real-life drag'n'drop to replace a failing natural organ without complications.
Knowing the above, are you really sure it's not 15 rather than 500 years?
[1]. (which is about as meaningful as saying we understand "weather", which is really a huge messy amalgam of many completely unrelated things ranging from fluidic motion and thermodynamics all the way to plate tectonics and even anthropology, after a fashion)
We are 'slow' only when we formalize the problem.
Probably not that many. Let's say you create a water-skipping robot with two different AI programs in it and a shaky, not very precise arm (but still as good as a human arm in terms of specs).
One is custom-made to the physics of the problem, and will calculate the angles and the forces and the pressures and the candy tasty physekz all over, and then decide on a particular motion of the robot arm, and the rock will skip. It'll take a lot of computing power, but it'll work.
The other just has a goal, to see the rock skip, and tries things at semi-random (though it has the general knowledge that it can be done and that it has to throw the rock towards the water in a particular way for it to happen and that it can control the outcome), and tries to figure out patterns between what it did and what happened as a result. Eventually, it has a particular tactic, a certain set of instructions, which could be "down down down the left thing and up up right down down the right thing and the up thing does down down up push push down push twist-force-2 at the same time", an entirely not at all complicated set of instructions with very little computation. This signal is sent through other nodes that might not have the perfect signal number, and eventually this outputs to the arm's motors... which nevertheless manage to make the rock skip, because the motion here is almost the same as the motion of the first AI, yet this one was obtained by eliminating the ones that didn't result in the rock skipping.
TL;DR: You don't do that many calculations on the spot. The "maths" in most situations like this is done by elimination throughout all the attempts you've made in your life to control a throw. Subsequent throws once you're already a practiced rock-skipper just involve firing "the same neurons as usual" which send "the same signals as usual" to your muscles, which result in the same skipping as usual, with very little math. If you already know that 2x^5 + 20 = 110 implies x = 45 from doing the same calculation twenty times in the past hour, your brain isn't performing "calculations" anymore, it's just repeating a pattern that's already there in your brain like a table lookup.
That is not an architectural design per se, and I highly doubt that it is optimal. Rather, it is good enough. Now, the Von Neumann computer architecture is of course lacking. Parallel computing as we know it works, but it's a total nightmare, and hardly competes with the incredible parallelism in nature. But I do think that there is sufficient power now to really feasibly compete with the brain, intelligence wise.
All this with a grain of salt. I have been reading stuff in the weird corners of AI research. I think this article is fundamentally a straw man. (I believe that...) Deep learning and probabilistic machine learning are fundamentally flawed for strong AI. Jeff Hawkins is a well respected AI researcher that I think seems to agree. Another problem is of course that these are all trained in a supervised fashion. Google does well because they have huge tagged data sets. However, the brain doesn't work by training on tagged data sets, it can learn on it's own, unsupervised. So long as we keep pointing mainstream AI down the supervised statistical machine learning path, we will always be far away from strong AI.
Firstly, the brain has a basic feedback mechanism, which can be thought of as basic supervision: it receives quick feedback on certain kinds of hardware issues its actions caused. Most computers don't even get a similar level of feedback (eg, that they tied up the cluster on junk computations).
Secondarily, modern humans are booted through this mechanism by already running humans, in what is direct supervised learning: from the time we're infants -- and for the next two decades -- our actions are supervised, commented on, corrected and our exposure to materials is metered and selected to create a (theoretically) optimum consumption plan.
That we get any results out of computers with a couple months of training when comparing them to humans with a couple decades of training is testament to the fact that computer learning is orders of magnitude more effective than human learning.
I find it very strange that people leave out the 2 decades of hardware tuning and supervised knowledge building that humans get when discussing about how awful it is we having to train our machines if we want them to be smart.
An interesting aspect of brains vs machines is how brains can learn from other brains indirectly. When someone tags a database for a machine to learn from, that is a form of direct communication (the kind machines are good at). A crow can watch another crow use a stick as a tool, and in turn learns that a stick can be used as a tool. This requires a complex understanding of the situation.
Facts don't require your agreement. They're still facts.
You're suggesting that the "2 decades of hardware tuning" is equivalent to programming, and that just isn't the case. While parents definitely provide instruction, each child's brain is fully on its own to program itself. It teaches itself how to make sense of the visual signals it's receiving from the retina... no one teaches a child up from down, how shading and colors differ, how to use both eyes in tandem to provide depth perception. It's all automatic and 'magical' in a way, because literally NOTHING you can do as a parent can speed that process up in any meaningful way. The same is true for hearing. You can't just upload your current understanding of the world... the closest you can come is by trying to help them along mechanically, by holding them up as they 'walk' their legs, for instance, but their brain is still figuring it out entirely independently. Showing the child how to move their legs is NOT the equivalent of showing them how to detect orientation using their inner ear, or teaching them how to send impulses to their muscles in the right order and at the right time, or teaching them to predict momentum so that they don't just fall on their face.
> computer learning is orders of magnitude more effective than human learning
The only reason one could even ATTEMPT to make that claim is that a computer's learning is something that can be copied digitally. A single machine can't even approach the learning abilities of the brain. Computers are so fundamentally different that it's not even a fair comparison to the computer... the brain will win every time.
> I find it very strange that people leave out the 2 decades of hardware tuning and supervised knowledge building that humans get
It's because it just isn't relevant. Take the most advanced AI available today, slap it in a robot and spend 2 decades raising it. I guarantee it won't end up anywhere close to a human in abilities or intelligence. If you can't see that, you're being intentionally obtuse.
Modern robotics has been around for more than 20 years and there is no autonomy whatsoever.
It might surprise many people who claim that AIG is 20-30 away from now that in robotics, something as simple as cup stacking is a major problem. If you can figure out how to do that you will get slightly famous.
The brain and a computer work so fundamentally differently that its impossible to provide a single framework to compare them. This is why I used the metric of computational complexity but even that is not at all accurate.
I think due to the tremendous amount of automation we have seen in the past 10-20 years, many programmer have difficulty appreciating the awesomeness of biological intelligence. Rather than view the success of automation as a failure of how human education works.
By fully appreciating how complexity of the biological intelligence can we start to learn from it and append it.
Its one of the shortcoming of natural selection and genetic programming.
We have better tools to do sequential computation than biology can match. And soon we will be able to harness the power of quantum mechanics to do our bidding.
But the point I am trying to make is whatever approach we use, sequential or parallel, Just in terms of computational complexity. The brain's capacity is greater than the sum of the computers humans have ever produced. Just using that metric we are still far away from having the tools to simulate brains, how long will it take for Intel to compress the complexity of ALL computers ever produced into a single chip ?
Surely its not tomorrow ? maybe 20-30 years ? I do not know.
What I do know is in 2015 it seems that there is a lot of work that needs to be done just from a hardware perspective.
Here are a couple links:
From today actually. Memristors are an exciting development if they can scale production: http://www.technologyreview.com/news/537211/a-better-way-to-...
A startup working on this: http://brainchipinc.com/
IBM's TrueNorth Architecture: http://www.research.ibm.com/articles/brain-chip.shtml
The more people who are interested in solving these problems the better.
Neural Networks are awesome - but modern AI tells us that there are also various other techniques, The problem with Neural Networks is they are essentially a black box and many mathematicians and engineers are not happy working with black boxes.
Right now the models for synapses and Neurons are very simple compared to what actually is happening in them. But regardless the approach is not in vain.
(My current research involves autonomous motor control with tactile feedback, which is very far away from AGI)
Also, because they're usually in some skull-like object, and because neurons are incredibly hard to culture, we simply lack the tools to observe them in their natural environment to the resolution you might want. Simple questions like how often a neural signal fails to cross a synapse in a brain are remarkable difficult to answer. Do we need to know how every neuron is connected to every other neuron in the brain to understand it? Even if we had such a map, would it be missing something so crucial as to be useless (for example how glia interact with the neuron)? Nobody can say for sure right now.
On the other hand, the brain and eyes of the fruit fly do amazing things. I think it's reasonable to say that vision is lacking both hardware and algorithms at this point.
Gains have continued in 2015.
Also, more recent reflections on his research which I think gives a bit more color to the OP: http://karpathy.github.io/2014/07/03/feature-learning-escapa...
Kind of like with the rise of electricity, the microprocessor, the PC, or internet -- in the beginning, only the people building it understood what all the fuss was about. But that changed quickly over the course of N years (where N ends up being sooner than everyone thinks). If you had started a career in any of those fields before they were obvious in hindsight, you would have probably done quite well.
The author of the post has not quit to go start a photo app as far as I know, he's still doing research on the cutting edge of deep learning because that's where the most promise is.
Neural networks continue to make progress in the narrow field they are designed for and researching these I'm sure continues to be interesting. That doesn't change the point that human don't interpret an image as a couple of annotations but as rich fabric of information far beyond what computer vision current does.
Basically, there is a ocean of interesting, useful and excite things computers can do before they arrive at what humans can do.
Just the fact that I was able to read that article the way I did is the product of something enormous. Discovery of electricity and semiconductors, mathematics, development of CPUs and memory, global computer networks, the entire software stack that allows me to display what someone wrote three years ago on a glowing rectangle with no involvement of any paper at all. The entire social and economic development that allows me to read this instead of walking through the woods and trying to impale some creature on an arrow.
That's a lot of solved problems, most of them figured out during the past century. I don't think some image segmentation, pose estimation and semantic reasoning is going to take quite as long, especially with more and more people working on it.
I was impressed by some video segmentation and object classification results that Microsoft showed off the other day at its Ignite conference. We're a lot farther along than some people realize.
Picture here: https://twitter.com/MS_Ignite/status/595365048547180545
Video clip at the 1:02:00 mark: https://channel9.msdn.com/Events/Ignite/2015/KEY02
Furthermore, I don't know which dataset they're using. Perhaps it only works on a small set of objects such as those shown in the video.
I don't mean to knock their results but it will still take time to get this to work on a more broad set of videos.
http://cs.stanford.edu/people/karpathy/deepimagesent/
This got a lot of press:
http://www.nytimes.com/2014/11/18/science/researchers-announ...
Partly the question goes to the difference between artificial intelligence, and artificial consciousness. According to some definitions, the former is ability to produce relevant information, while the latter is an autonomous system capable of using that information for self interest.
For example, no matter how complex a system like Watson is, it's no more conscious than a rock. Meanwhile, rudimentary life form is something we are nowhere near replicating artificially. This distinction is quite important, and very intelligent AI pundits seem to fail to make it (or understand it?).
While we are quite capable of producing intelligence artificially, the capacities associated with the consciousness become confused easily in the mind. While some intelligence is easy to produce with machines, there are some problems of experience that simply cannot be solved by artificial intelligence, and require consciousness.
But let's be clear: producing an artificial consciousness is orders of magnitude more complex an engineering challenge than building an artificial intelligence machine.
It is also potentially enormously destructive: many times more difficult to create, and at least as destructive as atomic bomb.
You are doing AI wrong. AI should learn all of that context by itself, from a large amount of stimulus. If it was a good one, it might be able to learn enough in less than N years, where N is the age of a human who would laugh at the photo.
That's basically what Nature is doing anyway.
Assuming sufficiently powerful Neural Networks, they will probably go through years of learning our world through interaction, just like a kid does, before it "gets" the joke. Doesn't mean it's impossible, and that's quit scary (in a good and bad sense I guess).
However this is information only, not interaction, I suspect this will have a serious distorsion effect on how the AI "perceives". It's a wild guess, but I believe interaction is at the root of understanding.
Edit : yes you also mention interaction through video games, which I skipped when I scanned your comment. But then again video games might be still far from the depth of real world interaction, more of a learning enforcer than the source of it - like books are for us....
One way of reproducing this aspect of interactions is simply using standard ML techniques to prevent overfitting, such as cross validation.
There's another aspect that's more difficult to reproduce that is the "online learning" aspect of interactions. If you can interact, you can form hypothesis in real time, test and modify them. This can greatly enhance learning efficiency I suppose -- you may directly explore fails in your models and improve in an optimal way.
This aspect also might be reproduced I believe simply through a large enough dataset. The learner could be given some capability to explore this dataset in a non-sequential way and look for informative results in it.
Interesting stuff.
"Neural network chip built using memristors (arstechnica.com)" https://news.ycombinator.com/item?id=9501119
http://www.image-net.org/challenges/LSVRC/2014/results Most teams are below 15%, GooLeNet is at 6%
http://www.image-net.org/challenges/LSVRC/2014/results Microsoft is now below 5%
http://arxiv.org/pdf/1502.03167.pdf Google is now below 5%
I can't really argue whether that's exponential or not, but it amazing progress in a short amount of time.
So much for the 'quick glance'. Which brings me to another matter. One of the reasons the author can extract all that information from that picture is because all the elements in it have been 'seen' already. A machine might not be able to extract the whole context, but things like the people involved and that they seem happy? Easy (-ish).
We need systems that learn from experience.
To me it seems we are still in the stage that we have to understand ourselves better, because we simply can't devise and algorithm to solve a problem we cannot solve ourselves.
We can do clever stuff with statistics and math, but that seems to me like more of a hack. For instance, people create models from very small datasets, if you have kids you surely can watch in amazement just how efficient we are wired in that regard. We try to mimic that by feeding huge datasets to algorithms but it still pales in comparison.
I remember one documentary in Discovery Channel, where some scientists make use of rat brain cells cultured on a circuit to build a small robot, which learns to avoid obstacles on it's path.
A similar video is here -https://www.youtube.com/watch?v=1-0eZytv6Qk.
there have been decades of research into 'Cognitive Architectures' (http://en.wikipedia.org/wiki/Cognitive_architecture) and 'Artificial Consciousness' (http://en.wikipedia.org/wiki/Artificial_consciousness)
there is massive amount of experimental observations on learning and cognition in neuroscience and cognitive sciences (from neurophysiology to psychology) that is largely ignored by Artificial General Intelligence and Machine Learning communities.
On the other hand the progress in Deep Learning, Computer Vision, NLP and Robotics is largely ignored by neuroscientists because these learning models do not respect biological constraints
There is a whole group of narrow domains like Formal Concept Analysis, Statistical Relational Learning, Inductive Logic Programming, Commonsense Reasoning, Probabilistic Graphical Models that don't talk to each other but all deal with cognition and conceptual reasoning using different tools
I think we have a chance to make progress if these fragmented domains converge.
There are researchers in all the different fields who's sole job is to report what other communities and doing and be the agents of cross pollination.
Everyone agrees that artificial general intelligence is a difficult problem.
Practically its not possible to converge all the different fields and also what is the point of that ?
Each researcher is interested in solving their own sets of problems what they find interesting or have the motivation to be part of the solution.
Progress is being made - maybe not at the rate of silicon valley start-ups but hard problems require time to solve.
It would not be ideal that Computer Vision people suddenly stop doing their research and take the massive risk of putting all their shoes into Deep Learning.
People doing Computer Vision have their sets of constraints and goals. If tomorrow the garbage man, cleaner, cook, etc all stop working and stay "we are all going to work on deep learning". The world will stop working.
As absurd as that sounds that is what the implications would be if theses separate fields try to converge. Even if we do solve the problem of AGI today, what direct change or improvement in human condition would be see tomorrow ?
When that AGI needs to be integrated in a framework like computer vision, robotic, or search engine you need that domain experts, practitioners in the those various fields to still exist tomorrow to maximize the economical benefit of such a technology.
>My impression from this exercise is that it will be hard to go above 80%, but I suspect improvements might be possible up to range of about 85-90%, depending on how wrong I am about the lack of training data. (2015 update: Obviously this prediction was way off, with state of the art now in 95%, as seen in this Kaggle competition leaderboard. I'm impressed!)
Thats more impressive than it sounds because each percentage point is exponentially harder than the last. Getting 95% accuracy is not 5% harder than getting 90%.
Just recently machine vision starting beating humans on imagenet. Imagenet is 1,000 classes, high resolution images, taken randomly from the internet. No one would have predicted that few years ago.
Sometimes a notable researcher like Hinton says that something like transcription of images into sentences might be possible in five years, only for researchers to demonstrate it in five months.
I remember reading something about early engineers working on computers were extremely skeptical of the rate of computer advancement. They were so focused on narrow technical problems they didnt see the big picture.
First: Look for focus, detect zone in which eyes are looking. Result: The focus is on the man at the right since eyes are directed at him.
Second: Sentiment Analysis Whatever he is doing people find it funny.
People like to play jokes that make you experience strange things that you can't understand at the moment.
Hypothesis: The man B near the main character A is interacting with A. It seems that the foot of B is interacting with the machine M. So B interacts with M that interacts with A.
Generated question: What kind of interaction could be exerted by B on M to cause M get A confused?
Hypothesis: B's foot is making the machine to malfunction in such a way to give a false message to A.
Hypothesis: To make this image more noticeable the men are well known people or famous people.
Data: The more serious the role this people play at society the more funny is the image, since their behavior is more unexpected.
Reasoning like this the machine could get a plausible hypothesis of what is happening in the scene:
A famous man B that probably do a serious and responsible role in society is playing a trick on a man A by making a machine M to malfunction in such a way that it gives a false message or information to A. The malfunction is caused by putting the foot on the machine. To make it funnier, the main character can't see that, ...
Having a general context like this the machine could look for machines and people which could play such a role and give a heavier weight to those that make the joke a better one.
The next generation of machines could modify the image to make the joke better since it can understand perfectly the context and purpose of it.
Also, notice that for this particular picture a lot of what the article is talking about doesn't matter:
- Does it matter that they're in a hallway?
- Does it matter there are mirrors?
- Does it matter that one person is the president?
The real joke, however, is in fact that it is the president. It's more humorous because it shows the president as a real person. It's unexpected, and out of context of how we typically understand the holder of the office of the president of the united states should behave.
That's the main joke. So yes, the finer details are important. It's not just a funny picture.
Anyway he mentioned what needs to be done at the very end of the article which is to emulate the embodied human development process, and there is quite a lot of progress in that area.
E.g. modern deep-learning classifiers can get high 90s F1-score on identifying entities in images, but what's our capability for recognizing e.g. product entities being mentioned in Amazon reviews? Good luck getting even 70% F1-score. It's still incredibly awful.
And this is just talking about very basic entity recognition in realistic settings. Lets not even consider relation extraction, and more meaningful tasks.
I think "true" AI of the sort described in this article is, likewise a pipe dream. Not in a 100 million years of scientific effort could we create the hardware and software necessary to do this. If you think otherwise, that's because you have an enduring and overriding faith in the power of science to overcome all barriers and achieve all goals. But on what basis could you place that faith? Sure, science is generally good at solving problems and advancing technology, but there are some "technologies" that are simply not attainable: indeed, not even clearly definable.
I think that deep AI of this kind is one of those. We think we know what it would mean for a computer to think like a person. It's not so clear that we do. Given that we deeply don't understand how we ourselves not only think, but also feel, want, assess, moralize, and experience, we're unlikely to produce thinking machines.
"Ah, but the Power of Science, may it be praised forever!" you say. "Science will help us to understand how we understand!" Yeah, maybe, nah, I don't think so. Just look at the construction itself. It's loopy. "Understand how we understand"? I doubt it's possible for any instrument to ever really comprehend itself; in other words, for any thinker to ever think accurately about how it thinks.
We should keep AI research within the realm of observable, measurable, useful utilities. "Comprehension" of any kind... it's never going to happen.
There I've said it. Now roast me at the stake, O ye of great faith.
I wonder if this was tongue in cheek since I thought people didn't start making fun of mo-lo-so until "Silicon Valley" aired: https://www.youtube.com/watch?v=J-GVd_HLlps.
The state of Computer Vision and AI:
we are really, really far. (2012)
No, the author means "we are really, really far away from our objective," which is not the same thing at all.
Was this deliberate, or did the author, in his focus on the question of interpreting images, simply not notice that his text was ambiguous as well?
Or is it just me?
"we are far" to me means "we are distant (from the objective)"
https://en.wiktionary.org/wiki/far#Adverb
If it was "far ahead", or "we've come far" I'd agree with you
It's almost gramatically incorrect to say "we're really far", unless it is a response to a question - "how far are we from home?", "we're really far". In that case it works because the subject is implicit.
It's like you can't have a title that is "It is the best smartphone yet."
I was disappointed.
The title is simply ambiguous.
Can there be no articles about how far we've come until we've reached the stars? Or singularity?
So in this context far and close are synonyms? English is so weird.
* We are really, really far along the way
* We are really, really far from our objective
It seems clear that most native English speakers in this thread have seen the ambiguity and started by assuming the first option, whereas non-native English speakers seem to have assumed the second, and may not have noticed the other option at all.
But see also: Contranym/contronym/Auto-antonym:
For example, a person who had no experience/knowledge of scales like that shown would have a tough time discerning what the reason for humour was.
Are we really far from our origin? Or are we really far from the destination?
In the absence of a complete statement, people tend to insert their own bias, which can lead to confusion.
Except the "along the path" the path part is neither stated or implied. Rather then just being a grammar or language issue I think people are projecting their optimistic opinion of AI onto the title, with no basis for it based on what's actually written. Objectively if you read the title how it should be construed as negative, we have a long way to go, etc.
Why is this so ambiguous? Far and distant are synonyms, right?
I suspect that it's because you can use "far" to describe a path you have traveled, in a way you can't with "distant". "We have come far", "We are far along our journey." So that "far" can mean far from a point of origin as well as a point of destination. Whereas "distant" only means distant from that point of reference.
So I thought the title was ambiguous without a preposition to clarify "far": "far away", for example.
Perhaps it's in the framing of the question: "the current state of x" implies that it's being considered as an ongoing process, which implicitly has an origin and a conceivable end.
Also, putting the subject as "we are..." puts it in the frame of an ongoing journey. If you say "That lighthouse over there, it is far." there is no ambiguity that the lighthouse is distant from us. Also "Effective computer vision: it is far" is not really ambiguous.
* http://english.stackexchange.com/questions/244893/what-is-th...
Yes, the man is confused because he's being pranked by the president. It would be funny if I was there.
But looking at this photo? It's not funny to me.
I suppose I would rank this picture a lot higher than a picture of a skyscraper in the list of funniest pictures, but neither would be above the "laugh" threshold.
Does everyone else here find it funny?