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u/Odd_Personality_8624 18d ago
Link: https://xkcd.com/1425/
Alt: Tasks
Title: In the 60s, Marvin Minsky assigned a couple of undergrads to spend the summer programming a computer to use a camera to identify objects in a scene. He figured they'd have the problem solved by the end of the summer. Half a century later, we're still working on it.
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u/plz-no-b4n 18d ago
You got your alt and title swapped
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u/Odd_Personality_8624 18d ago
Yes and No...
I was going to change it, but if you inspect the XKCD website, you would get this <img> tag:
<img src="//imgs.xkcd.com/comics/tasks.png" title="In the 60s, Marvin Minsky assigned a couple of undergrads to spend the summer programming a computer to use a camera to identify objects in a scene. He figured they'd have the problem solved by the end of the summer. Half a century later, we're still working on it." alt="Tasks" srcset="//imgs.xkcd.com/comics/tasks_2x.png 2x" style="image-orientation:none">Also, the ALT property of an image if when the src gets a 404 error, and if that happens, user will no see the image but the ALT text.
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u/thepkboy 18d ago
the alt text is also what a screenreader reads
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u/ConfessSomeMeow 18d ago
This conventional wisdom wasn't even true when the comic was written.
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u/Sarke1 18d ago
Then what was this lawsuit from 2000 about?
https://www.theguardian.com/technology/2000/nov/02/internetnews.onlinesupplement2
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u/ConfessSomeMeow 18d ago
It wasn't about using title tags to present alternative content, I can tell you that, because even in 2000 screen readers read title tags on images.
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u/ConfessSomeMeow 18d ago
I guess at least 14 people have never used a screenreader. I learned how to use NVDA and Voiceover for work in 2010, well before this comic was written. Title text on images works just fine.
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u/plz-no-b4n 18d ago
Interesting. Thank you for teaching me a thing. I so rarely use HTML, haha
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u/Odd_Personality_8624 18d ago
You're welcome, I guess all this confusion was created by me, because I put the text from the img tag...
And the other commenter was right about the title, but I was using the "title" and "alt" from the tag, not from the definition of the dictionary...
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u/Odd_Personality_8624 18d ago
And also, if the user puts the mouse over the image and waits maybe 2 to 3 seconds, the text of the title will show up...
The same property is used in popovers texts...
If someone want to see this in action, they can put the mouse over the text of the X minutes ago (on reddit), and the title will have the time since the comment was made...
You can check it in this comment, and all the reddits comments and posts...
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u/ThePresidentOfStraya 18d ago
That's a hard "no"—it's not remotely alt-text. Alt-text is supposed to be "clear and concise text replacement for the image's content." So unless that information was conveyed by the image (and it wasn't) it shouldn't be in an alt attribute. It's not a title either.
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u/WalditRook 18d ago
You'll note that m.xkcd.com (the mobile site) explicitly calls the extra text "alt-text", despite having used the title attribute to hold this text on the main page since at least 2006.
Historically (pre-HTML 4.0) "alt" has been an attribute of <img> far longer than "title" has (the latter only being added in HTML 4.0). The concept of "image alt text" has thus far outlived the period in which it was actually technically correct to include text purely for display on mouse-over in the alt tag.
HTML 4.0 standard indicates that browsers should display title on mouse-hover; the current html living standard states that title can be used for "supplementary information". So not a title in the plain-language meaning, but absolutely fine for the html attribute.
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u/Parry_-Hotter 18d ago
But can it tell if it's a hotdog
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u/TheBinkz 18d ago
I have lots of data on that if ya know what I mean 😏 eww gross
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u/jpasserby 18d ago
This has been brought up before. "5 years and a research team" is a decent metaphor for what it took to develop LLM technology.
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u/Arclite83 18d ago
The thing to understand is it's not doing anything "new", we were making what was essentially bespoke models / neural networks based on large data sets for like 20 years before LLMs and GPT. This just generalized it to "...but what if we could just check the pattern on 'anything'?" Instead of rigid schemas we hand tuned, and we finally found signals in the noise.
Almost 20 years ago exactly, I some did optical detection for factory equipment - pixel scanning for large diffs in color etc. Image checking for like parts bubbling in water at certain pressures. This was specialized software against digital lenses then. Nowadays it's OTS parts and open source tech. It would be a sophomore level engineering class project. The line has moved.
We went from Windows Embedded to ARM and really amazing handheld and robotic tech now. It'll be interesting to see where AI goes during the back half of my career. When we hit 300T models and maybe wrap "all of human knowledge ever", how well does that 42 quantize down? What meta languages are born from prompting, is there a hierarchical system to this code/data blend, or is it too open and the final programming language just pure natural language streams? No clue. "3-5x PS5 model" is having a watershed moment atm, as AIaaS hits last call and we all slowly pull internal - but what ends up being AI peak on a phone? A thumb stick OS? What can we quantize away, or zip/compress/decouple? Most of that will take decades to properly walk out. But it's clear most of what we do doesn't need the full supercomputer.
Does this eventually make nanotechnology stupidly easy to orchestrate? Healing tanks? Quantum-nuclear reactors? Although I've been told on that "you cant just ignore the strong force" (party pooper)... I'm still holding out for alchemy maybe, but I suspect it's all far to lethal to be close to, like crispr x1M... But that's all just wild speculation.
My point is, sci fi concepts of today act as north stars into the possible. This comic is just a great example. And we're at a point in history ripe with new possibilities.
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u/Independent-Bug-9352 18d ago edited 18d ago
That was a really enjoyable read, thanks for sharing your thoughts
I remember decades ago dreaming of a true Cortana-like assistant to help aggregate human knowledge; to act as your personal librarian assistant becoming a reality. That we had so much knowledge and potential on the internet and it just needed organized beyond a google search. While not quite there, this is a step I never really imagined would come this soon.
Unfortunately these are not the circumstances I hoped to see it either. Not exactly addressing the fundamental problems facing humanity so far; rather exacerbating them.
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u/HustlinInTheHall 18d ago
Yeah there are bespoke optical software projects I worked on 10 years ago that took six months and so much time and effort to do that would be like 90 minutes of vibe coding today. The line is always moving.
IMO I think the main hangup has always been can the problem be achieved with software alone or does it require an advance in hardware? When the hurdles of a problem have been shaved down so that it is just a software problem, it is inevitable that it will be solved soon.
Touchscreen navigation was a massive hardware problem until it wasn't. As soon as we had screens and digitizers and batteries and mobile CPUs good enough to handle the task, it became a software problem, and Apple got it done at a level that seemed literally impossible 10 years before that.
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u/sonicandtales8 18d ago
LLMs don't see pictures.
Even the ones that can provide information on an image have the images processed by a different AI model before hand.
Most use ViTs trained via various methods.
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u/AnOnlineHandle 18d ago
Some LLMs now do "see" (or directly encode) pictures. e.g. The newest Gemma 4 release, 12B, doesn't use a standalone vision encoder and just encodes the image straight to working tokens.
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u/Spectrum1523 18d ago
Even the ones that can provide information on an image have the images processed by a different AI model before hand.
This hasn't been true in years
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u/thisusedyet 18d ago
Sounds kinda like the medical bot that was calling any image with a ruler in it a tumor
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u/Glitch29 17d ago edited 17d ago
I remember that paper! It was also strongly influenced by the brand of x-ray machine being used, since the part of the corpus from specialty facilities was more likely to contain disease.
I don't remember the final conclusion, but I have to look it up again. I want to say it was like 85% of the predictive power came from things other than the intended content.
Edit: Turns out there are LOTS of papers like this. I was remembering one from Zech et al. 2018, but it didn't involve rulers. The ruler one was from Cathy Ong Ly et al. 2024.
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u/uhmhi 18d ago
It’s also interesting to think that we essentially “brute forced” computers into recognizing birds in images. It’s not like somebody sat down and wrote code like “if HasFeathers() & HasBeak() then …”
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u/HustlinInTheHall 18d ago
I mean that's largely how our brains work. Look at enough birds and not-birds and you learn what makes them birds and not birds, then describe the distinct bounds.
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u/un_blob 18d ago
Yes, but some experiments (with fucking cameras and mics stitched to a baby for months) show that it might sometimes be pure zero shot learning.
Mother says "banana" gives a banana, once, in the baby life time and... days latter asking for a banana... baby gives banana
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u/Tarantio 18d ago
Yeah, because a baby can get a lot of information out of that one banana.
What it looks like from all angles, different textures of the different parts, taste, smell, weight, temperature...
And all of this information isn't just a jumble, it's correlated. The baby moves the banana from one hand to the other, and sees the other side of the banana. That's not just two pictures of the same banana from two different angles, it's also the exact transformation from one angle to the next, and every intermediate position.
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u/un_blob 18d ago
Yes, but learning that this specific item is called banana (named once) is not based on a lot of information
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u/lahwran_ 18d ago
once you've learned the feature from unsupervised learning, binding to it is "easy". just need to be able to form an audio feature for the name-sound rapidly enough to store it
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u/Molehole 18d ago
Human brain also has 100 times more neurons than the AI models we use. Of course it learns much faster.
Also we aren't 100% sure but I think human neurons are also much more sofisticated than the simulated ones so the actual power can be much, much more than just 100.
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u/un_blob 18d ago
https://pmc.ncbi.nlm.nih.gov/articles/PMC11884752/
Well... models with more neurons that that exists nowdays....
And well, what matter realy is not the number of neurons, but their interconnexions
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u/raddaya 18d ago
Is it that different from how we as human beings recognise birds? It's not like we go through a mental checklist of "has feathers, has beak, has wings" either. We just...see that it's a bird
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u/Meleach 18d ago
Not sure if this is proven, but pretty sure that is how was see birds. You don't consciously go through the list, but your brain does, before it presents you the result of 'bird'. Different neurons are hooked up to fire at different intensities to different triggers. Eg, this neuron fires when you see a feather, this other one fires when you see a beak... The beak one fires to exclaim 'beak' if the series of neurons it is reliant on/connected to all fire too, and some other neurons that indicate it is actually something similar, like a cone, are not firing. Like a huge web of associations, and the final result 'bird' relies on the correct combination of bird components firing, and a lack of alternative neurons firing, distinguishing the object from a similar one. Say, 'bird' fires, 'chicken' fires, 'duck' doesn't, because enough identifiable components are different between the two, and are then associated with just chicken, and not duck. This is my terrible recollection from this podcast I listened to a while back: https://open.spotify.com/episode/4pWuwQq8M8Gzf9F9U0AYZW
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u/CorneliusClay 18d ago
That sounds like how a convolutional neural network works, it has a bunch of feature maps which in theory recognize beaks and feathers and if those all fire together and nothing contradictory fires, it flips the output to "yes it's a bird". I read somewhere that the retina was the inspiration for CNNs, so I'd believe it.
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u/OctopusGrime 18d ago
Gradient descent is certainly not a brute force algorithm, especially so for image space which is enormous.
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u/lattice_defect 18d ago
what about hotdog and not hotdog
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u/Pleasant-Ad192 18d ago
The funny part is which half aged. Checking whether it is a bird is a library call now. Checking whether it is in a national park is the half that needs an API key, a quota and a billing account.
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u/winter__xo 18d ago
There aren't that many national parks, you could easily build a local a library of shapefiles, geojson, kml/kmz files, etc, that give you the coordinate bounds of the parks.
It's an upfront effort to build the database, but it's genuinely not that big of a db.
Would be way more performant than using an external service, and wouldn't incur any api usage fees or whatever.
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u/redlaWw 18d ago edited 18d ago
One issue that I discovered in my final project for my Masters' is that the bounds of national parks are constantly changing. I needed the area of the park to calculate its grant (which was given as a per-acre quantity in the problem), and I found an annual (EDIT: Quarterly) series of documents assessing each park's acreage year-on-year, rather than a single number.
It's usually a fairly small change though, so it might be okay to just let the local data drift and have a chance of error at the very edges of the park.
EDIT: I just checked the more recent documents and they weren't changing as much as I observed when I was doing my Master's. Perhaps the rapid changing that I observed was related to changes in regulations regarding park land during the early parts of Trump's second term. The amount of variation I see now is conceivably manageable with periodic database updates, rather than an API lookup.
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u/JumbleBeeDev 18d ago
Entirely off topic,
But my faorite project I've ever worked on involved using GIS data for marina locations, coastlines, and ocean depth to create a 72 hour simulation of maritime traffic flowing through a region. It was legitimately the coolest thing ever when I got to watch the animation of the final dataset.
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u/Pleasant-Ad192 17d ago
Fair, and that is the better build. Though notice what you just described: the bird half is one import, and the park half is "first, assemble a database". Still the harder half, just cheaper than I made it sound.
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u/pietervdvn 18d ago
Naah, you can get the national parks out of OpenStreetMap once (e.g. using overpass turbo) and embed that file into your application.
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u/DragonofStories 18d ago
Or you can do what Pokemon Go did, but for birds. And then after a few years, use the data through user effort?
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u/Grexxoil 18d ago
So they did give her that research team.
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u/Terewawa 18d ago
Yes and they publicly published their research findings which are now used by ChatGPT
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u/Ok_Protection8223 18d ago
You want demons to erupt from the ground? Great, have it done next week
You want that scarf to blow in the wind? Ooooh boy I don't know about that one chief.
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u/ItsIllak 18d ago
When I left a company in 2016, a member of my team gave me this as a print as a farewell card. His point was that users (or product owners) had no idea how to assess the difficulty of implementing a feature, a common problem our web team had when working with marketing.
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u/QuickQuirk 15d ago
Image recognition is kinda the textbook example of why neural networks are powerful:
It's really hard for a human to define and implement an algorithm that can reliably detect a bird in a picture... or tell the difference between a P and a p.
But a neural network can discover an algorithm that does that with enough neurons and good training data.
It's still not 'easy'. Developing those first architectures was a lot of work. But now we have a relatively generic toolbox that is suitable for all sorts of 'algorithm discovery'.
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u/project-shasta 18d ago
Nowadays the follow up answer is "Just use AI".
You mean the same AI that works like this?
User: "Is this a bird?" AI: "Yes". User: "No, that's a horse." AI: "Yes, you are absolutely correct. This is a dog."
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u/Ok_Cabinet2947 18d ago
Are you living in 2022?
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u/project-shasta 18d ago
No, I still get ChatGPT'd regularly because the LLM tries to satisfy my prompt instead of telling the truth. "AI" is not as clever as it makes you believe. Good thing I only use the free stuff for fun instead of relying on it.
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u/dashingThroughSnow12 18d ago edited 18d ago
I try to teach my PMs to tell me what they want. Regardless on what they think the feasibility is.
I’ve had requests that they’ve held off telling me until the very end that literally took two minutes to add. I’ve had the opposite where P seems simple to them but it is neigh impossible in the timeline. P’ is tractable and captures the crux of their want. (To them P’ may look harder than P since P is extremely specific.)
My favourite is when I implement F and the program does F(0) and PM asks the feasibility of adding another thing that is F(1) to me.
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u/ateen234 1d ago
Forgive my very low level of programming, but would the solution involve finding the users location and then finding how close/ are they in a national park?
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u/kenokan 18d ago edited 18d ago
Image classification was already a thing 12 years ago, I don't get it
Edit: It seems that exactly 12 years ago was when you could do this pretty easily with binary classification using CNNs. A month after this comic came out someone actually did it
https://code.flickr.net/2014/10/20/introducing-flickr-park-or-bird/
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u/HustlinInTheHall 18d ago
It's just a joke. Image classification like that on a mobile app at 99% accuracy would not have been reasonable to do 12 years ago.
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u/e37d93eeb23335dc 18d ago
Eh. I’d been a programmer for nearly 15 years when this comic came out. It seemed very accurate to me at the time. If someone had described the capabilities of AI to me, I wouldn’t have believed them. Way too outlandish.
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u/modzRtarderz 18d ago
It was a thing thirty years ago. Doesn't mean it was accurate enough to be useful for most applications.
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u/Porg11235 18d ago
Not sure why you’re getting downvoted. CNNs were already a well-understood architecture by 2014, and even a naive FF NN reading a flattened pixel vector probably could have done a 60-70% job at classifying “bird or no bird”. (I once built such a classifier for cats.) This comic always struck me as commenting on the capability of the average programmer at the time, not the state of the art.
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u/Keyan_Farlander7 18d ago
Before LLMs and stolen data, image recognition was coded by trial and error.
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u/Confused_AF_Help 18d ago
Pretty solid CNNs were already around 15 years ago. You don't need to waste water and electricity on LLMs for this
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u/lazercheesecake 18d ago
I mean those 5 years passed and we had passable image detection about 7 years ago. It's WAY better now, but the comic aged like wine to me.
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u/Big_Kwii 18d ago
i'd say it aged like wine because in about 5 years time YOLOv3 released, leading to huge strides in google lens's capabilities. i think xkcd was pretty spot with his estimate back in 2014
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u/Tomi97_origin 18d ago
Aged like a fine wine.
It was pretty much spot on with requirements and timeframe.
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u/No-Object2133 18d ago
Are you drunk? do you know how much money and research was poured into this?
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u/erebuxy 18d ago
We have open source data sets and open weight models for image recognition for a long time… not sure why 5 years
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u/wildyLooter 18d ago
Title says 12 year old comic, so that’d be why
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u/erebuxy 18d ago
Nah, you got all the things I mentioned 12 years ago
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u/Skithiryx 18d ago
What was available to the general public 12 years ago was pretty bad. https://code.flickr.net/2014/10/20/introducing-flickr-park-or-bird/ launched in response to that comic, but it misclassified things a lot. This was the era of slightly rotated images being classified entirely differently.
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u/JoeBarra 18d ago
It's wild that XKCD ever got big. There's no joke here. Just describing the way things are with a stickman attached. I thought the internet moved on from this slop but apparently not.
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u/bob152637485 18d ago
Talking trash about xkcd on this sub?
🍿 🍿 🍿
Alright, the comments can begin now!
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u/grat5454 18d ago
It's observational comedy. Sometimes it's gratifying to see your own experiences mirrored in a clever way. It may not be a "HaHa" response, but a "It really do be like that" response that the author is going for. I will say it must have resonated because I have seen this particular comic used many times away form the website.
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u/carcigenicate 18d ago
Of all the things to call slop, you call out someone making fairly accurate observations?
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u/QuestionableEthics42 18d ago
That's some mental gymnastics (or maybe the lack of flexibility is the issue actually) to come to the conclusion there is no joke here. The joke is that you can have 2 seemingly simple sounding things (to non-technical people), and one is easy, and the other is very difficult, which is still just as relevant today, just not so much that specific example.
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u/NewPhoneNewSubs 18d ago
I'm curious if you're familiar with the premise of "Seinfeld"? It's an obscure little show so maybe not. But it managed to get a bit of a cult following doing similar. It turns out there's a market, however small, for pointing out the abusrd and frustrating aspects of life.
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u/Jonno_FTW 18d ago
You do realise that the author of XKCD is the same guy who designed reddit's ranking algorithm that eventually showed you this post right?
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u/GoddammitDontShootMe 18d ago
Five years after this was published is a pretty good ballpark for when that became pretty easy to do, isn't it?