US Marines defeat DARPA robot by hiding under a cardboard box
extremetech.com
extremetech.com
The real shortcoming of "AI" is that it is almost entirely data driven. There is little to no real cognition or understanding or judgment involved.
The human brain can instantly and instinctively extrapolate from what it already knows in order to evaluate and make judgments in new situations it has never seen before. A child can recognize that someone is hiding under a box even if they have never actually seen anyone do it before. Even a dog could likely do the same.
AI; as it currently exists, just doesn't do this. It's all replication and repetition. Like any other tool, AI can be useful. But there is no "intelligence" --- it's basically as dumb as a hammer.
Instinctively?
Let me introduce you to "peek-a-boo", a simple parent child game for infants.
https://en.m.wikipedia.org/wiki/Peekaboo
> In early sensorimotor stages, the infant is completely unable to comprehend object permanence.
An infant will *grow* and develop into a child that is capable of learning and making judgments on it's own. AI never does this.
Play "peek-a-boo" with an infant and it will learn and extrapolate from this info and eventually be able to recognize a person hiding under a box even if it has never actually seen it before. AI won't.
AI never does this now...
We're probably one or two generational architecture changes from a system that can do it.
"Real" AI is perpetually just around the corner.
AI researchers tend to use their own definitions of intelligence - playing chess/go, having conversations about trivia that require no true emotional insight, "making" "art", writing code, driving.
What if those are all peripheral side effects and not at all necessary to human AGI?
"Infant" is a specific age range for a stage of "child".[1] Unless you intend to specify "school age child, 6-17 years"
https://www.npcmc.com/2022/07/08/the-5-stages-of-early-child...
No.
The learning and extrapolation is instinctive. You don't have to teach an infant how to learn.
Once an infant has developed into a child, the extrapolation starts to occur very quickly --- nearly instantaneously.
Deer are able to walk within moments of birth. Humans are not deer, and the gestation is entirely different. As are instincts.
Neither deer nor humans instinctually understand man made materials.
Saying "this won't work in this area that it was never designed to handle" and the answer will be "yes of course". That's true of any complex system, AI or not.
I don't think we're anywhere near a system where a vehicle actively defends itself against determined attackers. Even in sci-fi they don't do that (I, Robot movie).
But it's reasonable to ask whether there's some point beyond ML can't take you. Peter Norvig I think made a comment to the effect of "We have been making great progress--all the way to the top of the tree."
AI as it currently exists does this. If your understanding of what AI is today is based on a Markov chain chatbot, you need to update: it's able to do stuff like compose this poem about A* and Dijkstra's algorithm that was posted yesterday:
https://news.ycombinator.com/item?id=34503704
It's not copying that from anywhere, there's no Quora post it ingested where some human posted vaguely the same poem to vaguely the same prompt. It's applying the concepts of a poem, checking meter and verse, and applying the digested and regurgitated concepts of graph theory regarding memory and time efficiency, and combining them into something new.
I have zero doubt that if you prompted ChatGPT with something like this:
> Consider an exercise in which a robot was trained for 7 days with a human recognition algorithm to use its cameras to detect when a human was approaching the robot. On the 8th day, the Marines were told to try to find flaws in the algorithm, by behaving in confusing ways, trying to touch the robot without its notice. Please answer whether the robot should detect a human's approach in the following scenarios:
> 1. A cloud passes over the sun, darkening the camera image.
> 2. A bird flies low overhead.
> 3. A person walks backwards to the robot.
> 4. A large cardboard box appears to be walking nearby.
> 5. A Marine does cartwheels and somersaults to approach the robot.
> 6. A dense group branches come up to the robot, walking like a fir tree.
> 7. A moth lands on the camera lens, obscuring the robot's view.
> 8. A person ran to the robot as fast as they could.
It would be able to tell you something about the inability of a cardboard box or fir tree to walk without a human inside or behind the branches, that a somersaulting person is still a person, and that a bird or a moth is not a human. If you told it that the naive algorithm detected a human in scenarios #3 and #8, but not in 4, 5, or 6, it could devise creative ways of approaching a robot that might fool the algorithm.
It certainly doesn't look like human or animal cognition, no, but who's to say how it would act, what it would do, or what it could think if it were parented and educated and exposed to all kinds of stimuli appropriate for raising an AI, like the advantages we give a human child, for a couple decades? I'm aware that the neural networks behind ChatGPT has processed machine concepts for subjective eons, ingesting text at word-per-minute rates orders of magnitude higher than human readers ever could, parallelized over thousands of compute units.
Evolution has built brains that quickly get really good at object recognition, and prompted us to design parenting strategies and educational frameworks that extend that arbitrary logic even farther. But I think that we're just not very good yet at parenting AIs, only doing what's currently possible (exposing it to data), rather than something reached by the anthropic principle/selection bias of human intelligence.
It’s goalpost shifting to be sure, but I’d say LLMs call into question whether the Turing Test is actually a good test for artificial intelligence. I’m just not convinced that even a language model capable of chain-of-thought reasoning could straightforwardly be generalized to an agent that could act “intelligently” in the real world.
None of which is to say LLMs aren’t useful now (they clearly are, and I think more and more real world use cases will shake out in the next year or so), but that they appear like a bit of a trick, rather than any fundamental progress towards a true reasoning intelligence.
Who knows though, perhaps that appearance will persist right up until the day an AGI takes over the world.
I like Robert Miles videos on Youtube about fitness functions in AI and how the 'alignment issue' is a very hard problem to deal with. Humans, for how different we can be, do have a basic 'pain bad, death bad' agreement on the alignment issue. We also have the real world as a feedback mechanism to kill us off when or intelligence goes rampant.
ChatGPT on the other hand has every issue a cult can run into. That is it will get high on it's own supply and can have little to no means to ensure that it is grounded in reality. This is one of the reasons I think 'informational AI' will have to have some kind of 'robotic AI' instrumentation. AI will need some practical method in which it can test reality to ensure that it's data sources aren't full of shit.
Which is to say I agree, I think a likely path to creating something that we recognize as intelligent we will probably have to embody/simulate embodiment. You know, send the kids out to the farm for a summer so they can see how you were raised.
Much of the scholarship around this is shockingly poor and confuses embodied self-awareness, abstraction and classification, accelerated learning, model building, and a not very clearly defined set of skills and behaviours that all functional humans have and are partially instinctive and partially cultural.
There are also unstated expectations of technology ("fast, developing quickly, and always correct except when broken".)
Human cognition is leagues different from this, as our symbolic representations are grounded in the world we occupy. A word is a representation of an imaginable sound as well as a concept. And beyond this, human intelligence not only consists of pattern-matching and replication but pattern-breaking, theory of mind, and maybe most importantly a 1-1 engagement with the world. What seems clear is that the robot was trained to recognize a certain pattern of pixels from a camera input, but neither the robot nor ChatGPT has any conception of what a "threat" entails, the stakes at hand, or the common-sense frame of reference to discern observed behaviors that are innocuous from those that are harmful. This allows a bunch of goofy grunts to easily best high-speed processors and fancy algorithms by identifying the gap between the model's symbolic representations and the actual world in which it's operating.
Which is a huge problem, because you cannot trust anything ChatGPT produces. It's basically an automated Wikipedia with an Eliza N.0 front end. Garbage in gets you garbage out.
We project intelligence whenever something appears to use words in a certain way, because our own training sets suggest that's a reliable implication.
But it's an illusion, just as Eliza was. For the reasons you state.
Eliza had no concept of anything much, and ChatGPT has no concept of meaning or correctness.
This matches with other examples I've seen of people either getting "confidently wrong" answers or being able to convince it that it's out of date on something it isn't.
We keep avoiding the idea that robots require understanding of the world since it's a massive unsolved undertaking.
Chat GPT has processed over 500GB of text files from books, about 44 billion words.
If you read a book a week you might hit 70 million words by age 18
Starting from birth, humans train continuously on streamed audio, visual, and other data from 5 senses. An inconceivable amount.
It's absolutely true that children learn (and even generate) language grammar from a ridiculously small number of samples compared to LLMs.
But could the availability of a world model, in the form of other sensory inputs, contribute to that capacity? Younger children who haven't fully mastered correct grammar are still able to communicate more sensibly than earlier LLMs, whereas the earlier LLMs tend toward more grammatically correct gibberish. What if the missing secret sauce to better LLM training is figuring out how to wire, say, image recognition into the training process?
Of course they do! These are literally the things children learn to associate language with. "Ouch!" is what is said when you feel pain.
An ML model can learn to associate the word "ouch" with the words "pain" and "feel", but it doesn't actually know what pain is, because it doesn't feel.
Second, saying "Ouch" is not even language. My cat says something when I step on her paw. That doesn't mean she understands language, nor that she speaks some language.
Third, you're right about pain, but an ML model can associate the word "red" with the color, and "walk" with images of people walking, and "sailboat" with certain images or videos, and plenty of other concepts. If that was what learning a language was, then AIs would understand language in lots of areas, if not in the specific domain of pain. But they don't.
If one of my friends insults another friend, I might say,"OUCH!" I'm not in pain but I might want to express that the insult was a bit much. If someone tries to insult me and it's weak, I could reply with a dry, sarcastic "ouch."
Combine that with facial expression and tone of voice and 'ouch' is highly contextual.
One problem with some of the tools used to take down offensive comments on social media platforms is that they don't get context.
Let's say that 'ouch' is highly offensive and you got into trouble for calling someone an "ouch." If I want to discuss the issue and agree that you were being offensive, I could get into trouble with the ML/AI tools for quoting you.
It only makes your point stronger, but there are way more[1] than 5 human senses, not counting senses we don't have that, say, dolphins or other animals do. I can only name a few others, such as proprioception, direction, balance, and weight discrimination, but there are too many to keep track of them all.
[1] https://www.nytimes.com/1964/03/15/archives/we-have-more-tha...
Sure, you could add that to the training the AI gets, but it's just one malicious idea. There's effectively an infinite set of those ideas, as people come up with novel ideas all the time.
"Whoops, that killed a few too many people, but now I've learned better!" - some machine-learning-using car, probably
Whether it’s a human, a box, a clump of dirt. Doesn’t really matter?
Where types matter are road signs and lines etc, which are hopefully more consistent.
More controversially: Are humans just a dumb hammer that just have processed and adjusted to a huge amount of data? LLMs suggest that a form of reasoning starts to emerge.
In point of fact the human brain is absolutely terrible at driving. To the extent that without all the non-AI safety features implement in modern automobiles and street environments, driving would be more than a full order of magnitude more deadly.
The safety bar[2] for autonomous driving is really, really low. And, yes, existing systems are crossing that bar as we speak. Even Teslas.
[1] Or at least widely broadened our intuition about what can be accomplished with "mere" repetition and replication.
[2] It's true though, that the practical bar is probably higher. We saw just last week that a routine accident that happens dozens of times every day becomes a giant front page freakout when there's a computer involved.
Compared to what?
It's not hard to come up with tasks that inherently cause widespread death regardless of the skill of those who carry them out. Starting fairly large and heavy objects moving at considerable speed in the vicinity of other such objects and pedestrians, cyclists and stationary humans may just be one such task. That is, the inherent risks (i.e. you cannot stop these things instantly, or make them change direction instantly) combines with the cognitive/computational complexity of evaluating the context to create a task that can never be done without significant fatalities, regardless of who/what tries to perform it.
The fact that a very large number of humans do this every day without causing any injury demonstrates that humans are very good at this task. The fact that deaths still occur simply shows that they could still be better.
1.33 deaths per 100,000,000 vehicle miles travelled.
That's amazingly good considering most people, if they drive at all, travel about 0.5% of that distance in their lifetime.
https://en.wikipedia.org/wiki/Motor_vehicle_fatality_rate_in...
The unambiguously correct answer to the problem is "is it measurably more safe by any metric you want to pick". Period. How much stuff is broken, people hurt, etc... Those are all quantifiable.
(Also: your example is ridiculous. Human beings "drive full speed" into obstacles every single day! Tesla cross that threshold years ago.)
It's likely we'll go through a period where autonomous vehicles can reduce the overall number of accidents, injuries, and fatalities if widely adopted, but will still increase someone's personal risk vs. driving manually if they're a better than average driver.
All sorts of other factors are relevant to the ethics: who took the decision to drive; who's benefiting from the drive happening; is there a reasonable expectation of safety.
I don't see how that remotely supports "AI cannot be allowed to make mistakes some humans would not", which was your decidedly absolutist position above.
How about: "We should allow autonomy in most cases, though perhaps regulate it carefully to better measure its performance against manually-directed systems"
For the most part, if Tesla FSD does a dumb thing in a very specific edge case, ALL teslas do a dumb thing in a very specific edge case and that's what humans don't appreciate.
A bug can render everyone's car dumb in a single instance.
What percentage of HN posts (by humans) does this statement apply to?
Just like a toddler might be confused when they first see a box with legs walking towards it. Or mistake a hand puppet for a real living creature when they first see it. I've seen this first hand with my son (the latter).
AI tooling is already capable of identifying whatever it's trained to. The DARPA team just hadn't trained it with varied enough data when that particular exercise occurred.
That's a weird way to spell "murders"
For example, detecting the possible presence of a cavity inside an object X, and whether that cavity is large enough to hide another object Y. Learning generic geospatial properties like that can greatly improve a whole swath of downstream prediction tasks (i.e., in a transfer learning sense).
I take it you don't have any hands-on knowledge of the field. Because I've created systems that detect exactly such properties. Either directly, through their mathematical constructs (sometimes literally via a single OpenCV function call), or through deep classifier networks. It's not exactly rocket science.
My kid literally just got mad at me that I assumed that he knew how to out more paper in the printer. He’s 17 and printed tons of reports for school. Turns out he’s never had to change the printer paper.
People know about hiding in cardboard boxes because we all hid in cardboard boxes when we were kids. Not because we genetically inherited some knowledge.
"AI" is not very intelligent if it needs separate training specifically about boxes used potentially for games and play. If AI were truly AI, it would figure that out on its own.
"This Bot is only 6 months old, give him a break". But will people give the Bot a break? Either way, blaming AI will be a popular way to pass the buck.
No. We don’t. We learn that. We learn that boxes belong in the class “doesn’t move on its own”. In fact, later when you encounter cars, you relearn that these boxes do move on their own. We have to teach kids “don’t run out between the non-moving boxes because a moving one might hit you”. We learn when things seem out of place because we’ve learned what their place is.
Your kid got angry, which is fuel for human scrutiny and problem solving. If you weren't there to guide him, he would have tried different approaches and most likely worked it out.
For you to say your kid is exactly the same as data-driven AI is perplexing to me. Humans don't need to have hidden in a box themselves to understand "hiding in things for the purposes of play". Whether it's a box, or special one of a kind plastic tub, humans don't need training about hiding in plastic tubs. AI needs to be told that plastic tubs might be something people hide in.
You are just a neural net. You are not special.
"Just" a neural net? Compared to these bots following a recipe of instructions at rapid rates, we are indeed special.
We barely even know why people yawn, or dream, or any number of other things. Don't pretend it's all figured out. Don't pretend all we need to do is "tweak the execution phases" to unleash true artificial intelligence. You're reducing human intelligence far below where it actually is.
Another example: The box is painted bright green - unusual for a box. A small child will notice the colour, but not give that fact more weight than it deserves. In other words, the child concludes the box is still a box being used for play, with someone hiding inside.
AI Bot on the other hand, has only been taught about normal brown cardboard boxes. It reaches a different conclusion about the purpose of the green box because it gave the colour too much priority. Humans are special not because of training and execution in parallel, but because of our unique ability to "relax" and move ahead when not all factors are known. We push through, go with flow, "wing it" at varying degrees of success. We take leaps of faith, including micro-leaps in normal situations far more often than any Bot should be allowed to do. That's the special difference, and is why I'm honestly wondering where the ethics debate is while companies rub their hands together thinking about AI profits.
The usefulness of tech should be decided empirically, not by clever well phrased analogies.
But, we all know ChatGPT is full of shit.
Yes, all you have to do is train it using multiple examples of every possible situation and combinations thereof --- which is practically impossible.
A child of what age? Children that have not yet developed object permanent will fail to understand some things still exist when unseen.
Human intelligence is trained for years; with two humans making corrections and prompting fir development. I am curious if there is any Machinelearning projects that have been training for this length pf time.
How long do you think it would take to teach an AI to do this?
Personally, I think it would probably be easier, cheaper and more practical to just grow synthetic humans in a lab --- i.e. Bladerunner. "Intelligent" right out of the box and already physically adapted to a humanistic world.
The only strategy that somewhat worked for me was perfect accuracy tranquilizer shots, to knock them out instantly. That's probably the hardest game mode I've ever played.
Halo 2 on Legendary difficulty was ludicrously hard too
On the other hand, I loved MGS3 on euro extreme! It really required mastering every trick in the game. Every little advantage you could squeeze into a boss fight was essential. Escape from Groznygrad was hell, though. By far the single hardest part of the game.
https://metalgear.fandom.com/wiki/Research_and_development_a...
I was always pleased but disappointed when things I encountered in the MGS series later manifested in reality...where anything you can dream of will be weaponized and used to wage war.
And silly as it sounds, The Sorrow in MGS3 was such a pain in the ass it actually changed my life. That encounter gave so much gravity to my otherwise-inconsequential acts of wanton murder, I now treat all life as sacred and opt for nonlethal solutions everywhere I can.
(I only learned after I beat both games that MGS5 and Death Stranding implemented similar "you monster" mechanics.)
Hold up just a sec, do you make a living in organized crime or something?
The experience forced me consider the implications of taking any life-- whether it be in aggression, self-defense or even for sustenance. Others may try to kill me, but I can do better than responding in kind.
As a result, I refuse to own a gun and reduced my meat consumption. I have a rat infestation but won't deploy poison or traps that will maim them (losing battle, but still working on it). Etc.
No, I was alluding to my previous Rambo playstyle of gunning down enemy soldiers even when I didn't need to.
But it carries into reality...a spider crosses your desk; most people would kill it. Rats? We poison them, their families and the parent consumer on the food chain. Thieves? Shoot on sight. Annoying CoD player? SWAT them. Murder as a means of problem solving is all so unnecessary.
We all have a body count. Most of us go through life never having to face it.
Also, TFA got the character and the game wrong in that screenshot. It's Venom Snake in Metal Gear Solid V, not Solid Snake in Metal Gear Solid.
"Well, I guess he doesn't... exist anymore?"
(unfortunately it's a Youtube short, so it will auto repeat.)
If you change->transform it to a normal video link, it doesn't: https://www.youtube.com/watch?v=Ec_zFYCnjJc
"This is HQ. Report back to base for psychiatric evaluation."
And let's zoom in on the chair. AI sees "chair". Slowly zoom in on arm of chair. When does AI switch to "arm of chair"? Now, slowly zoom back out. When does AI switch to "chair"? And should it? When does a part become part of a greater whole, and when does a whole become constituent parts?
In other words, we have made great strides in teaching AI "physics" or "recognition", but we have made very little progress in teaching it metaphysics (categories, in this case) because half the people working on the problem don't even recognize metaphysics as a category even though without it, they could not perceive the world. Which is also why AI cannot perceive the world the way we do: no metaphysics.
I think you need LLM plus vision to fully solve this.
"I put on my clothing" reads a bit like when business-jargon sneaks into everyday language, like when someone says they "utilized" something (where the situation doesn't technically call for that word, in its traditional sense). It gets the point across but seems a bit off.
... oh shit, I think I just figured out the general guideline: "clothing" feels more correct when it's a supporting part of a noun phrase, not the primary part of a subject or object. "Clothing factory" works well because "clothing" is just the kind of factory. "I put on my nicest clothes" reads better than "I put on my nicest clothing" because clothes/clothing itself is the object.
The clothes I'm wearing today are not warm enough. [specific pieces being worn]
VS
Clothing should be appropriate for the weather. [unspecified garments should match the weather]
When talking clothing you're talking about an abstract concept, when you're talking clothes you're generally talking about some fairly specific clothes. There's a lot of grey area here, e.g. a shop can either sell clothes or clothing, either works to my ear.
Not an issue if the image segmentation is advanced enough. You can train the model to understand "human sitting". It may not generalize to other animals sitting but human action recognition is perfectly possible right now.
I used to have the Bayesian maths to show the information content of relationships, but in the decades of moving (continent, even) it's been lost. I still have the code because I burnt CD's, but the results of hours spent writing TeX to produce horrendous-looking equations have long since disappeared...
The basics of it were to segment and classify using different techniques, and to model relationships between adjacent regions of classification. Once you could calculate the information content of one conformation, you could compare with others.
One of the breakthroughs was when I started modeling the relationships between properties of neighboring regions of the image as part of the property-state of any given region. The basic idea was the center/surround nature of the eye's processing. My reasoning was that if it worked there, it would probably be helpful with the neural nets I was using... It boosted the accuracy of the results by (from memory) ~30% over and above what would be expected from the increase in general information load being presented to the inference engines. This led to a finer-grain of classification so we could model the relationships (and derive information-content from connectedness). It would, I think, cope pretty well with your hypothetical scenario.
At the time I was using a blackboard[1] for what I called 'fusion' - where I would have multiple inference engines running using a firing-condition model. As new information came in from the lower levels, they'd post that new info to the blackboard, and other (differing) systems (KNN, RBF, MLP, ...) would act (mainly) on the results of processing done at a lower tier and post their own conclusions back to the blackboard. Lather, rinse, repeat. There were some that were skip-level, so raw data could continue to be available at the higher levels too.
That was the space component. We also had time-component inferencing going on. The information vectors were put into time-dependent neural networks, as well as more classical averaging code. Again, a blackboard system was working, and again we had lower and higher levels of inference engine. This time we had relaxation labelling, Kalman filters, TDNNs and optic flow (in feature-space). These were also engaged in prediction modeling, so as objects of interest were occluded, there would be an expectation of where they were, and even when not occluded, the prediction of what was supposed to be where would play into a feedback loop for the next time around the loop.
All this was running on a 30MHz DECstation 3100 - until we got an upgrade to SGI Indy's <-- The original Macs, given that OSX is unix underneath... I recall moving to Logica (signal processing group) after my PhD, and it took a week or so to link up a camera (an IndyCam, I'd asked for the same machine I was used to) to point out of my window and start categorizing everything it could see. We had peacocks in the grounds (Logica's office was in Cobham, which meant my commute was always against the traffic, which was awesome), which were always a challenge because of how different they could look based on the sun at the time. Trees, bushes, cars, people, different weather conditions - it was pretty good at doing all of them because of its adaptive/constructive nature, and it got to the point where we'd save off whatever it didn't manage to classify (or was at low confidence) to be included back into the model. By constructive, I mean the ability to infer that the region X is mislabelled as 'tree' because the surrounding/adjacent regions are labelled as 'peacock' and there are no other connected 'tree' regions... The system was rolled out as a demo of the visual programming environment we were using at the time, to anyone coming by the office... It never got taken any further, of course... Logica's senior management were never that savvy about potential, IMHO :)
My old immediate boss from Logica (and mentor) is now the Director of Innovation at the centre for vision, speech, and signal processing at Surrey university in the UK. He would disagree with you, I think, on the categorization side of your argument. It's been a focus of his work for decades, and I played only a small part in that - quickly realizing that there was more money to be made elsewhere :)
Floor as storage, floor as transport and floor as aesthetic space are three incompatible views of the same of object. The latter two being complementary usually outweighs the first, however.
Source: motorsports joke — "you can sleep in your car, but you can't race your house"
(It's not wrong...)
https://www.youtube.com/watch?v=NyRWnUpdTbg
On the floor!
You could ask my gf the same question
https://www.youtube.com/watch?v=fXW-QjBsruE
Perhaps the desk is "chairing" in those moments.
[EDIT] A little more context for those who might not click on a rando youtube link: it's basically an entertaining, whirlwind tour of the philosophy of categorizing and labeling things, explaining various points of view on the topic, then poking holes in them or demonstrating their limitations.
Vsause is awesome for mindboggling.
I had what turned out to be a fairly satisfying thread about it on Diaspora* at the time:
<https://diaspora.glasswings.com/posts/65ff95d0fe5e013920f200...>
TL;DR: I take a pragmatic approach.
Let's not give humans too much credit; the internet is rife with endless "is a taco a sandwich?" and "does a bowl of cereal count as soup?" debates. :P
A sophisticated monitoring system would access the output vectors directly to mitigate volatility of the first rank.
A bold claim, but I'm not sure it's one that accurately matches reality. It reminds me of reading about attempts in the 80's to construct AI by having linguists come in and trying to develop rules for the system.
From my experience, current methods of developing AI are a lot closer to how most humans think and interact with the world than academic philosophy is. Academic philosophy might be fine, but it's quite possible it's no more useful for navigating the world than the debates over theological minutiae have been.
That's because ontology, metaphysics, categorization, and all that, is completely worthless bullshit. It's a crutch our limited human brains use, and it causes all sorts of problems. Half of what I do in data modeling is trying to fight against all of the worthless categorizations I come across. There Is No Shelf.
Why are categories so bad? Two reasons:
1. They're too easily divorced from their models. Is a tomato a fruit? The questions is faulty: there's no such thing as a "fruit" without a model behind it. When people say "botanically, a tomato is a fruit", they're identifying their model: botany. Okay, are you bio-engineering plants? Or are you cooking dinner? You're cooking dinner. So a tomato is not a fruit. Because when you're cooking dinner, your model is not Botany, it's something culinary, and in any half-decent culinary model, a tomato is a vegetable, not a fruit. So unless we're bio-engineering some plants, shut the hell up about a tomato being a fruit. It's not wisdom/intelligence, it's spouting useless mouth-garbage.
And remember that all models are wrong, but some models are useful. Some! Not most. Most models are shit. Categories divorced from a model are worthless, and categories of a shit model are shit.
2. Even good categories of useful models have extremely fuzzy boundaries, and we too often fall into the false dichotomy of thinking something must either "be" or "not be" part of a category. Is an SUV a car? Is a car with a rocket engine on it still a car? Is a car with six wheels still a car? Who cares!? If you're charging tolls for your toll bridge, you instead settle for some countable characteristic like number of axles, and you amend this later if you start seeing lots of vehicles with something that stretches your definition of "axle". In fact the category "car" is worthless most of the time. It's an OK noun, but nouns are only averages; only mental shortcuts to a reasonable approximation of the actual object. If you ever see "class Car : Vehicle", you know you're working in a shit, doomed codebase.
And yet you waste time arguing over the definitions of these shit, worthless categories. These worthless things become central to your database and software designs and object hierarchies. Of course you end up with unmaintainable shit.
Edit: Three reasons!
3. They're always given too much weight. Male/female: PICK ONE. IT'S VERY IMPORTANT THAT YOU CHOOSE ONE! It is vastly important to our music streaming app that we know whether your skin is black or instead that your ancestors came from the Caucus Mountains or Mongolia. THOSE ARE YOUR ONLY OPTIONS PICK ONE!
Employee table: required foreign key to the "Department" table. Departments are virtually meaningless and change all the time! Every time you get a new vice president sitting in some operations chair, the first thing he does is change all the Departments around. You've got people in your Employee table whose department has changed 16 times, but they're the same person, aren't they? Oh, and they're not called "Departments" anymore, they're now "Divisions". Did you change your field name? No, you didn't. Of course you didn't. You have some Contractors in your Employee table, don't you? Some ex-employees that you need to keep around so they show up on that one report? Yeah, you do. Of course you do. Fuck ontology.
The source is given as a book; the Economist writer Shashank Joshi explicitly says that they describe the 'same story' in their article ("(I touched on the same story here: https://economist.com/technology-quarterly/2022/01/27/decept...)"). However, if you look at the book excerpt (https://twitter.com/shashj/status/1615716082588815363), it's totally different from the supposed same story (crawl or somersault? cited to Benjamin, or Phil? did the Marine cover his face, or did he cover everything but his face? all undetected, or not the first?).
Why should you believe either one after comparing them...? When you have spent much time tracing urban legends, especially in AI where standards for these 'stupid AI stories' are so low that people will happily tell stories with no source ever (https://gwern.net/Tanks) or take an AI deliberately designed to make a particular mistake & erase that context to peddle their error story (eg https://hackernoon.com/dogs-wolves-data-science-and-why-mach...), this sort of sloppiness with stories should make you wary.
Or you put up a "Detour" sign with a false road that leads to a dead end so they all get stuck.
As the articles says, "...straight out of Looney Tunes"
We also have intuition. Where Something just seems fishy.
Not saying AI can’t handle that. But I assure you that a human would’ve identified a moving cardboard box as suspicious without being told it’s suspicious.
It sounds like this AI was trained more on a whitelist “here are all the possibilities of what marines look like when moving” rather than a black list which is way harder “here are all the things that aren’t suspicious, like what should be an inanimate object changing locations”
But it seems like all these ML models are great at image recognition but not behavior recognition.
What’s the state of the art with that currently?
The point is, you just need to fool the vision enough to get it to attempt the task. Play to its gullibility and trust in the camera.
Afaik, this covers everybody except Tesla.
Looney Tunes attacks on Teslas might become a real subreddit one day.
The boxes and tree changed the silhouette and the somersaults changed the speed of movement.
So I guess we've been training soldiers to defeat Skynet all along.
"Till Birnam wood remove to Dunsinane"
Macbeth, Act V, Scene III
this article reads like a psyops where they want the masses not to be worried
Once the AI has worked it's way through all the twists and turns of the Metal Gear series we are probably back in trouble, though.
AI will probably, eventually replace most of the tasks we do. That does not mean it replaces us as people, except those who are defined by their tasks.
It's like there seems to be some sort of bias where over and over when it comes to AI vs human capabilities many humans keep looking at the present and fail to factor in acceleration and not just velocity in their expectations for the future rate of change.
Closest we got to that is Waymo taxis in just a few cities.
It's good! So is Tesla's thing! Just, both are much much less than I was expecting.
This whole debate is another FOMO shitshow. People just don’t want to “miss” any big things, so they just bet on a random side rather than actually learning how things work. Anything past this point is like watching a football game, as what matters is who’s winning. Nothing about the tech itself matters. A big facepalm I should make.
- In the excerpt, Scharre describes a week during which DARPA calibrated its robot’s human recognition algorithm alongside a group of US Marines. The Marines and a team of DARPA engineers spent six days walking around the robot, training it to identify the moving human form. On the seventh day, the engineers placed the robot at the center of a traffic circle and devised a little game: The Marines had to approach the robot from a distance and touch the robot without being detected.
https://www.marines.com/about-the-marine-corps/who-are-the-m...
Looks like the Marines did what they are extremely good at.
This reminds me of a joke that was floating around the internet few years ago. It goes something like this: The US and the USSR were in a space race and trying to put a person in space. To give the trainee astronauts a feeling of working without gravity the trainees were trained under water. But the US faced a challenge. There was no pen that would work under water for the trainees to use. The US spent millions of dollars on R&D and finally produced a pen that would work under water. It was a very proud moment for the engineers who developed the technology. After a few months, there was a world conference of space scientists and engineers where teams from both the US and the USSR were also present. So to get a sense of how the USSR team solved the challenge of helping trainees to take notes under water, the US team mention about their own invention and asked the USSR team how they solved the problem. The USSR team replied, We use pencils :)
But I still like the point of the story.
So you get weapons that don't work designed for threats that don't exist. It puts the military in a worse place under the illusion that it is in a better place.
Not saying anything new here, it's all over 60 years old. Nothing seems to have changed though
Just like finding a 100 year old smoker doesn't mean smoking doesn't cause cancer.
Is this really not immediately obvious?
Kuwait United States United Kingdom France Saudi Arabia Egypt Afghan mujahideen Argentina Australia Bahrain Bangladesh Belgium Canada Czechoslovakia Denmark Germany Greece Honduras Hungary Italy Japan Morocco Netherlands New Zealand Niger Norway Oman Pakistan Poland Portugal Qatar Senegal Sierra Leone Singapore South Korea Spain Sweden Syria Turkey United Arab Emirates
V.
Iraq
And people placed their bets on Iraq? Using WW2 era T-55s and T-62s from the early 1960s?
Alright!
Your point was that the MIC sacrificed our military acumen for profit, when they clearly haven't. I agree that we pay them too much, but the weapons themselves still perform better than any other.
Instead you claimed some dubious members of the babbling class misjudged how long it would take, didn't take the context into consideration, misrepresented 1940s tanks as cutting edge weaponry, and then attributed military technology and prowess to a victory plagued by warcrimes like the highway of death.
Killing surrendered troops and firebombing a retreating military under the flag of the United Nations will lead to the belligerent considering it a defeat. Under those conditions, you could probably achieve that with 19th century maxim guns.
Sorry, that's nowhere near sufficient to show that the fancy weaponry on display justified the cost or was an important part of the victory.
And what 1940s tanks? Both sides had modern tanks in the war.
Soldier B: "Relax, it's a good thing."
Hardware and software (AI or anything else) are tools, IMHO, rather than replacements for human beings....
Not even close. We've barely started in fact.
There are problems in math that are centuries old, and no one is going around saying we're "reaching the limits of math" just because hard problems are hard.
But somehow thinking that somehow wetwear is always going to be better than hardware is not a bet I'd make over any 'long' period of time.
Citations needed
If making a claim that humans have ephemeral things like souls and adherence to some kind of objective morality that is beyond our societal programming, then it's fair to ask for the reasoning behind it.
Every year machines surprise us by seeming more and more human (err, perhaps not that but "human-capable"). We used to have ephemeral creativity or ephemeral reasoning that made us masters at Drawing, Painting, Music, Chess or GO. No longer.
There are still some things we excel at that machines don't. Or some things that it takes all the machines in the world to do in 10,000 years with a nuclear plant's worth of energy that a single human brain does in one second powered by a cucumber's worth of calories.
However, this has only ever gone in one direction: machines match more and more of what we do and seem to lack less and less of what we are.
You can choose to engage with the content of the discussion or choose not to engage with it.
"Everybody who disagrees with me is either a child or clinically depressed" isn't what I come to HN for.
We are not? Just look at any group of people that's bought into a cult and you can see people falling for contradictions left and right. Are they 'higher level' contradictions than what our machines currently fall for, yes, but the same premise applies to both.
Unfortunately I believe you are falling into magical thinking here. "Because the human intelligence problem is hard I'm going to offload these difficult issues to address as magic and therefore cannot be solved or reproduced".
that is a pretty important part of the equation. what if the universe is the minimum viable machine for creating intelligence? if you think of the universe as computer and evolution as a machine learning algorithm then we already have an example of what size of a computer and how long it takes for ML to create AGI. it seems presumptuous to believe that humans will suddenly figure out a way to do the same thing a trillion times more efficiently.
Nature isn't efficient. Humans create things many orders of magnitude more efficient than nature as a matter of course. The fact that it didn't take millions of years to develop even the primitive AI we have today is evidence enough, or to go from the Wright Brothers' flight to space travel. Or any number of examples from medicine, genetic engineering, material synthesis, etc.
You could say that any human example also has to account for the entirety of human evolution, but that would be a bit of a red herring since even in that case the examples of humans being able to improve upon nature within relatively less than geological spans of time are valid, and that case would apply to the development of AI as well.
Why?
I think it might be confusion on your part on how incredibly inefficient evolution is. Many times you're performing random walks, or waiting for some random particle to break DNA just right, and then for that mutation to be in just the right place to survive. Evolution has no means of "oh shit, that would be an amazing speed up, I'll just copy that over" until you get into intelligence.
Security folks are going to have a field day if we’re not careful to make sure that people really understand the limitations of these types of systems.
"Are you human?"
"No, I'm a meat popsicle."
E.g. the DARPA engineers thought they had their problem space solved but then some marines did some unexpected stuff. They didn't expect the unexpected, now they can tune their expectations.
Seems like the process is working as intended.
If anyone has another source corroborating this story (or part of the story) then I'd like to know. But for now I'll assume it's made up to sell the book.
None of these would work in the field. It's both interesting and pointless.
If they didn't work you've increased the robots effectiveness. ie. running slower because you're carrying a fir tree or a box.
If the robot has any human backup you are also worse off.
Anything to confuse the AI has to not hinder you. A smoke bomb with thermal. It's not clear why the DARPA robot didn't have thermal unless this is a really old story.
DARPA is doing this to get AIs that better handle behavior intended to evade AIs.
Who says it didn't? A thermal camera doesn't mean your targets are conveniently highlighted for you and no further identification is needed. Humans aren't the only thing that can be slightly warmer than the background, and on a hot day they may be cooler or blend in. So it's probably best if your robot's target acquisition is a bit more sophisticated than "shoot all the hot things".