24 ms·
Ernst Dickmanns, a German scientist who developed self-driving cars in the 1980s
- squirrelicus 8y agoIt surprises many non techies I talk to (and techies too) to learn that AI hasn't really come very far in a few decades. We're a bit faster (by orders of magnitude to be sure), but AI "summers" are characterized primarily by it being a bit cheaper to put old limited things in products, and not by some abstract march of tech progress. Given Moore's law, I'd be surprised if we ever see another AI summer after the current one peters out.
- gambiting 8y agoIn the 60s, there was a rather famous project where scientists thought that solving image recognition will take them few months at most. It's 2018 and the best algorithms on the planet will answer with 99% confidence that a sofa in a zebra print is in fact, a zebra, unless they were very specifically trained against this scenario. I feel this exact same thing is happening with autonomous cars - yes, it's possible to get them to be extremely good at recognising the road and surroundings - but the last few percent, those crucial few percent that make the technology actually usable, I don't see those happening for another 50 years at least.
- nostrademons 8y agoMy unpopular (to me, even) prediction is that wartime will be the catalyst that makes self-driving cars ubiquitous. In times of peace having a car that kills its occupant even 1/10,000 of the time is unacceptable. In times of war having a car (or more likely, truck/tanker/tank) with no occupant is a huge competitive advantage, because someone is actively trying to kill the occupant. Build something with even a 90% chance of crashing and you still win, because you can take people off the battlefield entirely while your opponent loses precious soldiers with every vehicle that's destroyed. Then after the war, people's risk tolerances get reset because quibbling over a 1/10,000 chance of death seems ridiculous when people have been actively trying to kill you for the last 5 years and a countable percentage of your friends are now dead. The way global politics is going we probably don't even have to wait 10 years for this.
- zazen 8y agoYou seem to have a war like WW2 in mind. But a war between big powers in the 21st century couldn't drag on for five years while you frantically work on new technology. Even without use of nukes, one side or the other is going to be flattened a lot faster than in the days when bombers used propellers. Now, a new Cold War, that might push technological competition, although you won't get that "reset" of people's attitudes that you're after.
- reitzensteinm 8y agoIt sounds more like he has a war like Iraq and Afghanistan in mind. Imagine resupply convoys being driven autonomously, capable of launching drones for defense. https://www.army-technology.com/features/feature77200/ https://www.army-technology.com/features/feature77200/ "[...] every 55,702 barrels of fuel burned in Afghanistan by the US military forces corresponded to one casualty."
- Cthulhu_ 8y agoI've seen a number of projects already for self-driving supply trucks, and even walkers from Boston Dynamics. There's a (probably highly scripted) one featured in a later episode of Top Gear, for example.
- zazen 8y agoWell, you probably won't have to wait 10 years for another non-peer war like that. I think he's envisaging a big peer war with mass mobilization ala WW1/WW2, but he imagines such a war would be like a big Iraq/Afghanistan. It wouldn't.
- larkeith 8y agoIt seems like it might be feasible for a proxy war (like the one in Syria) to escalate significantly, to the point where multiple major powers are contributing manpower and materiel directly to the front lines; In such a case, it may be possible for an extended war to take place, while still incentivizing deployment of autonomous weaponized platforms - which are already in development today [1]. [1] https://en.wikipedia.org/wiki/Lethal_autonomous_weapon https://en.wikipedia.org/wiki/Lethal_autonomous_weapon
- denzil_correa 8y ago> In the 60s, there was a rather famous project where scientists thought that solving image recognition will take them few months at most. What project was this?
- tome 8y agoProbably a reference to this https://en.wikipedia.org/wiki/Computer_vision#History https://en.wikipedia.org/wiki/Computer_vision#History
- code_duck 8y agohttps://www.explainxkcd.com/wiki/index.php/1425:_Tasks https://www.explainxkcd.com/wiki/index.php/1425:_Tasks
- FabHK 8y agoI thought it might have been a reference to (LISP inventor) McCarthy's famous Dartmouth Conference in 1956, where they thought that 10 people over 2 months could make "significant advances" in making machines "use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves". https://en.wikipedia.org/wiki/Dartmouth_workshop https://en.wikipedia.org/wiki/Dartmouth_workshop EDIT: Probably the Summer Vision Project, as Tome said in sibling comment. https://dspace.mit.edu/handle/1721.1/6125 https://dspace.mit.edu/handle/1721.1/6125
- dalbasal 8y agoTo me, a fundamental question is "is this a problem?" Humans have similar problems too. Our intelligence is trained by experience and evolution to operate within certain parameters. More concerning to me is the fact that a human can conceptualize "couch" from a single example. ML algorithims needs to see thousands of couches before they can classify them.
- deleted 8y ago[deleted]
- ksdale 8y agoPreface: I didn't intend for this to be so long... I just got on a roll... I'm not sure it's true that we conceptualize couch from a single example. We've all seen thousands of couches in many different contexts, homes, schools, doctor's offices, on TV. If you had a person who had never seen a couch or a zebra before, and all you could tell them was right or wrong, it would probably take them (a lot?) more than a single try before they could distinguish between couch and zebra without fail. The only reason it's easy for us is that we have this giant scaffolding built around zebras as living things that look like a horses and donkeys, and couches as inanimate objects that look like things people sit on and regularly have some pattern on them. I think people tend to underestimate just how much "training" in the ML sense goes into a human brain. After all, humans spend the first... decade? of life incapable of all but the simplest tasks. That's a decade in which our brains are consuming petabytes of information and processing it constantly. Watching my children grow up, the way they learn seems remarkably similar to the way computers learn. If you watch a baby learn to move, it is purely an exercise in going too far in one direction and then too far in the other direction, repeated for basically years until they're coordinated enough to move roughly like an adult by the time they're 3 or so. It's the same with words and concepts too, they're just guessing based on things they already know (and the guesses are often waaaay off, because they don't yet know much), but they're constantly filing things away into their frameworks until their frameworks get big enough that this too resembles the way adults learn. By the time we're adults, the human brain makes ML algorithms look pathetic, but that doesn't take into the account the decades long head start that our brains got.
- azernik 8y ago> Given Moore's law, I'd be surprised if we ever see another AI summer after the current one peters out. On the other hand, the article notes that today's AI Summer gets its funding from a constant stream of profitable applications (though less dramatic than the blue-sky hype). The field is less reliant on animal spirits of academia, and has a more reliable source of funding. I still think things like self-driving cars are far in the future, but we're at a point where ongoing research into e.g. machine vision can be funded by less safety-critical commercial applications.
- romaniv 8y agoExpert systems had industry funding in the 80s. And contrary to what some people here keep implying, they delivered results. Some of them are still used right now.
- 2sk21 8y agoI completed my PhD in the subject of improving back propagation - in 1992! Many people are startled when I tell them how old the idea of NNs are :-)
- YorkshireSeason 8y agoIt's not widely known, but Alan Turing tinkered with neural nets in 1948, see [1], with his boss at the time calling Turing's work a "schoolboy essay". For a history of the subject, see Section 5.1 of [2]. Note that some parts of [2] are rather controversial. For a historical account of back-progagation, which itself was 'invented' many times, see [3, 4]. It is also not widely known that regular expressions, one of programmers' favourite tools, were introduced by Kleene in [5]: he was interested in characterising the behaviour of McCulloch-Pitts "nerve nets" (early versions of neural nets) and finite automata. [1] http://www.alanturing.net/turing_archive/pages/reference%20articles/connectionism/Turing's%20neural%20networks.html http://www.alanturing.net/turing_archive/pages/reference%20a... [2] https://arxiv.org/abs/1404.7828 https://arxiv.org/abs/1404.7828 [3] https://www.math.uni-bielefeld.de/documenta/vol-ismp/52_griewank-andreas-b.pdf https://www.math.uni-bielefeld.de/documenta/vol-ismp/52_grie... [4] http://people.idsia.ch/~juergen/who-invented-backpropagation.html http://people.idsia.ch/~juergen/who-invented-backpropagation... [5] S. Kleene, Representation of events in nerve nets and finite automata, see https://www.rand.org/content/dam/rand/pubs/research_memoranda/2008/RM704.pdf https://www.rand.org/content/dam/rand/pubs/research_memorand...
- FabHK 8y agoGreat references (though I can't open [4]). I sometimes think the contributions of numerical analysis pioneers is undervalued (eg Wilkinson, Kahan, Golub). Backpropagation is really just reverse mode AD, and has been discovered many times, as you point out. https://en.wikipedia.org/wiki/Automatic_differentiation https://en.wikipedia.org/wiki/Automatic_differentiation EDIT: [4] works now.
- s-macke 8y agoShow them this video: https://www.youtube.com/watch?v=cNxadbrN_aI https://www.youtube.com/watch?v=cNxadbrN_aI
- ahartmetz 8y agoI like your pessimism. But I'm also optimistic that interesting (mostly non-AI) stuff will happen when Moore's Law isn't a substitute for other innovations anymore. I am especially looking forward to Ray Kurzweil eating his hat when the singularity is delayed indefinitely.
- tim333 8y agoYou'll have a long wait - I think he projected 2045.
- Pica_soO 8y agoWhy was it again, that everyone reels in distaste from the church of singularity... Oh, yes... "Oh, powerful deus ex machina yet to come, the one above me is lost, take him into the loving arms of your personality review and do not judge your faithful followers by his threshold." the same reason all other powerful organized religion is distasteful. Rampant Stasi denunciation and a constant attempt to subdue all opposition voices.
- vegardx 8y agoPeople are often surprised that many planes can land completely autonomously on airports that support autoland (I think they call it ILS CAT 3). And that work on systems like that started in the 60s.
- adrianN 8y agoAutomating systems to interact with other automatic systems, likes planes and ILS, is a lot easier than automating systems that need to interact with humans.
- alxlaz 8y agoAnd it's even harder to automate systems that have to derive data from environmental cues. A line-following robot is something simple enough that undergrad students can build, and making it stop if it detects obstacles is a good choice for extra credit. A road-following car that stops when detecting obstacles is way, way, way more complicated than that.
- adrianN 8y agoI don't think stopping for obstacles is the hard part of autonomous driving.
- reitanqild 8y agoIt gets harder when you are: * Out of the lab * On roads that aren't level * When the obstacles move * etc
- rsynnott 8y agoIt's one surprisingly hard part of it. Note how Teslas enjoy driving into stationary objects with certain properties, say.
- adrianN 8y agoIt is my understanding that this is due to having the wrong kinds of sensors.
- agumonkey 8y agoTo me the major difference is the pressure differential of today's wave. It's a lot larger market than 80s AI, so it may provide more resources to overcome the hurdles. Or maybe it will get into another "comatose" decade.
- Shivetya 8y agoWe get bombarded with the term at nearly every turn. We see it in movies, television, and video games. So for the part what researches expect AI to become is not what the public thinks AI is. Marketing has a way of taking something that sounds cool, is just too complex to fully understand, but not so complex that they cannot convince you that you need it, and from there the terminology gets corrupted. Sure most people dismiss most over the top depictions in tv, movies, and games, but it is surprising how many out there still believe governments and the mega rich have something "close".
- ianai 8y agoI agree that linear thinking has its faults but I wonder if certain things can’t “add up” to opportunities for breakthroughs. Ie exascale computing, quantum scale Boltzmann machines, and faster communication between cpu cores. I’m wondering what new technologies will be possible after modeling at the scales possible by exascale.
- MaxBarraclough 8y ago"hasn't really come very far in a few decades"? How about AlphaGo Zero?
- romaniv 8y agoWhat about AlphaGo Zero? I'm honestly not sure why a system that plays board games is seen as some kind of giant leap in AI. Seems mostly a result of Google's marketing.
- MaxBarraclough 8y agoYou're really going to pretend that cracking Go wasn't a big moment for AI?
- romaniv 8y agoIn 1996 IBM made a system that was better than any person at playing chess. Chess has roughly 400 moves possible at any point. In 2016 Google made a system that was better at any person at playing Go. Go has roughly 130,000 moves possible at any point. That's approximately equal to 400 * 2^9. Moore's law states that the number of transistors (which is a proxy for computing power) in CPUs doubles every year. 2016 - 1996 = 20 years of computing power growth. In short, after our computing power has increased roughly by a factor of 2 ^ 20 someone made a system that plays a game that's 2 ^ 9 time more complicated than chess. Why is this seen as some giant, surprising leap forward?
- knight-of-lambd 8y agoBecause a chess engine isn't scanning 400 possibilities, and a Go engine isn't scanning 130k (where'd you even get that number?) possible game states. For example, a brute force search of a 19x19 Go board to a depth of 20 would yield on the order of 361^20 = 1.4E51 game states. With a reduction in search depth and better algorithms, state-of-the-art engines might cut this down by ten orders of magnitude, but can still be beaten by rank amateurs. Deepmind's approach to board game engines blows all previous approaches out of the water. The claim that their success is incidental to Moore's law is categorically false.
- rsynnott 8y ago"Ever" is a long time. But yeah, I wouldn't be surprised to see it die down again few a few decades at least.
- dalbasal 8y agoAt least to some extent, how you sees this relates to what you want/think AI to eventually be. A while back, I heard a particularly curmudgeonly interview with Noam Chomsky, just commenting on the state of AI and recent AI programs (Watson, Deep blue..). Chomsky basically called all these statsistical AI programs brute force parlour tricks, nothing to do with computer science or artificial intelligences. He stopped just short of calling the whole thing fraud. Real AI are things like bird flocking algorithims (did he work on something like this, can't remember). I think the jist was that to be an AI program, a program must embody a theory of intelligence. The theory-less nature of modern statistical algorithims seemed to really peeve him. maybe he's right. Anyway... I don't remember what point 9if any) i was driving at. I think there are abstract values at play, in how we narrate progress. Since we still don't know what inteligence "is," or even if it is a distinct thing, it's hard to know what the real milestones are. Personally, I like the fully realized, running at scale "proofs," but that probably says more about my ability to understand theory than anything else.
- braythwayt 8y agoAt some level: Alice: “Computers aren’t doing real intelligence, because they don’t think the way we theorize we think.” Bob: “Computers are doing real intelligence, because we don’t think the way we theorize we think.”
- nkozyra 8y ago> Chomsky basically called all these statsistical AI programs brute force parlour tricks, nothing to do with computer science or artificial intelligences. The counterargument is our own brains use brute force parlor tricks to solve problems. We learn by a priori (pre-built models), by experience (reinforcement learning) and viewing and weighing features that we observe against prior experience (NNs/ANNs). The entire point of ML is to produce approximately the same result a human does. Whether it's a parlor trick or a perfect simulation of the brain is irrelevant. There's no advantage to something novel and more "organic" unless it produces better results. This is a common problem I see - people are always trying to compare human reasoning to artificial reasoning rather than looking at the output. The output is all that matters.
- nkozyra 8y agoGiven how much the "brains" of AI is built upon fairly old (and very old) mathematics, I find this sentiment to be somewhat myopic. Yes, deep learning has its roots in something 50+ years old. Yes, we still use Bayesian concepts unearthed a century or more ago. Probability theory took foot 80 years ago. So what? Do we throw out the old models and approaches simply because they're old? But to say that AI "hasn't really come very far" because its technological achievements outweigh its computational side doesn't really make much sense to me. Progress is slow. Seeing a story like this and saying "wow, and we're only now seeing this in use in practice today" makes an implicit assumption that once something is proved it should become widespread. The original self-driving cars were not practical; it took advancements in computational power and speed to make it so. That's "coming far." We don't need some novel ML approach to advance AI.
- carlmr 8y agoAnother nice example are Fourier transforms. It took quite a while from 1822 until we had great applications for it in microcontrollers and DSPs for sound processing. However I can also kind of see the argument here that the deep learning ideas don't seem to have such a fundamental idea behind them. We modeled how biology works at a very simple and abstract level. Now we have more computing power (and especially more data) so we could find a few applications. But to me at least it doesn't look like a solid theory to build upon. It doesn't deliver insights into how things work like Fourier transforms did with signals and Bayes did with probabilities.
- bunderbunder 8y ago> We modeled how biology works That's the popular story behind neural networks. Like any good popular explanation of a STEM concept, it's as useful as it is inaccurate.
- carlmr 8y ago>at a simple and abstract level It would be nice if you quoted honestly.
- sergiosgc 8y ago> Given Moore's law, I'd be surprised if we ever see another AI summer after the current one peters out. I assume you are referring to the often declared demise of Moore's Law. Reports of such demise have been greatly exaggerated: https://ourworldindata.org/grapher/transistors-per-microprocessor https://ourworldindata.org/grapher/transistors-per-microproc...
- romaniv 8y agoAnother way of putting it: AI research of the 60s-80s was extremely impressive and is ridiculously underappreciated right now. Many ideas were simply way ahead of their time. It's sad that most software engineers today do not bother learning anything about the past of computing, assuming everything made in those decades is automatically "outdated". (This is not restricted to AI. For example, most OO programmers don't know what is Smalltalk.) The guy the article about says as much. “I’ve stopped giving general advice to other researchers,” said Dickmanns, now 82 years old. “Only this much: One should never completely lose sight of approaches that were once very successful.” I really feel for those researchers. Seeing how your life's work is ignored by people who brute-force their ways through the problems you've already solved must be rather depressing.
- squirrelicus 8y agoSo this
- kwhitefoot 8y ago> Given Moore's law, I'd be surprised if we ever see another AI summer after the current one peters out. This reminds me of a remark, possibly made by P. M. S. Blackett, which went something like this: "We've run out of money, now we'll have to start to think." It is possible that stagnation in computing performance will be just the stimulus that the field needs.
- agumonkey 8y agoWas Thrun already involved with German Computer Vision ? Sad that new AI is not doing the deeds on checking prior art..
- RealityVoid 8y agoThe Prometheus project is one of the most interesting projects almost nobody seems to know about. It is incredibly impressive and one of the most memorable and interesting interactions I've ever had on HN was with the user sokoloff, that recounted his time being an intern for the PROMETHEUS project: https://news.ycombinator.com/item?id=10328687 https://news.ycombinator.com/item?id=10328687
- lnsru 8y agoOne of these cars is displayed in German museum in Munich (Deutsches Museum Verkehrszentrum). I visit it every time when I go there. It’s very unspectacular big gray Mercedes loaded with old computers. Back then it was a huge improvement from 8 MHz 68k processors. We have now a huge improvement from 100 MHz to these in GHz range operating GPUs. Is it enough to solve this problem? Or will it take another 30 years?
- dekhn 8y agoCan somebody who understands what Dickmanns did, can you explain? Was he actually doing object detection on road elements? How did it stay in the lanes?
- blattimwind 8y agoIt didn't use GPS or any sort of pre-fabbed maps. It drove purely by eye / machine vision using four cameras. No radar or lidar.
- RealityVoid 8y agoI do believe they DID use a radar system. A HN user that worked on the project did mention something about using such a sensor.
- dekhn 8y agoI'm asking, specifically, what machine vision technology it used? In 1994 there wasn't much machine vision (that I'm aware of) that would even be able to do lane-keeping, except under incredibly idealized conditions (empty roads, no weather, good lighting). If so, I would request that whomever claimed this guy invented the self driving car temper their claim.
- jmiserez 8y agoHere is what someone on the project had to say about it: >Our vision system relied (heavily, not exclusively) on sensing prominent horizontal features symmetric about a common centerline. (Cars and trucks, especially at the time, have a lot of horizontal lines: bumpers, window top/bottom, valence, etc.) Thread here: https://news.ycombinator.com/item?id=10328687 https://news.ycombinator.com/item?id=10328687
- dekhn 8y agoAh! Yes, I also watched the video and I see they are identifying little "cups" (one horizontal line and two vertical). Bridges also have many hoizontal features symmetric about a common centerline. I would expect many false positives driving near bridges.
- mysterydip 8y agoI was recently reading up on GM's firebird concept/research cars from the 50s https://en.m.wikipedia.org/wiki/General_Motors_Firebird https://en.m.wikipedia.org/wiki/General_Motors_Firebird One of the features envisioned at the time: > It also featured a sophisticated guidance system intended for use with "the highway of the future," where an electrical wire embedded in the roadway would send signals that would help guide future cars and avoid accidents. So the idea has been around for a long time, approached from different directions.
- scotty79 8y agoThe idea is as old as a car because with a horse and cart you could doze off and trust the horse on usual routes. I wouldn't be surprised if at some point someone tried to train animals to do the driving and achieved modest success.
- mysterydip 8y agoGood point. I can't find it right now, but I seem to recall some experiments with a mouse/rat in a ball controlling a robot based on stimuli (LEDs or video). There's also this: https://www.technologyreview.com/s/401756/rat-brained-robot/ https://www.technologyreview.com/s/401756/rat-brained-robot/ Maybe someday horse neurons will control our cars!
- reitanqild 8y ago> I wouldn't be surprised if at some point someone tried to train animals to do the driving and achieved modest success. Somewhat relevant (animals, real time) but I think it failed: https://en.m.wikipedia.org/wiki/Project_Pigeon https://en.m.wikipedia.org/wiki/Project_Pigeon
- stephengillie 8y agoWe found that humans are the best animals to train for this: https://en.wikipedia.org/wiki/Bus_driver https://en.wikipedia.org/wiki/Bus_driver https://en.wikipedia.org/wiki/Chauffeur https://en.wikipedia.org/wiki/Chauffeur https://en.wikipedia.org/wiki/Taxi_Driver https://en.wikipedia.org/wiki/Taxi_Driver
- dubbel 8y agoA professor of mine who worked on the project as a PhD student once told us about the project: According to him they had to use the heavily armored version of the car, intended to protect presidents, and strip out all the amour, because only this car platform could carry the enormous weight of the computers at the time.
- RealityVoid 8y agoThe first version was basically a bus loaded with 486's .
- stephengillie 8y agoThey must have needed huge batteries - or could the bus engine power a large enough inverter to power all the 486s?
- owenversteeg 8y agoMy guess would be a big gas generator in the back of the bus.
- gsich 8y agowhy an inverter? I don't think DC-AC-DC is more efficient then DC-DC.
- stephengillie 8y agoThe bus alternator produces up to 80 amps at 20 volts - while the computer needs 12, 5, and 3.3 volts, in the 1-20 amp range. Using a common inverter and a common power supply is the cheapest way to convert the voltage.
- chrisdhoover 8y agoBus altenator is 24VDC? Plus charging voltage so a 27.6v output?
- TheArcane 8y agoThis is truly fascinating. As an engineer working with autonomous driving, I've always been under the impression that sel-driving car research exploded after the 2005 DARPA Grand challenge. It's embarrassing that I never knew about PROMETHEUS given that it was a €749m pan-European collaboration on autonomous driving.
- kakali 8y agoDoes Navlab not predate this work? It started in 1984. https://en.m.wikipedia.org/wiki/Navlab https://en.m.wikipedia.org/wiki/Navlab
- czr 8y agoThe Navlab project and Dickmanns' projects (VaMoRs / Prometheus) were mostly concurrent, but, yeah, I think CMU started & published initial work first.
- lorenzhs 8y agoJust an odd observation: in one of the photos, the UniBwM car has a Bundeswehr license plate (Y-320624), yet there's no mention of any military involvement in the article. I'm guessing here, but maybe that was just a trick to get around some regulations that only apply to civilian vehicles?