10 ms·

This happened right before the first AI Winter in the late 80s: AI (in the form of expert systems) solved a number of hard problems and was hyped as being able

• computerex 7y ago
  Right. There has been a lot of progress within the last decade in narrow AI, but AGI remains as elusive as ever.
• misterman0 7y ago
  >> We're not going to have AGI any time soon. In your mind, how many inventions are we from GAI? 1? 1000? Also, given we have our very brightest working in the problem space (MS, Google, FB, OpenAI ...) how long would each remaining invention take? It seems plausible to me that we are only one invention from GAI, one novel combination of two or more existing solutions and that this may come from anyone and at any time.
  • dorkwood 7y ago
    Applying this method of thinking, is there anything that we aren't one invention away from?
    • misterman0 7y ago
      I'm just trying to stay positive. If you talk optimistically about AI (or big data for that matter) you quickly get downvoted these days. I'm curious as to why that is. I thought this was 'marica. Isn't this 'marica? Edit: my apologies, that was condescending. I should have finished of with a wink of an eye.
      • striking 7y ago
        There's optimism and there's being too vague to disprove. You might be able to dodge downvotes by adding more objective content to your comments. History has played out a particular way quite a few times (https://en.m.wikipedia.org/wiki/AI_winter https://en.m.wikipedia.org/wiki/AI_winter). If you can tell us why today isn't like all the other days using facts, I'll bet the downvotes will stop.
    • solveit 7y ago
      Large scale colonization of Mars would require multiple inventions. We know this because we have a good understanding of what a successful colonization of Mars entails, and have a pretty good idea of the main technical obstacles. AGI, on the other hand, might be one breakthrough away or two hundred breakthroughs away. We just have no idea what makes intelligence tick.
  • onion2k 7y ago
    We've drawn a couple of circles and we want to draw the rest of the owl, and you're telling us there's only one step.
    • misterman0 7y ago
      Yes. Because we don't know it is an owl we should draw. It's equally likely AI is just three circles combined.
      • lsc 7y ago
        exactly... we don't even have a definition of consciousness that is better than Jacobellis v. Ohio - For that matter, I'm not sure we even have it that good. I'm not sure I'd recognize a non-human consciousness when I saw it. I'm not sure anyone would. I mean, sure, I can totally see the idea that consciousness is maybe some emergent property of complex systems... in which case, sure, we could stumble upon it by accident. That is totally possible; we're creating complex self-replicating systems all over the place. But as far as intentionally constructing the thing that will come after humanity? we can't do that until we know what, exactly, it is we are constructing, and as far as I can tell, we're pretty far away from any idea of exactly what that is.
        • pure-awesome 7y ago
          Is consciousness a necessary component for AGI, though?
          • lsc 7y ago
            >Is consciousness a necessary component for AGI, though? I always thought so. Like, without volition, it's just a really good natural language tool, and not a general intelligence. What would an artificial general intelligence without consciousness look like to you?
            • AstralStorm 7y ago
  • deleted 7y ago
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  • EliRivers 7y ago
    General intelligence? Does it even exist? I find the evidence that humans have it unconvincing, and I've seen nothing that makes me think we're "one invention" away. The direction of machine learning seems to be specialised tasks on specialised data sets; surely the very opposite of any kind of general intelligence.
    • z3phyr 7y ago
      So get whatever intelligence human have in computers. That was the goal. Call it general intelligence or call it x.
      • EliRivers 7y ago
        Human intelligence works well [0] from inside human shaped objects with human shaped biology, in human shaped societies, and to no small degree for doing human-style tasks. I am not convinced that putting that human intelligence, whatever it is, into a silicon box will give us the results we hope for (or is even a sensible ambition, given the degree to which our intelligence is part of our biology). [0] Actually, maybe it doesn't work really well - maybe it's a slow-motion car crash over millions of years. But it works well enough that we clearly want more of it.
        • antepodius 7y ago
          Whatever human intelligence is, there's clearly some part of it that can do things like design computers and build houses. That part, at least, people would like to be able to match in performance using machines. It's 'intelligence' in the sense of being able to do things like think up clever strategies in novel environments that people want to put in a silicon box.
          • EliRivers 7y ago
            If that is the goal, it's certainly not what the current batch of machine-learning and hype-train riders are chasing. They (or at least, the ones doing it via huge training sets) seem to be working on the very opposite of novel environment situations. I wonder if anyone is seriously pursuing that.
  • dreamcompiler 7y ago
    That's exactly what they thought in the 1950s and 1960s 1970s and 1980s. And oh by the way, our brightest minds were working on it then too.
• tintor 7y ago
  AGI is not needed for self-driving. None of what is mentioned above is a deal-breaker for self-driving car service: - lidar at night works just fine - plenty of cities with no or very little snow - construction zones: blacklisting, remote monitoring & manual mapping, detection of cones, barriers, re-painted lanes - self-driving cars with 360 degree view and plenty of patience and no distraction are safer for bicyclists than manually driven cars
  • SomeOldThrow 7y ago
    If I just wanted to drive around a city I’d grab a smart car. I have taxis to take me home at night when I’m drunk and the busses have stopped running. This solves a problem only rich people have: traffic is tedious. The rest of us just take public transit and listen to podcasts or music to bypass it. From my perspective the idea of a self driving taxi is the opposite of public transit in the worst possible way: pay more so fewer people have a job and you’re even more lonely and alienated than before. If I indulge I’m just making myself and everyone around me more miserable, and no amount of marketing will convince me otherwise. The area where I’d appreciate this the most is the country where uber and lyft haven’t reached yet, and that’s precisely the environment where self driving cars will take the longest to reach. We’ll see. Plus, I can’t wait to see the game of “beat the shit out of the corporate empire’s self driving car for fun when drunk”.
  • TheOtherHobbes 7y ago
    Self-driving cars may be safer than manually driven cars in certain contexts. What's missing from AI is reliability. It's brittle, it works well in some situations and not at all in others. Which is a problem, because you can never be sure how well it's working for you. There's no anti-Dunning-Kruger-function to say "I'm sorry Dave, I can't handle this, you should take over" - partly because that's not something you want to experience driving into a hail storm at 70mph on a freeway if you're asleep, but also because it would require a level of domain-specific AI self-awareness that is barely on the radar in most domains.
    • yonkshi 7y ago
      Unfortunately any AI systems will encounter the verification dilemma, the more powerful an AI system becomes, the less verifiable it would be.
• jsinai 7y ago
  These days it’s more 95% easy, last 5% hard.
• Wiretrip 7y ago
  So true. The real danger with 'AI' at the moment is the premature deployment of half-baked parlour tricks or the over reliance on simple algorithms where there are real consequences. i.e. prison sentences and insurance eligibility and yes, autonomous vehicles.
  • tempodox 7y ago
    > ...premature deployment of half-baked parlour tricks... And the “fake it till you make it” attitude in SV only makes this worse.
• bjourne 7y ago
  The first "AI winter" happened in the 60's. In 1949 Warren Weaver published a memorandum outlining his plan for universal machine translation. He likened the problem to cryptography, a field that had exploded due to WWII, and thought it would be solved in a few years. In hindsight, we know that his ideas were very naive but Weaver was an influential and charismatic person so they inspired lots researchers. They also attracted lot of funding from the US government, likely because of the envisaged military and political uses of machine translation. Then in 1966, the ALPAC committee setup by the US government published a report that concluded that machine translation were infeasible and thereby killing the funding. Work on machine translation resumed at IBM in the late 80's, but then based on statistical methods.
  • sjy 7y ago
    It feels harsh to call him naïve because I'm sure I wouldn't have had any more insight, but it is surprising how little we knew about the science of language pre-Chomsky, considering that we have been learning languages for millions of years. Now that we have had several generations of computer technology fall short of the abilities of human translators, it seems obvious that you're probably not going to be able to write a machine translation program on a computer with a few megabytes of memory.
    • skybrian 7y ago
      Did you mean post-Chomsky? It's my understanding that machine translation started to become semi-useful after abandoning traditional grammars (or anything Chomsky would recognize) and switched to statistics.
      • cr0sh 7y ago
        > It's my understanding that machine translation started to become semi-useful after abandoning traditional grammars...and switched to statistics. What's curious about this statement (and forgive me if I am in error here; I am not extremely familiar with the machine translation field) is that the community chose to go down the route of "traditional grammars" and their rules, for the purposes of translation, rather than down the route of statistics to begin with. Because as it was noted, this happened after WW2 and the advances in cryptography, which were by and large also driven by statistical analysis and other similar mathematically based advances; one would think that such insights would have carried over into the machine translation realm. But they didn't - which is simply a curious historical footnote. It's also something we see often in history - particularly as it relates to technological advances; that there are certain paths that are taken that lead to virtual dead-ends decades later, but which the path that should have been taken was presaged by earlier work, yet for one reason or another wasn't pursued. If we only had a way to avoid such "wrong turns", we could be much further along technologically - but of course, that would also be tantamount to "telling the future"...almost.
• JustSomeNobody 7y ago
  These are made up numbers, but I use this to explain the scale to people. If it costs $1Bn to reach 80%, it'll take another $100Bn to reach 90%. There isn't enough money to reach 100% for two reasons. 1) You can't get to 100% and 2) It will take so long people lose interest. You can't sustain the hype that long.
• jayd16 7y ago
  You're always going to see #2. Use cases get marketable before they get fully solved so you'll always see pretty good but not great examples. In the mean time there are a lot of things that are solved by neural nets that we take for granted such as voice and face recognition.
  • hodr 7y ago
    Voice is solved? Then wider hall cunt my giggle nexus ever get thaddeus tribbleshits?
    • burnte 7y ago
      That's MISTER Thaddeus Tribbleshits to you.
• Veedrac 7y ago
  What GOFAI technique got even close to 80% of the way? Maybe pathfinding, if I'm being particularly generous?
• ilaksh 7y ago
  The best self driving systems do not have trouble dealing with bicyclists. The one time it happened with Uber was a combination of user errors starting with a very serious misconfiguration of the system. Also, AGI and self-driving cars have almost nothing in common. Driving cars is a very narrow AI task.