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This AI Boom Will Also Bust
- chime 10y ago> Good CS expert says: Most firms that think they want advanced AI/ML really just need linear regression on cleaned-up data. Cleaning up data is very expensive. And without that, the analysis is good for nothing. AI helps provide good analysis without having to cleaning up data manually. I don't see how that is going away.
- LeanderK 10y agoi would say that this is one of the main advantages of ML. You can even use it to process unstructured data.
- tommynicholas 10y agoNot only is cleaned up data expensive, dirty data is expensive! Clean data pipes are now and will be increasingly really interesting businesses IMO.
- shadowmint 10y agohow so? Practically speaking isn't unsupervised learning one of the areas that hasn't really shown much practical application? I mean, if I'm wrong point me to some examples, but I was under the impression cleaning up data wasn't realky a ML strength at this point...?
- kristjankalm 10y ago> AI helps provide good analysis without having to cleaning up data manually. I don't even know where to start. I suppose you don't really think that what separates ML/AI (whatever that means) from your standard OLS regression is that denoising is not done manually? > Cleaning up data is very expensive. And without that, the analysis is good for nothing. I hope you are not saying here that linear regression cannot handle noise. In the end, as this blog post also points out, ML/AI is just a vague blanket term. What you want is a statistical method that captures the signal as fast and efficiently as possible and often the gain from going beyond simple linear models might be marginal.
- joe_the_user 10y agoI agree that using AI to get something that sort-of works out of dirty data is one of the appeals and is going to get people using it. If this works at all, the problem is that what you wind-up with is a system that's been heuristically taught to clean-up data for a single snap-shot of your data - and the teaching is expensive and requires experts who are going to move on. Less expensive than clean your data but still a cost. So when you wind-up with a different pattern of dirty data after a year's time, the system winds crappier than previously and no one will be able to agree how to fix it. Eventually AI is going to get an evil reputation and that may kill its appeal.
- cardine 10y ago> AI helps provide good analysis without having to cleaning up data manually. My own experience has shown that dirty data impacts advanced AI just as much as it impacts far more basic ML techniques. Even for the most advanced AI we work on, we spend just as much time worrying about clean data as we do anything else.
- throw_away_777 10y agoWhen you say "clean data", what exactly do you mean? I've often seen this claim that cleaning data takes a lot of time, but it seems like an ill-defined term.
- bonoboTP 10y agoIt can mean different things. In general: duplicate data, missing fields, different formats for different parts of the data, inconsistent naming schemes For text: character encodings, special symbols, escape characters, punctuation, extra or missing spaces and newlines, capitalization For images: different sizes, rotations, crops, blurry images For numbers: inconsistent decimal point/comma, outliers with obviously nonsense values or zeros, values in different units of measurement etc.
- nostrademons 10y agoFor user behavior: bots, clickfraud/clickjacking, bored teenagers, competitors who are sussing out your product, people who got confused by your user interface, users who have Javascript disabled and so never trigger your clicktracking, users who are on really old browsers who don't have Javascript to begin with. And then there's bugs in your data pipeline: browser (particularly IE) bugs, logging bugs, didn't understand your distributed databases's conflict resolution policy bugs, failed attempts at cleaning all the previous categories, incorrect assumptions about the "shape" of your data, self-DOS attacks (no joke - Google almost brought down itself by having an img with an empty src tag, which forces the browser to make a duplicate request on every page) which result in extra duplicate requests, incorrectly filtering requests so you count /favicon.ico as a pageview, etc.
- garysieling 10y agoAI techniques can help with cleanup too. I've been using entity recognition to find speaker names to use in faceted search of lectures for https://www.findlectures.com https://www.findlectures.com. The NER library just finds low hanging fruit that I check manually.
- BatFastard 10y agoGarbage in, garbage out. I have always been a stickler for making sure the data going in is good. Takes a bit longer, but makes life much much easier in the long run.
- mcguire 10y agoGarbage in, gospel out.
- iwritestuff 10y agoI plan to enter a PhD program in 1-2 years to specialize in ML/Deep Learning. Assuming it'll take 5-6 years to complete my degree how applicable should my skill sets be in industry at that point?
- adrenalinelol 10y agoNo one can tell you for sure, if it's your passion do it, if you're hoping for a big payday, I'd reconsider.
- whorleater 10y agoI don't actually think that's true, if the "AI bubble" bursts at some point in the near future, the people who'll be in trouble with be those without formal education to back them up.
- toomuchtodo 10y ago> the people who'll be in trouble with be those without formal education to back them up. The people who can't hack it are those who'll be in trouble. Tech has never much been the place where credentials are necessary. Don't specialize and saddle yourself with years of college debt if you're unsure of the field's long term prospects.
- nwjtkjn 10y agoFWIW, one does not accrue debt doing a PhD in something like Machine Learning.
- toomuchtodo 10y agoDepending on the program, possibly. But a PhD is not required to learn nor work in machine learning.
- randcraw 10y ago
- jeyoor 10y agoThis article matches what I've been seeing anecdotally (especially at smaller tech firms and universities in the Midwest US). I've been hearing more folks in research and industry express the importance of applying simpler techniques (like linear regression and decision trees) before reaching for the latest state-of-the-art approach. See also this response to the author's tweet on the subject: https://twitter.com/anderssandberg/status/803311515717738496 https://twitter.com/anderssandberg/status/803311515717738496
- IIIIIII 10y agoSaying that linear regression is easier to do properly than more complex methods like random forests, DL, boosting etc is like saying that people should code assembly instead of python
- kristjankalm 10y agoThis is a false dichotomy. Both OLS regression and, say, random decision forest regression have the same objective (predict values) and achieve it with similar means (build a generative model / function). They solve the same problem. Contrastingly, assembler and python are broadly aimed at completely different use cases. Broadly, whether you should move from OLS to random forest regression = SNR increase / increase in manhours and money spent.
- throw_away_777 10y agoIt is actually much easier to apply a random forest (or really gradient boosted decision tree, which almost strictly dominates random forests) than a linear regression. Decision tree methods require far less data preprocessing than linear regression, because the model is able to infer feature relationships. Obviously if your features are linearly related to your target than linear regression is much more viable.
- jbrambleDC 10y agoThis is absolutely true, the one caveat is that you can explain the significance of features and the relationship to the response variables in simpler terms.
- pesenti 10y agoWhen I was at Watson this is the first thing I told every customer: before you start with AI are you already doing the more mundane data science on your structured data? If not, you shouldn't go right away for the shiny object. This said I still believe the article is mistaken in its evaluation of potential impact (and its fuzzy metaphore of pipes). Unstructured or semi-structured or dirty data is much more prevalent than cleaned structured data on which you can do simple regression to get insight. Ultimately the class of problems solved by more advanced AI will be incommensurably bigger than the class of problems solved by simple machine learning. I could make a big laundry list but just start thinking of anything that involves images, sound, or text (ie most form of human communication).
- dheera 10y agoAnd before you do mundane data science on your structured data, you should figure out if there is a better way to get cleaner raw data, more data, as well as more accurate data. For example, I predict stereo vision algorithms will die out soon, including deep-learning-assisted stereo vision. It's useful for now but not something to build a business around. Better time-of-flight depth cameras will be here soon enough. It's just basic physics. I worked on one for my PhD research. You can get pretty clean depth data with some basic statistics and no AI algorithm wizardry. We're just waiting for someone to take it to a fab, build driver electronics, and commercialize it.
- taeric 10y agoI think the hidden strife in your triplet is a land mine. That is, getting more data is at odds with cleaner data for most folks. Adding accuracy? I hope you remember the difference between precision and accuracy. :) (None of which takes from your point.)
- pesenti 10y agoRight but in this case there is a trade-off: if you don't have the better data now how long before you will? Should you start now (and gain a time advantage at a cost) with what you have or wait for the better data (read sensor/process/technology/people etc). My experience is that waiting for cleaner data is often like waiting for Godot and will often be a project killer (sometimes justifiably). This is a key issue at the moment in advanced ML: clean large training sets ideal for supervised training are elusive and the companies making real-world advances are pretty much all using available data (and semi-supervised techniques) rather than expensive made up training sets.
- lowglow 10y agoWe're building an applied AI business by creating an experience through both hardware and software. You don't set out to create something with as big a breadth of vision by worrying about booms and busts. You continue your journey unwavering because the potential impact and fruitfulness of development is worth it. This is why you should work on something you're passionate about. Your time on earth is limited, so strive to leave good work and contribute to the progress of humanity on a larger scale.
- mmkx 10y agoThe technological singularity arrived November 15th. Plenty of AI/robots to come.
- kpwagner 10y agoAI is overhyped... sure that's probably true. But data science is here to stay in the same way that computer science is here to stay.
- tree_of_item 10y agoNo, I don't think "data science" is nearly as well defined as computer science.
- kpwagner 10y agoI agree; and it's probably not as deep either. I meant that computer science has staying power, while particular branches (or JS frameworks) may rise or fall in popularity over time. Likewise, the "trunk" of data science knowledge is not a mere fad. I'm not a data science academic or practitioner. My opinion is based on a small amount of tinkering and what I've read in various online sources.
- rampage101 10y agoThe more I get into machine learning and deep learning it seems like there is an incredible amount of configuration to get some decent results. Cleaning and storing the data takes a long time. And then you need to figure out exactly what you want to predict. If you predict some feature with any sort of error in your process the entire results will be flawed. There are a few very nice applications of the AI techniques, however most data sets don't fit well with machine learning. What you see is that in tutorials use the Iris data set so much because it breaks into categories very easily. In the real world, most things are in a maybe state rather than yes/no.
- ffwd 10y ago> In the real world, most things are in a maybe state rather than yes/no. Not to get too far afield, but I disagree with this on a certain philosophical level. All states are yes/no. All states of all things should result in a yes/no and be differentiable, with enough data. This doesn't speak to the practicality of that but as far as I can tell the theoretical potential is huge, almost infinite even.
- JonnieCache 10y agoIsn't this just shifting the ambiguity into your choice of state definitions, rather than the states themselves?
- ffwd 10y agoBut there should be no ambiguity, with enough data. Maybe that means there will always be ambiguity, but maybe it doesn't, especially not with man-made things and complex natural objects, and also if you can contextualize the data over time and 'geographically', there is more 'signal' there to differentiate
- jonathankoren 10y ago> But there should be no ambiguity, with enough data. What? I'm sorry but this runs counter to everything in my experience, both professionally, and just casual very day experience. More data, helps to a point, but then there's diminishing returns, and it certainly doesn't eliminate the ambiguity. On the contrary, you discover diversity, and you still have a misclassification and perhaps even a harder data cleaning problem, because now you're seeing cases that aren't actually clear cut. Even if you're only talking about adding more features, well again, that works up to a point, but then you hit sparsity issues.
- yegle 10y agoI started question the credibility of the article when the author mentioned "deep machine learning". Not an expert in ML, but it should be "deep learning" referring to a type of neural network based machine learning technique with deep hidden layers.
- deepnotderp 10y agoYeah,that's weird.
- h43k3r 10y agoA little off topic but I think the VR boom will bust much more sooner than AI. I can't think of normal people wearing those heavy gears in their normal life. There will be its use cases in specialized applications like education, industry, games but I don't think it will get popular like an iPhone. AR is still OK since it augments real life but there is a long way before it will become mainstream.
- thesimpsons1022 10y agohave you tried it? i own an oculus and every family member i've seen has been shocked and loved it. Obviously the oculus is prohibitively expensive but with the release of Playstation VR i think the mainstream is poised to adopt it. I really believe the next game consoles that come out will simply be vr headsets
- h43k3r 10y agoGame consoles will/should come with VR headsets. My point of view is daily normal consumption. I wouldn't wear an Oculus while I am home sitting on my SOFA with my family members and not many people play games especially considering third world countries like India. VR would have its place but not to the likes of smartphones.
- jroes 10y agoI have tried it. I owned a PSVR for a month and tried everything available. It was a pretty incredible experience, more immersive than I expected for sure. Most of my family and friends had a great time with it as well. Ultimately, however, I resold it after a month. There are too few interesting full games available. Nearly every game is mostly a short trial, and most of the games are also very experimental and uninteresting to me in general. As an example, a full 1/3 of games available were musical demos that seemed to be geared toward folks having fun experiences while presumably smoking weed or otherwise in an altered state. There was never a reason for me to come back to the system, but I would like to see if there are very imaginative useful practical applications that eventually see light. After surveying the other VR options I'm not convinced anything exists yet.
- tim333 10y agoIt seems a little odd that the author is focusing on machine learning not being terribly good for prediction from data to counter the "this time is different" argument. The reason this time is different is we are in a period when AI is surpassing human intelligence field by field and that only happens once in the history of the planet. AI is better at chess and go for example, is slowly getting there in driving and will probably surpass general thinking at some point in the future though there's a big question mark as to when.
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- coldtea 10y ago>The reason this time is different is we are in a period when AI is surpassing human intelligence field by field and that only happens once in the history of the planet. Citation needed.
- spsgtn 10y agoThis reminds me of the stem cell research boom and bust of the late 90s and 00s. It turns out that the new and shiny toy doesn't work for everything. However, it's not really a bust, as AI, similar to stem cells applications, will continue to do wonders where it is the best tool for the job.
- debaserab2 10y agoWe're not even close to reaping the benefits of stem cell research yet. It's way too early to deem it's success to be just mediocre.
- dekhn 10y agoStem cell research bust?
- jondubois 10y agoJournalists and investors only seem to get excited about buzzwords - Maybe that's because they don't actually understand technology. To say that technology is like an iceberg is a major understatement. The buzzwords which tech journalists, tech investors and even tech recruiters use to make decisions are shallow and meaningless. I spoke to a tech recruiter before and he told me that the way recruiters qualify resumes is just by looking for keywords, buzzwords and company names; they don't actually understand what most of the terms mean. This approach is probably good enough for a lot of cases, but it means that you're probably going to miss out on really awesome candidates (who don't use these buzzwords to describe themselves). The same rule applies to investors. By only evaluating things based on buzzwords; you might miss out on great contenders.
- hindsightbias 10y agoPerhaps an AI Recruiter is in order.
- ig1 10y agoWhile the article is right that in many situations a linear regression or tree based approach will be more effective, it downplays the real value add of deep learning which has been clearly demonstrated with image data and is likely to have significant impact in other areas (audio, biodata, etc.) where traditional statistical methods have failed.
- shmageggy 10y agoHere's why the pipes metaphor is a bad one: we already are doing everything we can and ever will do with pipes. Pipes have been around for a really long time, we know what they are capable of, we've explored all of their uses. OTOH, the current progress in AI has enabled us to do things we couldn't do before and is pointing towards totally new applications. It's not about making existing functionality cheaper, or incrementally improving results in existing areas, it's about doing things that have been heretofore impossible. I agree that deep nets are overkill for lots of data analysis problems, but the AI boom is not about existing data analysis problems.
- the8472 10y ago> we already are doing everything we can and ever will do with pipes. I'm pretty sure industrial engineers would disagree here. The romans certainly didn't clean their lead pipes with plasma and neither did they coat them with some fancy nano materials to reduce stickyness.
- throwaway729 10y ago> I agree that deep nets are overkill for lots of data analysis problems, but the AI boom is not about existing data analysis problems. This hits the nail on the head for me. The author's observations in the first 9/10 of the article could all be perfectly valid, but the conclusions he draws I. The last two don't follow for exactly this reason.
- taeric 10y agoIf there is a curse of our industry, it is almost willful ignorance of just how hard the physical engineering fields are. The simple things with pipes are simple. Yes. However, to think we haven't made advances, or have no more to make, is borderline insulting to mechanical engineers and plumbers. Ironically, deep learning will likely help lead to some of those advances.
- shmageggy 10y ago> However, to think we haven't made advances, or have no more to make Not what I was saying at all. My point was that pipes are used to transport something from point A to point B, and that regardless of what advances we make, they are still going to be used for that purpose, and that this is unlike the situation with AI.
- albertTJames 10y agohttp://www.dafont.com/forum/attach/orig/2/6/263344.jpg http://www.dafont.com/forum/attach/orig/2/6/263344.jpg
- AWildDHHAppears 10y agoThe "Big Data" boom--specifically the ability to monetize it--may bust, but I think computer vision and continuous speech recognition has truly made jumps in ability over the last decade and is here to stay.
- januscap 10y agoI think the article and the discussions are focusing on the wrong semantic definition of AI. Unsupervised learning is where the revolution is. Learning has nothing to do with boom or bust.
- januscap 10y agoI think the article and the discussions are focusing on the wrong semantic definition of AI. Unsupervised learning is where the revolution is. Learning has nothing to do with boom or bust.
- tootie 10y agoMy company has hitched itself to the AI hype train, but they're talking about NLP and conversational UIs not data analysis.
- RushAndAPush 10y agoI've read every comment in this thread and its filled mostly with peoples self congratulatory intellectual views. Nobody, not even Robin Hansen himself has given a good, detailed argument as to why the current progress in Machine learning will stop.
- bunderbunder 10y agoI doubt you'll get that, because nobody thinks that progress in machine learning will stop. An AI winter doesn't mean that progress stops. It means that businesses and the general public become disillusioned by AI's or ML's failure to live up to the popular hype, and stop throwing so much money at it. The hype then dies down. Research continues, though, until enough progress is made that machine learning starts to produce results that excite the public again, and the cycle goes into another hype phase.
- goatlover 10y agoWhy, as presumably rational agents, is there so much hype with certain technologies, even though past history should have taught us otherwise? Is it all for the VC? Is it the media just needing to sell stories? Usually there are smart, knowledgeable people involved in the hyping. They should know better.
- BeingIncubated 10y agoThere is no such thing as complete rational people, every person goes through bouts of rationality and irrationality, some are just more often rational than others. With that said nobody is immune from irrational emotion sometimes, for example have you been angry from something that wasn't worth it?
- goatlover 10y agoHell yeah I have been. And then realized it was stupid later.
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- blazespin 10y agoMl comes in big in cleaning up the data and making recommendations on what to look at.
- intrasight 10y agoFirst, there is no "AI boom" because there is no AI - there is machine learning. Second, booms that produce real, tangible results don't normally go bust. Finally, we've only just scratched the surface of what machine learning is capable of delivering, so no bust is to be expected.
- goatlover 10y agoThere is no AGI, or strong AI. There is weak or narrow AI. AI simply means that if a human performed the task, it would be considered intelligent. So Deep Blue was chess AI, but it wasn't remotely AGI. Same with Watson and every application of AI to date. In a sense, Artificial Intelligence is the encoding of human intelligence for some task or problem domain in machines.
- ma2rten 10y agoI think this field is suffering from some confusion of terminology. In my mind there are three subfields that are crystallizing that each have different goals and thus different methods. The first one is Data Science. More and more businesses store their data electronically. Data Scientists aim to analyze this data to derive insights from it. Machine Learning is one of the tools in their tool belt, however often they prefer models that are understandable and not a black box. Sometimes they prefer statistics because it tells you if your insights are significant. The second one is Machine Learning Engineering. ML Engineers are Software Engineers that use Machine Learning to build products. They might work on spam detection, recommendation engines or news feeds. They care about building products that scale and are reliable. They will run A/B tests to see how metrics are impacted. They might use Deep Learning, but they will weight the pros and cons against other methods. Then there are AI Researchers. Their goal is to push the boundaries of what computers can do. They might work on letting computers recognize images, understand speech and translate languages. Their method of choice is often Deep Learning because it has unlocked a lot of new applications. I feel like this post is essentially someone from the first group criticizing the last group, saying their methods are not applicable to him. That is expected.
- sidlls 10y agoIt's also suffering from hype. And the criticism you note isn't one-directional in the field at large. I'm finding that ML/AI researchers deriding ML/Data engineers and "scientists" as not doing "real" ML or AI is becoming a thing, similar to how some computer scientists deride engineering as not doing real computing.
- WhitneyLand 10y agoI hate this. Remember they did it to Michael Faraday in 1813, deriding him as not a real scientist as he was about to play a part in advancing one of the greatest challenges of mankind (unification).
- logicallee 10y agoIt is not suffering from hype. There is too little hype. People are vastly underestimating what is about to happen. See my comments here: https://news.ycombinator.com/item?id=13079598 https://news.ycombinator.com/item?id=13079598 under our recent article " Artificial Intelligence Generates Christmas Song". Basically, if there is no pixie dust that makes humans intelligent, and instead it is a matter of the architecture of the brain and the first few years of supervised sensory input, then neural net breakthroughs (which use a similar architecture/topology) have the potential at any moment in time to break through and match general human intelligence. What I mean is that if someone sent back source code from 80 years from now, but we had to run it on a bunch of Amazon / Google servers in a server farm, we're pretty much guaranteed to have enough computing power to do so! (This is a combination of the number of neurons, their number of connections, and their very slow speed.) We have the hardware. Now: we do not actually have the source code from 80 years from now that we can go ahead and run on those machines. So, we're at like heavier-than-air travel right before the Wright Brothers flew at Kittie Hawk. Except we have like jet engines already -- just no way to design them into something that flies. I think that AI is vastly underrated and I watch with incredible interest every single breakthrough. For the second category above, ML built into products or engineering solutions, Alphago surprised me, because Go had an intractibly large possibility space, it's not in any sense subject to brute-forcing or exhaustive search. Dragon's Natural dictation surprised me in that using its model its able to get basically perfect dictation. I've never worked as a transcriptionist, but a small search reveals it has basically annihilated the industry of medical transcription. These are not the big, general breakthrough. But the big, general breakthrough is right there, somewhere. The results researchers are coming up with are astounding, and they're doing it in many cases with neural nets, quite similar to the wiring of the human mind. The hype is faaaaaaaaaaaaaaar less than warranted for the stage that we're at. At any moment someone can put together something that achieves higher-level intelligence and can be set loose upon the world corpus of culture. True, there are no clear indications that this is about to happen (For example: people do not extract innate language algorithms from the human genome which encodes them), so we are not exactly taking many steps that we could be trying to. As far as I know we're not even genetically engineering people to see what different parts of DNA do -- which is obviously a very, very good thing, and who would allow anyone to bear to term a child made as an experiment to see what DNA does. But despite not going from a human starting point, the results that we are achieving in many cases match and surpass human ability - while we do know that in many cases some of their architecture is similar. I feel quite strongly that we have more than enough hardware for general intelligence - and I see advances every day that could end up going past the ppint of no return on it. -- EDIT: got a downvote, but I would prefer a discussion if you think I'm wrong.
- RandyRanderson 10y agoI'm old enough to have seen a lot of these boom/hype/bust cycles. I'm convinced that this time is, in fact, different. To temper this I believe most decent user-visible changes will take ~5 years (as most actually useful software does) but the changes will be huge: * The author cites computer driven cars. I think this will take place mostly on long-haul highway trucking instead of in cities first. Even so, this could mean a massive swath of truckers without work in a short 5yr epoch. * We've already seem the effects of heavy astro-turfing/disingenuous information/etc in the last US election. This certainly changed the "national psyche" and may have changed the election outcome. There is heavy ML research going into making the agglomeration of ads and content almost compulsively watchable. Our monkey brains can likely only handle a few simple dimensions and only boolean or maybe linear relations and they certainly get trapped in local maxima/minima. Even trivial ML techniques can bring this compulsion from say 50% effectiveness to 95%+ (by some reasonable measure). Imagine a web that is so completely tailored to the user such that search results, ads and content is completely tailored to you. Verbs, adjectives entire copy all written to get you to that next click. This is different. * Bots that seem like real ppl will be rampant. Are those 100 followers/likes/retweets actual ppl? Even years ago reddit (to gain popularity) faked users. Certainly this has only accelerated and will continue to as commercial and state actors see value to moving public opinion with these virtual actors. (ironically maybe only bots will have read this far?) * Financial Product innovation - Few ppl actually understand this market (even within the banks) however the deals are usually in the 100+ million range. The products take advantage of tax incentives, fx, swaps, interest rates, etc in an ever increasing complexity. These divisions are still some of the most profitable parts of banks. It's likely that on deals where profits are measured in tens of millions on a single deal (several are made per quarter, per major bank). It's likely that ML algos will be put to use here as well not only optimizing current products but in current prod elaborations. I beleive these products to be a major source of inflation. Whereas the official numbers are ~2% I believe the actual inflation (tm) felt by most is more in the 7%+ range. * State Surveillance and Actions - I hear ppl saying that mass surveillance hasn't been effective in stopping "terrorism", as if it would be ok if it did. Well, it will be effective and it will get very, very good at it. Of course terrorism is not defined anywhere so ... * Customer Support - this, like transportation, is a major employer of unqualified workers. I believe in 10 years there will be maybe 1% of the current workforce in CSR work. The technology is here the software just has to be written. It's not just the number of jobs displaced it's the velocity. If we look to the effective Predator-prey modeling: https://en.wikipedia.org/wiki/Lotka%E2%80%93Volterra_equations https://en.wikipedia.org/wiki/Lotka%E2%80%93Volterra_equatio... We see that the generally the solution takes 2 modes: * stability - wolf/rabbit populations wax and wane together * crash - the wolves kill enough rabbits to make the remaining pop crash Now I don't believe there will be a 'crash' but likely there will be a new normal (equilibrium) and getting there will not be pleasant. Disclaimer: Yes, I do work in ML.
- DelTaco 10y agoI would argue that it would often be easier to implement a ML solution on regular data than try and clean the data and then use linear regression
- mark_l_watson 10y agoI think the boom in AI jobs will go bust, when tools for data cleaning and injection, and for automatically building models will get so good that experts will no longer be required to use them. I have so frequently set up customers with procedures and code for ML, that I have seriously thought of writing a system to replace people like myself. Until there is real AGI however, there will be jobs for high end AI researchers and developers.
- Animats 10y agoHard to say. I'm seeing too many billboards near SF for "big data" and "machine learning". AI-type grinding on your stored business data is sometimes useful, but not always profitable. Everybody big already has good ad targeting technology, after all.
- vonnik 10y ago[Disclosure: I work for a deep-learning company.] Robin's post reveals a couple fundamental misunderstandings. While he may be correct that, for now, many small firms should apply linear regression rather than deep learning to their limited datasets, he is wrong in his prediction of an AI bust. If it happens, it will not be for the reasons he cites. He is skeptical that deep learning and other forms of advanced AI 1) will be applicable to smaller and smaller datasets, and that 2) they will become easier to use. And yet some great research is being done that will prove him wrong on his first point. https://arxiv.org/abs/1605.06065 https://arxiv.org/abs/1605.06065 https://arxiv.org/abs/1606.04080 https://arxiv.org/abs/1606.04080 One-shot learning, or learning from a few examples, is a field where we're making rapid progress, which means that in the near future, we'll obtain much higher accuracy on smaller datasets. So the immense performance gains we've seen by applying deep learning to big data will someday extend to smaller data as well. Secondly, Robin is skeptical that deep learning will be a tool most firms can adopt, given the lack of specialists. For now, that talent is scarce and salaries are high. But this is a problem that job markets know how to fix. The data science academies popping up in San Francisco exist for a reason: to satisfy that demand. And to go one step further, the history of technology suggests that we find ways to wrap powerful technology in usable packages for less technical people. AI is going to be just one component that fits into a larger data stack, infusing products invisibly until we don't even think about it. And fwiw, his phrase "deep machine learning" isn't a thing. Nobody says that, because it's redundant. All deep learning is a subset of machine learning.
- ma2rten 10y agoThis is somewhat tangental, but what do you think about Ladder Networks? (https://arxiv.org/abs/1507.02672 https://arxiv.org/abs/1507.02672, https://arxiv.org/abs/1511.06430 https://arxiv.org/abs/1511.06430) I was really impressed by their results, but I haven't seen it being applied successfully to other applications.
- jostmey 10y agoDisclaimer, I haven't looked at their work to closely. I think the process they use to select the hyperparameters overfits on the labels in the validation set. The true size of the labelled data includes the 100 from the training set and all labels in the validation set.
- WhitneyLand 10y agoThis article is tries to be right about something big, by arguing about things that are small and that do not necessarily prove the thesis. Notice now you can cogently disagree with the main idea while agreeing with most of the sub points (paraphrasing below): 1) Most impactful point: The economic impact innovations in AI/machine learning will have over the next ~2 decades are being overestimated. DISAGREE 2) Subpoint : Overhyped (fashion-induced) tech causes companies to waste time and money. AGREE (well, yes, but does anyone not know this?) 3) Subpoint: Most firms that want AI/ML really just need linear regression on cleaned-up data. PROBABLY (but this doesn't prove or even support (1)) 4) Subpoint: Obstacles limit applications (though incompetence) AGREE (but it's irrelevant to (1), and also a pretty old conjecture.) 5) Subpoint: It's not true that 47 percent of total US employment is at risk .. to computerisation .. perhaps over the next decade or two. PROBABLY (that this number/timeframe is optimistic means very little. one decade after the Internet many people said it hadn't upended industry as predicted. whether it took 10, 20, or 30 years, the important fact is that the revolution happened.) It would be interesting to know if those who are agree in the comments agree with the sensational headline or point 1, or the more obvious and less consequential points 2-5.
- jbrambleDC 10y agoanother point is that Linear Regression IS Machine/statistical Learning. Sure its been around for more than 100 years before computation, but regression algorithms are learning algorithms. Arguing for more linear regression to solve a firms problems, is equivalent to arguing for machine learning. Now, if instead he wanted to argue that the vast majority of a businesses prediction problems can be solved by simple algorithms, that is most likely true. but economic impact of this is still a part of the economic impact of machine learning.
- notahacker 10y agoIf we're classing linear regression as machine learning and agreeing it's a representative example of the type of simple algorithm that's most likely to benefit firms, I think it probably helps his point rather than harming it. It's a technique that's been around for ages, is far from arcane knowledge and every business has had the computing capability to run useful linear regressions on various not-particularly-huge datasets in a user-friendly GUI app for at least a couple of decades now. For the most part they haven't run those regressions at all, and where they have, they haven't been awe-inspiringly successful in their predictions, never mind so successful the models are supplanting the research of their knowledge-workers.
- skywhopper 10y agoI think what we're seeing is an explosion of new approaches to computerized problem solving made possible by huge amounts of data and enormous computing resources. A lot of what has become possible in the last couple of decades is indeed new, but the apparent rapid advance is really just a matter of applying the brute force of a massively upgraded ability to process huge quantities of data in parallel, and this has led us to make erroneous assumptions about future progress in these areas. Basically, these are new solutions to new problems, and we're rapidly seeing the easy 80% of this new generation of "AI" happen and it seems magical. But soon enough we'll hit the wall where further progress becomes harder and harder and brute force approaches are no longer sufficient to achieve interesting results.
- Phait 10y agoI understand that most people working with deep learning wouldn't want this type of thinking to spread amongst the public, and I surely don't want it either. But you have to be totally unaware of reality to think that DL is the definitive tool for AI. Most impressive results in DL in the past 2 years happended like this: >deepmind steals people from the top ML research teams in univerisites around the world >these people are given an incredible amount of money to solve an incredibly complex task >a 6000 layers deep network is run for 6 months on a GPU cluster the size of Texas >Google drops in their marketing team >media says Google solved the AI problem >repeat every 6 months to keep the company hot and keep the people flow constant >get accepted at every conference on earth because you're deepmind (seriously, have you seen the crap that they get to present at NIPS and ICML? The ddqn paper is literally a single line modification to another paper's algorithm, while we plebeians have to struggle like hell to get the originality points) I'll be impressed when they solve Pacman on a Raspberry Pi, otherwise they are simply grownups playing with very expensive toys. Deep learning is cool, I truly believe that, and I love working with neural networks, but anyone with a base knowledge of ML knows better than to praise it as the saviour of AI research. Rant over, I'm gonna go check how my autoencoder is learning now ;)
- madgar 10y ago> I'll be impressed when they solve Pacman on a Raspberry Pi, otherwise they are simply grownups playing with very expensive toys. I'm pretty sure "grownups playing with very expensive toys" accurately characterizes >100,000 software employees in the US right now.
- dwaltrip 10y agoI can't speak of their other work, but as someone who plays go and follows go bots, the AlphaGo project was a phenomenal piece of work that shouldn't be discounted. Go is a very difficult game that requires great intuition and a deep understanding of the patterns. Sure, maybe their timing was fortunate and it might have happened a year later anyways. But this is definitely not a guarantee, and they were still the ones to do it. P.S. For anyone who is interested, another very strong bot is out in the wild now. It has been playing on the KGS go server the past week under the name Zen19L, and has a rank of 9d. Some great games have resulted as people challenge it.
- Houshalter 10y agoRobotics and automation have been improving for a long time, and especially recently. Look at the rise in consumer drones, enabled by improvements in batteries, sensors, and computers. But the main thing holding them back is a lack of AI. Robots can do a rote action over and over again, but they have a hard time identifying where objects are, planning, and reacting to their environment. Just solving machine vision would be a massive step forward and enable a ton of applications. And that has sort of already happened. The best nets are already exceeding humans at vision tasks. They are learning to play video games at expert level, which is not conceptually distant from robot control. Its taking time to move this research out of the lab and into real applications, but it is happening. And so I totally believe that at least 50% of current jobs could be automated in 10 to 15 years. How many people are employed doing relatively simple, repetitive tasks, over and over again? Me and most people I know have jobs like that.
- siliconc0w 10y agoPretty much every large firm has multiple problems ML can solve better than linear/logistic regression. Smaller firms may still have one or two. In some industries the core competency will be how good your ML model is as everything else becomes a commodity. There are new advents that make ML better for small data-sets as well as opportunities for data-brokerage to increase access to data. And these are just current applications, new applications are still nascent (i.e self driving cars). Treating ML as a software problem instead of a science project - with a pipeline of adding/creating data, cleaning, modeling, analyzing, learning, and iterating is also incredibly important but it's not like most companies are doing this particularly well either.
- randcraw 10y agoAfter a good look behind the curtain of Deep Learning, I've come to agree with Robin. No, Deep Learning will not fail. But it will fail to live up to its promise to revolutionize AI, and it won't replace statistics or GOFAI in many tasks that require intelligence. Yes, DL has proven itself to perform (most?) gradient-based tasks better than any other algorithm. It maximizes the value in large data, minimizing error brilliantly. But ask it to address a single feature not present in the zillion images in ImageNet, and it's lost. (E.g. Where is the person in the image looking? To the left? The right? No DN using labels from ImageNet could say.) This is classic AI brittleness. With all the hoolpa surrounding DL's successes at single task challenges (mostly on images), we've failed to notice that nothing has really changed in AI. The info available from raw data remains as thin as ever. I think soon we'll all see that even ginormous quantities of thinly labeled supervised data can take your AI agent only so far -- a truly useful AI agent will need info that isn't present in all the labeled images on the planet. In the end the agent still needs a rich internal model of the world that it can further enrich with curated data (teaching) to master each new task or transfer the skill to a related domain. And to do that, it needs the ability to infer cause and effect, and explore possible worlds. Without that, any big-data-trained AI will always remain a one trick pony. Alas, Deep Learning (alone) can't fill that void. The relevant information and inferential capability needed to apply it to solve new problems and variations on them -- these skills just aren't present in the nets or the big data available to train them to high levels of broad competence. To create a mind capable of performing multiple diverse tasks, like the kinds a robot needs in order to repair a broken toaster, I think we'll all soon realize that DL has not replaced GOFAI at all. A truly useful intelligent agent still must learn hierarchies of concepts and use logic, if it's to do more than play board games.
- marcoperaza 10y agoUntil the taboo on talking about consciousness is broken and we seek to understand what role this incredible phenomenon plays in human cognition, there will be no progress towards the holy grail: true general purpose AI. That is my falsifiable prediction.
- Entangled 10y agoIt won't. It will get better. In a couple of years you will be able to take a picture of a rash in your arm and get exact diagnosis with treatment instructions. You will be able to take a pic of a flower, a leaf or any tree and get accurate info about its species, plagues and best techniques for growing them. You will be able to take a pic of any insect, spider, snake, animal, or anything that moves, any mineral, element or anything at all and get accurate info about that. AI is not only about robots thinking, it is about collecting information and making it available on demand. Agriculture and health will be the first beneficiaries, finances in a close second. Data mining is where the first stage of AI is, and Google is moving ahead of everybody else with their search engine, maps, translation, and all the information collecting tools. Once you have enough data, knowledge is just a couple of programs away.
- IshKebab 10y agoThat is a valid use of deep learning. I think the article is talking about people thinking that because deep learning is very impressive for some things, they should use it for everything - even problems where a much simpler solution works fine, or problems where deep learning doesn't apply. I work for a consumer product company, and there are often people talking about using 'big data' and 'machine learning'. They're just following the hype; they don't really know what machine learning is and I've only heard two or three potential applications of it mentioned that make any kind of sense.
- master_yoda_1 10y agoBy the way this earth will be bust in millian years, so what. Please stop wasting your time on these idiotic blog post and do something good for society. To HN: Please stop encouraging these kind of idiotic blog post there are better content on internet to rank higher on HN.
- sctb 10y agoPlease don't post empty generic dismissals on Hacker News. They don't contain any information, and that's what we're all here for.
- ntsclay 10y agoThe hacker
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- zamalek 10y agoOne of two eventualities exist: * The article is correct and the current singularity (as described by Kurzweil) will hit a plateau. No further progress will be made and we'll have machines that are forever dumber than humans. * The singularity will continue up until SAI. So help them human race if we shackle it with human ideologies and ignorance. There is no way to tell. AlphaGo immensely surprised me - from my perspective the singularity is happening, but there is no telling just how far it can go. AlphaGo changed my perspective of Kurzweil from a lunatic to someone who might actually have a point. Where the line is drawn is "goal-less AI," possibly the most important step toward SAI. Currently, all AI is governed by a goal (be it a goal or a fitness function). The recent development regarding Starcraft and ML is ripe for the picking, either the AI wins or not - a fantastic fitness function. The question is, how would we apply it to something like Skyrim: where mere continuation of existence and prosperity are equally as viable goals (as-per the human race). "Getting food" may become a local minimum that obscures any further progress - resulting in monkey agents in the game (assuming the AI optimizes for the food minimum). In a word, what we are really questioning is: sapience. I'm a big critic of Bitcoin, yet so far I am still wrong. The same principle might apply here. It's simply too early to tell.
- mmkx 10y agoIt's already happening. The world became hyper efficient. The hackers behind "Trumpbots" made money on the prediction markets. Understanding that mechanic makes me almost certain the singularity arrived after his election.
- inimino 10y ago...what?
- lmcinnes 10y agoI think you are making a false dichotomy here. It is perfectly possible for the article to be right, and there is still a future with general artificial intelligence and a singularity. If you believe the singularity is inevitable then you should read the article as saying that we are woefully misjudging where the asymptote is -- yes current progress looks impressive, but there are some really big steps that we are currently ignoring, and real human level AI is a century or two out, not a decade or two out. That's perfectly possible, and most philosophers who work in consciousness and philosophy of mind (as well as a very large portion of the machine learning community) will tell you that there is still some big hurdles that we don't even have the faintest idea how to cross (the whitehouse report on AI described it as a "chasm").
- erikj 10y agoWe already had the AI winter before. The worst thing about it is the death of Symbolics. The latest AI resurgence, unfortunately, created nothing comparable to the legendary Lisp machines.
- antirez 10y agoRecent AI progresses are a clear technological advancement. To meter them in terms of bay area biZZness meters is lame.
- felippee 10y agoThere is a never ending confusion caused by the term "AI" to begin with. Term coined by John McCarthy to raise money in the 60's is really good at driving imagination, yet at the same time causes hype and over-expecations. This field is notorious for its hype-bust cycles and I don't see any reason why this time would be different. There are obviously applications and advancements no doubt about it, but the question is do those justify the level of excitement, and the answer is probably "no". When people hear AI they inevitably think "sentient robots". This will likely not happen within the next 2-3 hype cycles and certainly not in this one. Check out this blog for a hype-free, reasonable evaluation of the current AI: http://blog.piekniewski.info/2016/11/17/myths-and-facts-about-ai/ http://blog.piekniewski.info/2016/11/17/myths-and-facts-abou...
- dmfdmf 10y agoThanks. This is why I love HN for finds like this, after poking around a bit this site looks like a really good blog that doesn't hype AI nor deny it which is congruent with my views.
- kodisha 10y agoSo, imagine that in 2011, 5 years ago, you approach some VC and say: "Hey, we are building this VR hardware and games for it, we would need ~1M to finish it". I think that there is high chance that you would get some weird looks, and possibly few remarks how that is a "dead technology, tried once, and obviously failed". And then, fast forward couple of years there is whole industry around VR, jobs, hardware, software, the whole eco system. You only need one strong player in a field, and suddenly everyone and your neighbour kid is doing it.
- mwfunk 10y agoI'm very curious to what degree there even is an AI boom right now, vs. AI and machine learning going through a phase as the buzzwords du jour used in corporate PR. People have doing all sorts of fascinating things with machine learning for decades, and (for example) Google has been arguably an AI-focused company from day one. In the tech press recently, I keep hearing how every huge tech company needs to have some sort of AI strategy going forward, so they don't miss out on an industrywide windfall, or even become irrelevant because they didn't hop on the AI bandwagon. I suspect that there are a few more people working in AI nowadays than we're doing so 10 years ago, but that quite a bit of the narrative surrounding AI in the press is some combination of corporate marketing and journalists eager to have something to write about. I'm not saying AI isn't important, rather that it's an important field that's only a little more important than it already was 10 years ago. The difference seems to be how often it pops up in PR and tech journalism vs. 10 years ago. Just a theory of course; I would love to know what the reality is.
- sprobertson 10y agoIt does seem largely a media boom, maybe somewhat due to the recent generative models that are much more interesting to include in an article.
- randcraw 10y agoI work at a large pharma, and from what I've seen in this business and anywhere else where data is important (like R&D), deep learning is perceived to be revolutionary. I think most of the rise in DL's corporate mindshare is the potential it has for disruption of the status quo. Business folks hate game changing tech. It destabilizes the priorities and practices they understand, and dishevels the grand hierarchy of corporate command and control they know and love. If DL really does reinvent any significant fraction of their part of the business, they know 1) they won't know what to do in response, and 2) their daily competition is going to get even more challenging until a new status quo emerges. And both of which may cause them to lose their tenuous position/status in the company hierarchy, much less, lose their very job.
- brudgers 10y agoMost firms that think they want advanced AI/ML really just need linear regression That's how AI always looks in the rearview mirror. Like a trivial part of today's furniture. Pointing a phone at a random person on the street and getting their identity is already in the realm of "just machine learning" and my phone recognizing faces is simply "that's how phones work, duh" ordinary. When I first started reading Hacker News a handful of years ago, one of the hot topics was computer vision at the level of industrial applications like assembly lines. Today, my face unlocks the phone in my pocket...and, statistically, yours does not. AI is just what we call the cutting edge. Open the first edition of Artificial Intelligence: A Modern Approach and there's a fair bit of effort to apply linear regression selectively in order to be computationally feasible. That just linear regression is just linear regression these days because my laptop only has 1.6 teraflops of GPU and that's measley compared to what $20k would buy. The way in which AI booms go bust is that after a few years everybody accepts that computers can beat humans at checkers. The next boom ends and everybody accepts that computers can beat humans at chess. After this one, it will be Go and when that happens computers will still be better at checkers and chess too.
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- kgwgk 10y ago> Open the first edition of Artificial Intelligence: A Modern Approach and there's a fair bit of effort to apply linear regression selectively in order to be computationally feasible. Does the book mention linear regression at all? The term doesn't appear in the index.
- unignorant 10y agoAlong similar lines, we did some work investigating public perception of AI over the past thirty years: https://arxiv.org/pdf/1609.04904.pdf https://arxiv.org/pdf/1609.04904.pdf From Figure 1, it's clear we are now in a boom.
- otoburb 10y agoThanks for the study - Figure 1 is impressively self-explanatory. It's interesting to note that the article's author Robin Hanson[1] worked in AI & ML industry roles starting at the peak of the last AI boom in '84 and witnessed the AI winter through to '93.
- eva1984 10y ago>> Good CS expert says: Most firms that think they want advanced AI/ML really just need linear regression on cleaned-up data Not nearly true. The simple counter-argument is that prior to DL, we don't have good approach to really 'clean' data like images. The author states this fact as if cleaning data is a piece of cake. No, it is surely not. In fact, part of the DL's magic trick is the ability to automatically learn to generalize useful features from data. From another perspective, the whole DL frontend, prior the very last layer, can be viewed as a data cleaning pipeline, which is learnt during the training process, optimized to pick the useful signals. The author clearly isn't an expert on the matters he trying to put claims on. Yet his statement comes with such big confidence or ignorance. This shows why this revolution will be a truly impactful one, for even some of the claimed intellectuals cannot understand its importance and divergence of its predecessors. They will be caught off-guard then left behind. It would be very enjoyable to watch what their reaction would be once it happens.
- pmrd 10y agoAlso - must remember that AI is a programmer on the sidelines letting data do the logic. It required a fairly different frame of mind than traditional programming
- randcraw 10y agoSo data as employed by DL plays the same role that it does in Brooks' subsumption architecture -- grounding and shaping the knowledge model -- but now doing it emergently, albeit requiring a lot of parameter tuning from the human-in-the-loop. An interesting prospect for the evolution of software developers.
- muyuu 10y agoThe immense availability of financial instruments and VC makes investment in any mildly promising technology overshoot. There's nothing really mysterious about this, especially after the dot com bubble. Is the web useless? no, but there's a limit to the number of players doing the same things the same way successfully - because that's what hype does, make people focus in not just a technology but a particular way. 3d-printing, mobile apps, tablet devices, VR, cryptocurrency, 3D TV, Neural Networks (in the 70s, then again in the mid 80s, then in the late 90s, and now), you name it. These are perfectly applicable technologies that may or may not warrant the swings in investment they attract, but they are valid and defensible nonetheless. They may also be monetisable - not always useful stuff brings proportional profit in the market. And of course, there's also timing. The exact same idea may work years later in a different environment. But the only way to find out is to try, and maybe try too much at some point.
- euske 10y agoI have a hard time understanding why even technical people use the term "AI" today. Its use should be limited to sensational media and cheesy sci-fi. It's roughly equivalent to saying "computery thingamabob". I would call a pocket calculator an AI too. Why not? It carries out certain mental tasks better than our brains do.
- throwaway729 10y agoWorth noting that that first AI program could run on a not-so-modern pocket calculator these days: https://en.wikipedia.org/wiki/Logic_Theorist https://en.wikipedia.org/wiki/Logic_Theorist The fact that AI is now a meaningless term is mostly a testament to the success of AI.
- randcraw 10y agoI think "AI" does still have meaning, in that the goal of AGI hasn't gone away. Yes, any goal that's tangential to AGI probably shouldn't be called AI. But as long as the constituent tasks needed by AI (vision, learning, speech in/out, etc) continue to improve rapidly, especially due to advances in a single technology like DL, it's inevitable and IMO appropriate that the umbrella moniker used to describe DL and its impacts remains "AI".
- itissid 10y agoThomson Reuter's ET&O and Risk division laid off 2000 people recently to fund a new center near university of Waterloo to provide "answers"(Its their mission statement) by recruiting people to provide deep learning solutions. NY suffered deep cuts. The sad part is it seems the leadership just want to use deep learning as a way to justify doing what they are doing, which is "starting from scratch"
- sjg007 10y agoNo. Image and audio recognition in many specific cases are now solved problems. This is substantial progress.
- srinikoganti 10y agoWhat is "new this time" is that computers/machines can see and hear and even speak, whether we call it AI or "Deep Learning" or "Machine learning". So there is going to be more impact from Vision/Voice based applications rather than data analytics/predictions. Eg. Self Driving Cars, Medical Diagnosis, Video Analytics, Autonomous machines/Robotics, Voiced based interactions. All of these combined could be as disruptive as Internet itself.
- AndrewKemendo 10y agoI'm sorry but I'm not buying it. ML companies are already tackling tasks which have major cost implications: https://deepmind.com/blog/deepmind-ai-reduces-google-data-centre-cooling-bill-40/ https://deepmind.com/blog/deepmind-ai-reduces-google-data-ce... http://med.stanford.edu/news/all-news/2016/08/computers-trounce-pathologists-in-predicting-lung-cancer-severity.html http://med.stanford.edu/news/all-news/2016/08/computers-trou... Those are just the two I had off the top of my head. We apply ML tasks for object/scene classification and they blow away humans. Not only that we're already structuring a GAN for "procedural" 3D model generation - in theory this will decimate the manual 3D reconstruction process.
- danielrm26 10y agoI think a key differentiation between ML and more common statistics is that ML is designed to improve itself based on data. Statistical methods don't do that. So maybe they're trying to do very similar things in a lot of cases, but self improvement is a major differentiator.
- rsimons 10y agoI recently made an appointment through an AI secretary to set up a meeting with them; it worked surprisingly well.. They are not hiring a secretary any time soon. Real effect. Also: https://www.theguardian.com/commentisfree/2016/dec/01/stephen-hawking-dangerous-time-planet-inequality https://www.theguardian.com/commentisfree/2016/dec/01/stephe...
- Noseshine 10y ago"Ai secretary" in what respect? Natural language processing? Because simply making an entry into a calendar is just a 1980s' level computer programming problem at best, so I think your example needs some clarification/elaboration. Whether something is "AI" also depends on the presence of learning (the stuff you do with Windows voice recognition to set it up is not "learning/teaching" in the AI sense). Most of the things touted as "I" (intelligence) have all the intelligence in the programming effort, but none in the actual program (apart from what was put in).
- willsher 10y agoIt seems the nature of this and VR that they come to boom for a while and then bust, having stagnated. They then wait for the next alignment of underpinning technology, knowledge and culture to emerge again. Last last one I'm aware of was mid-late 1990s, where VRML was gaining traction and new ways of thinking about AI were emerging. IBM Watson or similar (if I recall IBM was still calling their business AI system Watson back then) seems to be promenant in these two booms and both times the results it gives haven't matches it's marketing hype. The technology, having been significantly furthered fades into the day to day of computing somewhat until the next boom that drives more short burst innovation and awareness. Conscious AI and realistic VR is some way off, if we ever see it. Culturally and ethically we are not ready to answer the questions it poses and the cyclical nature gives us more time to digest the latest raft of questions in light of the progress.
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