IBM is not doing "cognitive computing" with Watson
rogerschank.com
rogerschank.com
I won't argue that the PR goes often too far, and that's a big debate we have internally (I work for Watson). But it's a pity that most of the negative opinions expressed here come from people who haven't even bothered to try any of the services we put out there or read any of the scientific papers that have been published by our team.
To me, the resentment is completely understandable. When Watson goes on Jeopardy -- an incredible technical and PR achievement -- the implication to the rest of the world is that ".... and now you can use it too!" The ads, too, convey that idea. But that isn't the case. Instead, I can access APIs for language translation, sentiment analysis, object recognition, etc...
You guys are selling Watson as a new paradigm of AI never before seen by man -- that I can plug my business and my app into this and do things never-before conceived in the history of computing. The service you let me access is basically a bunch of algorithms for which there are countless alternatives -- even in places like Algorithmia. There's a huge disconnect.
Many of the services that are on the platform can do things that were inconceivable just 5 years ago (like having a general visual classifier handle earth imagery with high enough accuracy to be useful http://www.huffingtonpost.com/entry/ibm-watson-california-wa...). Yes, there are alternatives - which is the sign of a healthy market - but we keep improving ours to stay state-of-the-art and to introduce higher level functions.
This should be priority number one. As others ITT have been saying, your marketing and PR departments have been writing checks that the product can't cash.
When people see that Watson can't do what it does on TV, you lose a sale, and that potential customer is now going to be skeptical of IBM's future marketing claims.
It doesn't matter if "answering factoid questions" doesn't have much real-world value — it's first of all what you're marketing the product as, so it should have been done and omigod amazing yesterday, and second of all the reason why your very smart marketing and PR people are pushing that angle is because it's a demonstration that is immediately and intuitively impressive to technical and non-technical people alike.
Getting a computer to "answer factoid questions" viscerally screams "major breakthrough!" in a way that "general visual classification of earth imagery" simply doesn't. It wows people at the sales meeting instead of boring them.
IBM needs to make sales. You need to build up a userbase toot sweet before Google et al start moving into this sector in a major way, as indeed they already are. There's a limited pool of enterprise customers, they don't change vendors often, and you need to get as many as you can as fast as you can, before Google/Amazon/whoever does, or you're done.
"Tout de suite" has never before sounded so appetizing!
[1] https://books.google.com/books?id=FFMADAAAQBAJ&pg=PA80&lpg=P...
It's the common spelling when used in English; I didn't want to seem pretentious. https://en.wiktionary.org/wiki/tout_de_suite#Alternative_for...
Besides, a really good AI should literally be able to sell itself, surely?
This, as a potential Watson customer, would give me a much better idea of Watson's potential and real world results. Plus it would establishes more confidence in IBM.
A lesson from my high school economics teacher that has always stuck with me is that you have to be your own product's biggest believer.
This isn't a matter of PR overhyping things a bit. Watson has (impressive) capabilities, and IBM's ads show Watson doing things, but the two circles essentially don't overlap. IBM is directly marketing Watson to the general public with promises that are entirely unsupported by the actual product. That's not just questionable business advertising, that's a level of inaccuracy that undermines AI work and understanding across the entire field.
Google, Facebook and MS also can't (try using Google translate in any other language combination than English and some other Romance or Germanic language). Why is that a problem for IBM specifically, or why will IBM's overpromising bring about the next AI winder, like Schank says?
Granted, Facebook Graph Search was horribly oversold, and Cortana is in the same "smart enough to be stupid" bucket as Siri and Alexa and every other vaguely-feminine chatbot on the market. Google Translate is incredibly fragile, and will get me from English to French but not from French to Arabic, or even from French to Quebecois.
But all of that is roughly consistent with their advertising. Obscenely selective demos have a long history in software, and it seems standard (though not good) to assume that you're seeing the best-case uses of whatever product is being advertised. Car ads don't work any differently.
Watson's advertising seems to take things to a whole different level. There's that Bob Dylan ad. They've got a North Face ad where Watson recommends an appropriate jacket for the Pacific Northwest in early spring. And so on. The commercial product Watson can't do those things. They aren't just showing off fragile best-case features, they're making stuff up, or hardcoding specific answers, or offering the North Face a different Watson than what you can get on their developer site.
That bothers me. It bothers me when Google claims Translate can live-interpret foreign street signs, too, but even that's a less wild claim than IBM is making. Promising people a product you simply haven't made isn't kosher, and that's especially true when you're selling AI kool-aid, which is a field that's already ripe with hysterical (and baseless) warnings and promises.
Agreed- but I'm saying that that's exactly what I get from ads of the products you mention, Siri, Alexa et al.
Case in point: that real-time Skype translation ad that made the rounds a while ago.
In any case, I don't pay that much attention to what is advertised. But I do pay attention to two things: on the one hand, (scientific) papers published by the various teams and on the other hand, the announcements those teams make in the technology press.
I've read most of the Watson papers and quite a few of the NLP stuff from Google and followed the assorted tech press announcements. I have to say that my impression is that Google is brazenly overhyping their stuff, they constantly try to fudge things as much as they can.
Their dependency parser beats previous efforts on the Brown corpus? It's the best natural language parser ever! Their image recognition beats imagenet? It's showing superhuman ability! And so on. IBM's advertisement is relatively tame in comparison.
All the other major players in this are equally unscrupulous and there's no point in singling anyone out.
I think I was probably put on edge by the Watson ads not just because of their scope (the Skype thing is similarly inaccurate) but because they conform to a very specific pattern of AI over-promising that I've grown to hate.
To a lot of nontechnical (or even just non-CS) people, strong AI is synonymous with Samantha from Her. We've been seeing people mistake existing tech for that since the 60s, when people were taking ELIZA seriously. Cue Cleverbot, Siri, and most obnoxiously Eugene Goostman (you don't get to claim you passed the Turing Test by convincing 30% of English speaking observers that you're a Ukranian child!)
So I look at the Watson ads and see exactly this pattern at work, but worse than ever. Most people know by now that Siri and Alexa are just voice assistants, and as good as the ads look no one is really claiming otherwise. The Watson ads, by contrast, throw around "cognitive computing" to imply real understanding, and then air scripted exchanges that are simply false, rather than selective. Worse, they're doing mass-market ads for a developer-only product, so viewers won't even try it out and see that it doesn't do what's being shown.
It may not be the worst on an overall-inaccuracy level, but I get twitchy when I hear people respond to discussions of strong AI with "Oh, basically what that Watson thing does?" And I do hear that, somewhat regularly. Crappy product hype is annoying, but I really wish we could stop telling people we've built real intelligence when we haven't.
Speech to text: https://developer.ibm.com/watson/blog/2016/04/28/recent-adva...
Emotion: https://developer.ibm.com/watson/blog/2016/02/29/another-ste...
That result makes a lot of sense - Google has been working with the huge corpus of English-European translations (e.g. UN reports) and getting impressive results largely because there's so much data available with such direct equivalences.
Just based on personal experimentation, they're much less capable with longer "translation distances" like phonogram<-->logogram and Indo-European<-->Sino-Tibetan. I didn't know IBM was leading on those long-distance translations, that's cool to hear.
Marketing aside, I must say that BlueMix's user interface is the worst. thing. ever. Buggy, extremely slow, fragmented, with cryptic error messages[1]. Took me 45+ minutes to reactivate my account (the old one was kindly deleted due to inactivity), and create the the TTS/STT services endpoints.
Funny; after all the investment to develop Watson, you'd think UI would be the easiest part to get it right.
On the positive side, the TTS and STT APIs are simply a pleasure to work with. The documentation is excellent, accuracy is pretty good, and the demos are spot on. Plus you have support for streaming audio through WebSocket for STT (which is a must for my project), and a few voices to chose from for TTS.
Thanks for the kind comments on our APIs. We are really trying hard to make them usable.
But I just tested again today, and the slowness is gone, so maybe it was an isolated incident. I was able to play with it a bit more, add services, create a few containers, etc. Definitely an improvement versus the classic interface.
I'm still getting used to the logic grouping and how to access your services (I'm coming from years of AWS / Google Cloud), but the ability to go back-and-forth quickly helps a lot.
Minor nitpick: any reason for API icon to be pink instead of blue, like all others? I keep looking at it as if it were a different state (e.g., "activated").
I work in academic research and learned long ago to ignore any PR related to AI/ML products. The ability to easily play with and test the abilities of Watson services is definitely helpful and enjoyable for us.
Most of my playing with it was focussed on the 'Explore' parts, not the 'Predict'/'Assemble'/'Social Media' functions. I was disappointed in the level of documentation for some stuff (you have to look quite hard to find a definition of 'summary' - on my data, it just sums the column, for example).
I was also disappointed that you can't fit distributions or access the distributions that Watson fits, and at the lack of statistics (you can't get at skewness, kurtosis and so on).
In general Watson's STT is slightly better than competition for long audio (like phone conversations), slightly worse for short utterances (like search queries). That's due to a bias in training set.
Hi. Do you happen to do any work with its parts that are written in Prolog?
I'm no fan of Watson-the-marketing-term, but this sounds like the bitter remarks of a symbolic AI defender who is so sure that their way of doing AI is the only way that anything else is fraud.
Watson-the-Jeopardy-winner did "cognition" (which he implies means following chains of logical reasoning) as well as any other system that has been built.
See for example "Structured data and inference in DeepQA" or "Fact-based question decomposition in DeepQA[2].
It's true that the the Watson image analysis services don't use this. I'm guessing that is because they don't actually work very well in that domain.
[1] http://ieeexplore.ieee.org/Xplore/defdeny.jsp?url=http%3A%2F...
[2] http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=6177...
He wasn't saying "Watson is crap", he was saying "Watson isn't doing cognition, stop saying it does"
Two points, though:
1) This is probably the most active area of research in machine learning/AI at the moment. The same type of progress that was made in image understanding 2 years ago using deep learning techniques is now being seen in NLP. See for example[1].
2) It isn't at all clear that "making connections" of the type he talks about is needed to generate the types on insights that he claims. For example, the work StitchFix is doing combining analogy reasoning on words along with other non-textual data[2] indicates to me that the current state-of-the-art absolutely could "understand" that Bob Dylan wrote about themes that were associated with anti-war sentiment.
[1] "Teaching Machines to Read and Comprehend" http://arxiv.org/abs/1506.03340
[2] http://multithreaded.stitchfix.com/blog/2015/03/11/word-is-w..., https://github.com/stitchfix/context2vec,
I still think there's a legitimate complaint here, though: IBM isn't doing unusually badly, but they're promising an exceptional amount. Even if Watson is a good product, and symbolic understanding isn't needed for good AI, they're still making claims they can't deliver on.
>AI winter is coming soon.
Perhaps, if so Watson is certainly evidence of this. It frustrates me to no end that machine learning has so much potential but is often lost in a sea of noise and buzz words (as much as I love deep learning, I'm almost tempted to lump that in here too given its outsized media coverage). Machine learning is in its infancy of impact, but the overselling by mediocre enterprise companies and ignorant press may shoot its credibility for years to come.
My wife was trying to find an old photo tonight and all I had to do was suggest that she go to google photos and search based on description. Same thing with Facebook recognizing images.
I am getting into using deep learning for NLP, and that looks promising. Google's parser is interesting but I want to see how quickly they can get many layer networks to do things like anaphora resolution (match up pronouns with previous noun phrases).
The trend I'm seeing is medium sized companies (think 10's of millions to low billions market cap) not solving simple, highly impactful problems using potentially simple machine learning techniques. A basic regression model that was state of the art 75 years ago can often add millions of dollars in immediate value if applied to the right problems. Instead, these companies often prefer to do what they know best, which is hire people to perform repeatable-but-not-easily-scriptable tasks.
The moment machine learning-illiterate executives start getting burned (one CEO to the other: "oh yeah, we spent X dollars on <insert hyped machine learning platform here> and it was useless"), is the moment that the hype can start destroying value.
This is a perfect description of how AI winters come to be. However, I think the upcoming winter will be more like an AI Fall. Some technologies over hyped and over sold will fall out of favor and people will be skeptical. However, companies like Google, Facebook, Microsoft, Apple who are using AI internally and can quantify improvements will continue to do so. The hype cycle is definitely going toward the slowly rising plateau rather than another crash. There is critical mass. Maybe this is what people though before in AI hype cycles (we are definitely in one), but in previous cycles we didn't have near human image recognition essentially solved.
The biggest hole in NLP is going from a parsed sentence to 'meaning.' Once we get that right, and are able to feed meaning back into the parser, then all our parsing problems will be fixed imo.
(How else would the computer understand "meaning" without beeing strong AI)
We don't even need true understanding of meaning, even a lousy approximation would be an improvement at this point (or I should say, a slightly less lousy approximation compared to what we have now).
Maybe that's the path to intelligence -- a bunch of ML and reasoning algorithms we already know about just working together and fed with a lot more data...
Once you take apart any AI thing (or any complicated thing n computing) you discover it is just a neural network, just SVD, just NLP + expert system etc. At least that is how I felt about it when I studied in college. The veil of mystery was lifted and it was just algorithms + math.
Having helped a friend use Watson a year ago, I sort-of agree with Schank's opinion in this article. IBM Watson is sound technology, but I think that it is hyped in the wrong direction. And over hyped. This seems like a case of business driven rather than science driven descriptions of IBM Watson. Kudos to the development team, but perhaps not to the marketers.
Really off topic, but as much as I love the advances in many layer neural networks, I am sorry to not also see tons of resources aimed at what we used to call 'symbolic AI.'
If you look at which AI projects the National Science Foundation is funding (or in Europe, the Horizon 2020 program), it's a lot more diverse. Even just in machine learning they're not putting all their eggs into the deep neural nets basket, with considerable funding going to the other major areas of ML (e.g. Bayesian methods). Symbolic methods have a decent amount of funding too, including some explicitly "cognitive systems" grants. Some other symbolic techniques are still funded but not as much by "AI" bodies, e.g. the logic-based branch of AI now gets a decent amount of its funding from the software engineering community, because verification is a big application of solver / theorem-prover techniques.
Attending this past year's AAAI in Phoenix was kind of funny in that respect. Maybe 95% of the consultants and recruiters there were solely interested in hiring people to tweak deep networks. But the scientific side of the conference didn't look quite like that.
Personally I'd love to see more research go into forms of symbolic AI that can deal with uncertainty, probability, partiality etc. The Research VP of Cycorp told me he wanted to go in that direction with Cyc, but couldn't find anyone with a promising and rigorous proposal. I think those sorts of considerations often lead either to pure theory with no thought to implementation, or the worst kind of adhoc "fuzzy logic" (scare quotes because fuzzy logic is a proper mathematical discipline).
I try to go to various "machine learning" and "AI" meetups around my city.
The most frustrating, but relevant, lesson I've learnt is to just stay away from everything IBM/Watson.
I can summarise every single bloody presentation they give because it's the following:
"Now, when we say cognitive computing, we don't mean any of that sci-fi and AI stuff, now here's a marketer and marketing materials that will explicitly imply that we're talking about that sci-fi and AI stuff for the next 59 minutes. There will be no technical information."
No one does.
Even with AlphaGo the hype was insane, but was mostly caused by people confusing weak AI with strong AI with Arnold Schwarzenegger. Any material that intentionally plays on this confusion is arguably false advertising and fraudulent.
The truth is, it's still impossible for anyone to talk about strong AI, because we have hardly been able to define what it even is. We just know we have it, and it's still unlike anything anyone is working on that has made it to the public. The people who write the papers absolutely know this. It's a common goal, but we have yet to engineer anything that remotely resembles strong AI.
For the most part, we're either still busy figuring out small but hard problems, or hacking resemblance and avoiding the important problems altogether.
If I'm trying to solve problem X and writing a paper about a new, better AI/ML approach to do it - this is most likely not even in the direction of strong AI. In fact, the new and improved solution is quite likely to be more specialized to the problem and thus even further from a general AI.
If I could magically build a strong general AI then it most likely wouldn't be an improved or even a good solution to the problem I'm looking at - it would be a horribly inefficient overkill, something comparable to using a human brain to perform arithmetic calculations - it can do it, but less accurately and more wastefully than a simple calculator.
Strong AI will most certainly appear magical, but any technology with this level of sophistication will require focused, intentional, intelligent effort. The person who accomplishes it will know exactly what they were doing.
This mystical emergence of intelligence is also somewhat metaphysical and unscientific. There are no ghosts in shells, and intelligence doesn't just rise from bare metal. If we were to ask if someone could invent bitcoin and not know it, it'd be a joke. Yet, with AI, there are those who still intuitively argue for some form of emergence. But across the board, these claims are made with little or no understanding of what underlies physical intelligence.
But it seems now they can't make sales in golf courses anymore and are trying to appeal to the technical staff for sales (but there might be one or two manager on these meetups that buys their BS)
Does anyone really think the next Big Idea will come from a bunch of stiff suits in Armonk, New York?
They are great comercializers, consultants and support engineers. They haven't been technical innovators for many decades.
However I don't think an AI winter is coming, as the author's asserts (towards the end). Applied AI research is driven by orgs like DeepMind and OpenAI, as well as academics. This powerful symbiosis of industry and academia will, in my opinion, continue to yield technological breakthroughs in the coming years and decades.
We seem to be oscillating wildly between three poles: Hawking's "AI will doom us all", IBM's "strong, friendly AI is right around the corner", and the skeptic's "strong AI is a pipe dream, or centuries away!" All three of those voices interfere with the possibility of sharing a useful, accurate view of what AI is and what it can be.
As bad as science news and public knowledge of science generally are, the situation is much worse on strong AI, and I think it's right to lay some of the blame for that with IBM and similar hype artists.
In general, every company (startups and big co. alike) seems to be hyping their "AI" capabilities out the wazoo, but three years ago saying those two letters together was a death sentence. I don't know if this is a good or bad thing (hype is bad, but general interest is useful for the visibility of the field) but it is definitely a sea change compared to the last 5-10 years.
I am extremely skeptical of most claims these days, and am a bit worried about AI Winter 2.0 due to hype around largely mundane technologies. There are exciting things happening in the space, but these things are rarely hyped to the extent the more mundane results with corporate backing are.
But he's done and continues to do a lot of good work. He helped found the Learning Sciences (an offshoot of cognitive science, AI, psychology, etc.) in the late 80s and early 90s. He continued and continues to do work in education, including founding experiential schools and writing books on education: http://www.amazon.com/s/?url=search-alias%3Daps&field-keywor...
I'll always remember his comments at that point that AI is mostly simple algorithms working against some database of knowledge.
I'm not sure I would still make that claim today, kernel based support vector machines aren't all that straight forward and many of the cutting edge machine learning and AI programs are far from easy to understand. Still, there is a feeling of disappointment when the curtain is pulled aside and the great Oz is revealed to be nothing that magical.
If they are unable to attract talent and innovate in their core business, how are they supposedly pursuing sophisticated AI, and the biggest question, is why?
What other products or innovations have come out of IBM Research? What is their overall reputation, and why should we believe them? Why don't they release Watson to the world, like Microsoft did with their twitter bot?
If I were a recent grad or even mid level in my career and wanted to work on as interesting projects as I could, I wouldn't be going to IBM. My first priority would be access to interesting and varied datasets, such as what can be obtained at Facebook, Google, Amazon, or another such company. A close second would be any of the players in the hardware ML industry such as Nvidia.
I don't understand what's so special about Watson, it all seems like marketing BS to me for a company in the death throws.
1986 Nobel Prize in Physics for the Scanning Tunneling Microscope
1987 Nobel Prize in Physics for High-temperature superconductors
Their blockchain project with Hyperledger is also full of baloney. There are plenty of groups that are innovating in that front, but IBM is far from it. But they're very good at marketing it.
Watson, Hyperledger, quantum computing... whatever. If it came from IBM it isn't worth the dime.
Are you asking just about recent developments in AI and ML, or in general?
If it were household names like Google or Apple, would help?
Also- the IBM models, like mentioned above. That's a bunch of NLP models that have been very influential and are still.
If you studied NLP now, for instance, you'd hear about them when learning about NER or WSD etc.
Deep Blue winning over Gary Kasparov?
$80B / year in revenue is not Apple's $200B, but not sure I'd say it is "not doing good" either.
I would disregard what the author has to say about Dylan even though it seems to be author's primary example. Dylan wrote a ton of songs and encompassed a couple different personas through his career. He's not one thing.
"if (sontexts[1980].contains("love") && songtexts[2014]contains("war"){print "Dylan changed his focus from lovesongs to protesting against war"}
Obviously the whole point is for it to figure it out for itself, not to be told.
"Recently they ran an ad featuring Bob Dylan which made laugh, or would have, if had made not me so angry."
"Ask anyone from that era about who Bob Dylan was and no one will tell you his main them was love fades."
"Dog’s don’s but Watson isn't as smart as a dog either."
etc
I like AI projects as much as the next HN reader, but compared to efforts by the other players in this space (Google, Apple, Tesla, Amazon, etc) whenever I hear a new marketing push about IBM Watson project my "BS detector" goes into the red zone.
(That said, it would be awesome if I'm wrong and IBM really is making some genuine advances...)
"It is used by nearly two dozen automakers, including Audi, BMW, General Motors, Ford, and Tesla Motors." -- http://fortune.com/2015/12/17/tesla-mobileye/
The Man Who Would Teach Machines to Think
http://www.theatlantic.com/magazine/archive/2013/11/the-man-...
AI winter will come if it isn't able to latch onto strong business cases. For the past few years, we've seen a slow uptake of low-grade AI, for example, Siri. As long as those sorts of things continue, the winter won't have a chance to set in.
It's true there is a lot of baseless hype around AI, but there's baseless hype around every new technology (probably around everything that catches people's attention). That said, if someone predicted that Watson is going to die, I would believe them, because they haven't seemed to get much business traction at all.
As long as there's a continual source of money there won't be another one - so basically as long as companies like Google and Facebook are profitable, and preferably money to be made elsewhere, things should be good.
I did a Masters at Stanford CS in 1985, and met most of the big names of that era. Stanford CS was dominated by the expert systems crowd, headed by Feigenbaum, and the logicians, headed by McCarthy. AI was being taught almost as philosophy. (Exam question: "Does a rock have intention?") You could graduate without ever seeing an expert system run, let alone writing one. Very little actually worked. But the faculty was claiming Strong AI Real Soon Now. From expert systems, which are just rules you write and feed to a simple inference engine. You get out pretty much what you put in. It's just another way to program.
Feigenbaum was running around, testifying before Congress that the US would become an agrarian nation unless Congress funded a big national AI lab headed by him. Really. There were a number of AI startups, all of which failed. There was a fad for buying Symbolics 3600 LISP machines, a single user refrigerator sized box with a custom CPU.
None of this delivered. That's why there was an AI winter.
That was a peak year for Symbolics.
In 1986 a Symbolics 3620 was about the size of a large tower PC.
http://bitsavers.trailing-edge.com/pdf/symbolics/brochures/3...
It would appear so, IBM hype aside. From chat bots to image recognition and playing Go; the media is having a field day around the AI theme.
If this hype feeds Investor and Consumer expectations, the next round of AI startups are doomed to underperform.
At the same time, there's a bit of a cop-out that goes on when we privilege our own cognitive processes over those of an AI just because our own minds are, more or less, a black-box.
I think he does a lot of that in this article. At the end of the day "human intuition" is just a filler until we figure out what's really going on.
Seems odd given alpha go and recent success of deep learning.
That may be; but then there's also this: http://arxiv.org/abs/1412.1897
Humans are no more robust against this kind of attack than any other system - consider stage magic, confidence scams, NLP, optical illusions, etc.
If you want a truly infallible system, then no, AI will never provide that. What you want is a magic deity.
This wave of AI from 2005 on, however, is affecting every facet of our lives significantly.
(Of course, the early AI research efforts lead to IMMENSE amounts of non-AI innovation in programming languages, database design, operating systems, etc so it may have been one of the most successful "failures" in history)
Also, the ridicolously simple "recency algorithm" used by Mozilla to show things in the awesomebar (the merged search and location bar) probably saved more man-hours than the Netflix recommendations, but I completely agree that it doesn't feel AI at all. Just as the wheel doesn't seem like technology compared to the loom, and sometimes the inverse is true, as the James Webb space telescope doesn't feel like better technological achievement than going to the Moon, but it probably is.
Although the author may be right overall, this paragraph certainly assumes a lot, and is probably wrong. Computer systems have been able to make such correlations for some time now.
How many times have you seen a TechCrunch article where the writer parrots the buzzwords the founder has thrown at them such as "x uses machine learning to sync your contacts with the cloud".
While true AI is a decade or two off, with each AI winter, an increasing number of human jobs are displaced. This next wave promises to be devastating to human productivity and a boon for machine productivity. The effects are real even if the intelligence isn't. When true AI is birthed, it won't need to be marketed. All that will be left are a few trillionaires, and food lines for the rest of us.
About the future "Wealth will be based on how many robots you own and control."
The author mentions himself that today's 20somethings have never heard of Bob Dylan, yet uses Watson's alleged ignorance of Dylan to dismiss it. Yet 20somethings are thinking entities.
Mostly it sounds like sour grapes, because his 1984 book didn't receive the recognition he thinks it deserves.
Not really. There's a lot more private funding for AI nowadays and a lot of research is happening in the industry, rather than in academia.
Machine learning is not cognitive computing, like Roger Schank puts it, but it's championed by most of the large tech corps (count them: Google, Microsoft, Facebook, IBM, Baidu). Those folks have the money to keep spring going for a long, long time, much longer than last time.
Just being indignant is not going to achieve anything. Roger Schank and those of us who think he's more or less right in spirit (if not in tone) have a very simple way to prove his point: make it all work. Show why Good Old-Fashioned AI is better than machine learning for achieving the goals it set itself back in the early days.
But we've not been able to do that. That's the fault of the people, like Roger Schank, who started various parts of the original AI project- and failed to take it to completion. Again, and again and again.
Google, IBM and the rest will do what they need to do to keep the money coming in and they'll fund a lot of research that way. The rest of us can suck it up- or come up with something that works better. Noone's stopping us.
Am I the only shmuck who thought Siri came from Apple, not Google?
Top result: "Currently, Siri defaults to the user's preferred Safari search engine, which is set by default to Google but can be changed to Yahoo or Bing. But with the debut of iOS 7, Siri search will default to Bing instead."
Maybe not, but "cloud computing" is getting a little pedestrian. New meaningless names used to market things we've been doing all along are a good way to gin up some interest.
Of course basic data mining is certainly not where the latest research is, but it covers many cases I see on HN or talked about in big data pitch decks. Regardless, it all seems a lot less fancy when you can get the job done issuing SQL commands that wouldn't confuse anyone who learned SQL in 1978. The whole thing is oversold and now largely commodified, to boot.
If and when this stuff starts to show real results you'll certainly feel it. The first wave of successful connect-the-dot bots will open up so many discoveries that opportunities for human labor will swell. But it's not chess, Jeopardy and a way to mine medical records. That's all obvious corporate bullshit.
And i sought you guys out and your company pissed in my face!
If every time you invite people to your party you serve nothing but turd sandwiches, don't bitch about how we didn't give you credit for the croquembouche you've got in the fridge out the back...
https://news.ycombinator.com/newsguidelines.html
https://news.ycombinator.com/newswelcome.html
We detached this comment from https://news.ycombinator.com/item?id=11751662 and marked it off-topic.
You never tried debugging, did you? (Ie computers can do surprising things, even when they do exactly as we tell them.)
Also, look at AlphaGo (or any modern chess engine): these programs play better than their their programmers could.
"Thinking" in the short-term means problem solving, whether through analysis and understanding. Machines are capable of that now, and do so much faster than do humans.
The parameters of learning will be bound - at least for now - by human input. But not the boundaries of learning.