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"The semantic web is the future of the web, and always will be." -- Peter Norvig

https://www.youtube.com/watch?v=LNjJTgXujno&t=1257s

"The semantic web is the future of the web, and always will be." -- Peter Norvig

https://www.youtube.com/watch?v=LNjJTgXujno&t=1257s

⬐ None

None

Peter Norvig put it best: "The semantic web is the future of the web, and always will be."

(For what it's worth, the startup school video that quote comes from is worth watching: http://youtu.be/LNjJTgXujno?t=20m57s)

Programming bubble will pop. Automated Machine learning is taking over. Even at google, Jeff Dean has recently stated that his Perceptual AI project is aimed at reducing feature engineering in their machine learning projects. In plain terms this means eliminating the need for programming in their search engines and other machine learning focused projects like google now and google goggles (the mobile app, not google glass). The only programmers left will be to manage cloud computing infrastructure and front end programmers. As things like siri and google now take off, front end programmers will also be in less demand. Microsoft, Apple, and Amazon are also increasingly focusing on machine learning. Automated analysis of text will take over the role of sql databases as demonstrated in IBM watson. In manufacturing, the role of programming also diminishing as vision guided robots like Baxter by Rodney Brooks's team take off.

All this is a good thing though, programming is slow and data is agile, as talked about by Peter Norvig in his startup school 2008 speech http://www.youtube.com/watch?v=LNjJTgXujno

Startups should aim at doing as little programming as possible and leaving it to data crunching.

⬐ disgruntledphd2

Citations needed. You seem to be commenting like this on many topics (wherever its tangentially relevant). I would really like to know why you think this will happen, what you think are the most promising machine learning methods for achieving it, and who you think is going to program these machine learning methods.

I love machine learning don't get me wrong, and I'd like to see Skynet (hopefully friendly) in my lifetime, but the current state of the field does not reassure me on this matter.

⬐ marshallp

You can read up presentations by Jeff Dean and also look at what those companies are doing. Listen to Craig Mundie from microsoft for example. See what's happening in competitions like kaggle or what are state of the art on difficult computer vision challenges like imagenet. Machine learning as used in android speech recognition and microsoft speech recognition has already decimated speech recognition researchers and computer vision researchers are next (in the sense that automated machine learning takes over feature engineering). Read all the big data hoopla. There's your citations. It's not all that difficult to figure this all out if you look into it.

⬐ seiji

It's as easy to buy into the church of the singularity as it is to buy into the "We'll have a million customers and $10MM/month in subscriptions within a week of launching" belief after releasing your four-years-in-stealth-mode-with-no-customer-feedback startup.

Beliefs based on other beliefs generate even further out beliefs unhinged from reality. It's not that they aren't true -- they just aren't true yet, and imagination doesn't count as ground truth (because magic doesn't exist).

⬐ marshallp

This is not about the singularity. This is about data vs programming.

And your attacks on the singularity are misguided. You think singularity people are retarded and your anti-singularity possie are holier, yet you don't have a coherent anti-singularity argument.

Any fool can tear down a barn, but it takes a carpenter to build one.

⬐ seiji

Oh, I certainly don't have an anti argument. We'll end up destroying ourselves, be obliterated by cosmic events, or eventually run civilization from computer simulations.

People have just been saying "real soon now" for 60 years. Obviously we are more "real soon now" than back then, but it's still unclear how soon we can be.

My favorite knock down is the "when computers get fast enough, we'll magically have AGI/hard AI." If that were the case, we could have really really slow versions now.

Theoretical vs. practical architectures matter. Nobody can imagine an iPad's software ecosystem if your model is vacuum tubes and single tape turing machines. But when you hit CPUs, shrinking processes, and GPUs with 6 teraflops on a card, you see how impossible your goals were with a 0.2 MHz room-sized computer with 500k of memory.

If you immediately know the candle light is fire, then the meal was cooked a long time ago.

⬐ marshallp

2011 and 2012 have been watershed years. IBM watson, speech recognition using neural nets beating speech researchers, neural nets on imagenet beating computer vision researchers. The trend is that automated machine learning wins. You can be on the right side of history (automated machine learning) or take opposite side (and be wrong).

⬐ seiji

I think we're on different pages of the same book. The current stuff is all well and good, but it's the Penn & Teller phase of AI/ML/GMs. We'll dispense with the façade of ability and instill actual ability eventually. I'm just not overly rah-rah about the recent incremental developments. I don't mean to overly pooh-pooh your rah-rah, but my brain is telling my fingers someone is wrong on the internet!

⬐ marshallp

Yeah, that wrong someone is you. You'll have to eat your words within 5 years at the most.

⬐ eropple

I'll take that wager. I mean that literally--put your money where your mouth is.

Email's in my profile.

⬐ wisty

In 1954: Programming bubble will pop. Compilers are taking over. Soon, anyone will be able to control computers using simple English commands.

⬐ marshallp

That kind of happened if you frame it in their era - HTML, CSS, PHP, SQL.

⬐ tabdon

It just makes sense that eventually we will create systems that do the work that is currently manual. That has been human history (printing press, manufacturing, etc). The good thing is that we are crafty buggers and will always find a new job to do even when we've just replaced the manual labor that was oh so painful to perform before.

⬐ seiji

The current incarnation of machine learning is nothing more than an ultra high dimensional two year old. "Here's a new item. Which pool of millions of items is this new thing like?" "Is the red square more like these other red squares or the green triangles?"

You can get pretty far extracting the math behind the meaning, but lack of meaning is still deathly apparent.

We should fix that sometime.

⬐ marshallp

I think most people are interested in practically solving problems. If the black box can solve it, there's no reason to care about "meaning". Once the machines have taken over all jobs, you can sit around and understand the meaning of things to your satisfaction at your own leisure surrounded by the luxury the machines have created for you.

⬐ seiji

Imagine how many more problems we could solve with meaning. Every secretary in the world could be fired. Software could actually know you. It would be a huge psychological problem for people.

You think MMOs kill brains in south east asia now? Wait until software acts like a boyfriend. Every site (read: website/app/software) could be unique, well designed, and work cross platform by telling the system what you want (remember: in this delusional scenario the system understands meaning to avoid the unintentional vengeful djinn problem).

You think multivariate testing is omgballz amazing? Imagine if ad networks share a sophisticated personality model (instead of: likes cats, doesn't like monster trucks) of you. Every site you visit could be rearranged to appeal to your individual design sense, buying patterns, and social expectations. A/B testing basically tries to suss out your average user so you can appeal to them. You leave your non-average users in the dirt by optimizing for the common moron.

It's possible and I think the incumbent giants will miss it whenever it happens (5-10-50 years out).

⬐ paulgb

Yeah, but the two-year-old scales to huge datasets and doesn't cry a lot.

Norvig's talk at Startup School '08 went into more details on this: http://www.youtube.com/watch?v=LNjJTgXujno

They do use SIFT (or at least a variant thereof) for finding and describing interest points, but by itself, there is no geometric matching in SIFT. There are various competing approaches on how do it, although in many cases, you can get very good results even without it. (It's very slow to do geometric matching so people often skip that step, or only apply it to the best matches.)

Landmark detection is a recent "hot topic" in computer vision, and given a large enough dataset, it essentially works now for the most part.

⬐ bfrs

Thanks for the link.

http://www.youtube.com/watch?v=i5E38frHo1U - (Steve Blank / Eric Ries - part 1)

http://www.youtube.com/watch?v=cwW2Q-09g9Y - (Steve Blank / Eric Ries - part 2)

http://www.youtube.com/watch?v=2mZUcWt2Q3M - (Steve Blank / Eric Ries - part 3)

http://www.youtube.com/watch?v=SSk2I8tlzdA - (Steve Blank at the Startup Lessons Learned conference)

http://www.youtube.com/watch?v=n-H7TAcqGko - (Steve Blank: The Democratization of Entrepreneurship)

http://www.youtube.com/watch?v=ynQasjpBTCk - (Alexander Osterwalder speaking @ Google)

http://www.youtube.com/watch?v=fEvKo90qBns - (Eric Ries speaking @ Google)

http://www.youtube.com/watch?v=q7K0vRUKXKc - (pg at Startup School '08)

http://www.youtube.com/watch?v=LNjJTgXujno - (Peter Norvig at Startup School '08)

Yes. See 21:00 http://www.youtube.com/watch?v=LNjJTgXujno

Money quote: "Semantic web: The future of the web and always will be."

⬐ abossy

Hilarious. The guy asking that question is a very close friend of mine and I've never stopped giving him crap about it.

This line of thought follows previous posts on the same subject.

It's very Norvig-esq (http://www.youtube.com/watch?v=LNjJTgXujno), but there's also http://anand.typepad.com/datawocky/2008/03/more-data-usual.h... and also Chris Anderson and Wired's flame bait http://www.wired.com/science/discoveries/magazine/16-07/pb_t... (that month's wired was dedicated to this subject)

And like someone at the previous discussions has said, this is the base of the scientific method, not it's death

⬐ ntoshev

I would also add the "Theorizing from data" talk from Norvig:

http://www.youtube.com/watch?v=nU8DcBF-qo4

⬐ andreyf

Norvig had a great response to that wired article: http://norvig.com/fact-check.html

⬐ bd

Golden quote from Norvig's talk at Startup School:

Q: What's your opinion about semantic web?

A: Semantic web. Future of the web. And it always will be.

Also:

If I assigned engineers to (semantic web) formats based on the percentage of pages that had those formats, then the correct number of engineers for semantic web was zero.