Kill your blog - <AUTHOR> http://www.wired.com/entertainment/theweb/magazine/16-11/st_essay <comment><USER2> A dissenting opinion:

Writing a blog helps you organize your thoughts, improves your writing skills, and teaches you to make good arguments (and recognize bad ones).

None of that requires having an audience. At least an audience larger than your friends and coworkers.

If you like to write, don't stop writing because it's not "where the buzz is at". There's more to life than following trends.

<comment><USER3> Well said. In addition to that, I use my blog to host content I hope people will find via google, like how-tos or coding gotchas. I'm not going to post a coding trick to my facebook account, even if it has more active readers.

<comment><USER2> Exactly. Some content, like status updates, is perfect for Twitter or Facebook, but this article seems to assume that that's all anyone uses blogs for.

Please god say it ain't so. I would like to think that people who keep blogs (at least the subset thereof that also read Wired) tend to write more interesting things in them, and I can't imagine that being replaced by 140 characters (this notwithstanding: <http://twitter.com/_why/statuses/881768089> )

<comment><USER4> If this is actually a ploy to get pointless blogs out of Google's index and hidden behind Facebook's garden wall, then I'm all for it!

</comment></comment></comment></comment><comment><USER5> And stop writing books people, it's so 1900. Surely you can convey everything you'd write about in a series of 140 char twitter messages.

<comment><USER6> I think that books _have_ lost prominence, though. People have found that there are mediums that convey their messages far better.

The book isn't dead. But it's not as used now as it was in the past. People with visions and dreams find that it's easier to make movies, or form bands. Some people write, yes, but the book is not as dominant as it was in 1900. The same stands for the play and the concerto.

Blogs still have their uses. But now, things exist that work better for a lot of people. Twitter and Tumblr come to mind immediately.

<comment><USER7> " People with visions and dreams find that it's easier to make movies, or form bands. Some people write, yes, but the book is not as dominant as it was in 1900. The same stands for the play and the concerto."

Any references to back this up?

I bet it's easier to self-publish a book than it is to make a movie.

<comment><USER6> I self-published a book in my senior year of high school, so yes. That's possible.

However, I referred to the set of genres overall. The book certainly is not dead. However, it takes up less of the "fine arts" market proportionally than it did a hundred years ago, and that's because other genres exist that allow people certain other freedoms.

It is easier to form a band now than it was a hundred years ago. A hundred years ago, it would have been extremely difficult, and there was less of a popular focus. There wasn't the insane level of attention to rock stars back then. And motion pictures didn't exist at all. Neither did animated pictures or video games.

This is applicable to the blogging situation as follows: 5 years ago, if you wanted to write online, a blog was the best way to go. Now? Not necessarily. Now you can Tweet if that serves your purpose, or you can maintain a videolog or a tumblelog. Many friends of mine abandoned their blogs for Tumblr for precisely that reason: it fits their needs. And yes, that's still technically a blog, but the format has changed. There are different rules and there is focus on different things. It's entirely different from maintaining a blog on Wordpress or Movable Type.

Nowadays, I don't see very many interesting posts from personal blogs. The blog posts I DO find interesting come from only a few places. The attention has changed to a variety of different mediums. Look at r/bestof/ on Reddit: suddenly, there are people who come to attention entirely through metaposting. Or, to take my example, I now link to my HN threads on my site and I've abandoned my blog for essays. It lets me build up a better relation between content that I write on the fly and content that builds up, that I slowly formulate and revise.

Of course the book isn't dead, or even dying, though in my opinion it's been 50 years since the last truly revolutionary piece of pure literature (Beckett). However, now if you've got a mind for expression there are more writing-based venues available. 100 years ago, you either wrote poetry, wrote novels, or wrote plays. Now you have lyric writing, film-writing, and game writing available. That fits what people want much more.

Look at the invention of science fiction: when movies first appeared, literature stopped dealing with sci-fi largely for several decades, because the minds that were attracted to sci-fi were attracted to cinema. The same thing is happening with video games right now: not all but many writers are joining on the science fiction game bandwagon. And so, if I were a game designer looking at a bunch of college students interested in science fiction, I might very well advise the students that novel writing might NOT be the best way to go on with sci-fi. Some people would ignore that and go on and try, but with so many other venues available, there's less of a pressure for books. That's how I see this article: it's an argument that a blog isn't necessary anymore. That doesn't mean people won't like it, and use it, but it means that blogging is no longer the primary source of content on the Internet.

<comment><USER8> _"I self-published a book in my senior year of high school."_

I'm curious, what did you write about? Fiction/non-fiction? Still writing or just hacking?

Putting a book together at that age is a respectable achievement.

<comment><USER6> It was a novel. I published it through CreateSpace, so it could be found on Amazon; it's online at Scribd at <http://www.scribd.com/doc/3319195/gstepl>.

I still write, though I haven't written anything as ambitiously. My start-up is one that focuses on creative writers who want to learn to write at a more professional level, so it lets me get away with doing both. And I'll probably be writing something for NaNoWriMo in November: if I like that, I'll spend a little bit revising and seeing if any publishers are interested.

The two things go nicely together, because with literature you get used to putting things together haphazardly until they work: hacking gives you a more utilitarian approach to doing things. I find that when I'm in the middle of programming, I write better - if that makes any sense to you.

</comment></comment></comment></comment></comment></comment><comment><USER9> If you want to share medium-sized amounts of timely information, blogs and screencasts are the best conduits that I know of.

As for "expressing yourself" with Flickr, Facebook, and Twitter, photos, throwing pillows at people, and 140 character ephemera do not substitute for a solid, well-edited blog post.

<comment><USER10> "The time it takes to craft sharp, witty blog prose is better spent expressing yourself on Flickr, Facebook, or Twitter."

One word: SUBMARINE.

</comment></comment><comment><USER11> What a linkbait title. Blogs aren't going anywhere, people are just figuring out what's appropriate on their blog versus Twitter/Facebook/[insert social network site of month here].

Personally, I see my blog and Twitter feed as two _very_ different types of communication. Twitter, etc is great for quick little comments, sharing links, short updates of what's going on my life, questions to friends, etc, whereas my blog is good for more in depth posts like technical projects I'm working on, longer opinion pieces, tips and tricks, etc.

Similarly, I hate reading individual's blogs that simply talk about their boring day to day life, but 140 character chunks of that stuff is ok. On the flip side, there's no way to fit anything substantial (like my typical blog post) into 140 characters.

 _"A stand-alone commentator can't keep up with a team of pro writers cranking out up to 30 posts a day."_

My approach is to not to subscribe to these kind of high volume "professional" blogs, but rather lots of little low traffic blogs.

I figure if there's something truly important posted on one of these mega- blogs my peers will inevitably post about it (on their blog, Twitter, or Facebook) and I'll find out about it anyway. Plus I'll see a huge variety of posts and little gems that never make it up to the big guys, rather than a zillion "xyz company released abc cell phone" posts on Engadget. I'm a gadget fiend but that shit gets old after awhile.

So don't let Wired and the mega-blogs discourage you from blogging. If you write something interesting the big guys will link to you anyway. My blog has been linked to from Slashdot, Daring Fireball, Ajaxian, TUAW, etc over the last year, and I only post about once a week when I have something I think is at least moderately interesting.

</rant>

</comment><comment><USER12> Kill your blog? Say it aint so!

[http://weblog.raganwald.com/2008/07/brief-history-of- dangero...](http://weblog.raganwald.com/2008/07/brief-history-of-dangerous- ideas.html)

</comment><comment><USER13> I think Paul Boutin might have a crush on me.

 _some anonymous troll named r0rschach or foohack_

Um... "foohack" is only used by one troll, afaik, and I'm not anonymous. :P <http://tr.im/h9f>

This is the same Paul Boutin who was so impressed by my resume. <http://tr.im/h9g>

<comment><USER14> Was there ever a follow-up story to the VW post on your resume?

<comment><USER13> No, not that I know of. Though I did get pinged randomly by a yahoo employee from china who saw the story and looked me up in the corporate directory.

</comment></comment></comment><comment><USER15> _"Cut-rate journalists and underground marketing campaigns now drown out the authentic voices of amateur wordsmiths."_

 _"Paul Boutin (email@example.com) is a correspondent for the Silicon Valley gossip site Valleywag."_

This made me giggle even though I sometimes view Valleywag as having more journalistic integrity than TC.

</comment><comment><USER16> They're just trying to be sensationalistic.

To each his own. If you prefer the blog format, by all means keep blogging. If you prefer twitter, go do that..

<comment><USER17> exactly what I was thinking... Soo, just because some people stop blogging and move to other platforms everyone should? Screw that.

Of course, if you intend to use your blog to make some dinero and pay some of the bills, it's probably not going to work... but neither will twitter or facebook for that matter. If you just want to express yourself, use whatever you want and whatever your public will use.

And can't you have a blog AND twitter, facebook, etc?

</comment></comment><comment><USER15> This is awful advice. Our blog has, on some days, received more traffic than our actual site. In addition to generating sales, it has also helped us substantially with SEO and connecting with our users.

This is quite possibly the worst advice I've ever seen on HN in recent memory. Just because you can use facebook/twitter et al doesn't mean you can't have a blog. They aren't mutually exclusive. One word of caution, if you're spending tons of time on your blog with very little to show for it, maybe consider getting rid of it or cut down your post frequency.

</comment><comment><USER18> Gary V. had great advice: "people ask what social networking tools I should use ... twitter, jaiku, etc. The answer? Use all of them"

There is nothing exclusive about twitter & blogging. You'll note those with the biggest personal brands and most followers are often bloggers. This is because facebook and twitter are both bad at content/person discovery.

Blogging is a good way to let people find out who you are.

<comment><USER6> Ugh. I dislike that advice very much.

I'm not a fan of people who use social networking to advertise. I know there are practical benefits, but the idealist in me wishes that people wouldn't pick up media just to abuse it. Some things are great: I remember there was a comment here about somebody who complained about Comcast on Twitter and got an instant response. But I've seen too many people who use Twitter and Jaiku and Tumblr to post links back to their content and that's that, and I hate that.

Of course, the idealist in me thinks that you shouldn't advertise in every venue possible, that you should work until you've got something so useful that word-of-mouth brings people in, and that's not entirely practical either.

<comment><USER15> Disclaimer: TicketStumbler uses Twitter, Facebook and Blogging

What if you have people come to you instead of vice versa? For example, you never follow anyone on twitter you make people follow you. I agree that the spammers are a huge problem, but I think some of these tools can be used for good.

I raged about Comcast on Twitter yesterday and they haven't done anything. Bastards.

<comment><USER6> The ones you mentioned are okay if you're using them decently. I can see why TicketStumbler would benefit from Twitter, absolutely. And Facebook and blogs are different: blogs help you reach out to your current users, and Facebook Pages are built to let you attract fans. I guess that's not the misusage I was thinking about: I referred more to the spammers and the people who sign up for every service imaginable just to attract people. (The owner of AllFacebook, which I used to write for, did that a lot on Twitter, and it always just seemed slightly wannabe to me.)

</comment></comment></comment></comment><comment><USER19> So a guy who presumably makes his living on being a professional blogger is lamenting the lost of the personal touch in blogs... hmm

</comment><comment><USER20> writing a blog can demonstrate your subject matter expertise to investors, employees, and business partners. philmichaelson.com has very small readership, but the leads its generated have already been worth the effort.

Twitter, Flickr, and Facebook don't allow you to share the same length and quality of content outside your friend network

</comment><comment><USER21> He assumes blogs make media hard to upload. Posterous breaks that rule. Then there's my site, OurDoings, where the photos you upload form the skeleton of a blog, backdated by when the photos were taken. You can leave it as just photos or fill it in with as much writing as you want.

</comment><comment><USER22> Please do quit if your goal is to bask in the adoration of other bloggers.

If you've got useful information or clever insight, by all means continue. For god's sake, I wouldn't know how to program these days without all the "5 steps to XXX" blog posts out there.

</comment><comment><USER23> Why are they mutually exclusive? Seems like they each work best when supported by everything else. The defining characteristic of 2008 is not twitter, as it was blogging in 2004, but rather the growing interconnectivity of all web services.

</comment><comment><USER24> What is this blog thing? A site with a RSS feed? The BBC website has RSS and news aren't posted in batches. So is it a blog? Nope.

Isn't a blog supposed to be personal? The mentioned sites aren't really blogs, so the article is irrelevant.

Blogs still matter when it comes to details that aren't discussed in the media. In my native language blog posts and forum discussions are among the top results and I often learn more and understand my country better from this content than from the PR-dominated media.

I am glad that some of the bloggers I read have started micro-blogging - I am not interested in reading what they ate for dinner last night.

I like traditional personal webpages.

</comment><comment><USER25> He makes the mistake of thinking there's some limited number of blog readers and you have to compete against TechCrunch for them.

Also Calcanis may have quit blogging, but his posts now end up on top 10 blogs every time.

</comment><comment><USER26> I totally disagree. I don't see the point in stopping to write web log entries just because twitter or facebook exist. I do not write on my web log in search of some form of hype. The reason I do write on it, is to serve two purposes: To keep track of stuff that I know I will eventually forget (and it will be extra fun to re-read it later :)), and to communicate some form of information to my friends and family.

Maybe, now that I think of it, it might be more interesting to start storing carrier pigeons or messages inside bottles, hmmmm...

</comment><comment><USER27> Although this is a worthy topic to explore, the reasons the article mentions are are lousy. So what if there are people making a living blogging? Who cares if Jason Calacanis or Scoble changed their blogging patterns? Who cares about all the noise in the blogosphere, and competing services like Twitter.

The most valid reason is that blogging is hard, and most people aren't willing to put forth the effort to make their blogs readable.

</comment><comment><USER28> In the programming world, there is a healthy ecosystem of bloggers. If Steve Yegge switches to Twitter, I quit.

One great thing about the web is that you can structure your content however you want.

</comment><comment><USER29> How about this: kill your blog if it sucks and no-one reads it and you can't think of any improvements you could make to get people to read it.

</comment><comment><USER30> You need your blog, because it's part of your portfolio. It's what people who want to find out more about you read.

</comment><comment><USER31> clearly a flamebait ...

</comment><comment><USER32> Shiny object!!!</comment>


Ask HN: Review my startup playwire.com - <AUTHOR>

We are doing a video hosting solution with a very strong focus on monetization.<p>This is our first public release so if you have suggestions for features they are more than welcome (API is coming soon). <comment><USER2> I like your idea. The site also looks nice. Do you host these videos yourself? I can see bandwidth charges getting out of control if you do.

<comment><AUTHOR> Thanks for taking a look. We are using a CDN for the bandwidth otherwise it would be impossible.</comment></comment>


Aramco listing plan halted, oil giant disbands advisors – sources - <AUTHOR> https://www.reuters.com/article/us-saudi-aramco-ipo-exclusive/exclusive-aramco-listing-plan-halted-oil-giant-disbands-advisors-sources-idUSKCN1L71TZ <comment><USER2> It is rumored that the Saudis made the 100 billion they wanted from the Ritz- Carlton crackdown, so no more interest in the IPO.

<comment><USER3> > the Ritz-Carlton crackdown

What's this? Are you referring to the use of these hotels as luxury prisons during the purge?

<comment><USER4> Numerous princes were detained at the Ritz-Carlton hotel and weren't allowed to leave until they admitted to corruption and surrendered significant amounts of cash. A few fought this but most folks just handed over the money.

[https://www.bloomberg.com/news/articles/2018-01-25/saudi- ara...](https://www.bloomberg.com/news/articles/2018-01-25/saudi-arabia- struck-gold-with-corruption-crackdown)

<comment><USER5> It wasn't only confinement. One general appears to have been tortured to death, and many others had to be hospitalized and showed signs of torture.

[https://www.hrw.org/news/2018/03/14/saudi-arabia- allegations...](https://www.hrw.org/news/2018/03/14/saudi-arabia-allegations- abuse-death-custody)

<comment><USER3> Wow. How in hell did they (Saudi Arabia) manage to become the Head of the UN Human Rights Panel ?

[https://www.independent.co.uk/news/world/anger-after- saudi-a...](https://www.independent.co.uk/news/world/anger-after-saudi-arabia- chosen-to-head-key-un-human-rights-panel-10509716.html)

Reeks of corruption at the highest level.

<comment><USER6> I assume it's something along the lines of: "Never refuse a US Military request, be mad at Iran, and don't bother Israel."

</comment></comment></comment></comment></comment></comment><comment><USER7> I am not surprised by this news. No doubt the transparency that a listing would require was simply too much for the Kingdom.

<comment><USER8> I think of the biggest issues was they expected to get a far higher market cap then they got.

</comment></comment><comment><USER9> I think they were asking $2 Trillion, and deals with developing countries always involve a "no-layoffs clause", which is mostly nepotism. See Air India's "proposed sale" for an example.

Additionally, the recent royal family instability and waiting until solar and wind took off (ie. competition) didn't help.

It would be cheaper to just hire Xe and take over the country, which you would need to do anyway to enforce your ownership stake.

<comment><USER10> Only fools would have ever bought into the Aramco IPO. The vast majority of IPOs are sucker bets for investors, companies selling high to take advantage of their likely peak value.

But in Aramcos case you'd have actual self dealing despots controlling your investment, and eventually they'd make clear how junior their junior partners really were.

</comment></comment><comment><USER11> They are denying it ... [https://www.cnbc.com/2018/08/23/saudi-aramco-ipo- riyadh-deni...](https://www.cnbc.com/2018/08/23/saudi-aramco-ipo-riyadh- denies-its-scrapping-plans-for-listing.html)

<comment><USER12> "The Government remains committed to the IPO of Saudi Aramco at a time of its own choosing when conditions are optimum."

Not much of a denial.

</comment></comment><comment><USER13> This was all but announced over the past few months. Like mentioned in article in the article, they're targeting SABIC which probably requires a big bond deal. Bloomberg says current market cap is 98bn USD so...

There are definitely some junior bankers who want to kill themselves over this.</comment>


Quantum computing's also-rans and their fatal flaws - <AUTHOR> https://arstechnica.com/science/2019/11/quantum-computings-also-rans-and-their-fatal-flaws/ <comment><USER2> Strong agree, the future of quantum computing is photonics.</comment>


Programming a problem-oriented language by Chuck Moore - <AUTHOR> http://www.colorforth.com/POL.htm <comment><USER2> Mirror without obnoxious __BOLD __:<http://strager.net/POL></comment>


Every Black Hole Contains a New Universe - <AUTHOR> http://www.insidescience.org/?q=content/every-black-hole-contains-new-universe/566 <comment><USER2> In Einstein's theory of general relativity, Riemann tensor is assumed to be symmetric, due to great simplification of the theory compared to the one with non-symmetric Riemann tensor, and an absolute lack of experimental evidence to the contrary for 100 years since it was discoveried. Torsion, which is the cause of asymmetry of the Riemann tensor is set to zero. I do not know why this author is so excited about torsion tensor, the idea that torsion could be non-zero has been around for at least 90 years, but got zero traction. Disclaimer : I have a phd in string theory.

<comment><USER3> I _don't_ have a PhD in string theory, but would like to make the point that many times in the history of science ideas that were originally regarded as uninteresting lie dormant for a long time only to usefully surface much later in the context of new evidence.

In this case, the researcher seems to be excited _specifically_ because of the potential of torsion to explain dark energy -- a recently discovered phenomena (although of course, oddly presaged by Einstein's cosmological constant hack).

<comment><USER2> The classical theory of gravity with torsion certainly does not explain dark matter. The author is trying to build some quantum theory of gravity, where torsion is required, speculating that it would explain dark matter. I should add here that the attempts to build a quantum theory of gravity without torsion failed in the last 100 years (string theory is the best candidate), and the author is trying to add even more complexity by adding torsion tensor.

<comment><USER4> I'm trying to follow you as a (very interested) layman, so excuse me if I'm being stupid. Couldn't the failure to build quantum gravity be BECAUSE torsion has been excluded (which is I believe what the author suggests)? Maybe this is something that has to be looked into.

I'm thinking that a marriage of physics, mathematics and CS might be necessary to overcome our limits in understanding these structures. Something like an IBM Watson for physicists, where a computer is fed with all informations we have and solves an optimisation problem to come up with a unified theory explaining all the phenomenons with the least complex solution (i.e. the least universal constants). Another requirement would be to have 42 as an error code for all possible failures in the calculation ;).

</comment></comment></comment></comment><comment><USER3> Of course headlines have to be catchy, but this universe-in-a-black-hole concept is hardly a new one. Rather, the novel part here seems to be the idea that the connection in GR might might possess non-zero torsion, contrary to what is normally assumed.

In the whole black-holes-create-universes vein, last century the theoretical physicist Lee Smolin presented the quite delicious idea (described in his book The Life Of the Cosmos) that entire universes might be subject to the process of natural selection.

This would work if singularity formation involves the transmission of physical constants to the 'daughter' universe with slight modification, quite a thing to suppose given we don't really know what physical constants 'mean'.

If it occurred, this natural selection would optimize these constants for the production of black holes, which luckily for us co-incides with the production of life-bearing stars. Of course, all very unfalsifiable, but kinda epic.

<comment><USER5> One SF short story takes this further. The best form of interstellar travel generates black holes, so the universe is optimized for producing the type of life which does interstellar travel via that process. No need for coincidences here, and even more un-falsifiable.

<comment><USER6> What's the name of the short story? Sounds like it could be interesting.

<comment><USER7> "What Continues... and What Fails..." by David Brin, perhaps?

<comment><USER5> That's the one - thanks!

</comment></comment></comment></comment></comment><comment><USER8> Will theories like this ever produce any new testable predictions, or is this the modern equivalent of counting angels on the head of a pin?

<comment><USER9> Funny thing: the Talmudic sages actually came to a conclusion on how many angels could dance on the head of a pin. I think it was about 16 or so.

<comment><USER10> Well, it's obvious in retrospect, but I'd imagine that took some pretty awesome reasoning powers.

</comment></comment></comment><comment><USER11> I am not a physicist but I have a question about this. If matter is being sucked into a new universe from an old one, I can understand that the mass of the new universe would count on the old universe's balance sheet if you will, showing that the sum of matter and energy is still constant, but wouldn't the new universe be only partially logically self-contained in this regard? Wouldn't the sum of matter and energy be increasing?

And if our universe is on the inside of a black hole, shouldn't the amount of energy and matter be increasing here as well? If not, why not? Why don't we get to suck in fading stars from our parent universe and get an increasing amount of m or e?

<comment><USER12> I'm not sure that the extra matter ever "enters" the black hole in a traditional sense...

As matter approaches it (pass the Schwarzschild radius) and increases in velocity, time slows down more and more for it.

When velocity approaches C, time approaches zero.

Though in another thought, I'd guess that due to the forces involved, the matter is ripped apart on a sub-quantum scale to the point that it literally disintegrates into its energy component and basically adds energy to the black hole (which can be interpreted as mass). But again, it never enters it.

<comment><USER13> This is a well-understood problem in GR. To an outside observer, things never appear to cross the Schwarzschild radius (R_S), giving the impression that time stops for in-falling matter when it gets to R_S. This, however, is an illusion forced on a distant observer.

The stuff that is actually falling into the black hole never notices R_S because the velocity they measure is not greater than c, and crossing the event horizon is a completely benign event. It's the later extreme tidal forces / spaghettification that destroys in-falling matter.

So it is fair to say that stuff enters a black hole.

</comment></comment></comment><comment><USER14> So what happens to those universes when Hawking radiation causes the black hole to evaporate?

And if we're in a black hole, what evidence, if any, do we have for the effects of evaporation?

<comment><USER15> Interpreting various equations from my limited knowledge of such a discussed area, I've come up with this:

Hawking radiation makes a black hole lose mass, hence diminishing its Schwarzchild radius. The Schwarzchild radius came up because it's a singularity (in the mathematical sense) in a solution to Einstein filed equations. The same way matter collapses onto itself up to creating such a singularity and being encompassed into the now existing event horizon, as soon as there is not enough mass to sustain the singularity, the event horizon vanishes, hence the remaining matter "pops" back into our sight.

You could view it otherwise with a thought experiment: take particles out of the black hole one by one. Each time it will reduce the mass and maybe the radius. At some point the mass/radius ratio may not be small enough to hold light and the bubble bursts in plain sight as the event horizon breaks down. In such a thought experiment, the worst case would be that the black hole would require every single particle but one to evaporate (unless you assume a single particle could be a black hole in itself).

An interesting back of the envelope calculation is computing the mass contained in a Schwartzchild radius of Planck length (the size at which quantum effects take over).

        l_P = sqrt(hG/c^3)
        r_s = 2Gm/c^2

hence m = sqrt(hc/4G) = 1.0882546265651108e-08 kg which is a bit more than 1e22 electrons sitting at a position 1e-15 smaller than a single one of them.

<comment><USER16> Is there anything to preclude there being an ultra-massive particle that might be able to cause a singularity by itself?

The particles we observe today exist in a relatively low-energy environment. Whatever's going on in the furious intensity that is the inside of a neutron star that's on the edge of becoming a black-hole could be quite spectacular in comparison.

</comment></comment></comment><comment><USER17> Ok, so if our universe is inside a black hole which is in a "parent" universe and so on, doesn't there have to be some kind of "root" universe? So where does that "root" universe come from?

<comment><USER18> Since there's no concept of space and time "inside" a blackhole, I think it would be better to think of blackholes as doors to different rooms in a huge mansion rather than each room having a smaller room contained inside it.

<comment><USER13> Space and time still are well defined inside of a black hole. They only break down at one point, termed the singularity. But nothing mystical happens and neither space nor time break down when you pass the event horizon. So there is indeed a well-formed notion of "inside", as long as you're not in the very center, where the singularity breaks everything.

<comment><USER18> A black hole is technically a single point. The part of the hole that we colloquially refer to is the event horizon. The headline speaks of universes "inside" a black hole, not an event horizon.

There's a brilliant explanation on reddit by RobotRollCall on this topic. I'll update my post when I find the link.

<comment><USER19> Perhaps you mean this thread:

[http://www.reddit.com/r/askscience/comments/f1lgu/what_would...](http://www.reddit.com/r/askscience/comments/f1lgu/what_would_happen_if_the_event_horizons_of_two/c1cuiyw)

<comment><USER18> Yes! Exactly that one. Thank you.

</comment></comment></comment></comment></comment></comment><comment><USER10> So, what happens when black holes collide, and merge into a single one? Serious question, btw - would we experience it, or does our universe contain all matter to ever enter our black hole, therefore all it has merged with?

If the latter, how big can black holes get? Can they consume a universe? That would be a neat end-result - the whole universe goes down the drain, to cause a new one.

<comment><USER20> interesting notion! who's to say the big bang wasn't caused by a supermassive blackhole on the other 'side'

<comment><USER21> Why you need 'other side'?

Our Universe has disproportion between (visible) matter and antimatter. Black holes my contain missed anti-matter. So when black hole will consume all matter of our Universe, it will bang again, without help of any parent Universe.

<comment><USER20> I said "other 'side'", not 'other side'! (dimensions are silly :) ) However, I'm not a physicist and really have no idea what I'm talking about :( reading about cosmology is only a hobby/daydream of mine.

</comment></comment></comment></comment><comment><USER22> What the article doesn't mention is that the universe inside the black hole is _exactly the same as this one_. So if you head over to a black hole, dive in, and travel to earth, everyone welcomes you "back" and asks how it went.

<comment><USER23> I don't think that that follows in any way from the article. But the idea is interesting. Care to explain or give references?

</comment></comment><comment><USER24> The esact mechanism of 'Torsion' may not be right, but there have been indications for a long time that there may be another universe inside a black hole -- because if you look at the solution to Einstein's equations, there's a place on 'the other side' that is flat.

If there is some process like cosmic inflation triggered by high energy densities (like Andre Linde's chaotic inflation) it's pretty believable that some kind of 'bang' happens when a black hole formss and the output of that bang fills the 'other side'.

</comment><comment><USER25> IANAP but if universes could be born from black holes, would they also disappear if the black hole were to evaporate via Hawking radiation?

</comment><comment><USER26> On a related note, I find it very intriguing that the three properties that distinguish black holes from one another (mass, charge and spin) are the same ones that distinguish quarks. That's a beautiful symmetry between some of the smallest and largest things in existence.

</comment><comment><USER27> What impact would this have on scenarios of the end of our universe?

</comment><comment><USER28> Perhaps this helps explain Fermi's paradox (at least partly) ... some of the many, many aliens we should have found by now might be living either out in the parent universe, or inside other black holes in our universe. I'm way out on a limb here, but it's an interesting speculation. Whadda you think?

<comment><USER29> Fermi's paradox would still hold for _this_ universe, methinks.

<comment><USER30> Yeah, but there could be a new parenthetical, "likelihood of an alien living in another universe, multiplied by all the effects of Fermi's Paradox itself, and multiplied by the likelihood of that alien's ability to traverse universes."

<comment><USER31> I think that would be a very small extra factor, especially if you also add the likelihood of that alien's interest in traveling to specifically this part of this universe.

And of course, we have no reasonable basis for estimating any of those numbers. We have a sample size of one universe and we don't even know the rate of occurrence of life in that one. Still, it can be fun to speculate.

</comment></comment></comment></comment><comment><USER32> Does this mean that black holes can be inside black holes?

<comment><USER33> Black hole version of inception! <http://www.imdb.com/title/tt1375666/>

Actually, this is much to complicated for my feeble mind. I am sticking with the "Parallel Universe Box" from the Futurama series (<http://futurama.wikia.com/wiki/Parallel_Universe_Box>). It makes my head hurt less.

</comment></comment><comment><USER34> So Black Holes are actually closures

</comment><comment><USER35> "You're very clever, young man, very clever," said the old lady. "But it's turtles all the way down!

</comment><comment><USER36> With my highschool physics i was lost at "since energy can create matter"...

</comment><comment><USER37> Sheldon Cooper, what do you think? Bazinga?

</comment><comment><USER38> Black holes and dark matter are likely to be just artificial constructs to rescue certain mistaken orthodoxies of astro physics.

Electrostatic force is much much stronger than gravity. It is known to be ubiquitous both on micro (sub-atomic) scale and on macro (cosmic) scale.

Once you acknowledge its existence, it is no longer necessary to postulate black holes in the centres of galaxies to hold them together.

<comment><USER39> I hope your kidding, but if your both serious and not trolling:

Like charges repel and opposite charges attract which prevents large scale accumulation of charge in even a tiny area by cosmic scales. Gravity while locally weak does not have these issues and you can keep dumping more mass into the same area more or less indefinitely.

<comment><USER38> Dumping more and more mass into a black hole until it holds a whole universe is, apparently, not enough. But, hey, isn't it lucky that there is now also an unobservable _dark matter and energy_ and just enough of it to make the 'gravity only' sums come out right? Hmmmm.

Does this really sound more plausible to you than that widely separated bodies in vacuum, large and small, can hold charge?

I hope you are kidding.

<comment><USER16> Keep reading your high-school textbook on physics and be sure to note the equations on calculating the force of an electrostatic charge over very large distances.

If you rubbed the entire galaxy with a cosmic-sized cat you still wouldn't have the effects you're postulating.</comment></comment></comment></comment>


The BS-Industrial Complex of Phony A.I. - <AUTHOR> https://gen.medium.com/the-bs-industrial-complex-of-phony-a-i-44bf1c0c60f8 <comment><USER3> At the Biennale in Venice (one of the most important art shows there is) I saw a work which looked like this:

There was a metal frame holding two glas plates with ventian sediment inbetween (sand, soil, mud). In the center there was another metal frame which formed a hole. There also were to PCB boards with ATMEGA micro controllers.

In the text the artist claimed she controlled the biome of the soil with an AI using various sensors and pumps.

This was clearly a fake, as you could see nothing like that on the PCB.

Accidentally (?) she managed to create the best representation of AI I have seen in art: all that counts is that you _call_ it AI even if it is a simple algorithm. AI is the phrase behind which magic hides and people _love_ magic. Everything that has the aura of "humans don't fully understand how it works in detail" _will_ be used by charlartans, snake oil salesmen and conmen.

If even artists slap "AI" onto their works to sell it, you know we are past the peak now.

<comment><USER4> >> Accidentally (?) she managed to create the best representation of AI I have seen in art: all that counts is that you call it AI even if it is a simple algorithm.

Backpropagation, which most researchers will agree is an AI algorithm, is a "simple algorithm".

So are many other AI algorithms, some of which are simple enough to be understood so well that most people don't recognise them as AI anymore: search algorithms like depth- breadth- or best-first search, game-playing algorithms like alpha-beta minimax, gradient descent/ hill climb, are the examples that readily come to mind.

I think the above article and your comment are assuming that, for an algorithm to be "AI" it must be very complicated and difficult to understand. This is common enough to have a name: "the AI effect". A few years down the line I bet people will say that "this is not AI, it's just deep learning".

There's no reason for AI algorithms to be complicated. Very simple algorithms can create enormous complexity, even infinite complexity. The state of deterministic systems with even a couple of parameters can become impossible to predict after a small number of steps if they have the chaos property. Language seems to be the application of a finite set of rules on a finite vocabulary to produce an infinite set of utterances. Complexity arises from very simple sources, in nature.

<comment><USER3> The point was that her PCB wasn't connected to anything at all. She claimed there were pumps and sensors, but there was literally nothing. There were cables etc and it certainly would fool someone who has no idea of circuit design and electronics, but I happen to know a bit about it and the circuit almost certainly didn't do what it claimed it did.

<comment><USER4> Ah, I see. I must have misread your comment. I thought you meant that the PCB didn't have anything like (a hardware implementation of?) an AI algorithm on it, not that it had nothing at all on it.

</comment></comment></comment></comment><comment><USER5> This happened right before the first AI Winter in the late 80s: AI (in the form of expert systems) solved a number of hard problems and was hyped as being able to solve _every_ problem. Reality set in when we figured out:

1\. It didn't scale and

2\. Getting 80% of the problem solved was easy, but getting that last 20% was very, _very_ hard. Maybe several orders of magnitude harder than the first 80%.

Nowadays we don't seem to have problem 1 quite so much, but problem 2 is still there in a big way. Witness self-driving cars, where driving on an interstate highway in broad daylight is easy, but driving through a snow-covered construction zone at night is impossible. Or just dealing with a bicyclist on the road without killing them.

We're not going to have AGI any time soon.

<comment><USER6> AGI is not needed for self-driving.

None of what is mentioned above is a deal-breaker for self-driving car service:

\- lidar at night works just fine

\- plenty of cities with no or very little snow

\- construction zones: blacklisting, remote monitoring & manual mapping, detection of cones, barriers, re-painted lanes

\- self-driving cars with 360 degree view and plenty of patience and no distraction are safer for bicyclists than manually driven cars

<comment><USER7> > _plenty of cities with no or very little snow_

But what happens that rare day it snows? Hundreds of deads? Cars get recalled for much less than that.

<comment><USER8> Drive it in manual mode like we do right now? I mean even an autonomous car that could handle 80% of normal driving just fine would be great. And by 80% I'm talking about driving on a freeway without crashing into a barrier in broad daylight.

<comment><USER9> Gonna suck for those people who don't own cars and are relying on the fleets of privately-owned pseudo-taxis.

</comment></comment></comment></comment></comment><comment><USER10> In 1949, some years after the invention of neural networks, Norbert Weiner, one of the leading minds of the time, was convinced that AI (AGI as you may call it) or a full understanding of the brain is no more than five years away. Alan Turing thought Weiner was delusional, and that it may take as much as fifty years. Seventy years later, we are nowhere near insect-level intelligence.

I don't see any fundamental barrier preventing us from achieving AI, but if someone from the future came to me and said that AI will be achieved in 2130, I would find that quite reasonable. If they said it will be achieved in 2030 or 2230, I would find those equally reasonable. Our current scientific understanding is that we have no idea how far we are from AI, we don't know what the challenges are, and we don't even know what intelligence is. We certainly have no idea whether the approach we are now taking (statistical clustering, AKA deep learning) is a path that leads to AI or not.

In the sixties, the leading minds of that time were also working hard on the problem and did not find it any further away from us as we do today. That some people are optimistic is irrelevant. The fact is that we just have no idea.

<comment><USER11> > Seventy years later, we are nowhere near insect-level intelligence.

That's arguable: For instance, we have the entire connectome of c. elegans mapped out; we can easily simulate it, and it seems to act the same as the actual nematode. So, in one sense, we are at that level.

However, we still have no clue how such a simple system actually works to produce the level of "intelligence" it has. So in that sense, we're not at that level at all.

> We certainly have no idea whether the approach we are now taking > (statistical clustering, AKA deep learning) is a path that leads to AI or > not.

One clue we do have:

We may not be on the right path with that method; it's something the "grandfather" (or whatever) of AI (Hinton) has mentioned, and which I have stated before about...

That is, the fact that we currently have no understanding of the mechanism by which biological neural networks implement anything like "backpropagation". From what we currently understand, as I currently understand it, we have yet to find such a mechanism that would allow for it.

It's also one of the leading reasons why our current artificial neural networks consume so much power, as compared to biological systems...

<comment><USER10> > For instance, we have the entire connectome of c. elegans mapped out... So, > in one sense, we are at that level.

Well, whatever "intelligence" C elegans has, I think everyone would agree that it's far from insect-level; it's microsocopic-nematode-level. But I am not sure a _simulation_ of C elgans rises to the level of "artificial". As you note, we don't understand it yet. But we may have already built systems that are more "intelligent" (whatever that means) than C elegans, and we may have done that decades ago.

> From what we currently understand, as I currently understand it, we have yet > to find such a mechanism that would allow for it.

True, but our path to artificial intelligence may not end up going through neural networks at all. We've not achieved flight by mimicking biological flight. I'm not saying it won't, either, but we cannot say for sure that it will. We really don't know.

</comment></comment></comment><comment><USER4> >> Deep learning algorithms have proven to be better than humans at spotting lung cancer, a development that if applied at scale could save more than 30,000 patients per year.

It's not easy to scale deep learning because deep neural nets have a very strong tendency to overfit to their training dataset and are very bad at generalising outside their training dataset.

In a medical context this means that, while a particular deep learning image classifier might be very good at recognising cancer in images of patients' scans collected from a specific hospital, the same classifier will be much worse in the same task on images from a different hospital (or even from a different department in the same hospital).

To overcome this limitation, the only thing anyone knows that works to some extent is to train deep neural nets with a lot of data. If you can't avoid overfitting, at least you can try to overfit to a big enough sample that most common kinds of instances in your domain of interest will be included in it.

So basically to scale a diagnostic system based on deep neural net image classification to the nation level one would have to train a deep learning image classifier with the data from all hospitals in that nation.

This is not an easy task, to say the least. It's not undoable, but it's not as simple as having someone at Hospital X download a pretrained model in Tensorflow and train its last few layers on some CT scans.

<comment><USER12> This statement is false, as recently demonstrated by DeepMind on retinal scans. Not only did they generalize outside of the training dataset _but they were able to use the features learned by the model on an entirely different type of scanning device_.

[https://www.nature.com/articles/s41591-018-0107-6.epdf?autho...](https://www.nature.com/articles/s41591-018-0107-6.epdf?author_access_token=PAbvHEuv_YYmrPVbG5HqKdRgN0jAjWel9jnR3ZoTv0P43NEH20hFuvBoJk6cvICihn8kmL6tmejFlnuPlbT_0KmJgK6N07SPh_ZLy0Nxb0-LAGIDBaH1fjJTkD9ahUEQpRlEudtlG9E1v3ca9xNQcQ%3D%3D)

"Moreover, we demonstrate that the tissue segmentations produced by our architecture act as a device-independent representation; referral accuracy is maintained when using tissue segmentations from a different type of device."

<comment><USER4> In the paper you link to, the researchers trained an image classifier on data collected from 32 sites of the Moorfiel NHS trust. The trained model was tested on, presumably held-out, data from the same dataset.

This is an example of scaling a model beyond a dataset collected from a single site. It is not contrary to what I say in my comment.

The researchers further tested their model on data obtained from a different device than it was originally trained on. This data was collected from the same hospital sites. The original model performed poorly on this new data and was re-trained to improve its performance.

This does not demonstrate an ability to generalise to unseen data- only an ability to adjust a model to new data, by re-training.

<comment><USER12> It contradicts your statement: "it's not as simple as having someone at Hospital X download a pretrained model in Tensorflow and train its last few layers on some CT scans" Because in this case it _was_ as easy as taking a model from a totally different modality and retraining the first (in this case) few layers to accommodate the new device. Furthermore the original training used 15k scans and the retraining only required 152 scans. This is totally reasonable and clear evidence of transfer and generalization. Moreover, even human operators require retraining on new devices!

<comment><USER4> My Tensorflow comment was a bit unclear. I meant that you can't just download a generic model like the kind that is readily available, e.g. one trained on ImageNet or CIFAR etc, and expect that you can retrain it easily and get a diagnostic tool that is competitive with an expert. The models in the paper you link were specifically trained on medical imaging data.

My point is that you need a lot of work to make this work even for one hospital, let alone scale to many, even more so scale at the level of a national health service. I don't see that the paper you link contradicts this.

Edit: if I may summarise: I said "it's not simple" not "you can't do it".

Transfer learning is not generalisation to unseen data. If the pre-trained model and the end model don't have any common instances it doesn't work [Edit: "don't have any instances with a common feature space" is more clear].

Also, you're talking about generalisation to new devices. My understanding is that this is only one aspect of the difficulties with scaling image recognition for medical diagnoses to data from different sites.

</comment></comment></comment></comment></comment><comment><USER13> In my opinion the term intelligence itself is misplaced for machine learning tasks. Every problem that is solved with ML and "big data" appears to me to be a perception problem (which wouldn't be surprising because the mechanism is inspired by human vision, not cognition, which it lends itself to naturally).

As a specific example, a few months ago or so openai released their text generation tool and branded it as "too dangerous too release", claiming it could , with the help of AI, generate believable texts.

But what it generated was simply natural sounding gibberish. There were plenty of sentences in the text along the lines of "before the first human walked the earth, humans did..""

What, for me at least, lies at the core of intelligence is understanding semantics. An intelligent system can recognise the sentence above as flawed because it could extract _meaning_.

Everything coming out of the field of ML seems to me just like sophisticated statistics. In many ways symbolic AI to me still seems more valuable, profit aside.

<comment><USER14> In the text generation tool outlined above (and indeed many of the convnet- based visual networks), the hidden layers are there precisely to extract 'meaning'. The lower layers (closer to the source input) deal with syntax and feed upwards to hidden layers that extract semantic features, which in turn feed upwards to more layers, each with a bigger overview of the semantic features and thus ultimately the context. That's the idea anyway.

<comment><USER15> >the hidden layers are there precisely to extract 'meaning

That is just wishful thinking, no? I mean, there is no particular reason to think that the hidden layers will actually do this with any high degree of success.

</comment></comment></comment><comment><USER16> I can attest. while doing research in a T1 university, all the professors were mildly disgusted by the hype pushed out by startups, and even Google's own internal marketing department.

Nonetheless they too are minting the same nonsense in the "introduction" part of academic research, it's a clear case of everyone is playing the game, so "I have to play or be left behind."

<comment><USER17> I used to work on fundamental molecular microbiology. We looked at what happened when DNA replication went wrong in E. coli.

What I used to do when writing or speaking about it was to start with cancer or antibiotic resistance as if anyone in my field gave a crap about either of those topics. Sure, we do care about those things in the broad sense, but we didn't consider ourselves to be on the front line of solving either of those problems.

</comment></comment><comment><USER18> The author seems confused about what artificial general intelligence is. People have not meaningfully moved towards AGI - it's still a distant pipe dream.

The closest we've gotten is probably a Dota bot that's pretty good as long as you give the bot a huge advantage. Which is an incredible piece of technology, but about as close to AGI as an ant is to a human.

<comment><USER19> Not even an ant. If AGI is a human then what we have is the equivalent of synthetic RNA molecules.

<comment><USER20> What? Ants don't have general intelligence, and even ant colonies' decision making (= simple swarm intelligence) is readily replicable in a programmed system, and has been, for a while.

I don't think a gradual scale is very helpful because I don't think that the progression from current-generation AI to AGI is going to be gradual (it will require at least one paradigm shift). That said, if you want to compare AI progress to actual animals then our current-gen AI _way_ beyond ants. Note that, while we haven't fully mapped the neurons/connectome of ants yet, this is unnecessary to emulate their decision-making power. And we _have_ mapped (and can simulate) the full connectome of simpler animals (e.g. _C. elegans_ , _P. dumerilii_ ) so we're definitely a long way beyond single molecules.

<comment><USER21> If you are referring to the open worm project, then the conclusions you have drawn are exactly the opposite of what I have drawn.

As I understand it open worm is a hodge podge of statistical and numerical methods to try and replicate the sensorimotor behavior of c elegans. Open worm is neither complete, accurate nor elegant, despite knowing c elegans connectome and having mapped the some 900 cells in the worms body.

<comment><USER20> I wasn't explicitly referring to that, it's just one of many efforts. Anyway, you're certainly right that none of the existing efforts are "elegant" but that's hardly relevant. What matters is that the connectome is fully mapped, and that we _can_ accurately simulate arbitrary behaviour. The issue with projects such as OpenWorm is that they have so far not been successful in generating new _insight_ (this may be connected to your issue with lack of elegance) but this is distinct from being able to accurately simulate behaviour. Another issue is that of simulating the physical environment because — surprise, surprise — simulating the worm neurons without any realistic external stimuli is a pretty pointless exercise for most purposes.

But pick any set of stimuli you like, feed it into the models and you get a response that corresponds exactly with empirical observation. I'd therefore definitely call the neuronal model itself accurate and complete.

<comment><USER21> No actually we can't do arbitrary simulation of c elegans. Can you link me towards a publication which contains validated results supporting your assertion?

</comment></comment></comment></comment></comment></comment><comment><USER22> The hype is BS but narrow AI in the context of automation is here. Job are so specialised nowadays (driving, cashiers, fulfilment, paralegal, diagnostician ...) that a narrow AI (i.e. a glorified automation algorithm) that can do just 10% better at a cheaper cost will take down the job. The confusion is real (AI, AGI, terminator...) but pattern recognition softwares powered with big data has already proven business value and are here to stay.

</comment><comment><USER23> > The technologists know it's bullshit. Fed up with the fog that marketers > have created, they've simply ditched A.I. and moved on to a new term called > "artificial general intelligence."

Not to detract from an otherwise excellent BS takedown, but unfortunately the author fails to mention that there's a non-zero possibility that AGI itself is merely taking the bullshit to the next level.

It continues to astound me how some technologists actually believe AGI is not just inevitable but around the corner. When to my naive perspective (as a machine learning rank amateur but with several decades experience as a professional human being) all I see is machines that can do some form of pattern recognition, but nothing resembling the common sense that the words "general intelligence" seemed to indicate at one point.

Minor quibbles about truth and meaning of words aside, I have to support any article that skewers the soft underbelly of the phony AI ecosystem as effectively as this one does.

<comment><USER24> The real issue we are facing is that everything that we thought was not going to be pattern matching and tree search has turned out to be pattern matching and tree search. I remember my father telling me computers were never going to be able to play Chess, because it required creativity for example. Nowadays a neural network with tree search plays chess that looks remarkably human. A lot of problem domains have fallen to what is basically pattern match and tree search.

Extrapolating the trend of the last 30 years, there _is_ evidence that computers will be able to solve every task a human can using pattern matching. If that isn't AGI, it might turn out to be better than intelligence.

The technological future is unknowable, so believing AGI is certain is too much. But believing it certainly isn't around the corner is also too little. If computers can do anything a human can intellectually, they have reached AGI. The list of discrete tasks (games, decision making once the parameters are defined) a computer can't do is a very short list.

If someone finds an objective function for deciding what decision parameters are important AGI could be upon us very quickly. As a postcript, I think people radically overestimate human intelligence.

<comment><USER25> I kinda see it this way:

AGI is Data in Star Trek TNG - trying to be human, making decisions to want to be alive, eventually dreaming and finally using an emotion chip. Another alternative here would be Moriarty or the various doctors in Voyager.

AI is the Ship in TNG - lots of heuristics to figure out what the user is trying to do. Past usage of commands and relating major events outside the ship with algorithms for battle, life support, etc.. Events categorized by importance and automatic handling to save lives when necessary. Basically an extremely advanced Siri that doesn't really misunderstand you - while at the same time not really caring about you or knowing anything about being alive other than the priorities built into its software.

I think for the next 100 years we're going to have AI progressing like the ship in TNG. I don't think we'll have AGI until maybe 100-200 years.

Then again when I was born no one had a fucking clue eventually we would have something like the iPhone and talk to someone in China with <1 sec lag. So my estimates could easily drop to half.

<comment><USER26> Your comment reminds me of this xkcd cartoon: [https://xkcd.com/1425/](https://xkcd.com/1425/)

It's 5 years old now (coincidentally the time span quoted to develop a solution), but recognizing a bird was already considered a solved problem 3 years ago, less than 2 years after the publication of the cartoon.

Predicting the future is hard.

<comment><USER27> Surprisingly, recognizing a bird is much harder when you count rare species and running birds. Thus, sparse data. Does it recognize penguins too?

AIs today still fail at it. Some folks were trying to train one to match endangered species and they had to pull mighty tricks to have some 70% accuracy. I think it was here on HN some time ago, but can't recall a link.

<comment><USER28> And yet, average humans can classify even fewer species.

<comment><USER29> With the same amount of training/data as NNs? I doubt it...

<comment><USER26> Do you take into account billions of years of training due to evolution?

<comment><USER29> Technically that's network architecture, not training data... admittedly though humans are "pre-trained" from birth.

</comment></comment></comment></comment></comment></comment></comment></comment></comment><comment><USER30> I've been collecting examples of where the ads that I see are based on extremely simple algorithms of the type that could have easily been supported 30 years ago, and yet I keep reading articles that suggest that the advertising industry is deploying sophisticated tools to target ads to me. I wrote about this recently:

\</comment><comment></comment><comment></comment><comment></comment><comment>-

Despite much talk about Machine Learning and AI improving advertising results, what I'm seeing is getting worse and worse. Despite billions invested, the ads shown to me are much less relevant than that ads that I saw on the Web 10 years ago.

I hired 3 developers from Fullstack Academy. They were all great, so I went and checked out the website, curious about the curriculum. And now, every website I go to, I see an advertisement for Fullstack Academy. (See screenshot.)

I've been writing software for 20 years. I've written semi-famous essays about software development. I am not going back to school. I do not need to go to a dev bootcamp. So why show me ads, as if I'm thinking of going to school?

For the last several years I've been seeing articles about the surveillance economy. In theory, advertisers know more about me than ever before. In theory, they know about my entire life. And yet, the ads I see are less targeted than what I used to see online 10 years ago.

[http://www.smashcompany.com/business/when-will-machine- learn...](http://www.smashcompany.com/business/when-will-machine-learning-and- ai-improve-advertisings-ability-to-target-people)

<comment><USER31> _So why show me ads..._

To keep the brand in your head so you post about it on Hackernews.

<comment><USER30> That's a "just so" story. You're looking at something that is easily explained by incompetence, stupidity and irrationally, yet you're working to transform it in your head into something rational. Take a moment to think of the money they've wasted and what do they gain? How likely is a sale? Did Fullstack imagine this scenario when they authorized their marketing firm to spend this money? Or is the marketing firm simply trying to spend money so they can bill for something?

<comment><USER31> I was being a bit facetious about you posting on here, but the point I was making was serious. In advertising there's a thing called the "effective frequency"[1] which is the number of times you need to see an ad before it has an impact on you. Obviously this series of adverts has worked on you - <USER2> know the brand and you use it as an example of which ads you remember. If the company is advertising in order to raise the level of engagement they're getting that's a fail; if their ads are intended to get people talking about the company that's actually a pretty good result.

There are more reasons to advertise your business that simply "getting more sales". Indirect communication is _very_ useful.

[1] [https://en.wikipedia.org/wiki/Effective_frequency](https://en.wikipedia.org/wiki/Effective_frequency)

</comment></comment></comment></comment><comment><USER32> If everyone sophisticated enough to be on this site would just use the term "applied computational statistics" (even just in their own thoughts) instead of "deep learning" or AI, the world would be a better place. Gradient descent finds some fun minimia (my current venture is heavily based on that idea) but to assign more agency to Adam or RMSProp than they merit is just an exercise in feeding the trolls.

<comment><USER4> Could you please explain in what sense deep learning is "applied computational statistics"?

What about classical planning, SAT solvers, automated theorem proving, game- playing agents and classical search? Could you please explain how one or more of those are "applied computational statistics"?

Further- I don't understand the comment about "agency". Could you clarify? Why is "agency" required for a technique or an algorithm to be considered an AI technique?

<comment><USER33> I don't know anything about the underlying algorithms for the examples you rattled off, but deep learning trains a graph of neuron weights such that they are statistically optimized to minimize error in computed output labels for some domain of input data. Very much "applied computational statistics".

<comment><USER4> The examples I gave are classic AI algorithms that are very easy to look up on wikipedia. They do not compute any statistics.

I'm not sure what you mean about "neuron weights that are statistically optimised". Modern-era, deep neural nets train their weights with backpropagation, which is basically an application of the chain rule, from calculus. They do not use statistics for that.

For example, calculating the mean of a set of values or calculating the pearson correlation coefficient of two variables are computations typical in statistics.

Could you please clarify what you mean by (applied) "computational statistics", so that I don't have to double-guess you?

Edit: Do you really not know what a SAT solver is? Not to be rude but if that is the case, from where do you draw your confidence about the correct terminology to use for AI?

<comment><USER34> He means that neural networks are applied statistics in that they solve a statistical regression problem. It's not conceptually different from classical methods of regression like least squares. The phrase "statistically optimized" is certainly a funky one, but regression is certainly as much a part of statistics as the two problems you mentioned.

<comment><USER4> That doesn't sound like what the OP was saying.

</comment></comment></comment></comment></comment></comment><comment><USER35> [https://outline.com/FP487e](https://outline.com/FP487e)

</comment><comment><USER36> Getting ready for the next AI winter.... this is a cyclic phenomena.

<comment><USER37> Hopefully we don't take decades again for a simple but important change like changing tanh to relu activations.

<comment><USER38> my bet is on capsule networks, Hinton is usually on point with his stuff

</comment></comment></comment><comment><USER27> Next: NoAI, like NoSQL. All natural real intelligence, fully organic and explainable. Just add caffeine. ;)

<comment><USER39> Brilliant.

</comment></comment><comment><USER40> In 1996 I made this AIML (Artificial Intelligence Marketing Language) parody by taking an actual VRML article from some shameless trade rag, and globally replacing "Virtual Reality" with "Artificial Intelligence".

(from "ArtificialPostModernIntelligenceInterActivity", V2 #4 April 1996, p. 20)

[https://www.donhopkins.com/home/catalog/text/SupportForAIML....](https://www.donhopkins.com/home/catalog/text/SupportForAIML.html)

Another closely related technology is BSML: Bull Shit Markup Language. (Note: most of the features described in the BLINK tag extension were eventually implemented by FLASH!)

[https://www.donhopkins.com/home/catalog/text/bsml.html](https://www.donhopkins.com/home/catalog/text/bsml.html)

At one point years later, somebody actually emailed me, asking me to take it down, because they were developing a "real AIML [TM]" product, and found my parody of their unique original idea to be beneath their dignity, distracting, and confusing to their potential customers using google to search for their prestigious "AIML" product.

</comment><comment><USER41> > In this way, Dynamic Yield is part of a generation of companies whose core > technology, while extremely useful, is powered by artificial intelligence > that is roughly as good as a 24-year-old analyst at Goldman Sachs with a big > dataset and a few lines of Adderall. For the last few years, startups have > shamelessly re-branded rudimentary machine-learning algorithms as the dawn > of the singularity, aided by investors and analysts who have a vested > interest in building up the hype. Welcome to the artificial intelligence > bullshit-industrial complex.

As an AI researcher, I think a lot of people are a little too sensitive to the term "AI" and make a lot of big assumptions upon hearing it. It's a very general term that doesn't really imply any particular degree of complexity or sophistication. Labeling simple machine learning algorithms and heuristics as "AI" isn't at all unique to this era of hype that began in the last ~5 years -- rather that's how the term has been used in academia for many decades. If you took a college class called "AI" or looked up some of the most popular textbooks on AI [1], you'd find that a lot of it is dedicated to search algorithms (breadth-first, depth-first, A*), linear classifiers, and feature engineering. If you think "artificial intelligence" is a bad name for these things, fine -- but don't blame the recent wave of hype, this is what the term AI means and has pretty much always meant. So go ahead and call your startup's linear regression "AI", and if the VCs leap to fund you under the impression that it means you'll be behind the singularity, that's on them. AI != deep learning. AI != AGI.

[1] e.g., "Artificial Intelligence: A Modern Approach" by Russell and Norvig

</comment><comment><USER42> "Deep Learning projects are typically written in Python. AI projects are typically PowerPoints."

</comment><comment><USER43> Of all the hypes going around (blockchain mostly lol) I think ai is going to have the most substance to it though. I would say the breadth of problems being solved are much wider and there is still a lot of research which hasn't really found its way to actual implementation yet

</comment><comment><USER44> I think honestly think westworld (yes the tv-series) has the best explanation of why general intelligence is a hard problem to solve.

They mention consciousness but I think the same apply to intelligence in general. Humans in my mind aren't different from say a program you write except that we have a lot more input and possible outputs depending on a much larger variant of external variables.

If we could build machines that have eyesight just as we do, muscles just as we do etc I'm sure we could reverse-engineer the human being.

[https://www.youtube.com/watch?v=S94ETUiMZwQ](https://www.youtube.com/watch?v=S94ETUiMZwQ)

<comment><USER45> I find this analysis reductionist. You're basically saying "brains aren't hard to reproduce once you have biological sensors and actuators." Why not? They're _extremely_ delicate, intricate organs.

Claim 2 is also a difficult one: of course you can easily claim consciousness doesn't exist, but it is impossible to argue by logic. You need a metaphysical philosophical framework, and then it's already left the realm of empirically observable truths.

<comment><USER46> I'm not sure the claim is that consciousness doesn't exist. More that it is an emergent property of complex systems rather than something that is (or can be) deliberately programmed.

<comment><USER44> Precisely. It's the complex system that gives us an illusion of consciousness. At least, that is what I naively believe in since there is a lack of evidence for anything else.

<comment><USER47> So you think experience is itself an illusion? When you kick a rock and feel pain, you're not really experiencing pain? Is the rock also an illusion?

<comment><USER44> Well it depends on how you view it. You feel the pain from kicking the rock and remember it, so you won't kick the same rock against a few minutes after.

That is an experience to me. An experience is simply a memory of an event/feeling etc. Without any memories, you won't remember any events or feelings and will gladly kick the rock again since you won't have any memory of it hurting you.

Or how else would you define an experience? A memory isn't an illusion, there is definitely something physical in your brain that say that that specific event has happened. But you can also remember things that haven't happened, which is probably why a lot of people believe in ghosts, religion etc.

I don't know why, but it probably serves a biological purpose and people are probably more likely to survive if they are afraid of things and are careful.

</comment></comment></comment></comment></comment></comment><comment><USER48> Everything is bullshit until is not, humans were talking about transportation without using animal forces for decades before it became a reality, and a lot of people were highly skeptical of such thing being even possible until it actually happened in 1804 (first steam train), same thing happens with Artificial Intelligence, and we are in such uncharted territory that someone could say AGI is just 10 years away and someone else say 100 years away and both get the same amount of credibility, meaning near none cause we don't even know what is that we don't know to achieve AGI.

<comment><USER49> Your example isn't quite as it seems : "trains" (cars running on rails) were used in mining for hundreds of years prior. The steam engine was first documented in 1698. What happened in 1804 was someone figured out the manufacturing processes to make a steam engine light enough and powerful enough to usefully pull a train of cars over some reasonable distance.

</comment></comment><comment><USER50> There is a lot of froth, as in any hot field. However, unlike before, there are many cases where AI actually works now. Some perceptual tasks work better than a human, in fact. We can quibble about the naming and whatnot, but that's not something you can say about the last AI winter. It's sort of like dotcom bust of 00, sure things imploded back then, but there's no sign whatsoever e-commerce will implode at any time in the future because unlike before it actually works this time.

<comment><USER51> _> Some perceptual tasks work better than a human, in fact. [...] that's not something you can say about the last AI winter_

Eh. I'd say that's somewhat apples to oranges.

A) There were some useful and successful expert systems.

B) Things seemed to be going swimmingly, until they hit a fundamental wall.

C) We're working with a few orders of magnitude greater compute than they had access to.

<comment><USER50> Sure, but we did figure out how to make things more robust and generalizable, at least for perceptual tasks so far. Knowledge representation and probabilistic reasoning are still non-existent, though. Moreover, nobody is even working on any of that, for fear of being compared to Doug Lenat.

<comment><USER52> Representation learning and probabilistic methods are huge sub-areas of modern machine learning, just take a look at the proceedings of ICLR2019.

<comment><USER50> Representation learning != knowledge representation, probabilistic methods != probabilistic reasoning. I'm talking foundations of AGI, which as far as I'm aware, nobody is seriously working on at the moment.

</comment></comment></comment></comment></comment><comment><USER53> AGI is a gradient, not an arbitrary threshold.

We are not capable of recreating human level intelligence yet, but our modern algorithms had become magnitudes better at generalization and sample efficiency. And this trend is not showing any signs of slowing down.

Take PPO for example (powers the OpenAI 5 dota agent), the same algorithms can be used for robotic arms as it does with video games. Two completely different domains of tasks now generalizable under one algorithm. That to me is a solid step towards more general AI.

<comment><USER54> It's a gradient but according to the marketers it's basically going to overtake humanity any week now.

<comment><USER55> "AI any week now"

What marketers proclaim that? Are they saying that or are they saying there is _utility_ in AI, now? Because me thinks, there is real utility, now, but it's going to take years until it overtakes us. Years!

<comment><USER56> I'm not sure anyone said any week now but Musk probably came closest [https://www.entrepreneur.com/article/323278](https://www.entrepreneur.com/article/323278)

</comment></comment></comment></comment><comment><USER57> Consider this article:

[http://fortune.com/longform/single-family-home-ai- algorithms...](http://fortune.com/longform/single-family-home-ai-algorithms/)

If you read it, you'll find that their methods to value homes and renovations are based on algorithms written to value mortgages in the 80s, 90s, and early 00s.

I'm going to bet that there's not much of what the average HNer would think constitutes AI going on in there.

</comment><comment><USER58> What's the most difficult thing AI should be be able to solve but can not as of yet?

I would say it is writing a program which writes an AI program. Why? Because it is so difficult for us to define what exactly an AI program should be able to do.

This shows that we have an issue with not being able to ask the right question. If we could answer exactly what the AI should be able to do then it would be much easier to create such a program and also create a program that writes such a program.

We could say that an AI program should pass the Turing Test and many have written programs that more or less pass it. But so now, write a program that writes several different programs that all pass the Turing Test one better than the previous one.

I don't really have an idea how I would start writing such a program that writes a program that passes the Turing Test better than previous AI programs. That makes me guess we are still far off from General AI. But I of course may be wrong, just because I don't know how to do something does not mean others would not.

<comment><USER59> We know what we want it to do, we want it to have some basic ability to think for itself. That is something we just simply can't do. Back propagation is far from a spanking for acting out of line. AI has no ability to understand it's acting like a fool or how to deal with uncertainty with emotions which cause us to act without regards to consequences and in many cases reality. It lacks understanding of what future consequences its trying to prevent such as our daily decisions to get up and go to work each morning. The AI has no understanding of it's future and the consequences of not going to work until it's fired 40,000 times for not showing up or it's children are taken from him/her/it.

I'm glad people are finally waking up to the fact that AI is not ML and AI is all hype at the moment. Google used algorithms quite effectively to adapt and learn, but they have no greater understanding of what we want, just what we and others have wanted in the past.

</comment></comment><comment><USER60> These types of AI promotion seem to be a sort of cop-out that suggest we all look forward to a hands-off future where computers will be able to do everything for us.

Then human minds will be allocated away from thoughtful interaction with their environment and into an all-hands-on-deck scenario where neural net operations are given top priority so they can churn out some answers.

<comment><USER61> My main short-term fear is that increased automation will lead to an increasingly isolated society. I can already get almost everything delivered through amazon, order takeout through an app without speaking to anyone. Watch movies on Netflix without needing to go to a video store. What's the world going to be like when drone deliveries become a thing, and I don't even have to speak to a delivery driver? How will it affect kids if they do all their schooling online?

I think that, even before AGI happens, AI assistants will become placeholder friends for a lot of people. You'll be able to have a conversation with Siri or Alexa. Eventually, people might have pseudo relationships with robot boyfriend/girlfriends. Imagine having a friend who is anything you want them to be, does everything you want, and most importantly, never challenges you or tells you anything you don't want to hear. People will get used to that, and it will become difficult for them to have real human relationships.

In other words, technology is enabling everyone to function without directly interacting with others. People might choose not to interact with other humans out of convenience, insecurity, fear. Japan already has a population of "herbivores", people who choose not to get into relationships, and the rest of the world could become like that too. I hope we find a way to reverse this trend.

Short documentary on hikikomori in Japan: [https://www.youtube.com/watch?v=wE1UIK85E3E](https://www.youtube.com/watch?v=wE1UIK85E3E)

<comment><USER62> I think your fear is misguided. People have been complaining about how technology is causing humanity to become more socially isolated for literally thousands of years, and the actual evidence has been that those complaints are unfounded. If anything, we've probably become more socially interconnected, but that's more due to the increased population density of our environs than technology changes.

What a lot of people miss, I think, is that human beings are fundamentally social animals, and we crave social interaction. And I say this as a strong introvert--as someone who has to be alone to recharge myself emotionally. Things like distance learning or working from home are not well-received by most people, especially not on a long-term basis. Sure, some people will find it comfortable, but those people are a tiny majority, and I should point out that it's not a new phenomenon: Emily Dickinson for the last 10 years or so of her life or so refused to meet visitors face-to-face and rarely left her house, which is more severe than most hikikomori.

</comment></comment></comment><comment><USER63> I totally agree with the gist of this article. This hype is being propagated by a lot of folks who are willingly clueless, as for example, in the data science crowd. This band-wagon is crowded and isn't stopping any time soon.

It irks me to no end to comme across tutorial-style articles proclaiming to teach an AI algorithm, also known as 'linear regression'.

What bugs me the most though, are the countless 'influencers' on LinkedIn which spew rubbish about machine learning, AI and all the wonderful things that are just around the corner.

Lastly, it doesn't help when countless articles/books are written on the subject of AI dangers, AI ethics and are 'robots coming for us?'. These add fuel to the fire of hype.

In the end, this behavior will only guarantee the eventual blowing-up of the bubble, when promises are not delivered.

</comment><comment><USER64> Is Medium for pay now? They told me to sign up to get "one more free story".

<comment><USER65> Medium lets writers opt-in to a paywall. It is not the default, but does come with some perks for authors.

</comment></comment><comment><USER66> So can I call it "second year linear algebra" now instead of "AI"?

</comment><comment><USER33> Sure, "AI" as it is used today implies "software that codifies decision-making using data". No, it's not the T3000. But as the author acknowledges:

> Dynamic Yield can pay for itself many times over by helping McDonald's > better understand its customers

Ok, so it's not hype - it is delivering real value. "AI" is just a marketing term to help C-suite suits and Silicon Valley sales reps get on the same page about what's being sold with as few words as possible. What's being sold is software that helps make optimal decision using data.

AI isn't a rigorously defined academic term, so people will use it how they want. It's only hype when real value isn't delivered.

<comment><USER67> > What's being sold is software that helps make optimal decision using data

Doesn't this apply to virtually all software?

<comment><USER33> In an extremely reductionist sense, maybe. Do I use Microsoft Word to automate decision making? No. Does Facebook help me make important life choices? Heh.

How about this: Amazon.com is not AI, but their recommendation engine is.

</comment></comment></comment><comment><USER68> There is a massive positive, though, for the 'geeks building the future": AI is where everyone else is looking. If you know where you _should_ be looking, you have a decisive advantage over the rest of the market.

<comment><USER69> Doesn't your last sentence go for literally everything?

<comment><USER68> Of course it does, but we can say with confidence that attention and capital are misallocated toward a specific, identifiable set of activities. _That 's_ rare.

</comment></comment></comment><comment><USER70> Been deleted, cached is at [https://webcache.googleusercontent.com/search?q=cache:RV8OOz...](https://webcache.googleusercontent.com/search?q=cache:RV8OOzgmjJsJ:https://gen.medium.com/the- bs-industrial-complex-of-phony- a-i-44bf1c0c60f8+&cd=1&hl=en&ct=clnk&gl=de&client=safari)

</comment><comment><USER71> > The Turk, also known as the Mechanical Turk or Automaton Chess Player > (German: Schachtürke, "chess Turk"; Hungarian: A Török), was a fake chess- > playing machine constructed in the late 18th century. From 1770 until its > destruction by fire in 1854 it was exhibited by various owners as an > automaton, though it was eventually revealed to be an elaborate hoax.[1]

[https://en.wikipedia.org/wiki/The_Turk](https://en.wikipedia.org/wiki/The_Turk)

</comment><comment><USER72> Progress of civilization could be summarized in the slow march of BS elimination parallel with the creation of creative new forms of BS (people don't actually learn anything, they just form the same crazy opinions about something new)

Strikeout "of Phony AI." The BS-Industrial Complex is huge and the rise of the Internet has made it worse by empowering the less-informed to share ideas. That is somewhat the price you pay for progress.

The hopeful idealistic information superhighway myth of the 90s turned into something else.

<comment><USER51> I look at BS as an inevitable symptom of the Singularity.

As we approach the capacity of human reason, fewer people are able to keep up with the world, and are therefore more susceptible to it.

<comment><USER72> I don't know, look back two thousand years and you see plenty of it. More like it's a symptom of humanity. Animals are stupid machines, humans aren't nearly as far away from them as we'd think ourselves.

</comment></comment></comment><comment><USER73> AGI will arrive as soon as someone can arrive at a reasonable definition of intelligence.

Try it. Everything I've seen is already achievable by computers.

<comment><USER27> Solving novel problems. Show me.

By novel I mean multiple categories. A system that can serve as archive, mathematician, calculator, can move a robot, drive a car and additionally make coffee from scratch. Oh and talks (speaks and understands and acts upon orders) in 3 human languages at decent levels plus can roughly explain what it's doing. Oh and can learn more unrelated skills.

Hey, people do it all the time.

<comment><USER73> _A system that can serve as archive, mathematician, calculator, can move a robot, drive a car and additionally make coffee from scratch. Oh and talks (speaks and understands and acts upon orders) in 3 human languages at decent levels plus can roughly explain what it 's doing. Oh and can learn more unrelated skills._

I'm a bit unclear if this is supposed to be a definition of intelligence.

Stephen Hawking would fail this test, but no one would argue he isn't intelligent.

</comment></comment></comment><comment><USER74> Imho it has gone further. In a way, all the things described as not actually AI now "are" AI, because the term AI has been used in that way so many times.

I don't think we'll ever use a better (more accurate) term for the ML- and data-driven value current systems create. Instead "true" AI will get a new fancy name to build the next hype around in several year.

</comment><comment><USER75> We should be focused on designing "smart systems" that optimize measurable outcomes

Who cares how complex the algorithm is! What matters is that it _works better_. Is there a measurable outcome that matters? Can the system optimize that outcome over time, through a coordination of human processes and technology design?

That is what organizations need. Not hyperparameters.

</comment><comment><USER76> It seems the author has deleted the post. Maybe Dynamic Yield asked him to take it down? Anyway, currently it is accessible through [https://outline.com/FP487e](https://outline.com/FP487e)

</comment><comment><USER77> It is pretty much spot on, but I'm not convinced anyone should really care. When was software not hype driven?

</comment><comment><USER78> This is no different than anything else. You hype what you're working on so people get interested and throw money at you. AR/VR glasses, AI, self driving cars, it's all the same. You generate interest, make lots of money and who cares if it ever gets to market.

</comment><comment><USER79> This reminds me of the article the other day about the internet being an SEO wasteland

Basically our business networks run the same way (not a shock at all): sycophants spam aristocratic investors with half assed bullshit solutions to juice the odds of hooking one

</comment><comment> East-Link Rudimentry machine learning algorithms are indeed AI, by common usage.

Try typing into Google Images something like "ai machine learning deep learning venn diagram" and you'll see that by common usage, machine learning is a strict subset of AI.

</comment><comment><USER14> For a real emperor's new clothes moment, look at SpinVox!

[https://en.wikipedia.org/wiki/SpinVox](https://en.wikipedia.org/wiki/SpinVox)

</comment><comment><USER80> The problem is when you work at a company that tells you, "we are different, we are not BS like the other A.I. companies"

</comment><comment><USER81> Just AI? IT is _filled_ with BS and fads. Dilbert is a documentary, not just a comic strip.

</comment><comment><USER82> Repeat with me: Machine Learning != AI

</comment><comment><USER38> I _despise_ the term Artificial Intelligence. This is all _PROBABILISTIC MODELLING_. Nothing to do with AI/AGI/whatever.

The computers aren't thinking or learning. It's just modelling fancy probability statistics.

E.g. classical neural networks are basically a load of linear regression equations with an activation function stuck on the end of each of them. No magic. Just lots of linear regression.

This stuff only works when:

1) you are trying to solve a specific problem that is suited to probabilistic models

2) you have a data set that is sufficiently large, varied and specific

3) the model is developed, trained, tested, implemented and updated in a rigorous and sensible manner

<comment><USER52> Actually most modern neural networks are not probabilistic, they are deterministic function approximators.

Also your point 3) isn't quite correct either, often a "standard" architecture and training procedure (e.g. ResNet50 with Adam) will work on a new task with sufficient training data and minimal modification of the model.

<comment><USER38> The only nnets I mentioned were "classical" as a purposefully over simplified example. Yeah, they can model any function, but historically they were used for probabilistic density functions (if I remember correctly).

Most of what the article talked about can be done with much simpler models, which is what I get peeved about.

Also, yes, you can transfer learn with resnet. But if I throw my bank statements at it, it'll do bugger all.

Similarly, if I throw new images at resnet in a silly way, it won't transfer properly.

<comment><USER52> You might be confusing the historical use of the sigmoid activation function with probabilistic modeling, neural networks in the 80s were used similarly to how they are today, albeit at a much smaller scale due to hardware limitations at the time.

The development of neural networks is a major contribution of the machine learning community, so even if you'd like to split hairs about whether the "computer is learning" ("learning" has a a precise technical definition by the way), NNs are not "just statistics."

<comment><USER38> Ok, it seems like there are some crossed wires or missing context here. Also, widely off topic.

I never said anything about the term machine learning. Check my bio, see what I'm working on. Fully aware of neural network contributions.

I'm all for machine learning. Just not "AI". "AI" is hype bullshit.

"Learning" when used by the people who spout this BS is not the technical definition version, and is what I was referring to.

Could probably have made that clearer, but I'm 1.5 days without sleep.

What does feeding test data into a network yield? Inference results. Inference seems vaguely familiar from probabilistic modelling?

Bayes rule applies to neural nets too. Two different models may give vastly different results. Whilst they can be very good approximators, they can also be very unreliable if care is not taken during training.

G(x) ~ f(w.f(w.x+b)+b) is literally a fancy weighted sum. A linear regression. It is some easy stats combined together with a few other things that aren't explicitly necessary, eg activation function can be identity to cancel out f().

EDIT Both the parameters of a network and the training data are variables in the application of Bayes rule. Which inherently deals with likelihoods (probability). /EDIT

So at their fundamental, they are "just some stats" stuff. They may have a few more bells and whistles to make them complex (and better) systems, but they still output a classification/regression based on inference.

You can, of course, approximate many functions with them. I've built a network with only weights of +1/-1, for example.

But those examples have extremely specific use cases that are not applicable to anything the article discusses.

</comment></comment></comment></comment></comment><comment><USER83> Seems like the post has been taken down.

</comment><comment><USER84> if there is no natural intelligence around, you need an artificial one

</comment><comment><USER85> TLDR; _machine learning == "AI"_, just as much as _colocated servers == "cloud"_

</comment><comment><USER86> Ben Goertzel.

</comment><comment><USER87> Paywall.

<comment><USER88> Not when you have Javascript turned off.

</comment></comment><comment><USER89> > The BS-Industrial Complex of

Brilliant! This is really a thing, and the computer industry is (and has always been?) rife with it.

<comment><USER90> IBM Global Services. Oracle. Accenture. Any company with 100+ employees who does consulting involving the design, implementation and maintenance of computer systems for _any_ government bureaucracy.

Is there anyone around here who thinks this industry sector is something else than industrial grade BS and if every single one of those companies disappeared overnight that we would not be in a better place as a civilization very, very quickly as we were forced to pick up the pieces.

Industrial quantities of BS are the norm, right? Most of us do startups to do _something_ more than to schmooze, threaten and ultimately bilk customers paying with other peoples money. We kind of want to do tech.

<comment><USER91> > Most of us do startups to do something more than to schmooze, threaten and > ultimately bilk customers paying with other peoples money.

Do you know Theranos ? That's the definition itself of bullshit and it was a "startup". [https://en.wikipedia.org/wiki/Theranos](https://en.wikipedia.org/wiki/Theranos)

Bullshit comes from 5000+ employees to companies with 5 dudes. Scale does not change anything.

 _Business culture_ , _profit as only value_ and the culture of _fake it until you do it_ are the source of the problem.

And against that their is not magic solution, excepted trust _a lot less_ the ones that speak and trust _a lot more_ the one that do. In the good old Nerd world, we named that _Show me the code_

</comment></comment></comment><comment><USER92> "I was able to bullshit about A.I., so the whole field is bullshit."</comment>