
AI is transforming Google search – the rest of the web is next - nzonbi
http://www.wired.com/2016/02/ai-is-changing-the-technology-behind-google-searches/
======
lowglow
I can't be the only one that considers all these AI articles just smoke and
mirror puff pieces to prop up a company's value by capitalizing on the hype
(hysteria?), can I? I think the first flag is that the journalists don't seem
to really understand the technical capabilities or limitations of current
ML/AI applications. They accept grandiose claims at face value because there
is no way to measure the real potential of AI (which the promise of seems
limitless, so anything appears plausible especially coming from a big company
like GOOG).

I think there are a couple of really overhyped areas right now, AR/AI/ML and
IoT/IoE. Now while I don't mind the attention and money being thrown at tech,
I can't help but feel we're borrowing more against promises, hopes, and
dreams, while simultaneously under-delivering and I think that's going to hurt
tech's image and erode investor confidence sooner than later.

~~~
hh2222
And ... I can't be the only one that thinks Google search results just aren't
that good anymore.

~~~
joe_the_user
Yeah, if anything, the "AI" part of the search has been part of the decline.
Google aggressively gives me what it thinks I want rather than what I ask for.
It seems like it's very clever in giving me something like what an entirely
average person would likely want if they mistakenly typed the text that I
knowingly and intentionally typed ("Kolmogorov? do you mean Kardashian?" etc).

The search does seem able to understand simple sentences but there's much less
utility in that than one might imagine. Just consider that even an intelligent
human who somehow had all of the web in their brain couldn't unambiguously
answer X simple sentence from Y person whose location, background and desires
were unknown to them. Serious search, like computer programming, actually
benefits from a tool that does what you say, not what it thinks you mean.
Which altogether means they're a bit behind what Alta Vista could give in the
Nineties but are easier to use, maybe.

Part of the situation is the web itself has become more spam and SEO ridden
and Google needs their AI just to keep up with the arms race here. So "Two
cheers" or something, for AI.

~~~
vram22
The "did you mean" part reminds me of this:

[https://www.google.com/search?q=recursion](https://www.google.com/search?q=recursion)

Typical Google :)

------
osmode
There is a tendency among non-technical admirers of ML to regard deep learning
methods as beyond their creators: independent entities that will one day,
given refined enough algorithms and enough energy, out-comprehend their human
creators and overwhelm humanity with their artificial consciousnesses. The
term “neural networks” is itself a misnomer that doesn’t at all reflect the
complexity of how human neurons represent and acquire information; it’s simply
a term for nonlinear classification algorithms that began catching on once the
computing power to run them emerged.

The question of whether or not deep neural networks are capable of
“understanding” is largely a theoretical concern for the ML practitioner, who
spends the bulk of his or her time undertaking the hard work of curating
manually labeled data, fine-tuning his or her neural classifier with methods
(or hacks) such as dropout, stochastic gradient descent, convolution and
recursion, to increase its accuracy by a few fractions of a percentage point.
Ten or twenty years from now, I imagine we’ll be dealing with a novel set of
ML tools that will evolve with the rise of quantum computing (the term
“machine learning” will probably be ancient history, too), but the essence of
these methods will probably remain: to train a mathematical model to perform
task X while generalizing its performance to the real world.

As fascinating and exciting as this era of artificial intelligence is, we
should also remember that these algorithms are ultimately sophisticated
classifiers that don't "understand" anything at all.

~~~
proc0
This is true of ANN, and deep learning. They are mathematical models of
learning that are finally practical after a couple decades (not to diminish
anything the researchers have accomplished which is incredible).

Then there are biologically inspired neural networks, like Hierarchical
Temporal Memory (HTM), that actually correlate directly to how the cortex in
mammals work. These have also demonstrated learning capabilities, and seem a
lot more promising in the road map to general AI, in my opinion, because after
all we should be piggy-backing on evolution (not that we can't find a
mathematical model first).

So yeah, the hype is just hype, but it could be justified for the wrong
reasons if we see breakthroughs in biologically inspired AI (the Brain
Project, to name another example).

~~~
argonaut
Demonstrated learning capabilities? I have not seen HTM models make any
breakthroughs on any benchmarks. It's also stretching the facts to say it
directly correlates to how mammalian cortexs work. At best, you could say it
directly correlates to some theories on how mammalian cortexes work -
neuroscience has an incredibly poor understanding of brains in general.

------
rifung
> At one point, Google ran a test that pitted its search engineers against
> RankBrain. Both were asked to look at various web pages and predict which
> would rank highest on a Google search results page. RankBrain was right 80
> percent of the time. The engineers were right 70 percent of the time.

I don't really understand the point of this metric. Why are they predicting
what ranks highest on Google search? Wouldn't a better metric be who predicts
the correct place a user was looking for?

Is the thinking that if they are using machine learning, than whatever the
user is looking for should have bubbled up to the top?

~~~
speeder
I am in the recent days having the impression that Google whatever it is doing
is focused more and more in presenting to the user google biggest clients, and
hoping that it will be useful.

Because I am having more and more trouble finding what I want, people used to
consider me a master of google fu, finding whatever random stuff they wanted,
now I am struggling, specially after google changed the + and "" meaning (+
went from "mandatory" to mean "google plus search" and " went to mean "literal
string" to mean "a sort of mandatory thing")

If I need to find some obscure term, I know now that google won't find it,
despite finding that same term in the past, finding pages with a certain
information on it never happen anymore, even using the "" thing.

For example I own a ASUS N46VM laptop with nVidia Optimus... this laptop is
terrible, and I am always having to look online how to make it behave
properly, before the "+" change, I could type +N46VM and be guaranteed I only
would get relevant ifnormation... recently I was desperately searching for
some stuff, and found out no matter what I input on google, it returned
completely bogus results, where the string N46VM was nowhere in the page, not
even in the "time" dimension (ie: if I load the page on archive.org for
example and scan every version of it, N46VM never had been on it, google just
heuristically decided the page was relevant and gave it to me wrongly).

EDIT: I am having some success with DuckDuckGo

although their research system is clearly cruder than google, having much less
heuristics and whatnot, frequently I find the stuff I want easier on
DuckDuckGo anyway, after some pages of browsing results... while on google I
browse 40 pages and all of it is completely irrelevant and unrelated (while on
DuckDuckGo it shows me 40 pages with the term I want, but in the wrong
context).

~~~
camhutch
There is a "verbatim" mode of search that may help in these cases, which looks
like it turns off a bunch of search heuristics. When you get your result,
there's a "Search tools" button that reveals a dropdown that defaults to "All
results". In that dropdown is a "Verbatim" option. Try that.

You can add the query parameter "tbs=li:1" to get verbatim results right away.

~~~
username223
> "verbatim" mode... which looks like it turns off a bunch of search
> heuristics.

Ugh. This sounds like one of those families of PHP escaping functions:
`escape_string()`, `really_escape_string()`, `escape_string_all_the_way()`,
`no_really_i_mean_it_this_time_escape_string()`, etc. Google seems to have
improved somewhat at fighting SEO spam, but their efforts to "helpfully"
change queries have consistently made their service worse.

------
Houshalter
This is very interesting. As late as 2008, Google said they don't use any
machine learning in search. Everything was hand engineered with tons of
heuristics. They said they didn't trust machine learning, and that it created
bizarre failure cases.

~~~
adenadel
Do you have a source for that? It's a really fastcinating claim that I'm
interested in reading more about.

~~~
dhj
I believe it considering 2006-2008 was when all the deep learning pieces came
together (some parts were decades old, some 5 years, some 2 years). Google's
main push in ML is with deep learning. Although, I would like to see the
source too. Tried to find it using Google, but no luck! :)

~~~
dvlat
"Are Machine-Learned Models Prone to Catastrophic Errors?"

[http://anand.typepad.com/datawocky/2008/05/are-human-
experts...](http://anand.typepad.com/datawocky/2008/05/are-human-experts-less-
prone-to-catastrophic-errors-than-machine-learned-models.html)

------
ohitsdom
> The truth is that even the experts don’t completely understand how neural
> nets work.

I'm no AI/ML expert, but I can't believe this is true... Is it?

~~~
johnm1019
In my experience doing 3D image reconstruction from 1D and 2D sensors, there
are often situations where the engineers can only explain what's going on
using math equations - and not in any other qualitative kind of way. For some
people, this might be considered not "completely understanding" something.

~~~
niels_olson
I submit I know of no one who can honestly and verifiably say they understand
quantum spin without equations either. It just falls out of the math that way
and is highly verifiable by experiment.

~~~
goldfeld
Can't you at least explain the fundamental need for quantum spin (the why) out
of symmetry principles of the universe?

------
reza_n
RIP big data. Hello AI. Makes sense, data drives a lot of 'AI' tech. I guess
what I find amusing is the push from Google to rebrand themselves as an AI
company. My guess is it won't be too long until we see everyone else jumping
in the AI branding boat. That will kind of dilute a lot of what is being done.

------
varelse
I can understand Amit Singhal's opposition to replacing hand-coded features
with machine learning models. He's right that ML models have bizarre failure
cases across large sample sizes, but he's apparently career-endingly wrong to
seemingly believe that one cannot do anything about it. He's also wrong IMO to
not recognize that hand-crafted signals and features lack bizarre failure
cases themselves.

IMO this shifts the focus from lovingly hand-crafted signals and features to
lovingly hand-crafted loss functions and variants of boosting and training
algorithms to address those bizarre failures as they occur. For example,
recently much ado was made about minimal changes to the input data to image
recognition convolutional nets to spoof the object ID. And the simplest remedy
is to augment the training data with these cases and perhaps boost the
gradients of outputs that are wrong. It's not perfect, but Google search was
never perfect either. Evidence: I was on the Google search team for a bit and
we had all sorts of meetings to address such failures as they happened.

While I agree that the quality of Google technical searches has declined
dramatically recently, I believe there's huge opportunity to fix them by
understanding why the ML models are failing (shooting from the hip, I suspect
it's a long-tail problem writ large) and changing the loss functions, models
and training algorithms to address these failures as they're detected.

Anything less IMO is a failure of imagination in an age of 6.6 TFLOPS for
~$1000 and the ability to stuff 8 of them into a $20K server and go wild.

~~~
tyingq
Older quora answer from a googler citing some of Amit's thoughts on the
matter: [https://www.quora.com/Why-is-machine-learning-used-
heavily-f...](https://www.quora.com/Why-is-machine-learning-used-heavily-for-
Googles-ad-ranking-and-less-for-their-search-ranking)

------
amelius
The current trend seems to be to put a human behind a web API.

I guess when AI is sufficiently advanced, those humans can be seamlessly
replaced by computers.

------
hyperpallium
I recall google engineers complaining that their clever insightful carefully
engineered code was soundly beaten by a statistical approach.

The current approaches aren't so much AI as having really, really,
ridiculously large datasets.

------
known
No alternative to AI for Google
[http://www.bbc.co.uk/news/technology-23866614](http://www.bbc.co.uk/news/technology-23866614)

------
graycat
Tech hype is a little like old spontaneous combustion of some oily rags in the
corner: No telling just when they might ignite, but when they do the result
can be a big fire, for a short while.

Once the hype gets a flicker, there are good sources of more fuel to make the
fire bigger. E.g., the situation is old, say, back to the movie _Lawrence of
Arabia_ where a news reporter was talking to Prince Faisal and said: "You want
your story told, and I desperately want a story to tell.". So, tech people who
want their story told get with tech journalists who desperately want a story
to tell.

One such case doesn't mean very much, but once the _fire_ starts, more techies
and more journalists do the same because the fact that there are already lots
of stories gives each new story some automatic credibility.

But, fairly soon the stories get to be about the same, with little visible
progress (usual situation in reality), and interest falls, the bubble bursts,
becomes yesterday's news. Then, the world moves on to another source of a hype
conflagration, bubble, viral storm, whatever.

For AI, by 1985 DARPA funding at the MIT AI Lab had gotten AI going. There
were _expert systems_ and more. Lots of hype. In a few years, the fire went
out, the bubble burst, and there was _AI winter_.

For the next bubble, say, System-K (right, doesn't mean anything), print up
some labels about System-K. Then order a gross of children's bubble bottles,
right, soapy water with a plastic stick with a circle at the end good for
blowing bubbles. Put the labels on the bottles and send them to various
departments at Stanford, start up companies in Silicon Valley, VC firms on
Sand Hill Road, and tech journalists. Then stand back and watch the media
conflagration for System-K! So, get stories:

"System-K -- Next Big Thing"

"System-K Deep Background"

"Ex-Googlers Respond on System-K"

"System-K, Son of AI"

"Leading VC Talks about System-K"

"Silicon Valley Goes All in on System-K"

"System-K, Bigger Than the Internet"

"The First System-K Unicorn?"

"System-K Trending up"

------
PaulHoule
With the Google Knowledge Graph they don't need the rest of the web.

It's starting to get rare to see organic results to web pages more than most.

------
inaudible
I don't quite understand why people want to dismiss examples of machine
learning as valid techniques for understanding the human environment.. It's
not as if the human brain was built and guided from nothing, many of the same
adaptive principles are as present in our minds as they are in other mammals
and equally so from where all the branches divide. Even tiny organisms. And we
seems to center the brain at the core of humans intelligence, when there's a
range of chemical and metabolic coordination going that might bypass the brain
entirely.

It's efficient, failure resistant models that matter. We're talking about
accelerated learning, finding the models that work out of all those many
iterations that fail. You can model it, decompile the results and try to
understand and emulate what makes things seem real, but we don't even need to
analyze it, because case by case it changes and it's circumstance makes things
very different. 'Many ways to skin a cat'.

I think the challenge of the future is finding the general API that can
negotiate all the things and make all the parts communicate, the kernel if you
want. We can determine optimum speech algorithms, babel communication, create
seeing eyes that recognize objects, optimize forms that can negotiate physical
terrain, work out what is meant in human expression, but it's not until all
these units work together that the 'AI' will seem seamless in human terms.

All of those parts have discreet forms, they generate a lineage of algorithms
from iterations based on code, languages often derived from need. A Lisp might
be the best way of interpreting language, a Haskell might be work best for
defining strict biomechanics and area physics. Different abstractions are
better for the results they are designed to intuit. But when we are to create
the ultimate neural net, the composite of all these machine languages that are
constantly required to optimize beyond human intelligible understanding, what
will be using? What structure will state 'this works good enough' to not
bother with the computation any more - in the familiar context of why don't
our eyes have faster frame rate, need better detail, or need us to see into
UV. What regulates such a machine, and how does a machine understand failure
without guidance?

I like to think of these questions when I see rough examples posited around
potentials in machine learning. Getting one human system sorted is one thing,
communicating the results to other sub-systems an optimize concurrent results
is another. The data model is too huge to even comprehend!

I'm just excited that these things exist, that there are individuals, research
groups and companies looking at the what makes us 'us'. It might help us
unlock the features of the brain and evolution.. Used for commercial gain -
who cares, just a small cog, with revenue to continue development.

Just going to add my favourite example of machine learning, not because it's
'best' but because it's so dynamic that you feel the wonder.
[http://www.goatstream.com/research/papers/SA2013/](http://www.goatstream.com/research/papers/SA2013/)

------
jonesb6
[https://en.wikipedia.org/wiki/List_of_fallacies](https://en.wikipedia.org/wiki/List_of_fallacies)

If anyone wants to practice their critical think skills, see how many
fallacies you can spot in this article.

------
wangii
One part of me truly hope Google to success in ML/AI, although I consider
Google an evil company. AI, Singularity, they are the most important things in
this century. The implication is simply beyond our imagination. I don't care
too much if Skynet takes over the earth and kicks human being into the
dustbin. If it's the destiny, so be it.

Another part of me believe it's a sign of folks don't know what they are
doing, writing. How can we achieve AI without understanding? Google will fall
apart.

