
One data scientist on the hype around artificial intelligence (2017) - contrarian_
https://builttoadapt.io/why-the-ai-hype-train-is-already-off-the-rails-and-why-im-over-ai-already-e7314e972ef4
======
rm999
This is a negative value article. It spends a lot of time dwelling on a low-
value semantic argument that "when people in business say 'AI' they really
mean machine learning". Sure, but these words have been used synonymously for
a long time - my grad degree ~15 years ago concentrating in machine learning
was called "AI"; who cares? Then the article goes on to claim:

>I anticipate that after this passes, we can start to do the right thing —
focusing on using machine learning to build things that are meaningful and
realistic.

UGHHH. Why did you hide this in a long article making the 180 degree
contradictory point that AI (which really means machine learning, remember?)
is dumb?

This article is a disservice to its reader because it downplays a huge shift
in the world that any ML practitioner should understand very well: machine
learning/AI will ultimately replace a lot of what humans do, and it's moving
at a faster pace than ever. I've led several small-ish projects (1-2 people,
3-6 months) that could replace dozens or even 1000s of experts in their
respective fields. There are thousands of people like me, and there are more
every day. The buzz may wear off, in the same way Time Magazine stopped
talking about how the internet was going to take over the world after the dot-
com crash, but the effects and the efforts will continue unabated.

~~~
maaand
> I've led several small-ish projects (1-2 people, 3-6 months) that could
> replace dozens or even 1000s of experts in their respective fields.

Can you give some specifics on projects?

~~~
rm999
In one project at a previous job we were able to pinpoint the genre, mood,
instrumentation, etc of any new song with (usually) better-than-human accuracy
in milliseconds using deep neural networks. This is better in pretty much
every way than the 1000s of music experts that are employed by competitors and
vendors.

[https://tech.iheart.com/mapping-the-world-of-music-using-
mac...](https://tech.iheart.com/mapping-the-world-of-music-using-machine-
learning-part-2-aa50b6a0304c)

Not to mention personalized recommendations, which basically aren't possible
at scale without some level of ML:

[https://tech.iheart.com/mapping-the-world-of-music-using-
mac...](https://tech.iheart.com/mapping-the-world-of-music-using-machine-
learning-part-1-9a57fa67e366)

[https://news.ycombinator.com/item?id=12269568](https://news.ycombinator.com/item?id=12269568)

The thing to keep in mind is every machine learning practitioner who is worth
their salary is doing stuff like this. A lot of our every day work isn't as
sexy as teaching a computer Go, but it's game-changing to most industries.

------
selljamhere
> artificial intelligence (AI), a sub-branch of machine learning

I could have a failed mental model, but I'm under the impression that the
relationship is the other way around. AI is a broad field encompassing various
strategies to build intelligent machines. ML is one particular strategy where
large volumes (think Big Data) of training data is used to teach by example.
(Which makes Deep Learning a subset of ML, where "deep" neural networks are at
play.)

~~~
solomatov
It's actually neither way. AI and ML have some intersection. There're machine
learning methods which have little to do with AI, for example, logistic
regression. There're AI methods which have nothing to do with ML, for example,
logical inference.

------
purplezooey
Dumb article. He talks about inflated expectations vs. real productivity, but
does not give any evidence.

I was expecting some evidence and then I hit "I hope you liked this article."

------
microtherion
The article mentions IBM trying to cash in on the AI hype with Watson, and
towards the end wonders what the next overhyped trend will be. This ad might
hold the answer:
[https://www.youtube.com/watch?v=2O2CLoCxAWA](https://www.youtube.com/watch?v=2O2CLoCxAWA)

------
solomatov
This is not hype. See here: [https://rajpurkar.github.io/SQuAD-
explorer/](https://rajpurkar.github.io/SQuAD-explorer/)

In this benchmark people or models are given a text, and later asked a number
of questions. Questions are quite real. See for example here:
[https://rajpurkar.github.io/SQuAD-
explorer/explore/1.1/dev/S...](https://rajpurkar.github.io/SQuAD-
explorer/explore/1.1/dev/Super_Bowl_50.html)

Models already have performance which are as good as human's. This is real.
This is not hype.

~~~
mark_l_watson
I agree! The question answering datasets, and some of the models built with
them are getting good. I work on GAN and general classifiers at work, but on
my own time my main interest is in sequential language models.

------
bitL
This time the hype is founded - Deep (Reinforcement) Learning truly pushed
many AI domains out of uncanny valley. Image recognition/generation, speech
recognition and synthesis, shallower language understanding - we truly have
tech we have never seen before and only dreamed about. Of course, it won't
solve everything, but the "solved level" got a massive upgrade with recent
advancements. If we get GPUs that can compute DNNs 1000x faster, then we will
see magic everywhere around us; so far the good models take very long to
train, making them less adaptable to changing conditions.

------
xtracerx
The cute robot soccer game is a disingenuous argument. Put a machine gun on
one of those Boston Dynamic robots with image recognition targeting and tell
me it's not scary.

~~~
mcguire
Well, yeah, 'cause you will never know when or where it's going to start
spewing bullets. (I'll just note that a Tesla just ran into a parked fire
truck.)

------
cosmosa
I stopped reading when it said AI is a sub-field of machine learning. It’s
actually the other way around.

------
strebler
This is a really good article, I'm impressed. If you read only one deep
learning article this month, make it this one.

I'm going to borrow that analogy of "teenagers perceptions of sex" \- it's
hilariously accurate for deep learning.

~~~
ghaff
I fully agree that big data has essentially been renamed to AI. Which makes
sense because AI today ~= ML/DL which require lots of data.

And, as someone who went through the data warehousing fad in the late 90s,
there's a lot of naive belief in pouring in a lot of data and magic happening.

That said, there has been a lot of advance that, once it's happened, we just
don't call it AI any longer. Route optimization (Google Maps), predictive
analytics in some domains, image recognition. Yeah, a lot of it is just fuzzy
pattern recognition but some of it is pretty good.

The more fundamental question IMO is how far DL can even take you. We've
actually seen a lot of progress there but we also haven't seen a lot of
forward motion in cognitive science for example. So do we just run out of
steam in some of the areas, like autonomous vehicles, where we think we're
doing pretty well today.

~~~
solomatov
Big data is not AI. Big data is processing of a giant datasets with quite
classic models, such as Logistic Regression in a distributed way. That's what
frameworks such as Spark and Hadoop do. AI or deep learning is different.
Usually, they don't distribute across many machines so well.

------
freecodyx
looks like the guy is trying to convince himself,

------
mcguire
But machine learning is a sub-field of AI.

------
Bucephalus355
I really enjoyed this article. AI has dramatically overpromised every decade
since 1960. Advances have always been made, but why do they have to exaggerate
so much about its potential?

I’m too scared to comment or say anything else though. It would be the
equivalent of saying something negative about bitcoin on r/Bitcoin.

~~~
aalleavitch
Part of why we hype is because it’s how we get advancement. Yeah realistically
the AI we all really want to see might be another three decades out, but if we
want that to happen we have to get investors hyped about it now.

~~~
jack9
> Part of why we hype is because it’s how we get advancement

That's a partisan overstatement. It's how some other "we" gets funding, at
best. Yes there's a correlation between getting funding and success, but to
say the correlation between hype and success directly, seems disingenuous.

