
The Business of Artificial Intelligence - matco11
https://hbr.org/cover-story/2017/07/the-business-of-artificial-intelligence
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randcraw
Recent advances in AI still have some big limitations, especially that all the
benchmarks at which it has excelled are 1) synthetic and 2) end points.

First, real world telemetry is very often less clean than the synthetic data
presently used to train face or word recognizers. In the wild, bad lighting,
poor focus, motion artifacts, occlusions, odd angles, floppy hats and
unfamiliar expressions will reduce accuracy enormously when used in the real
world. This often isn't a game stopper when the usage is lightweight (single
picture matches, willingness to re-scan the object, patience from the user,
etc). But in "live fire" situations like driving a car, the cost of
misidentifying road signage, pedestrian behavior, or failing to perceive
imminent risk can be very dire. In fact, good performance in synthetic situs
like a lab are a LONG way from practical utility outside the lab.

Second, because these benchmarks are end points, where the only objective is
to put a label on each input and add them up to gain a high total score, there
is little impact after a label fails, when it misclassifies an object or
misunderstands a word, and has to deal with the consequence.

Beyond mere benchmarks, when a situated AI is obliged to process a sequence of
data and reactions to each appropriately (driving past street signs like
"BRIDGE OUT" or "BEWARE OF DOWNED POWER LINES" or "DEAF CHILD") and the AI
fails to understand some or all of an unfamiliar observation, unless it's also
able to ask itself questions like "What ELSE might that sign mean?" or "Maybe
I should slow down here because I may have misread, or because flooding is
common hereabouts", the consequences of a 5% error rate on synthetic
benchmarks looks a lot less favorable.

IMHO, AI is still a long way from doing more than advising humans. In a great
many practical use cases, I suspect AI is a nowhere near robust and reliable
enough to take the reigns from the status quo.

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hacker_9
I agree, and there has already been one fatality from when a Tesla system
failed to classify a lorry that was in the way. What we have is really a great
advancement of mimicking the sensory organs (eyes, ears), but we still haven't
figured out the thinking component.

'Seeing' is more than just classifying pixels, it's also about understanding
the visual relationships between objects and being able to act on that data.
How this fits into our current view of neural networks I've no idea, and worry
that self driving engineers just fill this part in with lots of 'if .. else if
...' statements, which of course really isn't the same as actual thinking.

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greglindahl
A fatality caused by a driver not remaining in control of the car. If the
Tesla system was Level 3, it would be Tesla's fault. It was Level 2.

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amelius
I'm looking at this table of applications, but one application is mysteriously
missing: Search. What kind of ML is Google using in its engine?

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jorgemf
On one hand you have all the information it extracts from non text sources:
books, images, audio, video, non English languages, etc. They use machine
learning from them. On the other her hand they also use ML in the search
engine to understand better your query (that is why it shows results that
sometime doesn't have the keywords you typed), to rank the results based on
what you would like more, etc

So the question is where Google doesn't use ML. If there is data there is a
way to apply ML and create value.

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mark_l_watson
Great article. I also believe that for the near term (next ten years [1]) the
most significant use of AI will be systems that cooperate with human expert
users.

I also agree that leaders who are creative in determining novel use of ML and
who support rapid experimentation, might have more impact than technology
leads.

[1] I believe that further out than ten years, general AI systems with broad
knowledge and less need for labeled training data will become more valuable
than AI/human teams.

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radarsat1
> the most significant use of AI will be systems that cooperate with human
> expert users.

except instead of calling it AI, we'll call it "photoshop filters" :P

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mark_l_watson
You might enjoy this article about AI enhanced animation workflow
[https://medium.com/@lara.petrov/towards-new-ai-based-
animati...](https://medium.com/@lara.petrov/towards-new-ai-based-animation-
ba61cf7e89ba)

~~~
radarsat1
Oh yeah, cool, I agree there are lots of applications of this technology for
user-centered stuff. :) My point was more that as we get "used to" using it,
we'll often cease to call it "AI". The moving goalposts problem..

