
AI Playbook - febin
http://aiplaybook.a16z.com
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stanfordkid
It's really quite interesting how A16z is playing this. I've been following
the types of content that they release -- and I think their vision is that the
a16z brand can almost function as a consultancy with (not only) direct
channels to the enterprise, but also deep technical knowledge of their
problems. In the old world consultancies most of the money went to the
partners -- but top engineers didn't get to rake in the profits.

In the modern world, top engineers can band together, raise VC funding, build
some stupid app and get acqui-hired for 5-10x the salary. Huge discrepancies
in comp.

The natural progression is that VC funds build channels and in-house expertise
on technical problems in enterprise. Top engineers raise funding and are
guided by partners towards solving these problems.

The new model is not that enterprises pay consultancies to solve problems, but
instead, they form long standing trust based relationships with VC's who then
fund companies that solve their problems (and profit when the companies
profit). A big part of making this differentiation happen is releasing content
that educates leaders and implementers within such enterprises.

~~~
g10r
Yes, similar to what CAA did for the entertainment agency world. You may find
it interesting to read, Who is Michael Ovitz?

~~~
tw1010
Woah, now it suddenly makes sense why the a16z podcast interviewed people from
CAA a while ago.

~~~
g10r
There's a long-standing relationship there.

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lvh
> We've met with hundreds of Fortune 500 / Global 2000 companies, startups,
> and government policy makers asking: "How do I get started with artificial
> intelligence?" and "What can I do with AI in my own product or company?"

You're definitely never in a concerning part of a hype cycle when you have a
technology in search of a problem. How many of these organizations just needed
someone who could write a SQL query?

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brahmwg
I generally agree that SQL could do alot of what these companies need, and not
AI/ML. That said, there is definitely proper use cases for AI/ML and SQL
cannot address them all. Furthermore, employing AI/ML may not just be to solve
whichever problem they are trying to solve, but it may also be used to impress
investors and stakeholders through the use of buzzwords; AKA, using AI/ML may
be out of FOMO, used not only to address a real business problem but also to
show stakeholders that they are keeping up with trends.

Related
[https://news.ycombinator.com/item?id=16898827](https://news.ycombinator.com/item?id=16898827)

~~~
lvh
Sure! My position is definitely not "ML is useless"! I've written plenty of ML
and particularly think the current focus on DL is missing a lot of
opportunities for augmented human intelligence (and risk mitigation of
systematized bias) with explainable models.

As for showing stakeholders they're keeping up with trends: yeah I'd
definitely categorize that as regrettable :-)

~~~
g10r
Well, one reason to "keep up with trends" is because if your direct competitor
does use ML to unveil some market/product/business opportunity or
optimization, and you (executive/CIO/CEO) weren't at the least looking into
the technology, heads are going to roll.

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tedmiston
It looks like this was launched in May 2017. There's more context in the post
below:

> We’ve met with hundreds of Fortune 500/ Global 2000 companies, startups, and
> government agencies asking: “How do I get started with artificial
> intelligence?” and “What can I do with AI in my own product or company?”

> While there are many excellent tutorials out there that show how to use
> TensorFlow or the beautiful math behind neural network training, we couldn’t
> find a broad overview — a “Chapter 0”, if you will — for product managers,
> line of business leaders, strategists, policymakers, non-AI developers to
> read first before moving on to more technical materials. So building on our
> popular primer on artificial intelligence, today we’ve launched a microsite
> to help newcomers — both non-technical and technical — begin exploring
> what’s possible with AI. The site is designed as a resource for anyone
> asking the two questions above, complete with examples and sample code to
> help get started; no computer science degree required! Ultimately, it’s
> aimed at people who aren’t only studying AI in universities or labs and just
> want to get their hands and heads around it as they explore options for
> their own companies.

[https://a16z.com/2017/05/12/ai-playbook/](https://a16z.com/2017/05/12/ai-
playbook/)

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saurabh20n
It would be useful to have a section: "What AI cannot do" or to be pedantic:
"What the current AI techniques cannot do."

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sonnyblarney
I appreciate this article; I'm impressed when intelligent people can take
complex things and explain them in laymen's terms.

I buy the idea that AI will be like RDBMS.

Except ...

RDMBS is tangible, straight forward. Easily applicable.

AI is indirect, soft.

So while I agree AI will find it's way into most things - and - will be a
critical feature of some things (i.e. it will enable self driving cars) ... I
still think it's over hyped.

It's a new and interesting field that is just too vague and 'non-
parameterizeable' to provide value in so many ways.

Remember 'Big Data' \- it was mostly an optimization. Most businesses simply
don't depend on data in such quantities, and when they can make use of it,
it's often just a tweak to their business, not a deep strategic insight.

If we see an explosion in GPU type computing, wherein AI experiments are able
to grow maximally, perhaps we can dream a little bigger ...

But in the meantime 'there be a lot of hype' around this subject.

Kudos for the article, though.

~~~
hadsed
I think it is soft to the extent that you need an expert to define precisely
the bounds of your problem and the AI solution. However, consider the
following situation today: you have millions of images that are fairly related
(perhaps on a real estate listing site) and tags. You can now
straightforwardly build a product for predicting hashtags and describing the
photos without knowing hardly anything about AI. There are plenty of products
to help engineers do this already, one of which is Google cloud's ML products.

Now imagine that at some point a lot of the AI problems get to a point where
they can figure out the correct training procedures automatically. People are
working on this with varying amounts of progress but I think the future looks
like we'll be able to do enough of this to sell/open source something as well-
defined as a SQL database.

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sirthemano
I wonder if they made this because they're struggling to get enough high
quality AI startups coming their way. A decade ago you wouldn't really see VCs
do this kind of stuff.

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whyte_mackay
Interesting that I always used Andreessen's: Why software is eating the world?
[1] as a temper to AI hype. You can replace "blockchain" with "distributed
database" or "AI" with "software" and look at the result for if the use of AI
or blockchain was just buzzword lingo or actually semantically relevant.

But now they have a chapter: "Giving Your Software AI Superpowers", which
breaks this technique hard.

AI's history, to me, starts with Operational Research:

> Employing techniques from other mathematical sciences, such as mathematical
> modeling, statistical analysis, and mathematical optimization, operations
> research arrives at optimal or near-optimal solutions to complex decision-
> making problems. Because of its emphasis on human-technology interaction and
> because of its focus on practical applications, operations research has
> overlap with other disciplines, notably industrial engineering and
> operations management, and draws on psychology and organization science.
> Operations research is often concerned with determining the maximum (of
> profit, performance, or yield) or minimum (of loss, risk, or cost) of some
> real-world objective. Originating in military efforts before World War II,
> its techniques have grown to concern problems in a variety of industries.

Later authorities were just (in part) rebranding OR for Darpa/Iarpa grant
money.

I like this executive summary though: It is good reading for managers and
CEO's who may not be familiar with AI and its possibilities. For practitioners
it is mostly fluff though and any intro course will give a better overview. A
real playbook has yet to be written.

[1]
[https://www.wsj.com/articles/SB10001424053111903480904576512...](https://www.wsj.com/articles/SB10001424053111903480904576512250915629460)

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dmos62
This seems to be, to quote, "a pre-tutorial -- a Chapter 0". I was expecting
an actual playbook, as in strategies and approaches to different challenges, a
list of things ML and AI can do well at this time. For me, that would be a
very interesting resource.

~~~
codeisawesome
Take a look at “Algorithms for the Intelligent Web” - particularly the second
edition.

