
Solving Industry-Specific Problems by Combining AI and Subject Matter Expertise - tomaskazemekas
http://www.bradfordcross.com/blog/2017/6/13/vertical-ai-startups-solving-industry-specific-problems-by-combining-ai-and-subject-matter-expertise?imm_mid=0f3d3e&cmp=em-data-na-na-newsltr_20170626
======
daliwali
Vertical AI = Expert System, just an updated rebranding from 1980s to now. An
expert system may be defined as:

>a piece of software which uses databases of expert knowledge to offer advice
or make decisions.

The methods of inference have improved from predicate logic to statistics and
"machine learning" now that computers have gotten much faster.

(I'm bootstrapping an expert system myself)

~~~
bluetwo
I don't see these things as equal, but I see where you are going with the
comparison. Both require an understanding of a specific domain and problem(s)
that need to be solved in that domain.

I really think striving to find specific problems where AI can add value is
critical to making money in the space, and I agree with this article strongly.

~~~
daliwali
The term "AI" is so overused that it can mean anything. Expert systems are
considered "AI" for that matter. They may not necessarily use the most
computationally expensive methods like neural networks, but "AI" doesn't
prescribe any specific technique.

~~~
bluetwo
Yes, but "Vertical AI" is the thing the author was trying to better define.

------
fab1an
Hands down one of the best articles on the topic I've read - so many "wish I
had known this years ago" types of insights.

The video contains bits that aren't fully reflected in the post itself, so
it's worthwhile to watch the whole thing. The most important part IMHO is that
you absolutely have to start with a known problem and work out the solution
from there, not vice versa. This is a trivial insight for most startups, but a
surprisingly common trap for even the smartest AI/ML people. It's very easy to
get blinded by the sheer awesomeness of the data/model you stumbled upon, all
while ignoring that you haven't fully grasped the problem space yet.

------
JasonCEC
My company[1] Analytical Flavor Systems is a vertically integrated domain-
expert based AI for new product development, flavor profile optimization, and
predictive manufacturing in the food and beverage industry.

Like the article and some of the comments here suggest, it took years of
domain expertise (most of the team comes out of the Tea Institute at Penn
State, a research Institute for tea and tea tasting), followed by years of R&D
to collect the proprietary data-sets and develop the models. And then it took
a year or so to build a product around the AI's predictive capabilities - this
isn't the shortest or easiest path, but we're still going strong!

I think companies like this are hard to build, hard to fund, and hard to
compete with.

Where I disagree with other comments is on the competitive side; we've
developed a few of our own algorithms[2] (not generic or even "played with
some options" neural nets / deep learning) trained on specialized and
proprietary data set from years of work and collection - now that we've dug
our moat, I don't think anyone will be competitive with out specialized AI for
modeling human sensory perception and predicting preferences[3] of food and
beverage products anytime soon!

[1] www.Gastrograph.com

[2] [https://gastrograph.com/resources/whitepapers/local-
fisher-d...](https://gastrograph.com/resources/whitepapers/local-fisher-
discriminant-analysis-on-beer-style-clustering.html)

[3][PDF] [https://gastrograph.com/resources/whitepapers/2017-market-
pr...](https://gastrograph.com/resources/whitepapers/2017-market-preference-
tasting-panels.pdf)

~~~
kurusii
Did you forget footnote number one?

------
denzil_correa
AI is not like ordering your lunch : "I don't like to sour, can you make it a
bit spicy", doesn't work for AI. Cutting edge AI solutions require highly
customized frameworks. The number of ingredients involved are very large in
number and you can't go on making on creating a list of parameters to create
your own "recipe". Therefore, a platform or stack will only affect solutions
which are have least variance across industries - language translation, object
recognition, photo tagging etc.

Industry specific customized solutions would require SMEs to build solutions.
At this point, you are essentially buying people skills and not products.
You'd also see a large increase in price points here which companies may not
be willing to pay. Everyone wants something quick and cheap. AI is not a
panacea or a magic wand. The hype is more detrimental to the progress of AI.
Once the disillusionment sets in - people will blame the technology rather
than people who took decisions to use AI in the wrong contexts.

------
dmix
What's the opposite of Vertical AI? Is it a monolithic chunk of code like an
operating system with millions of lines?

I've always been a bit confused about what the end goal will look like for
'general AI'.

Or is there potential for some general platform that these vertical AI systems
can plug into, similar to the app store, but with some type of cross vertical
communication so they be layered on top of each other.

~~~
bluetwo
I think the opposite is AI for the sake of AI.

He is trying to show that AI that solves business problems is more valuable
than AI that scores 1% higher on some benchmark.

------
cannonpr
What I would like to understand is why are these startups defensible.

The tech value proposition is in running these algorithms, often tensorflow at
scale cheaply in production. Companies like Google/Facebook/Palantir often
have access to very similar supposedly hard to get to datasets, plus a lot
more engineering expertise to running these systems at scale.

Why can't they start playing whack-a-mole pumping out vertical products
presenting a serious threat to these smaller startups. Maybe it's not worth it
for them but there is a fair bit of cash there ?

For example Deepmind with healthcare, and the google jobs API ?

~~~
fest
The same reason small companies exist at all- they are more nimble and don't
require to operate at large enough scale to survive.

IMO, it would be pretty mad to start a company today around general-purpose
conversational AI or general purpose photo recognition service. Applying the
same technologies for smaller, more specific groups of users is much less
risky path.

I highly doubt Google/Baidu will get into business of recognizing
manufacturing defects in fidget spinners or analyzing sensory data to predict
when a punching press is about to fail.

~~~
crypto5
> I highly doubt Google/Baidu will get into business of recognizing
> manufacturing defects in fidget spinners or analyzing sensory data

They will build platforms for:

\- IoT - single robotic KIT/SDK, which allows you to easily install sensors
and integrate data into rest of the platform

\- Cloud ML - image recognition/models optimization - will allow to train
model and detect defects from previous step by two mouse clicks

and take a lot of added value from small companies.

------
chasely
As someone who is a subject matter expert that is using more and more AI/ML
for their research, this makes intuitive sense. I could keep up with current
AI research, but I would barely be treading water and couldn't create anything
of substance.

However, incorporating mature ML methodologies (meaning it has a library) to
subject-matter problems is now adding a tremendous amount of value in the
research I do.

------
s3nnyy
Since a long time, I try to figure out an AI-based vertical in the tech
recruiting space. However, I am stuck in agency mode
([http://coderfit.com](http://coderfit.com)). If you have good ideas how AI
can help to source and hire software engineers, I have done the tedious work
of getting paying clients and a database with engineers who are looking for a
job.

~~~
ousta
[https://www.blog.google/products/search/connecting-more-
amer...](https://www.blog.google/products/search/connecting-more-americans-
jobs/)

~~~
s3nnyy
Thank you, I am aware of Google entering the market since November, when they
presented the Jobs API.

------
pplonski86
In my opinion the mainstream AI startups are described in article, mainstream
AI startup = unique data + algorithms. I think it is good to not be in AI
startup mainstream and work on things that maybe won't make you rich but will
make you happy.

~~~
jorgemf
but working at AI makes me happy

------
shadowmint
If you can deliver 'a totally new opportunity through rich domain modelling'
(ie. You have a solution to a problem that hasn't been solved before), and
your solution:

\- Is fundamentally tied to data that is proprietary and difficult to gather.

\- Is intrinsically extremely complex to build and maintain.

\- Can only be built by a diverse team which is difficult to gather.

Then... well, yes, I guess you could say that those of good metric for
determining if a product can easily _be replicated_.

...but that's not the same thing as it being _useful_ or _profitable_.

It just means that the team has a bit more time to try to figure out those
other two important things before someone else comes along and copies what
they've done.

> My claim is that Vertical AI startups are inherently defensible.

Putting 'vertical' in front of 'AI' doesn't magically make things better than
just 'AI'.

The problem with these products is you can't take a trivial 'proof of concept'
or MVP, pitch it and then roll it out 'into production'.

This isn't some 'smoke and mirrors' jazzy demo of a website & app combo you
can go away and implement properly later... the proof of concept you build may
not scale. Like... it may actually _not be possible_ to scale. Maybe it takes
too much compute to train; maybe it takes data you don't have; maybe it turns
out your data isn't suitable.

You think building a business and getting users and sorting your workers and
so on isn't hard enough?

Try adding a product that may or may not _actually ever work_ into the mix.
Sound scary yet? It should sound scary. That's the sound of money draining out
of a hole in the floor.

...and sure, you argue, these models _do work_ and they _are_ good; but
there's the catch right there... :)

...if you use a model that _does_ work and isn't risky and does use available
data... then you lose all the points at the top that made it an interesting
business to invest in.

~~~
blennon
I'm not trying to be offensive, but what point are you trying to make? It
sounds like you disagree with the author but your commentary seems like
incoherent rambling to me.

I think the author makes excellent points.

It's hard to build a general AI business. E.g. a computer vision API provider.
The technology is so democratized that you can't compete on algorithms alone.
These APIs/services are more or less commodities nowadays.

Compare that to credit card fraud detection. For many years, there was one
company/product (HNC/FICO/Falcon) that dominated the market (and largely still
does) because they had a monopoly on the data. They smartly created a
consortium and only they have the rights to train models on the data. They
still use a relatively simple feedforward neural network with a ton of hand-
tuned features. This is an example of vertical AI that created a wildly
successful company.

~~~
shadowmint
tldr: The author claims 'Vertical AI startups are inherently defensible'.

My point is, so what? That's not a good indicator of anything meaningful.

The values that make them 'defensible' only make them difficult to replicate,
that might look good on paper or in a pitch ('no one else can do this...') but
they're not indicators of value, and the pose significant risks to execution.

/shrug

You don't have to agree, I really don't care; but this article sounds more
like AI hype to spin to investors than meaningful advice.

Picking ML models that are difficult to replicate, hard to obtain data for and
require a diverse team is _not_ what you should be trying to do.

~~~
bluetwo
I think he is looking at it from the point of view that you want to build a
company that has value.

Focus on a problem that has value in an industry that has money. Collect as
much data as possible to prototype and use the tool itself to collect
additional data as it is used.

I don't see that as hype, I see that as a useful roadmap.

------
hamilyon2
Not a word about adtech. Nice and informative

