hello world it's Suraj and how are you
supposed to start an AI startup I'm
gonna go over the steps you need to take
to do this using a combination of my own
startup experience and analyzing some
best practices that some recently
successful AI startups have implemented
the public's interest in AI will
continue to rise in the coming years and
we're just at the start of a larger AI
revolution innovative startups continue
to pop up every single week tetra for
example uses recent advances in speech
recognition to generate detailed notes
from your phone calls hyper science
extracts data from forms easily using
optical character recognition and jet
lore uses consumer behavior as input to
a model able to output structured data
so it's 2018 and you want to get in on
this your first step is to study AI you
can't create an AI solution if you
yourself don't understand how this
amazing technology works right I've got
some great free playlists for you to
help you get started with this if you're
just getting started watch them in this
order learn Python for data science then
intro the tensorflow
then intro to deep learning and lastly
the math of intelligence
additionally Andrew Aang's newer deep
learning that AI course is a must-have
and Jeremy Howard's fast on AI course is
excellent as well my favorite book on
deep learning is the deep learning book
by Ian Goodfellow it's free online and
where it really excels is in helping you
get to terms with the math of deep
learning deep learning has proven to
help perform every other ml model almost
every time on a wide range of tasks so
the hype is justified however you should
really know how the other machine
learning models work because there are
use cases where you don't have access to
a lot of data and you just need to make
a simple prediction in those cases
support vector machines or even a simple
decision tree would be a more useful
approach than a deep neural network
the only way to know what model to use
and when to use it is to develop an
intuition and that comes after studying
using a wide variety of sources all the
ones I've talked about will be in the
description once you've got a grasp of
the landscape of algorithms that make up
AI you've got to find a problem that you
feel passionate about some of the most
successful companies in the world were
started because the founders were trying
to solve a problem that they themselves
faced write down a list of problems that
you or your friends have faced in your
personal lives that you would like
solved these will very likely be good
candidates for problems that you are
passionate about solving since those
affected are people that you very much
care about if you can't think of any
good ones you could also use social
media to search proactively for people
looking for a solution search in quotes
for words and phrases that indicate that
the poster is frustrated by something or
looking to solve a problem like how do I
or can't seem to this will work on
reddit Twitter just about any community
where a target market might congregate
on but perhaps the best way that I found
to find problems is to travel and meet
people I couch surfed around Europe for
three months a few years ago and
happened to stay with Alex macaw in
London he gave me the inspiration
necessary to change my trajectory in my
early years of university from economics
to computer science one of the most
impactful changes I've made in my life
it's what got me to eventually start a
robotics startup lucid robotics while I
was a third-year student at Columbia and
more recently travelling through South
and Southeast Asia for four months
helped put things into perspective for
me
I left the bubble of Silicon Valley and
got to see problems firsthand that real
people faced in their day to day lives
problems related to infrastructure
banking communication and of course
education that was the one that really
affected me simple conversations I had
with locals compounded into later
experience
is that resulted in a larger plan to
educate the world on AI by making my
content the product as you own musk says
being an entrepreneur is like eating
glass and staring into the abyss of
death if you don't have the passion to
solve a problem you're not gonna stick
around when the going gets tough no
matter how many negative comments I've
gotten I've always persevered because
it's not about me it's about solving AI
once you have a problem to solve it's
time to do some market research who are
you selling to where are they going to
buy it how much would they pay what are
the competing products or services in
this space what's the cost to deploy
what does the history of this market
look like usually Gove's and big data
analytics firms have publicly available
reports on this generally AI startups
are classed into two varieties there are
the horizontal AI startups these are
working on one fundamental problem that
would serve many different industries
like say general natural language
processing then there's the vertical AI
startups these are solving problems for
a very specific type of customer
belonging to one specific industry just
about every major tech company is
working really actively on AI they're
able to hire all the AI rock stars from
hinton to Laocoon they've got the talent
they've got the massive amounts of data
they've collected from users over the
years which remains closed only to their
teams these tech giants have a huge
advantage when it comes to building
horizontal products that can apply to
many industries like image recognition
or language translation or
infrastructure but the advantage that
you have is that you can move fast on a
single problem vertically they don't
have the time to tackle every single
niche problem but you do and as a whole
they're focused on the consumer more
than they're focused on the enterprise
so a niche solution that would help
companies would be a great choice create
a landing page that describes your
product with a simple email signup
to get notified of release and share it
on social media
one way to raise awareness of your
product is to raise your own personal
profile establish yourself as an AI
thought leader create blog posts or
other content that answers fundamental
questions about AI build an audience see
if you get any signups
if you do especially after you've stated
what the price of your product will be
it means there's a demand for what you
could be offering this is crucial I went
through the whole process of raising
money building a product and the team my
last startup only to find that the
eldercare market didn't want a $5,000
robot to help pick things up off the
floor people with ALS would much prefer
a simpler tool I was too obsessed with
the solution not the problem if you
remove AI from the company but it still
has a valuable product you're on the
right track but if AI is your only thing
then neither the customer nor investors
will be excited about it once you've
done your market research it's time to
build a product but before you build a
model you've got to collect organize and
label your data as much as you can the
quality of the data is usually the most
important part of the machine-learning
pipeline even more so than the
architecture of your model the phrase
garbage in garbage out comes to mind the
easy way to get data is to search for
public datasets there's an awesome list
of datasets on github the University of
California Irvine has a giant ML
repository of datasets and of course
Kaggle has some great ones as well if
those don't work another way is to
create data yourself using an existing
data set like if you're trying to
classify handwritten characters you
could generate new data points from
existing ones by adding some noise via a
distribution if you've got unlabeled
data crowdsourcing via Amazon's
Mechanical Turk is a great option you
could pay people to label it for you
there are also data marketplaces out
there like data circle where you can buy
or exchange datasets directly from other
people lastly if
feeling creative you can scrape the data
yourself using Python in a library like
scrap PI or a web service that does this
for you like Digger not calm when it
comes to building models tensorflow is
still the best most battle-tested
machine learning framework out there and
there's a whole host of tools in the
tensorflow ecosystem like tensorflow
serving that make it production ready
build a model train it on your data via
a service like AWS Google Cloud or Floyd
Hub use your model to make a prediction
and this is your product more data means
smarter algorithms means better products
means more users means better data and
the cycle repeats raising money is a way
to accelerate the scaling process the
ico route may be hot right now but if
you're in it for the long term and don't
want to mess around with legalities I'd
avoid this for now a friend of mine who
was the first designer for aetherium
didn't do an ICO for his company balance
he instead raised money using a
crowdfunding tool called we funder his
reasons were really inspiring and I've
got an article on it in the description
venture capital is of course another
option and while it's easy to convince
some VCS that your product is cool by
just repeating a bunch of buzzwords the
best ones will understand the technology
well so be prepared to explain the
technicalities of it to them you're
always in a better position to talk to
VCS when you've already got to cash flow
than if you don't that's why I got
rejected by so many VCS in New York I
hadn't proven my robot would sell now
I've got a few offers to invest in what
I'm doing since I built an audience so I
can afford to be picky but I'm not
focused on raising money right now when
it comes to hiring there's a limited
supply of talent and it's distributed
globally so it makes sense to hire
globally look at the leaderboard on
Kaggle the first hires you make are the
most important I worked really hard to
find the right fit to help me with
editing my videos if you really love
something it's hard to ever let go
control of
but if you find the right person it
becomes much easier as Steve Jobs said
the neatest thing that happens is when
you get a core group of you know ten
great people that it becomes self
policing as to who they let into that
group at some point you'll be able to
exit whether that be through an aqua
hire or by one of the big tech companies
or an IPO and if all else fails there's
always the Consulting route it's not as
sexy as building a product but hey it
puts bread on the table find a couple of
friends and start a consulting firm get
some initial clients and slowly get more
impressive brands on board as you prove
yourself
I hope my tips helped please subscribe
for more programming videos and for now
I've got a startup to grow so thanks for
watching