Database Reference
In-Depth Information
have a lot of businesses that control many of the things we use, though we're
still going to have a lot of governments essentially doing the real mundane
things like collecting taxes. It's going to be a completely different interaction.
So in order to make decisions about those interactions, we have to take an
interest in how governments work together, what types of relationships are
available to them, what diverse steps they're taking, and what data exists and
shows these relationships. If we gather all this data, we'll be able to analyze it
and make it into a much more scientific process that is a much more thought-
ful way about decisions and how they affect citizens.
Gutierrez:
What is something you think will be huge in the future?
Radinsky:
I think that eventually we're going to have some sort of genetic
hardware made from real genes grown from a lab that will allow us to com-
bine computer and genes. There's a lot of work already in this area of people
trying to do that. But I still think that trying to find a way of combining genes
and computers will generate new kinds of problems, new kinds of data, and
new kinds of hardware for us, which goes back to the stacks I was talking
about—hardware to software and data to algorithms. It's going to be a com-
pletely new stack.
Gutierrez:
What advice would you give to someone just starting out?
Radinsky:
Find a problem you're excited about. For me, every time I started
something new, it's really boring to just study without a having a problem I'm
trying to solve. Start reading material and as soon as you can, start working
with it and your problem. You'll start to see problems as you go. This will lead
you to other learning resources, whether they are books, papers, or people.
So spend time with the problem and people, and you'll be fine.
Gutierrez:
What is something someone starting out should understand
deeply sooner than later?
Radinsky:
Understand the basics really deeply. Understand some basic data
structures and computer science. Understand the basis of the tools you use
and understand the math behind them, not just how to use them. Understand
the inputs and the outputs and what is actually going on inside, because other-
wise you won't know when to apply it. Also, it depends on the problem you're
tackling. There are many different tools for so many different problems. You've
got to know what each tool can do and you've got to know the problem that
you're doing really well to know which tools and techniques to apply.
