Douglas Goldstein, CFP®, Financial Planner & Investment Advisor
Ami Gal is the co-founder and CEO of a company called SQream Technology.
Douglas Goldstein, financial planner & investment advisor, interviewed Gal on Arutz Sheva Radio.
Douglas Goldstein: Some people say that Israelis are a little bit loud and they like to scream
from time to time but I’m aware that’s not the main goal of SQream Technology.
Can you give us a summary of what you’re all about?
Ami Gal: SQream is about delivering business [triumph] with data and that’s sometimes
when you have business [triumph].
We enable organization, research institutes and others to actually leverage their data
and to do miracles in their business goals.
For example, we enable streams of data coming from different sensors to allow better preserving
of boarders.
As I mentioned earlier, we also allow to advance rapidly on a [configuring] research and many
other ecosystems that have the data and until recently didn’t have the right tools in
order to actually leverage the data.
Douglas Goldstein: It sounds to me like you’re working with organizations whether they are
military or medical research who have huge bodies of data and helping to somehow take
that data and put it into a workable form.
Am I understanding you?
Ami Gal: You’re completely right.
We have massive datasets just like you said and sometimes it’s impossible even to analyze
simple stuffs on top of that and what you need is infrastructure that is much more scalable
than anything on the planet today and this is exactly what SQream does.
SQream enable them to actually that and understand what it means to their goals, whether it’s
business, financial, telecommunication, healthcare or defense.
There’s plenty of that in the world and everybody knows that but being able to actually
analyze that, that’s pretty tough and this is where SQream is actually enabling them
to do that easily.
Douglas Goldstein: As a financial advisor, I work with clients and we develop retirement
plans or try to figure out how to best manage their portfolio and when we do this, the engine
that we use to analyze data is something called a Monte Carlo simulation which takes many
thousands or millions of different possible scenarios and test them all and then tells
you the odds of anyone particular scenario ending up.
It’s a tool that also deals with huge bodies of data.
So tell me how you fit on that spectrum.
Ami Gal: When you use the Monte Carlo algorithm, the thing that actually makes you more accurate
is if you can get more iteration.
So if you can get into not only ten thousands of hundreds of thousands iterations but the
millions and billions of iterations, it’s actually do risk your decision because you
have more scenarios analyzed.
So this is actually a common big data task that we’ve been facing to enable to do just
more iterations and actually the risk points that you get are much better so this is actually
not only Monte Carlo, also Las Vegas and other common mechanics to the point that you just
mentioned, so certainly big data tools enable much more accuracy in these algorithms.
Douglas Goldstein: Normally when we run a Monte Carlo simulation for a client, we’ll
look at maybe a thousand different iterations and you said that if you run a million or
billions, you get better results.
I remember once that I was testing this and I ran just 20 iterations and I forget the
exact number but it ended up being something like the client had a 70% chance of success
and then I ran the same test but with a thousand iterations and pretty much ended up being
around the same results, around the 70% chance of success.
So I was interested because I realized maybe it only to have billions of different trials.
Tell me why it helps to be able to test so many different iterations?
Ami Gal: If you get an excellent chess player to play with a mediocre chess player and they
play 10 games with each other and if you take the same players and you get them to play
1000 games with each other, what do you think will be the odds in two different cases.
Surprisingly, if they only play 10 games probably it’s going to be 10-0 in one case and it’s
going to be about 93% in the other case.
So most of the algorithms, you have to have enough statistical datasets in order to be
more accurate.
So I don’t think the difference between 20 to few hundreds or a thousand is big enough.
What we saw at least in one case that when you change the number of iterations from a
few tens of thousands to a few millions and a few ten millions, you actually got good
five more points in the prediction capability which was enormous for this case.
This is one thing I wanted to mention about it.
The other thing that is important not for the a guy who is doing a personal finance
but the other things that is important to the bank or the asset manager that is doing
that for you is the fact that he is running it fast.
They can do that today but you will have to wait a month for the results but if you can
get the results in a few seconds then it’s a triumph, it’s a business triumph.
This is exactly where SQream gets in and usually we don’t have personal finance guy that
we uses to use our technology, usually it’s a much larger datasets which also got back
to your specific question.
It’s the size of the datasets as well at the beginning and not only at
the end.
Douglas Goldstein: The idea
you have is interesting but I’m guessing that the source of the idea came from something
special that came up.
Where did you come up with the idea to create this technology?
Ami Gal: Back in 1997, I was running a call center company that had some performance issues
and wanted to do the same queries that I did in the call center, much faster and then running
it to an Oracle and I tried to do it with a GPU, graphical processing unit, which happens
to have thousands of cores as opposed to a few cores on a CPU and I failed.
It didn’t work for me at that time.
About 13 years later, I met my co-founder Kostya Varakin who came up with the idea how
to actually do it right and together we founded the company.
So it’s about using an extreme parallel tool that was created for streaming, for videos,
for pictures and utilize the same power for big data query.
So this is one side of the story and the other side of the story is about it includes both
on the floor.
Douglas Goldstein: Is Israel the right place to be developing this kind of technology and
maybe you can give just a thought of either inspiration or warning to people who want
to do business here?
Ami Gal: As you know, if you want to build a 9-billion dollar company, your market is
not in Israel.
Your market is in somewhere else but if you want to build a technology like that, the
best place in the world is in Israel.
You have floods of talent from all over the place and it’s much more suitable to do
groundbreaking technologies like that in Israel than any other center of talent in the world
and that includes the Silicon Valley because here people are not afraid to break things
that our thought is at best practice.
Here people are being motivated when you tell them that it’s impossible to do it and you
get it the next morning.
So for me, it’s going to be a company that enables the world to be a better place and
the center of this company is in Israel.
That’s the right place today and will be the right place tomorrow.
Regarding the business side, you probably understand that our main market is not America
and we have people on the run right now that are bringing business in America.
Douglas Goldstein: How can people follow you and follow your work and the company?
Ami Gal: I would be happy if you can get them to www.SQream.com.
We are looking for talent right now and we’ll be delighted if people can learn more about
the projects we’re doing right now with this technology.