- The rise of emerging markets has made it essential
for investors on Wall Street and other financial centers
to develop a deep understanding
of the opportunities and risks across the entire world,
not just in the U.S. and Europe and Japan.
How likely are lawmakers in Brazil
to pass a controversial bill to overhaul the pension system?
What are the odds of India's ruling party
winning upcoming local elections in the biggest state?
Should we bet on the Nigerian president
liberalizing the agricultural sector?
Trying to answer these questions
by reading online English versions
of each country's main newspapers
could never give any investor
any type of information or analysis advantage
simply because doing so merely scratches the surface
of available and relevant information.
So what could the savvy investor do?
The answer lies in using the power of AI
to browse through, digest and synthesize
millions of bits of information,
not just in newspapers but also on TV broadcasts,
Web pages and social media feeds such as Twitter,
much of it in local languages.
Startups such as Arkera in London and Sigmoid in New York
do exactly this and have found ready clients
around the world.
In the case of the Brazilian pension overhaul bill,
Arkera's AI system kept a close watch
on everyone of the 500 certain legislators.
A few weeks before the bill's passage in July 2019,
the AI system was able to predict the outcome
almost to the letter,
giving its hedge-fund clients significant
early mover advantage regarding movements
in Brazil's currency.
So how does the AI system do this?
The starting point is that computers
can access far more data points,
including social media feeds,
simultaneously than humans ever could.
Further, AI driven translation systems
are now able to translate all of these into English
with almost the same degree of accuracy
as that of human translators.
The biggest value of AI algorithms, however,
lies in making sense of what all of this information means.
Some of the early work in this area
began with scientists training algorithms
to understand of content of first sentences
and then paragraphs.
For example, the AI would be given thousands of paragraphs
as well as questions such as, "Does John like Jane?",
and the right and well as wrong answers.
Through a process of trial and error learning,
the AI would eventually become smart enough
to understand the rules of grammar
and make sense of new previously unseen paragraphs.
Over time, scientists in the field
of natural language processing
have been able to develop AI systems
that can understand movie scripts,
summarize CEOs' earnings calls,
and even write drafts of newspaper articles
by first digesting what's being said around the world
on a particular issue.
The leap from these capabilities to the AI systems
of Arkera and Sigmoid was not particularly dramatic.
Given the perpetual arms race
among those who develop these technologies
as well as those who use them,
it's a safe bet that natural language processing
will become far smarter and far more widely used
within just the next three to five years.