Machine learning is a buzzword in the technology world right now,
and for good reason: It represents a major step forward in how computers can
learn.
Very
basically, a machine learning algorithm is given a “teaching set” of data, then
asked to use that data to answer a question. For example, you might provide a
computer a teaching set of photographs, some of which say, “this is a cat” and
some of which say, “this is not a cat.” Then you could show the computer a
series of new photos and it would begin to identify which photos were of cats.
Machine
learning then continues to add to its teaching set. Every photo that it
identifies — correctly or incorrectly — gets added to the teaching set, and the
program effectively gets “smarter” and better at completing its task over time.
It is, in
effect, learning.
1.
Data Security
Malware is a huge — and growing — problem. In 2014, Kaspersky Labsaid it had detected 325,000 new malware
files every day. But, institutional intelligence
company Deep Instinct says
that each piece of new malware tends to have almost the same code as previous
versions — only between 2 and 10% of the files change from iteration to
iteration. Their learning model has no problem with the 2–10% variations, and
can predict which files are malware with great accuracy. In other situations,
machine learning algorithms can look for patterns in how data in the cloud is
accessed, and report anomalies that could predict security breaches.
2.
Personal Security
If you’ve flown on an airplane or attended a big public event
lately, you almost certainly had to wait in long security screening lines. But
machine learning is proving that it can be an asset to help eliminate false
alarms and spot things human screeners might miss in security screenings at
airports, stadiums, concerts, and other venues. That can speed up the process
significantly and ensure safer events.
3.
Financial Trading
Many people are eager to be able to predict what the stock markets
will do on any given day — for obvious reasons. But machine learning algorithms
are getting closer all the time. Many prestigious trading firms use proprietary
systems to predict and execute trades at high speeds and high volume. Many of
these rely on probabilities, but even a trade with a relatively low
probability, at a high enough volume or speed, can turn huge profits for the
firms. And humans can’t possibly compete with machines when it comes to
consuming vast quantities of data or the speed with which they can execute a
trade.
4. Healthcare
Machine learning algorithms can process more information and spot
more patterns than their human counterparts. One study used computer assisted diagnosis (CAD) when to review the
early mammography scans of women who later developed breast cancer, and the
computer spotted 52% of the cancers as much as a year before the women were
officially diagnosed. Additionally, machine learning can be used to understand
risk factors for disease in large populations. The company Medecision developed an
algorithm that was able to identify eight variables to predict avoidable
hospitalizations in diabetes patients.
5.
Marketing Personalization
The more you can understand about your customers, the better you
can serve them, and the more you will sell. That’s the foundation behind
marketing personalisation. Perhaps you’ve had the experience in which you visit
an online store and look at a product but don’t buy it — and then see digital
ads across the web for that exact product
for days afterward. That kind of marketing personalization is just the tip of
the iceberg. Companies can personalize which emails a customer receives, which
direct mailings or coupons, which offers they see, which products show up as
“recommended” and so on, all designed to lead the consumer more reliably
towards a sale.
3.
Fraud Detection
Machine learning is getting better and better at spotting
potential cases of fraud across many different fields. PayPal, for example, is using machine learning to fight money
laundering. The company has tools that compare millions of transactions and can
precisely distinguish between legitimate and fraudulent transactions between
buyers and sellers.
4.
Recommendations
You’re probably familiar with this use if you use services like
Amazon or Netflix. Intelligent machine learning algorithms analyze your
activity and compare it to the millions of other users to determine what you
might like to buy or binge watch next. These recommendations are getting
smarter all the time, recognizing, for example, that you might purchase certain
things as gifts (and not want the item yourself) or that there might be
different family members who have different TV preferences.
5.
Online Search
Perhaps the most famous use of machine learning, Google and its
competitors are constantly improving what the search engine understands. Every
time you execute a search on Google, the program watches how you respond to the
results. If you click the top result and stay on that web page, we can assume
you got the information you were looking for and the search was a success.
If, on the other hand, you click to the second page of results, or type
in a new search string without clicking any of the results, we can surmise that
the search engine didn’t serve up the results you wanted — and the program can
learn from that mistake to deliver a better result in the future.
6.
Natural Language Processing (NLP)
NLP is being used in all sorts of exciting applications across
disciplines. Machine learning algorithms with natural language can stand in for
customer service agents and more quickly route customers to the information
they need. It’s being used to translate obscure legalese in contracts into
plain language and help attorneys sort through large volumes of information to
prepare for a case.
7.
Smart Cars
IBM recently surveyed top auto executives, and 74% expected that we
would see smart cars on the road by 2025. A smart car would not only integrate
into the Internet of Things, but also learn about its owner and its
environment. It might adjust the internal settings — temperature, audio, seat
position, etc. — automatically based on the driver, report and even fix
problems itself, drive itself, and offer real time advice about traffic and
road conditions.
Courtesy of Bernard Marr
0 comments:
Post a Comment