Technology has taken over the world and
our lives. Things are now very different from how they previously were. Now, we
have become very dependent on technology and that too without realizing it.Technology
has evolved too. It has become more immersive, user-friendly, and smart. Where
once humans were a necessary element to keep machines running, Data Science and
Machine Learning are making machines smarter. Just look at self-driving cars.
However, what is Machine Learning? Machine Learning
is the in-depth study of statistical models and algorithms that are used by computer
systems so they can carry out a specific task. All of this is done by using
only inference and patterns instead of anyexplicit instructions.Machine Learning
is considered to be a subset of AI (artificial intelligence). However, it isn’t
exactly like AI.
Timeline of the Era
Machine Learning has been around for a
long, long time. Way before many of us were born. Although when we think about
AI or Machine Learning, robots automatically come to mind. Yet, MA and AI are
not just about that. There is a lot more to it. AI has been integrated with
other technologies to make them more adaptive, intelligent, and perceptive.
Nonetheless, how did all of this come about?
Let’s have a look at the era in which Machine
Learning was introduced, also called as the era of innovation, to learn more
about the milestones of MA.
1950s
·
IBM’s Arthur
Samuel developed the first computer game program in the 1950s.
·
In 1952, Arthur
Samuel coined the phrase 'Machine Learning.'
·
Frank Rosenblatt
combined Arthur Samuel’s Machine Learning with Donald Hebb’s model of brain
cell interaction to form the Perceptron.
1960s
More layers were discovered in the
Perceptron, so it would offer the higher processing power, which led to the back propagation and feed forward neural networks.
1970s
A period of reduced funding in AI called
‘AI Winter’was caused.
1980s
A resurgence was witnessed in Machine
Learning after the rediscovery of backpropagation.
1990s
·
It was the golden
era for Machine Learning.
·
Multiple
developments were made in the technology, algorithm, and hardware.
·
Machine Learning
shifted to a data-driven approach from a knowledge-driven approach.
2000s
Several contributions were made in the
field of Machine Learning and Artificial
Intelligence, including Deep
Learning, which now saw commercial use.
2010s
Machine Learning received much publicity
and is now widely used in software services.
All the information above showcases the
timeline of the era of innovation for Machine Learning. During this era, there
were various developments, innovations, and breakthroughs in the field of Machine
Learning. There is more to it, but now it is time to move on towards its usage
in the real world, to help understand that firstly Machine Learning is
different from Artificial Intelligence; secondly how Machine Learning can be
leveraged in business.
Integration In Industries
Machine Learning and Artificial
Intelligence have become part of many technologies and industries. They
immersed themselves deeply into various processes, and as a result, offer
multiple benefits. Below is a list of all the sectors that are relevant to
Computer Learning and how this concept can benefit businesses.
Let’s look at Artificial Intelligence on
the whole to learn more about its contributions to various industries and how AI
is changing lives.
Manufacture
A number of processes have become
automated in the manufacturing process. Where once machines had reduced the
need for workers, they will soon replace the labor forcefully.
Artificial
intelligence (AI) in the manufacturing market in 2018 valued to be around USD
1.1 billion. By 2025, it is expected to reach around USD 18.5 billion. (Source:
Zion Market Research)
Healthcare
Machine Learning is flourishing in the
healthcare sectorthrough sensors and wearable devices that use data to evaluate
patients’ health. In 2017, around 7 million Americans were using wearable tech
to monitor their vital signs with the help of digital health platforms.
AI has made its way into the hospital
system, and diagnostics, but very soon, robot arms might be seen performing
surgeries on people.
Artificial
intelligence (AI) in the healthcare industry in 2018 valued at around USD 1.4
billion. By 2025, it is expected to reach around. (Source: Zion Market
Research)
Gaming
Artificial Intelligence has also
immersed itself into gaming, and we are not just referring to the
computer-generated opponents seen in combat games.
“The
only similarity Machine Learning has to Artificial Intelligence is that both
are smart technologies.” – Joe Nast (Writer, crowdwriter)
Processes Get Easy Through Machine Learning
Now let’s have a look at all the sectors
that have integrated Machine Learning into their processes.
Financial Services
Banks, along with other financial
institutions, utilize Machine Learning for a number of reasons including:
·
To prevent any
kind of fraud
·
Identify
insights into data such as investment opportunities
·
Identify
vulnerabilities using cyber-surveillance
·
Pinpoint clients
that have high-risk profiles
Retail Industry
Machine Learning is being used to gather,
analyze, and make use of data where people can personalize their shopping
experience. Machine Learning capabilities provide online shoppers with personalized
product recommendations. Meanwhile, it also includes coupons, pricing, and
other incentives.
More
than 28% of retailers are integrating Artificial Intelligence/Machine Learning
solutions into their business, which is seven times more than the number of
retailers in 2016. (Source: Google Trends)
Automotive
Big Data analytics and Machine Learning
are being used by the automotive industry to improve operations, customer
experience, and marketing. Predictive analytics allows manufacturers to share
and monitor critical information about potential parts and vehicular failures. Also,
it helps identify patterns and trends from large datasets.
In
2018, a 3% marginal increase was seen in the number of companies deploying AI
at scale in the industry. (Source: Accelerating Automotive’s AI Transformation,
Capgemini)
Government Agencies
Government agencies use Machine Learning
to deliver utilities andpublic safety because they consist of multiple sources
of data, whichneeds to be mined. This analyzed data can be used to identify
ways of saving money, increasing efficiency, minimizing identity theft, and detecting
fraud.
Transportation
Machine Learning’s modeling aspects and
data analysiscapabilities are utilized bypublic transportation, delivery
companies, and other transportation businesses.Analyzing the data can help
identify trends and patterns to predict problems in increasing profitability
and making routes more efficient.
AI
is expected to reach 3.5 billion dollars in the transportation market by 2023.
(Source: P&S Intelligence)
Oil & Gas Industries
Machine Learning can be used in business
operations to translate data sets into insights and to collect large volumes of
information and translate data sets into actionable insights. This can, in
turn, reduce costs, improve safety, save time, and boost efficiencies.
AI
in the oil and gas sectoris expected togrow to $2.85 billion by 2022. (Source: Markets
and Markets)
Conclusion
Machine Learning is essential for all
sorts of businesses to collect and analyze data, which then can be used to
identify patterns and garner insight. This can be used to cut costs, find
vulnerabilities in their techniques, and find ways to maximize efficiency and
more.
“These
are the pros of Machine Learning now. Imagine what benefits it could offer once
the technology evolves further.” – Belinda Newman(Staff Writer, Academist Help)
Author Bio:
Claudia Jeffrey is the Founder and CEO
of WordCountJet.She is also serving as the Assistant Manager of
Networking at Assignment Assistance.
A staunch believer in Machine Learning, Claudia makes use of it to streamline
processes in her own business.
