Plunkett Research estimates the U.S.
market for AI on this basis at more
than $14.4 billion for 2018.
Researchers
at Gartner estimated that the global business value derived yearly from AI had
already reached $1.2 trillion by 2018, while analysts at PwC estimated that
AI's contribution to the global economy could soar to $15.7 trillion by 2030.
Artificial
Intelligence (AI) and machine learning will create vast changes in nearly all
segments of business and industry over the mid-term.
The effect of AI on consumers and households
is already in broad evidence, although the people benefitting from such
technologies may not be aware of the process or the significance of what's going
on around them.
For example, utilizing
machine learning, Amazon.com pioneered the development of advanced software
that learns from a shopper's actions online and then makes product
recommendations tailored to the individual.
In its early years, Netflix famously offered a $1 million prize to
anyone who could engineer an algorithm that would learn from a subscriber's
movie rental habits in a manner that would increase the accuracy and usefulness
of its online recommendation engine by 10%.
The more that Amazon or Netflix can display perfectly curated products
for individual shoppers, the happier the consumer and the greater the amount of
sales completed.
(Yes, Netflix paid off
on this Progress Prize offering, selecting the work of a team of engineers that
called themselves “BellKor's Programmatic Chaos.”)
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Artificial
Intelligence (AI) spending worldwide was estimated at $24 billion for 2018 by
analysts at IDC and is expected to grow to $77.6 billion by 2022. This is an estimate on a broad basis that includes
spending on software and services.
Plunkett Research estimates the U.S. market for AI on this basis at more
than $14.4 billion for 2018. Researchers
at Gartner estimated that the global business value derived yearly from AI had
already reached $1.2 trillion by 2018, while analysts at PwC estimated that
AI’s contribution to the global economy could soar to $15.7 trillion by 2030.
Artificial
Intelligence (AI) and machine learning will create vast changes in nearly all
segments of business and industry over the mid-term. The effect of AI on consumers and households
is already in broad evidence, although the people benefitting from such
technologies may not be aware of the process or the significance of what’s going
on around them. For example, utilizing
machine learning, Amazon.com pioneered the development of advanced software
that learns from a shopper’s actions online and then makes product
recommendations tailored to the individual.
In its early years, Netflix famously offered a $1 million prize to
anyone who could engineer an algorithm that would learn from a subscriber’s
movie rental habits in a manner that would increase the accuracy and usefulness
of its online recommendation engine by 10%.
The more that Amazon or Netflix can display perfectly curated products
for individual shoppers, the happier the consumer and the greater the amount of
sales completed. (Yes, Netflix paid off
on this Progress Prize offering, selecting the work of a team of engineers that
called themselves “BellKor’s Programmatic Chaos.”)
Search engines like
Google and Bing utilize similar technology to serve up billions of dollars’
worth of online ads weekly to carefully-targeted browsers of news,
entertainment and data online. These recommendation engines run in the
background 24/7; they learn more as time goes by and interactions with
consumers increase; they benefit from frequent, incremental improvements made
by software engineers; and they make the owners of these technologies highly
efficient, effective and profitable in their business operations, enabling the
firms to offer better service at lower retail prices. Today, Amazon’s cloud computing subsidiary,
Amazon Web Services (AWS), offers the “SageMaker” tool to enable companies of
all sizes to quickly build machine learning tools.
Consider the
implications of machine learning for critical industrial processes. For example, airlines around the world spend
hundreds of millions of dollars monthly on fuel. Imagine the benefit, both financially and in
terms of reduced carbon emissions, if the air transport sector can reduce fuel usage
a mere five percent through the utilization of machine learning—determinizing
the most efficient air routes in light of current weather, setting the optimum
engine speeds for fuel efficiency and assigning the most efficient flight paths
in and out of airports by computer-aided air traffic controllers. Airlines will thereby reduce both total time
in the air and total fuel burnt. This is
but one example from tens of thousands of potential applications—virtually all
factory, supply chain, and transportation sectors can benefit through such uses
of AI.
Technologies
that Have Deep Synergies with Artificial Intelligence
Robotics and Automation
The Internet of Things (IoT)
Sensors and Wireless Networks
Predictive Analytics
Big Data and Data Mining
Imaging and Facial Recognition
Voice Recognition
Cloud Computing
Digital Assistants (Siri, Alexa,
etc.)
Source:
Plunkett Research, Ltd.
How AI works: Simply put, AI and machine learning work by
finding patterns in data. The larger the
pool of data, the more observable the patterns and the better the accuracy and
outcomes of the machine learning process.
Amazon, for example, not only uses AI broadly in its online services, it
is successfully applying it in physical retail stores. Amazon operates an AI-assisted, 1,800-square-foot
brick and mortar convenience store called Amazon Go in one of its Seattle,
Washington office buildings. Customers
may pick up drinks, snacks and prepared meals.
Initially open only to Amazon employees, the store was made available to
the public during 2018. Shoppers scan an
app on their smartphones when they enter the store, so that they can be
properly identified as individual shoppers.
Cameras throughout the store track shoppers and note which products they
have selected while totaling the cost. The store runs without cashiers, utilizing
electronic checkout and payment, while sensors based on AI determine which
products were removed from the shelves by which customer, facilitating both
checkout and restocking. There is the
potential for a very large rollout of these Amazon Go stores in the U.S., and
Amazon may export the concept to the U.K. under a new trademark, “No Queue, No
Checkout. (No, Seriously.)” Technologies
refined in the Amazon Go stores may show up in stores at Amazon’s Whole Foods
subsidiary, and in its specialty stores that sell books and electronics. Based on the experience of Amazon and a few
other pioneers, Artificial Intelligence will have a very significant effect on
the way we shop in stores in the near future.
One of the more
promising advancements is called “deep learning.” In 2014, Google spent nearly $600 million to
acquire UK-based DeepMind, an intensive learning research group. Deep learning is sometimes referred to in
conjunction with phrases such as “machine learning” and “neural networking.” The main point is that software can be
trained by being constantly fed data, queried as to its meaning, and receiving
feedback to its responses. It is
essentially training a machine to respond correctly to data of a given nature
or to data within a given set of circumstances.
Industry
Sectors with Significant Near-Term Benefits from Artificial Intelligence and
Machine Learning
(The higher the amount, recency and
frequency of data available, the more useful the outcomes from applying AI to
such data. Health care is a perfect example, with vast amounts of patient and
outcome data captured daily on a global basis.)
Health Care
· Disease
diagnosis and analysis of scans, samples, symptoms and imaging
· Recommendations
for optimum treatment
· Personalized
drug therapies
Agriculture
· Enhancement
of the “Precision Agriculture” trend, for more effective irrigation, planting
and harvesting
· Prediction
of weather and rain
Transportation
· Providing
traffic flow management
· Enabling
self-driving cars and trucks
· Optimizing
operations for aircraft, truck fleets, railroads and ships
Energy
Efficiency and Production/Environmental Controls
· Developing technologically-advanced
“smart cities” and green buildings
· Improving
energy efficiency in air conditioning, lighting and other systems
· Improving
operations and outcomes at all types of energy production operations, from
selecting better sites for drilling oil wells to gaining optimum output from
windmills
· Enhancing
air and water quality monitoring and control
Manufacturing
· Reducing
plant downtime
· Increasing efficient
use of materials and personnel
· Optimizing
actions of robotic equipment
Financial
services
· Better
analyzing risk for insurance underwriting
· Analyzing
optimum investments for specific goals
· Approving
loans and controlling credit risk
Supply
Chain
· Optimizing
timing of orders and shipping
· Reducing
inventory wastage and delays
Source:
Plunkett Research Ltd.