27 Incredible Examples Of Artificial Intelligence (AI) And
Machine Learning In Practice
There are so many amazing ways artificial intelligence
and machine learning are used behind the scenes to impact our everyday lives
and inform business decisions and optimize operations for some of the world’s
leading companies. Here are 27 amazing practical examples of AI and machine
learning.
Consumer goods
Using natural language processing, machine learning and
advanced analytics, Hello
Barbie listens and responds to a child. A microphone
on Barbie’s necklace records what is said and transmits it to the servers at
ToyTalk. There, the recording is analyzed to determine the appropriate response
from 8,000 lines of dialogue. Servers transmit the correct response back to
Barbie in under a second so she can respond to the child. Answers to questions
such as what their favorite food is are stored so that it can be used in
conversation later.
Coca-Cola’s global market and extensive product list—more than
500 drink brands sold in more than 200 countries—make it the largest beverage
company in the world. Not only does the company create a lot of data, it has
embraced new technology and puts that data into practice to support new product
development, capitalize on artificial intelligence bots and even trialing
augmented reality in bottling plants.
Even though Dutch company Heineken has been a worldwide brewing leader for the last
150 years, they are looking to catapult their success specifically in the
United States by leveraging the vast amount of data they collect. From
data-driven marketing to the Internet of Things to improving operations through
data analytics, Heineken looks to AI augmentation and data to improve its
operations, marketing, advertising and customer service.
Creative Arts
Culinary arts require the human touch, right? Yes and no.
AI-enabled Chef
Watson from IBM offers a glimpse of how
artificial intelligence can become a sous-chef in the kitchen to help develop
recipes and advise their human counterparts on food combinations to create
completely unique flavors. Working together, AI and humans can create more in
the kitchen than working alone.
Another way AI and big data can augment creativity is in
the world
of art and design. In one example, IBM’s machine learning
system, Watson, was fed hundreds of images of artist Gaudi’s work along with
other complementary material to help the machine learn possible influences for
his work including Barcelona, its culture, biographies, historical articles and
song lyrics. Watson analyzed all the information and delivered inspiration to
the human artists who were charged with the creating a sculpture “informed” by
Watson and in the style of Gaudi.
Music-generating
algorithms are now inspiring new songs. Given
enough input—millions of conversations, newspaper headlines and
speeches—insights are gleaned that can help create a theme for lyrics. There
are machines such as Watson BEAT that can come up with different musical
elements to inspire composers. AI helps musicians understand what their audiences
want and to help determine more accurately what songs might ultimately be hits.
Energy
Global energy leader, BP is at the forefront of realizing the opportunities
big data and artificial intelligence has for the energy industry. They use the
technology to drive new levels of performance, improve the use of resources and
safety and reliability of oil and gas production and refining. From sensors
that relay the conditions at each site to using AI technology to improve
operations, BP puts data at the fingertips of engineers, scientists and
decision-makers to help drive high performance.
In an attempt to deliver energy into the 21st century, GE Power uses big data, machine learning and Internet of
Things (IoT) technology to build an “internet of energy.” Advanced analytics
and machine learning enable predictive maintenance and power, operations and business
optimization to help GE Power work toward its vision of a “digital power
plant.”
Financial Services
With approximately 3.6 petabytes of data (and growing)
about individuals around the world, credit reference agency Experian gets its extraordinary amount of data from
marketing databases, transactional records and public information records. They
are actively embedding machine learning into their products to allow for quicker
and more effective decision-making. Over time, the machines can learn to
distinguish what data points are important from those that aren’t. Insight
extracted from the machines will allow Experian to optimize its
processes.
American Express processes $1 trillion in transaction and has 110
million AmEx cards in operation. They rely heavily on data analytics and
machine learning algorithms to help detect fraud in near real time, therefore
saving millions in losses. Additionally, AmEx is leveraging its data flows to
develop apps that can connect a cardholder with products or services and
special offers. They are also giving merchants online business trend analysis
and industry peer benchmarking.
Healthcare
AI and deep learning is being put to use to save lives by Infervision. In China, where there aren’t enough radiologists to
keep up with the demand of reviewing 1.4 billion CT scans each year to look for
early signs of lung cancer. Radiologists need to review hundreds of scans each
day which is not only tedious, but human fatigue can lead to errors.
Infervision trained and taught algorithms to augment the work of radiologists
to allow them to diagnose cancer more accurately and efficiently.
Neuroscience is the inspiration and foundation for
Google’s DeepMind, creating a machine that can mimic the thought processes
of our own brains. While DeepMind has successfully beaten humans at games,
what’s really intriguing are the possibilities for healthcare applications such
as reducing the time it takes to plan treatments and using machines to help
diagnose ailments.
Manufacturing
Cars are increasingly connected and generate data that
can be used in a number of ways.Volvo uses data to help predict when parts would fail or
when vehicles need servicing, uphold its impressive safety record by monitoring
vehicle performance during hazardous situations and to improve driver and passenger
convenience. Volvo is also conducting its own research and development on
autonomous vehicles.
BMW has big data-related technology at the heart of its
business model and data guides decisions throughout the business from design
and engineering to sales and aftercare. The company is also a leader in
driverless technology and plans for its cars to deliver Level 5 autonomy—the
vehicle can drive itself without any human intervention—by 2021.
The AI tech revolution has hit farming as well, and John Deere is getting data-driven analytical tools and automation
into the hands of farmers. They acquired Blue River Technology for its solution
to use advanced machine learning algorithms to allow robots to make decisions
based on visual data about whether or not a plan is a pest to treat it with a
pesticide. The company already offers automated farm vehicles to plough and sow
with pinpoint-accurate GPS systems and its Farmsight system is designed to help
agricultural decision-making.
Media
The BBC
project, Talking with Machines is
an audio drama that allows listeners to join in and have a two-way conversation
via their smart speaker. Listeners get to be a part of the story as it prompts
them to answer questions and insert their own lines into the story. Created
specifically for smart speakers Amazon Echo and Google Home, the BBC expects to
expand to other voice-activated devices in the future.
UK news agency Press
Association (PA) is hoping robots and artificial
intelligence might be able to save local news. They partnered with news
automation specialist Urbs Media to have robots write 30,000 local news stories
each month in a project called RADAR (Reporters and Data and Robots). Fed with
a variety of data from government, public services and local authorities, the
machine uses natural language generation technology to write local news
stories. These robots are filling a gap in news coverage that wasn’t being
filled by humans.
Big data analytics is helping Netflix predict what its customers will enjoy watching.
They are also increasingly a content creator, not just a distributor, and use
data to drive what content it will invest in creating. Due to the confidence
they have in the data findings, they are willing to buck convention and
commission multiple seasons of a new show rather than just a pilot episode.
Retail
When you first think of Burberry, you likely consider its luxury fashion and not first
consider them a digital business. However, they have been busy reinventing
themselves and use big data and AI to combat counterfeit products and improve
sales and customer relationships. The company’s strategy for increasing sales
is to nurture deep, personal connections with its customers. As part of that,
they have reward and loyalty programs that create data to help them personalize
the shopping experience for each customer. In fact, they are making the
shopping experience at their brick-and-mortar stores just as innovative as an
online experience.
As the world’s second-largest retailer, Walmart is on the cutting edge of finding ways to transform
retail and provide better service to its customers. They use big data, machine
learning, AI and the IoT to ensure a seamless experience between the online
customer experience and the in-store experience (with 11,000 brick-and-mortar
stores, something rival Amazon isn’t able to do. Enhancements include using the
Scan and Go feature on the app, Pick-up Towers and they are experimenting with
facial recognition technology to determine if customers are happy or sad.
Service
Central to everything Microsoft does is leveraging smart machines. Microsoft has Cortana,
a virtual assistant; chatbots that run Skype and answer customer service
queries or deliver info such as weather or travel updates and the company has
rolled out intelligent features within its Office enterprise. Other companies
can use the Microsoft AI Platform to create their own intelligent tools. In the
future, Microsoft wants to see intelligent machines with generalized AI
capabilities that allow them to complete any task.
When you bring together cloud computing, geo-mapping and
machine learning, some really interesting things can happen. Google is using AI
and satellite data to prevent
illegal fishing. On any given day, 22 million data points
are created that show where ships are in the world’s waterways. Google
engineers found that when they applied machine learning to the data, they could
identify why a vessel was at sea. They ultimately created Global Fishing Watch
that shows where fishing is happening and could then identify when fishing was
happening illegally.
Always at the top of delivery extraordinary service, Disney is getting even better thanks to big data. Every
visitor gets their own MagicBand wristband that serves as ID, hotel room key,
tickets, FastPasses and payment system. While guest enough the convenience, Disney
gets a lot of data that helps them anticipate guests’ needs and deliver an
amazing, personalized experience. They can resolve traffic jams, give extra
services to guests who may have been inconvenienced by a closed attraction and
data even allows the company to schedule staff more efficiently.
Google is one of the pioneers of deep learning from its
initial foray with the Google Brain project in 2011. Google first used deep
learning for image recognition and now is able to use it for image enhancement.
Google has also applied deep learning to language processing and to provide
better video recommendations on YouTube, because it studies viewers’ habits and
preferences when they stream content. Next up, Google’s self-driving car
division also leverages deep learning. Google also used machine learning to
help it figure out the right configuration of hardware and coolers in their
data centers to reduce the amount of energy expended to keep them operational.
AI and machine learning has helped Google unlock new ways of sustainability.
Social Media
From what tweets to recommend to fighting inappropriate
or racist content and enhancing the user experience, Twitter has begun to use artificial intelligence behind the
scenes to enhance their product. They process lots of data through deep neural
networks to learn over time what users preferences are.
Deep learning is helping Facebook draw value from a larger portion of its
unstructured datasets created by almost 2 billion people updating their
statuses 293,000 times per minute. Most of its deep learning technology is
built on the Torch platform that focuses on deep learning technologies and
neural networks.
Instagram also uses big data and artificial intelligence to
target advertising and fight cyberbullying and delete offensive comments. As
the amount of content grows in the platform, artificial intelligence is
critical to be able to show users of the platform information they might like,
fight spam and enhance the user experience.
Bernard Marr
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