LOOK FOWARD TO ARTIFICIAL INTELLIGENT

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AI NRTTIEFLILCIIGAELNT

IN SELF DRIVING VEHICLE



Artificial intelligence (AI) is at the heart of self-driving car systems. To 5

create self-driving cars, self-driving car developers use massive
amounts of data from image recognition systems, as well as machine
learning and neural networks.

The data is fed into machine learning algorithms via neural networks,
which recognise patterns in the data. Images captured by self-driving
car cameras are used to train the neural network to recognise traffic
lights, trees, curbs, pedestrians, street signs, and other elements of
any given driving environment.

Google's Waymo self-driving car project, for example, integrates data
from sensors, LiDAR (light detection and ranging — a technique
similar to RADAR), and cameras to recognise everything in the
vehicle's vicinity and forecast what those items will do next. This
happens in fractions of a second. Maturity is critical for these systems.
The more the system drives, the more data it has to feed into its deep
learning algorithms, which allows it to make more precise driving
decisions.

The following outlines how Google Waymo vehicles work:

 The driver (or passenger) decides on a route. A route is calculated
by the car's software.

 A rotating, roof-mounted LiDAR sensor scans a 60-meter radius
around the car and generates a dynamic three-dimensional (3D)
representation of the vehicle's current surroundings.

 A sensor on the left rear wheel detects the car's position in relation
to the 3D map by monitoring sideways movement.

 Front and rear bumper radar systems calculate distances to
obstacles.

 The AI software in the car is connected to all of the sensors and
collects data from Google Street View and video cameras inside the
vehicle.

 The AI uses deep learning to simulate human perception and
decision-making processes and controls actions in driver control
systems like steering and braking.

 The car's software uses Google Maps to get a heads-up on
landmarks, traffic signs, and lights.

 There is an override function that allows a human to take control of
the vehicle.

Google's Waymo project is an example of a self-driving car that is

nearly fully autonomous. A human driver is still required, but only
when the system cannot be overruled. It isn't truly self-driving, but it
can drive itself in ideal conditions. It has a high level of autonomy.
Many consumer vehicles today have a lower level of autonomy but
still have some self-driving capabilities. As of 2019, the following self-
driving capabilities are available in various production automobiles:

 Hands-free steering centres the vehicle without requiring the
driver to take their hands off the steering wheel. The driver must
continue to pay attention.

 Adaptive cruise control (ACC) maintains a preset distance
between the driver's car and the car in front even when the
vehicle comes to a complete stop.

 Lane-centering steering automatically nudges the vehicle
toward the opposite lane marking when the driver crosses lane
markings.

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The National Highway Traffic Safety Administration (NHTSA) of the

United States defines six levels of automation, beginning with Level
0, where humans drive, and progressing to fully autonomous vehicles
via driver assistance technologies. The five levels of automation that
follow Level 0 are as follows:

 Level 1: An advanced driver assistance system (ADAS) assists the
human driver with steering, braking, and accelerating, but not all at the
same time. An ADAS system includes rearview cameras as well as
features such as a vibrating seat warning to alert drivers when they
stray from the travel lane.

 Level 2: An ADAS that can steer, brake, or accelerate while the driver
remains fully conscious behind the wheel and acts as the driver.

 Level 3: Under certain conditions, such as parking, an automated
driving system (ADS) can perform all driving tasks. In these cases, the
human driver must be prepared to retake control and must remain the
primary driver of the vehicle.

 Level 4: In certain circumstances, an ADS can perform all driving tasks
and monitor the driving environment. In those cases, the ADS is
reliable enough that the human driver does not need to pay attention.

 Level 5: The vehicle's ADS acts as a virtual chauffeur, driving the
vehicle in all conditions. The human occupants are only expected to be
passengers and are never expected to drive the vehicle.

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As of 2019, automobile manufacturers had attained Level 4.

Manufacturers must clear a number of technological milestones and
address a number of critical issues before fully autonomous vehicles
can be purchased and used on public roads in the United States.
Despite the fact that Level 4 autonomous vehicles are not yet
available for public consumption, they are used in other ways.

Waymo, a subsidiary of Google, recently partnered with Lyft to launch
Waymo One, a fully autonomous commercial ride-sharing service.
Riders can direct a self-driving car to their destination while providing
feedback to Waymo. The cars still have a safety driver in case the
ADS needs to be overridden. As of late 2019, the service was only
available in the Phoenix metropolitan area, but it plans to expand to
cities in Florida and California.

In China's Hunan province, autonomous street-sweeping vehicles are
also being produced, and they meet the Level 4 requirements for
autonomously navigating a familiar environment with limited novel
situations.

Manufacturers' estimates for when Level 4 and 5 vehicles will be
widely available differ. Ford and Volvo both anticipate releasing a
Level 4 vehicle to the public in 2021. Elon Musk, the CEO of Tesla
and a pioneer of both self-driving and electric vehicles, has stated that
his company will have Level 5 vehicles ready by 2020. A Level 5
vehicle must be capable of responding to novel driving situations as
well as, if not better than, a human.

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The primary advantage touted by proponents

of self-driving cars is safety. According to a
United States Department of Transportation
(DOT) and National Highway Traffic Safety
Administration (NHTSA) statistical projection
of traffic fatalities for 2017, 37,150 people died
in motor vehicle traffic accidents that year.
According to the NHTSA, 94 percent of serious
crashes are caused by human error or poor
decisions, such as drunk or distracted driving.
Autonomous vehicles remove those risk
factors from the equation; however, self-
driving vehicles are still vulnerable to other
factors that cause crashes, such as
mechanical issues.

The economic benefits of autonomous cars
could be enormous if they can significantly
reduce the number of crashes. According to
the NHTSA, injuries have an economic impact
of $57.6 billion in lost workplace productivity
and $594 billion in loss of life and decreased
quality of life.

Autonomous trucks have been tested in the
United States and Europe to allow drivers to
use autopilot over long distances, allowing
them to rest or complete tasks while also
improving driver safety and fuel efficiency.
Truck platooning is a cooperative ACC
initiative powered by ACC, collision avoidance
systems, and vehicle-to-vehicle
communications (CACC).

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One disadvantage of self-driving In one case from March 2018, Tesla's
Model X SUV was on autopilot when
technology is that it may be unsettling it collided with a highway lane divider.
to ride in a vehicle without a driver According to the company, despite
behind the wheel, at least at first. visual and audible warnings to put his
However, as self-driving capabilities hands back on the steering wheel, the
become more common, human driver's hands were not on the wheel.
drivers may become overly reliant on Another collision occurred when a
autopilot technology, leaving their Tesla's AI mistook the shiny reflection
safety in the hands of automation, on the side of a truck for the sky.
even when they should be acting as
backup drivers in the event of
software failures or mechanical
issues.

 Autonomous vehicles must learn to recognise a wide range of
objects in their path, from branches and litter to animals and
people. Tunnels that interfere with the Global Positioning System
(GPS), construction projects that cause lane changes, and
complex decisions, such as where to stop to allow emergency
vehicles to pass, are all obstacles on the road.

 The systems must make instant decisions on when to slow down,
swerve, or maintain normal acceleration. This is an ongoing
challenge for developers, and there have been reports of self-
driving cars hesitating and swerving unnecessarily when objects in
or near the roadways are detected.

 This issue was highlighted in a fatal accident involving an Uber self-
driving car in March 2018. According to the company, the vehicle's
software detected a pedestrian but deemed it a false positive and
did not swerve to avoid hitting her. Toyota has temporarily halted
testing of self-driving cars on public roads as a result of this
accident, but testing will continue elsewhere. Toyota Research
Institute is building a test facility on a 60-acre site in Michigan to
advance the development of automated vehicle technology.

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ARTIFICIAL
I NTELLIGENT

IN THE TRAVEL INDUSTRY



Examples of Artificial
Intelligence in the Travel

Industry

The role of artificial intelligence in the business world
has grown dramatically over the last decade, with the
last few years seeing much more widespread adoption
in the travel industry in particular. Three of the most
significant ways the technology is currently being
deployed are listed below.

1. Online Customer Service and Chatbots

One of the most exciting 13

applications of artificial
intelligence for hotels and other
tourism businesses is providing
online assistance to customers.
There has already been
widespread adoption for the
purpose of powering chatbots on
social media platforms and instant
messaging apps, in particular.

AI can respond to questions and provide
valuable information to customers even
when a customer service representative
is unavailable. Customers are
demanding faster and faster response
times on online platforms, and artificial
intelligence enables businesses to meet
these demands in ways that humans

t

2. Face-to-Face Customer Service

One emerging trend is for the technology to be used

for face-to-face customer service interactions as well.
Importantly, this has the potential to reduce wait times
at information or reception desks while also
improving overall efficiency.
The AI robot 'Connie,' which has been deployed by
Hilton, is one example of this technology in action.
This robot provides tourist information to customers
who speak to it using artificial intelligence and speech
recognition. Each human interaction also contributes
to the robot's learning, which improves the quality of
all future communications.

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3. Data Processing and Data Analysis

Finally, it is critical to understand that

the applications of AI in the travel and
tourism industry go beyond customer
service. In fact, one of its most popular
and effective applications is data
collection and interpretation in order to
draw conclusions about customers,
business practises, and pricing
strategies.
The key advantage of artificial
intelligence in this field is its ability to sort
through massive amounts of data
quickly and accurately, whereas
humans would take much longer and
potentially contain more errors. The
Dorchester Collection hotel, for
example, has used AI to sort through
customer feedback from surveys,
reviews, and online polls in real-time to
build a more accurate picture of current
opinion.

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AI RNTTIEFLILCIIGAELNT

IN INFORMATION TECHNOLOGY



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Machine Learning

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Speech Recognition

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Biometrics

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AI NRTTIEFLILCIIGAELNT

IN EDUCATION



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AI RNTTIEFLILCIIGAELNT

IN MANUFACTURING



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AI NRTTIEFLILCIIGAELNT

IN BUSINESS



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AI can create the
trailer

The process of creating a According to John R.
Smith, multimedia and
trailer for new horror movie vision manager at IBM,
“Morgan” involved using the capacity to reduce a
machine learning techniques process from weeks to
and experimental APIs hours reveals the true
through IBM’s Watson power of AI.
platform.

Watson was taken to film
school as it analysed
hundreds of existing horror
movie trailers to learn what
kept viewers on edge before
being fed the entire final cut
of the upcoming movie

The analysis resulted in the
program selecting the 10
most usable moments in the
film and then a human editor
created this finished trailer
from those clips. Pretty cool,
isn’t it?

The entire process took about
24 hours to complete,
compared to a 10 to 30 day,
labour-intensive, manual edit
that would be the norm.

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Another example of the Impossible Things is,
according to Zhang’s
use of AI in the movies Kickstarter page the first
comes with “Impossible co-written feature film that
Things,” an independent marks “the next step in
horror movie co-written by human-computer
AI software. collaboratively created
original content.”
The movie is the brainchild
The software uses the
of Jack Zhang whose machine learning process
of Natural Language
Greenlight Essential Processing (NLP) to analyse
thousands of movie plot
company has developed AI summaries correlated to
box office performance.
software which “allows
The result they say is an AI
users with neither system that’s smart enough
to recognize plot patterns
programming nor that lead the way to
successful box office
mathematics background performance.

to explore and discover 42

repeatable patterns from

decades of film data.”

AI can Personalizing
Choices

Machine Learning is also We aren’t talking about

helping entertainment simple viewing

providers recommend recommendations though

personalized content, —machine learning

based on the user’s applications are also being

previous viewing activity used to fine tune the way

and behaviour. Take Netflix the home page is presented

for example. to users, in particular the

They run a large number of “Continue Watching” (CW)

machine learning row.

workflows every day to be Using data based on factors

able to predict what we such as subscription

want to watch. history, previous

The tech team at Netflix has interactions with content,
created an AI framework
called Meson to support and even contextual
their efforts.
features such as time of day

and device, the content and

placement of the CW row

And you see the work of can be amended for
Meson every time you open
Netflix and are served up maximum effect.
suggestions on what to
watch next. Although still in its infancy,
it’s not hard to see the
massive potential here.

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AI as Creative
Director

Advertising is another McCann Erickson in Japan
developed an AI creative
creative industry where director, AI-CD ß that
machine learning is analyzes the client’s brief
beginning to have an before producing creative
impact. ideas.

The Drum, in partnership The machine has even been
taken to a meeting where
with native advertising ideas are presented to the
client, communication
company Teads has planner at McCann Erickson
saying, “We want to treat
recently produced a short AI-CD ß like a normal
creative director.
documentary to
And it’s important for a
demonstrate how some in creative director to be in
the meeting.” That is a pitch
the ad industry are I would’ve loved to have sat
in on.
exploring how AI can aid

the creative process.

The Automation of
Creativity features some
interesting new concepts.

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Advertising Display A London ad company
at the Bus Shelter
created a bus shelter ad
display incorporating facial
recognition and AI
techniques.

The display, which featured
a fictitious coffee brand,
presented different images
and copy depending on how
engaged people were
judged to be.

The system was designed to
“learn” which ads created
the most engagement and
reaction in an ongoing
process of improvement.

AI create Documented
Painters that bring

back historic materials.

The Rembrandt is a

creative experiment
where machine learning
was used to produce a
new portrait based on
previous work by the
artist.

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ARTIFICIAL
I NTELLIGENT

IN HEALTHCARE