Abstract our emotions are. Research is taking

 

Abstract

A mobile device
is a portable computing device that performs many functions of a computer with
a mobile operating system and integrated cellular network connection for
communication. Artificial intelligence (AI) is the ability of a computer system
to perform tasks that requires human intelligence like decision-making, speech
recognition, and translation between languages. Artificial intelligence is
being used in automotive industry for self-driving cars, video-games, health
care, etc. With the advancement in technology and innovation in smartphone
manufacturers, artificial intelligence made its way into mobile devices. AI in mobile
devices learns about the user preferences over time by tracking the activities.
The data collected by AI is stored in the device which keeps it personal and
under the user’s control. Some of the apps share this information anonymously
to their servers to enhance their AI. Although AI is in nascent stage, there is
a lot of controversy about its advancement. Experts believe that AI will soon
turn smartphones into intelligent entities that will know how we feel and what
our emotions are. Research is taking place on the said AI technology which will
know the mood of every person and how it is likely to change. Such advancements
in AI will give mobile devices the power to learn our emotions and to predict
them. AI has made everything from virtual assistant to face recognition
possible on a smartphone. Tech giants, Google and Apple have already integrated
artificial intelligence into their maps; Apple Map or Google Maps as they can
now predict and make suggestions on where you might want to go. Another popular
application of AI that comes with modern smartphones today is the AI personal
assistant. To name a few, we have Apple’s Siri, Microsoft’s Cortana, Google
Now, and Huawei’s upcoming Kirin 970. The continuous improvements to the mobile
device AI assistants made them more ‘human’ like in various aspects such as the
way they talk, behave, and act. Further innovations in the AI technology would significantly
affect the way these virtual assistants would work. Besides all the
advancements and advantages of AI in mobile devices, it also poses some serious
threats. The vulnerability of mobile devices to electronic spying and hacking
adds up to the existing threats in AI. This paper provides a study on “The
potential implications, benefits, and challenges of integrating AI technology
into mobile devices”?

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 Keywords: Artificial
Intelligence, Smartphones, Technological Advancement, Human Intelligence,
Virtual Assistant

1.Introduction

From science
fiction to reality, artificial intelligence is now an everyday term which once
enjoyed a superior status. John McCarthy is the Father of Artificial
Intelligence (Rajaraman, 2014). He coined the term
Artificial Intelligence in the year 1955 that describes computer programs which
display intelligence, that is, these programs have the ability to perform tasks
which when performed by humans potentially requires intelligence (Rajaraman, 2014). He invented LISP, a programming
language designed specifically to solve the issues in Artificial Intelligence (Rajaraman, 2014). A rise in artificial intelligence
was observed from 1957 to 1974 (Anyoha, 2017). It was the time when
computers became faster, cheaper and easily accessible (Anyoha, 2017).  Also, there was an improvement in machine
learning algorithms and people started understanding the best fit algorithms to
their problems (Anyoha, 2017). Machine learning is an
increasingly powerful tool which can be applied to wide variety of areas
ranging from object recognition, language translation, and more (Thorat & Smilkov, 2017). This makes use of learning
algorithms that helps computer systems to learn new tasks instead of
programming them continuously to perform tasks (Society, 2017).

Figure 1: Artificial Intelligence
Evolution (Anyoha, 2017)

The above figure
shows the timeline of artificial intelligence starting with whether machines
can think in 1950’s to inventing the first speech recognition software in
2000’s (Anyoha, 2017).

A trend in
computer technology which is observed over the past decades was that the
devices turning portable and personal (Malaka, n.d.). Mobile devices are designed
in such a way that they are small enough to be carried around with computing
capabilities of a personal computer or desktop (Tan & Lindberg, 2008) (Wikipedia). With significant
progress in technology in the past few years, mobile devices now have become
multifunctional devices that are capable of handling wide range of applications
(Tan & Lindberg, 2008). For example, besides making
phone calls and sending messages smartphones are capable of connecting to the
internet enabling sending of e-mails and web browsing (Tan & Lindberg, 2008). For the success of mobile
systems, the design of user interface and software play an important role that
allows access to personal information and provides flexibility in handling changes
according to user requirements (Krüger & Malaka, 2004). There are a lot of difficulties
that needs to be solved before building useful infrastructures for mobile users
(Krüger & Malaka, 2004). This is where artificial
intelligence (AI) comes into picture and helps in solving many of the user
difficulties (Krüger & Malaka, 2004). AI technology is made
available in mobile phones for a while now, providing voice assistant features
like Siri, Cortana, or Google Assistant (Oliva, 2017). Additional AI applications
in mobile devices include: Maps that compute the optimal driving route and
distance to destination, song suggestions, friend recommendations on various
applications, job recommendations based on previous searches, product
recommendations, all these are a result of AI in mobile devices (Deloitte, 2016).

This paper
provides information that helps in understanding the implications, benefits,
and challenges on AI in mobile devices.

1.1  Research Question

To understand
the potential implications, benefits, and challenges of integrating AI
technology into mobile devices.

2. Literature Review

Artificial
intelligence is firmly rooted into our day-to-day lives (Deloitte, 2016). Though AI technologies have
been in existence for several decades, the explosion of massive amounts of
data, which is the raw material for AI that led to its rapid advancement (Marr, 2017).  Many voice-controlled intelligent personal
assistants, such as Cortana by Microsoft, Siri by Apple, Google Assistant by
Google, Bixby by Samsung, are progressing to be a part of users’ daily lives, largely
on mobile devices (Kiseleva et al., 2016). There is a significant difference
when information is accessed by the intelligent assistant in contrast to
traditional web search (Kiseleva et al., 2016).

Figure 2: Interaction with voice
assistant: Cortana (Kiseleva et al., 2016)

Figure 2 shows
two examples of tasks performed by the assistants  (Kiseleva et al., 2016). These intelligent assistants
help users with communication, information, and time management  (Azvine, Dijan, Tsui, & Wobcke, n.d.). Each of these assistants are
designed to have a model of their user and a learning model to better
understand their users and to provide more personalized services (Azvine et al., n.d.). Users communicate with their
assistants in order to control their mobile devices such as making a phone
call, setting up reminders and alarms, or to manage their calendars (Kiseleva et al., 2016). These interactions are made
possible with the help of automatic speech recognition (Kiseleva et al., 2016). Another important application
of artificial intelligence in mobile devices is its usage in Global Positioning
System. GPS navigation systems make use of stored map information for selecting
the best route (Duffany, 2010).  GPS system uses measures GPS location, date,
and time information in determining the best possible route (Duffany, 2010). Learning based artificial
intelligence provides GPS system with the ability to recognize traffic, traffic
lights, stop signs, individual driving habits, weather, direction of travel,
speed limits, day of the week and time of the year between the source and
destination points (Duffany, 2010). Further, recommender systems
(RS) in various mobile applications use artificial intelligence (AI) methods to
provide recommendations to the users (Portugal, Alencar, & Cowan, n.d.). Artificial intelligence also
aims at improving the level of safety from malware attacks in android operating
system mobile devices (Lopez, Cadavid, & Garcia, 2016). This is achieved by machine
learning classifiers where the algorithms are trained with known features in
order to predict the classification of unknown features (Amos, Turner, & White, 2013) . Another newest feature added to
mobile devices by integrating AI is Google Lens launched by Google in its
mobile phones Pixel and Pixel 2 (Statt, 2017). This makes use of the
technology computer vision (Statt, 2017).

 2.1
Artificial Intelligence Benefits and Technologies:

Artificial
intelligence tends towards creating a personalized user experience (Wertz, 2017). It analyzes enormous data
sets efficiently than a human being (Wertz, 2017). It can rapidly identify
patterns of information, such as purchase trends, credit checks and other
common threads which are analyzed every day to cater to a single customer (Wertz, 2017). For example, the AI-powered
feature of Now Playing in Google Pixel 2, with a database of 100,000 songs that
are updated weekly, will tell the user the song playing in the background by
automatically sending a notification onto the home screen (Oliva, 2017). Another example is the
Google Translator that understands different languages and translates them to
the user language (Oliva, 2017). The most important benefit
of AI is that it makes the mobile devices better understand the way user uses
it and will provide more relevant features and applications (Oliva, 2017). To achieve these benefits
following technologies are incorporated with AI.

Forrester’s
TechRadar methodology is used in identifying and analyzing the current and future
trends of different AI technologies (Forrester, 2017). Figure 3 shows the Forrester
report on artificial intelligence technologies.

 

Figure 3: Artificial Intelligence
Technologies (Forrester, 2017)

There is a
significant rise in investment and adoption of artificial intelligence by many
tech giants (Press, 2017). The most widely used AI
technologies are:

·      
Text Analytics and Natural Language Processing:
The ability of a computer to understand and communicate with humans in their
natural language (Weischedel et al., n.d.). NLP makes use of text
analytics to understand the sentence structure, meaning, sentiment and intent
through machine learning methods (Press, 2017). NLP is used in number of
disciplines such as computer and information science, linguistics, mathematics,
electrical and electronic engineering, artificial intelligence and robotics (Chowdhury, 2003).

·      
Speech Recognition: This is an ability of the
computer to map an acoustic speech signal to text (Forrester, 2017).

·      
Facial Recognition: The ability to analyze
images and videos of human facial expressions to identify users and to detect
their emotional responses and engagement levels (Forrester, 2017).

·      
Machine Learning Platforms: Making available
different development and training toolkits, models, algorithms, computing
power to applications and machines (Press, 2017).

·      
Deep Learning Platforms: This makes use of
artificial neural networks that provide computers with the capability to learn
and improve on their own (Parloff, 2016). This is primarily used in
pattern recognition and classification applications (Press, 2017). For example, recognizing the
make and color of a car, identifying the landmarks in picture (Triggs, 2017).

·      
Biometrics: This enables the interactivity
between humans and machines in the form of image and touch recognition, speech
and body language (Press, 2017).  These are majorly used for authenticating or
identifying the user (PRNewswire, 2016).

2.2 Challenges of AI in mobile devices

Though there are
multiple benefits of integrating AI in mobile devices there are few challenges associated
with it. They are:

·      
With the increase in Neural Networks, Machine
Learning, and Heterogeneous Computing there are emerging use cases for
smartphone users spread across a wide range of fields (Triggs, 2017). These technologies improve
user experiences by offering them with enhanced audio, image and voice
processing, language processing and improved database search speed, among
others (Triggs, 2017).  Due to limited processing capabilities of
smartphones these use cases cannot be easily obtained (Mao, Zhang, Song, & Letaief, 2016). Hence it is harder to
implement artificial intelligence on a mobile platform (Zhang, Gu?sgen, & Yeap, 2004).

·      
The artificial intelligence revolution involves
sending information to vast data centers on the cloud server, where it is
processed before obtaining the results (Burgess, 2017). Sending data back and forth
is slowing down the processing of data and also posing serious privacy concerns
(Burgess, 2017).

·      
Another serious concern of AI is its Intuitive
thinking or Judging power. That is, artificial intelligence or machine learning
works by training the software to identify patterns or trends in the data (Nogrady, 2016). After it is trained, it
performs analysis on fresh, unseen data and derives the results (Nogrady, 2016). There is no proper reasoning
on how it got to that particular result (Nogrady, 2016). Therefore, the performance
of the system depends on the data it learns from (Nogrady, 2016). Since the data that is fed
to AI’s is not perfect, the results obtained are not accurate (Nogrady, 2016). There should be more
scrutiny on the decision-making process of AIs (Nogrady, 2016).

 

 

2.3 Artificial Intelligence in Mobile Apps

The expansion of
AI technology has led the mobile users to expect an improved user experience
and mobile app performance (Dossey, 2017). With the increase in user
expectations many companies have developed their intelligent mobile
applications (Dossey, 2017). Apps are the first step
towards introducing AI to mobile devices (Burgess, 2017). For example, Starbucks
released its mobile app “My Starbucks Barista” that places orders for users (Dossey, 2017). Another example is of
“TacoBot” released by Tacobell that recommends personalized menu suggestions based
on the user prior purchase trends (Dossey, 2017). Few such apps are “The Roll”,
this organizes thousands of phones taken with the phone by grouping similar
pictures together thereby deleting the copies (Eaton, 2016). “EasilyDo” which connects
the email accounts like Gmail to other services and apps like Facebook,
LinkedIn so that it can send an alert reminding you of the scheduled meeting in
the calendar (Eaton, 2016). There is a significant
growth in development of these smart apps as they help in accomplishing daily
tasks (Dossey, 2017). Many apps are being
developed with algorithms that adjust based on the user behavior (Dossey, 2017). These algorithms help to
create more meaningful applications that can engage users (Dossey, 2017).

3.Applications of AI
in mobile devices:

3.1 Voice controlled
personal assistants

There is a significant growth in the usage of voice-controlled
intelligent devices, such as Microsoft’s Cortana, Google’s Google Assistant,
Apple’s Siri, and Samsung’s Bixby (Kiseleva & de Rijke, 2017). These personal assistants
assist user in performing different tasks, both at work and in their daily
lives considering context as a crucial factor (Kiseleva & de Rijke, 2017). For example, they help in
making phone calls, planning night outs, performing web search and many more (Kiseleva et al., 2016).

 

3.2 Face Detection

Deep learning techniques are used to detect faces (Apple, 2017). This technique captures the
face attribute features available such as age, emotion, gender, smile, and
facial hair (Microsoft, n.d.). Example of this is the
iPhone X “Face ID” feature which is used to secure the phone (Staff & Fleishman, 2017).

3.3 Recommender
Systems

Recommender systems are vital in mobile commerce as it
benefits users by providing highly personalized products and services (Frey et al., 2017). For example, on a social networking app, RS suggests profiles
which are similar to user, based on their interests (Portugal et al., n.d.). Another example is of
Amazon, it is known for its personalization and recommendation, which helps
customers find items of their interest by using these recommender systems (Linden, Smith, & York, 2003).

3.4 Computer Vision Systems

Computer vision deals with the technology of image
processing, pattern recognition, physics, and artificial intelligence (Shapiro, 1985). An example for this is “Google
Lens”. Lens is a computer vision system that is capable of retrieving
information of any object in real time by pointing the Pixel or Pixel 2 camera
at it (Statt, 2017). It can read text and saves
information from business cards, saves URL from fliers, identify landmarks, can
retrieve information on books, art, posters by focusing the camera on to them (Statt, 2017). Lens also works as barcode
and QR code scanner (Statt, 2017).

4 Future of AI in mobile devices

Technology is
ever changing and there is a constant advancement in the capabilities provided
by artificial intelligence. Besides the general applications of AI in mobile
devices mentioned above, many mobile tech giants are moving towards enhancing
the use of artificial intelligence to its fullest in their mobile devices. To
accommodate the benefits offered by this ever growing technology on mobile
devices,  a new custom AI processor that
can handle these tasks is to be designed (Triggs, 2017). This not only improves the
computational power but also efficiency in three main fields: size,
computation, and energy (Triggs, 2017). An extra dedicated processor
for handling complex sorting algorithms will help improve the computational
power of smartphones and enhance faster processing, from automatic image
enhancements to faster video library searches (Triggs, 2017). Major mobile vendors are
working towards implementing this custom chip on their mobile devices for
improving its computational power. Apple, the biggest mobile phone vendor believes
that its mobile devices will be a major platform for implementation of
artificial intelligence (Reuters, 2017). Apple’s new iPhone X is billed
as “the future of the smartphone” with its new enhanced neural engine with a
capability to process “up to 600 billion operations per second” (Vincent, 2017). Till today, AI features that
are made available on mobile devices are powered by the cloud (Vincent, 2017). This is one of the major
shortcomings as a steady internet connection is needed to make it work and it
is less secure as the personal data is sent to the main servers (Vincent, 2017). To overcome this, Apple is
focusing on doing AI on the local mobile devices (Vincent, 2017). By incorporating AI
compatible hardware in phone itself protects users’ privacy by sending less
data off-device (Vincent, 2017). Chinese tech giant, Huawei
is building a neural processing unit on its Kirin 970 system-on-chip which has
higher processing speeds and can process image recognition 20 times faster than
a regular CPU (Vincent, 2017). It is the first advancement
in bringing powerful AI features to the local mobile devices (Business Wire, 2017). On-Device AI is equipped
with capabilities that lays the foundation for understanding and assisting
people (Business Wire, 2017). Sensors present in the
on-device AI are capable of producing huge real-time, personalized data (Business Wire, 2017). Backed by powerful chip
processing capabilities, devices will become more cognitive of user needs and
will provide highly personalized services (Business Wire, 2017). Google also is working on methods
to implement on-device AI called “federated learning” (Vincent, 2017). Further, research is going
on at Google in making google maps find parking spots on arriving at
destinations and to distinguish between people using taxis versus actually
driving the car (Bohn, 2017). The most important factor
people are more concerned about is privacy and how to mange the privacy
settings on their mobile devices (Bohn, 2017). Google is positive that AI
could fix this problem heuristically by making the systems more sophisticated
towards user emotions (Bohn, 2017). With the increasing
advancements of AI in mobile devices, chip design companies ARM and Qualcomm
are configuring AI compatible mobile chips (Vincent, 2017). With the ever increasing
cyber attacks improvements to mobile security is important (Howarth, 2016). Mobile security can be
preserved with the use of machine learning that helps the organizations in
better interpreting of the data which leads to effective detection of security
threats and vulnerabilities (Howarth, 2016).  Further research in AI will lead to solutions
to increasing cyberattacks (Howarth, 2016).

5 Implications of AI in mobile devices

The growth of
artificial intelligence is creating a whole new world for mobile devices.  It has advanced to such an extent where it
shows messages and images that correspond to user thoughts and opinions (Forbes, 2017). The ads that are recommended
to the user are tailor made specially for that user based on his prior searches
(Forbes, 2017). AI enhanced chatbots are
used for quick communication (Forbes, 2017). For examples, messenger app
by Facebook has enabled 11,000 chatbots that allows its users to do anything (Forbes, 2017). In search technology, voice
searches increased from ‘statistical zero’ to above 10% of global searches in
2015; About 25% of windows 10 taskbar searches were made via voice as reported
by Bing, and 20% of mobile android searches are by voice which shows the new
expectations set by AI (Accenture, 2017). People interact with their
smartphones regularly to check the weather app every morning or to book an Uber
after checking Citymapper in the night; AI based interface will learn this
behavior and perform them automatically as a routine (Deloitte, 2016). With the growth of AI towards
user experience, it might soon be more than an intelligent interface  (Accenture, 2017). Because of its simplicity,
each user engagement might become more personal, powerful and natural (Accenture, 2017). Deploying AI across
interfaces will open doors towards greater adoption of complicated tools (Accenture, 2017). AI tools are seamlessly
integrated with mobile devices which made them an essential component today (Accenture, 2017). The way developers and users
see intelligent interactions within mobile applications is taking a profound
turn with the advancement and availability of machine learning (Dossey, 2017).

6 Personal Contribution

After
researching rigorously on artificial intelligence, I can say that AI is
changing the world at a breakneck speed. The goal of artificial intelligence is
to improve the user experience and make it more personalized. In order to
achieve this, the mobile vendors like Google, Apple are collecting user information
such as location details, frequently used app details and how these apps are
used by the user with the help of the mobile devices and sending them back to
their cloud servers. This invades the user privacy. AI requires tons of data to
perform analysis which makes privacy implications bigger. There is a potential
that a large amount of personally identifiable data is being gathered. If
proper masking or de-identification techniques are not performed before
performing the analysis on user data, it can lead to leak of personal
information. Sometimes not performing anonymization properly can also lead to
stigmatizing analytics which can have a significant negative impact on a user
or a group of users. Solution to protect privacy of a user can be achieved by
implementing AI on-device instead of using the cloud server as used currently.
This is still in its nascent stage and major tech giants are working towards
achieving this.

With steady
increase in chatbots in different applications for helping users to get various
products and services, it will have a serious impact with respect to human jobs.
Bots being cheaper and quicker to develop are gaining prominence significantly.
Few jobs that are already automated by bots are Customer Service agents and
Fast-food servers. For example, Tacobell’s Tacobot. There is a possibility that
in the coming years these chatbots might replace human workforce.