1.0 learning is a branch in computer science

 

1.0  
INTRODUCTION

 

1.1        
Objective of the study

 

A.    
To investigate the difference
between machine learning and artificial intelligence.

B.    
To investigate the use of
machine learning in changing the lives of the people.

C.    
To investigate how machine
learning is being incorporated in the smartphone industry.

D.   
To investigate about the machine
learning library i.e. Tensor flow

 

1.2        
Artificial intelligence and
machine learning

Machine
learning is a branch in computer science that allow the computer the ability to
learn without being programmed explicitly.  Artificial Intelligence is the broader concept
of machines being able to carry out tasks in a way that we would consider
“smart”. Machine Learning is a current application of AI based around the idea
that we should really just be able to give machines access to data and let them
learn for themselves.

1.3        
         Uses of
machine learning

One of the most popular use of AI in machine learning, where
computers, software and others perform through cognition (like human brain).
Example of area where machine learning is heading include the following: –

 

 

1.4        
Virtual personal assistants

Siri, google Now, Alexa and many more are some of the many popular
examples of virtual assistants. As the name says they try to assist in finding
information when asked to find something over the voice. All you have to do is
ask “what is the weather today?”,” when is the Manchester united playing” or
“set an alarm for 3pm”.

Machine learning is a very important part of personal assistant
for they gather and refine the information on the basis of previous encounter
with them. Later this set of data is used to make results that are specific to
your preferences. Virtual assistants are used in a variety of platforms like
smart speaker like google home and amazon echo, smartphones like Samsung Bixby
and google pixel as google assistant and mobile apps like google allo.

1.5        
Video surveillance

Video surveillance is a very hard task and a boring but with
machine learning it can be an automated process since training the computers
they can handle this task .Computers can detect crime just by tracking unusual
behaviour using machine learning.

 

1.6        
Social media

Social media like Facebook, twitter, Instagram and many others use
machine learning to personalize news feed, adds and many more.

On application like camera the use of face recognition in order to
identify people in a specific scene and also identify their faces so as to add
effects like smoothing on their face.

 Machine learning is also
being used in apps like Facebook so as to identify people we may know and
suggest that we add them as our friend. Also apps like Pinterest uses computer
vision to find objects in images and suggest similar pins accordingly.

1.7        
Email spam and malware
filtering

Apps like Gmail use machine learning to classify email into
primary, social, important and spams. This is with the help of filtering being
done under the hood using machine learning. Over 325,000 malwares are found
every day and each piece of code is 90-98%similar to its previous versions. The
security program that are powered by ML understands the pattern for coding.

1.8        
Online customer support

Microsoft bots are being used to provide chatrooms where people
can report about the services they get and this is due to machine learning.
similarly, when one opens a browser the search is customized for that specific
person. For example, YouTube is highly customized for each and every person
according to what they like watching thanks to machine learning.

1.9        
Product recommendations

When you buy something online you start receiving email relating
to that product in other stores also when you browse online you see some sites
suggesting things to buy which are close to your taste. This is due to machine
learning which compiles your likes and taste as you browse the internet
combined with an algorithm working under the hood.

 

 

 

1.10    
Fraud detection

Online fraud detection is among the frontier that machine learning
is taking head on by trying to analyse illegal online transaction and
preventing money laundering e.g. PayPal. This is done by using a set of tools
that can help compare millions of transactions happening and distinguish
between legit and illegal transactions.

 

1.11    
          Introduction of
machine learning to smartphones

First phone was made by alexander graham in 1876 and it became a
revolutionary gadget as the 1900 approached. The phone was used for basic
services like calling and texting at the end of 20th century. As the
years progressed the phone morphed from basic phone to feature phone and later
to smartphone which was introduced in 2000 i.e. Sony Ericson R380. This was a revolutionary
idea in its time since it featured a capacitive touchscreen something that has
never been seen before on a phone.

As the smartphone momentum started and many companies joined in
namely Apple, Android and many more. Due to demand of new features the
smartphone industry has tried to out do one another and in the process, sell more.
This has made the companies making the phone to invest huge in research and
development so as to come up with new features. Artificial intelligence has
always been a new frontier for the phone but the computing power has always
been a constraint. The smartphone computing power cannot be able to be enough
to train models which are necessary for the learning process of a artificial
intelligence. Training a model entails providing a lot of data to that model
until it can recognize a certain data. This is taxing on a smartphone which is
has low computing power and so training is done on a computer workstation and
once that is done it is then ported back to the device via tensorflow
framework.

 

1.12    
Tensorflow

Tensorflow an open source software library for dataflow
programming in a wide range of tasks. The main tasks is its application in
neural network which for the base for training data models. Its mainly utilized
by google in machine learning with which it provides through its range of
application e.g. Google keyboard which has predictive typing. Tensorflow is a
lightweight library which is perfect for smartphones.

 

 In may 2017 google released
tensorflowlite which main aim is to provide light weight machine learning in
android powered smartphones (especially android 8.0 Oreo). The core of
tensorflow is programmed in c++

 

 

 

 

 

 

 

 

2          
Research methodology

Research data was collected using the methods like direct
observation, interview and online data collection. It outlines and specify the
way in which the research is to be carried on.

2.1        
Research methods and design

A research design is a blueprint of methods and procedures used in
collecting and analyzing variable when conducting a research study. A specific suitable
question for study in a research project should be considered and then choose a
suitable method of conducting the research. This is important for
successful coverage of the highlighted objectives and completion of the
research. Research data was gathered through participants observation e.g. use
of senses like eyes by examining people in a targeted population. There was
also the case of examining earlier records on artificial intelligence from
where we have valuable information pertaining to inception of this technology

2.2        
Target population and study
area

Target population involves the people I want to gather information
from and in my case, involves any person who owns a smartphone. The features
like predictive typing which involves use of google keyboard will be an easy
task.

2.3        
Direct observation

It is also called observational study and it is a method of
getting evaluative information which entails an evaluator watching his/her
subject in his/her place of living and not changing the environment. This type
of data collection is used together with other data collection procedures e.g.
survey, questionnaires etc. 

The main aim of this is to evaluate a happening behaviour process,
event or when results can be seen. When observing the subject one should not
make them aware of your purpose since this can alter the observation and for
that reason the subject should not be aware. There are two types of direct
observation i.e. structured direct observation and unstructured direct
observation. Structured direct observation are used when we want to get
standardized information and result in quantitative data while unstructured
direct observation involves looking at natural happenings and get qualitative
data.

This involves observing the functions offered by some smartphones
that range from facial detection in photos to predictive typing in keyboards.
When one uses the google keyboard it learns a person typing patterns and makes
out the words a person types this intern stores those words in a database and
later reproduces them when one is writing a text of a Facebook post.

2.4        
Online data collection

Online data collection was among the main research methodology used
to come up with this information. Sending online interviews can be a tricky
since then evaluator and the participant have never met and this makes sharing
private information very hard. Therefore, one must first come with ways for the
participant to trust you and accept to share that information

 

 

 

 

 

3          
Literature review

3.1        
Global perspective

It is estimated that more than 80% of
all households in United States now have computers in their home, and of those,
almost 92% have internet access. As computers became more prevalent in American
society, the next natural advancement in communication was through the
internet. This is rising trend is due to invention of the smartphones.
Smartphone features are the main selling point today i.e.  the one who outsmarts the other in terms of
providing better features that customer is willing and able for such a
smartphone then he/she takes the day.

Apple a tech company is a leading
competitor in this field ,innovating every year to come with gadgets that
impresses everyone and this has created a group of apple royalists.

They are willing to spend more than a
1000$ for a smartphone.

The company latest innovation is
powered by machine learning to provide security to its flashy iPhone x. The
innovation could not have happened without machine learning ,no matter how much
you code i.e. control statement, methods, api it couldn’t be done . Machine
learning is about training the computer to think like a person without
explicitly coding it. This would then involve training a model by showing it a
lot of data and later it would be able to make guesses about that specific data
e.g. imagine how can you code a program that distinguishes between apple and
oranges ,one would argue that you can by saying that apple an apple is between
red and maroon and orange is yellow and so you can develop a program that
analyses the pixel in those pictures and if the colour coincides with the
colour of yellow .This can be true but wat about if the program is  fed of black and white photos then the
program cannot determine .This is where machine learning comes in and by
training a model it can be able to make pretty close guess of a black and white
picture of apple and orange  since it was
trained with a wide range of data e.g. black and white pictures of apple and
oranges ,shape of apples and many more.

Apple use in machine learning was
inform of face id in which the phone would make a mesh of your face when you
want to unlock it and compare it to the stored mesh in the phone and if it
coincides it would unlock the phone.   The problem is with face id the person could
just hold a picture of your face and the phone would unlock. So they came with
a system that would project dots on your face and the camera using this dots it
would be able to make a 3d mesh of your face and using machine learning a lot
of your facial characteristics would be taken for comparison and the model
would be able to guess it was that actual person close to probability of a
million to one. This made it possible even to unlock your phone wearing
glasses, wearing a lot of make up or changing ones look by maybe growing a
beard.

4          
 Finding and observations

The
possibilities with machine learning is endless as long as the computing power
of the smartphone keeps up. Apple had to develop A11 fusion chip (the most
powerful chip in smartphone to this date) to handle such computing requirements
needed by the smartphone to train models of one’s face.

 

 

 

 

 

 

 

5          
 Recommendations

The bottom line is that everything related to
machine learning and artificial intelligence is tasking to smartphones as of
this date, but as companies continue innovating and coming up with better CPU
and GPU architecture then this task will run buttery smooth and even open new
frontiers in this specific field.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

References

1.     
“Build and train machine
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2.     
Drury, C. G. (1995). Methods for
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3.     
Alpaydin, Ethem (2010). Introduction to Machine Learning. London: The MIT
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4.     
 “Machine
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5.     
 Mohri, Mehryar; Rostamizadeh,
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TensorFlow.org. Retrieved November 10, 2015.”TensorFlow Release”. Retrieved Dec 22, 2017

7.     
 Metz, Cade (November 9, 2015). “Google Just Open
Sourced TensorFlow, Its Artificial Intelligence Engine”. Wired. Retrieved November 10, 2015.

8.     
 Perez, Sarah (November 9, 2015). “Google
Open-Sources The Machine Learning Tech Behind Google Photos Search, Smart Reply
And More”. TechCrunch. Retrieved November 11, 2015.

9.     
Savov, Vlad (September 12, 2017). “iPhone X announced with
edge-to-edge screen, Face ID, and no home button”. The Verge. Vox Media. Retrieved December 13, 2017.

10.  Brandom, Russell
(September 12, 2017). “The five biggest questions
about Apple’s new facial recognition system”. The Verge. Vox Media. Archived from the
original on November 15, 2017. Retrieved November 9, 2017.

11.   Ng, Alfred
(September 27, 2017). “Is Face ID
secure? Apple takes on lingering questions”. CNET. CBS Interactive. Archived from the
original on October 2, 2017. Retrieved November 9, 2017.

12.   Daniel B. Kline
(2017-01-30). “Alexa, How Big
Is Amazon’s Echo?”. The Motley Fool.

13.   “Watson – Stories of how AI and Watson are
transforming business and our world”. Ibm.com. Retrieved 2017-12-10.

14.   Lynn La
(2017-02-27). “Everything
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17.   Dickson,
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Tan, Steinbach, and
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20.   Bishop, C. M. (2006), Pattern
Recognition and Machine Learning, Springer, ISBN 0-387-31073-8

 

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