1.
In Which Area AI is Used?
• This technology lets us accurately, unobtrusively and inexpensively collect wildlife data, which could help
catalyse the transformation of many fields of ecology, wildlife biology, zoology, conservation biology and
animal behaviour into 'big data' sciences. The information in these photographs is only useful once it has
been converted into text and numbers. Not only does the artificial intelligence system tell you which of 48
different species of animal is present, but it also tells you how many there are and what they are doing. It
will tell you if they are eating, sleeping, if babies are present, etc.
• More than 1 million Americans require daily physical assistance to get dressed because of injury, disease and
advanced age. Robots could potentially help, but cloth and the human body are complex.
• Computer scientists at Rice University have created a deep-learning, software-coding application that can
help human programmers navigate the growing multitude of often-undocumented application programming
interfaces. Bayou is a considerable improvement," he said. "A developer can give Bayou a very small amount
of information -- just a few keywords or prompts, really -- and Bayou will try to read the programmer's mind
and predict the program they want. Bayou is based on a method called neural sketch learning, which trains
an artificial neural network to recognize high-level patterns in hundreds of thousands of Java programs. It
does this by creating a "sketch" for each program it reads and then associating this sketch with the "intent"
that lies behind the program.

2.
There are a number of areas where AI can be applied, including the
following:
Expert systems, where computers can be programmed to make decisions in real-life situations. The
integration of machines, software, and specific information allows the system to impart reasoning,
explanation, and advice to the end user.
Natural Language, where chatbots can recognize natural human language if communicating directly with a
user or a customer.
Neural Systems, simulate intelligence by attempting to reproduce the types of physical connections that
occur in human brains. For example, neural systems can predict future events based on historical data.
Robotics, are programmed computers which see, hear and react to sensory stimuli, such as light, heat,
temperature, sound and pressure.
Gaming Systems can manipulate strategic games, such as chess or poker, where the machine can think of an
exponential number of possible positions to play effectively against a human opponent.

3.
17 Everyday Applications of Artificial Intelligence in 2017
Smart Cars
Surveillance (Security Cameras)
Detecting fraud
Writing simple news stories (Fake News)
Customer Service
Video games
Predictive purchasing
Work automation and maintenance
prediction
Smart recommendations
Smart Homes
Virtual Assistants
Preventing heart attacks
Preserving Wildlife
 Search and Rescue
Cybersecurity
Hiring (and perhaps firing)

4.
An Example of ML -Netflix Pattern-
Measurable attributes gave Netflix the foundations to start analysing their
consumers and provide them with relevant and personalised content. Netflix’s
tagging system allows them to suggest and recommend other films and series’ they
think people will enjoy based on their previous viewing history. These suggestions
drive users to click and engage further with content.
Not only this, but thanks to targeted recommendations and advertising, Netflix has
been able to lower its promotional campaign budgets by being able to target only
the most relevant and valuable people at a time.
As of May 2018, Netflix has 125 million worldwide streaming subscribers. Having
this large user base allows Netflix to gather a tremendous amount of data. With
this data, Netflix can make better decisions and ultimately make users happier with
their service.

5.
The goal of the technology is to stop recommending movies based on what you've seen,
and instead make suggestions based on what you actually like about your favourite
shows and movies.
That's why Netflix is moving into a field of research known as "deep learning." That
means that Netflix is "training" its software to provide better recommendations by
feeding massive amounts of information to a tech called "neural networks." Neural
networks mimic how the human brain identifies patterns.
The company took the lessons learned by researchers at Google, Stanford, and Nvidia
and created deep learning software that takes advantage of Amazon's powerful cloud
infrastructure, according to a new post on Netflix's technology blog.
Built upon a strong foundation of strategic decisions, Netflix has come a long way into
building a great learning model to predict what their users’ next favourite unwatched
movie could be, at a considerably high level of accuracy.

6.
What's required to create good machine learning systems?
• Data preparation capabilities.
• Algorithms – basic and advanced.
• Automation and iterative processes.
• Scalability.
• Ensemble modelling.
Did you know?
• In machine learning, a target is called a label.
• In statistics, a target is called a dependent variable.
• A variable in statistics is called a feature in machine learning.
• A transformation in statistics is called feature creation in machine learning.

7.
Machine Learning
• Supervised Machine Learning; The system uses past data to predict future outcomes.
For instance; classification of spam. The system is able to detect what characteristics an
email that`s spam or not spam exhibit. After learning this, it is able to categorize oncoming
emails as spam or otherwise.
• Unsupervised Machine Learning; Unsupervised learning only deals with the input data.
It basically work on the incoming data set to make it more readable and organized.
Basically, it analyses the input data to find out patterns or similarities or anomalies in
them.
For instance; Amazon take into consideration your prior purchases, and are able to suggest
other things that you may be interested in.

8.
• Reinforcement Learning; Reinforcement Learning allows systems to learn based on past
rewards for its actions. Every time a system takes a decision, it is punished or rewarded
for it`s actions. For each action, it gets a feed back, with which it learns whether it
performed a wrong or correct action. This type of machine learning is strictly dedicated
to increase efficiency of a function/tool/program.
For instance; Let` s consider a game, say chess.
Determining the best moves would require a lot of research on numerous factors. Building
a machine designed to play such games would require a lot of rules to be specified. With
reinforced learning, we don`t have to deal with this problem, as the machine learns by
playing the game. It will make a move (decision), check if it`s the right move (feedback),
and keep the outcomes in mind for the next move it takes(learning).

9.
How to Apply Machine Learning to Keyword Planner
When it comes to Keyword Difficulty or Organic Competition, no one really can accurately
predict the outcome as there are so many factors involved. It may even be difficult to
impossible for Google themselves to predict accurately beyond general guidelines and
suggestions.
For AdWords Competition or Paid Competition, having accurate data also will be important
because we will need to accurately plan and budget our SEM campaigns. Twinword Ideas is a
free keyword tool that gets its AdWords competition and CPC data directly from Google.

10.
User journeys are complicated. It’s hard to give the correct “value” of a touchpoint early in
their journey.
Until AI surpasses general human intelligence, search advertising will always require some
degree of human involvement. For some campaigns, human control is needed at a far
more granular level.
This might be when an account lacks data, has a fluctuating or limited budget, or requires
personally chosen bid weightings on individual keywords.

11.
How to Apply Machine Learning
to Keyword Planner
Searched word: Depilacja laserowa w pobliżu
So, instead of searching this word in AdWords and get
some ideas, it would be better for us to copy the links
of our competitors into AdWords and centralize these
keywords in excel.
Why we are doing this?
Rather than start creating a new keyword list, we can
take advantage of our competitors ` keywords. Our
competitors have already spent so much effort to
create this list, so we can get handy keywords from
them.
If we apply this transaction for the first 30 websites,
for instance, we will have;
• Various kind of keywords
• Keywords who can not be seen by others as long as
they do the same.
In short, `a big data` of keywords which is need to be
trimmed.

12.
How we can do it! (Continued)
However, to do this we need to write a `script` to do it automatically (JavaScript). By doing this, our
boot will copy all these `URLs` (say top 30 ones) into a dossier and search every single of them in
AdWords to get different keywords.
At the end of this transaction, we will have a long keyword list which is need to be trimmed. So far,
we have received some help by boot, however from this point we, as human being, need to get
involved, and it requires personally chosen bid weightings on individual keywords.
Laser hair removal is such a huge sector that there are lots of keywords to be used. It is indeed that
there will be some keywords which are not related to laser hair removal, for this reason an
individual should get involved in to trim them.
Deleting poor keywords, we may use `Stanford Classifier` programme however, we need two kinds
of data:
• Trainer data
• Test data

13.
Trainer Data;
A machine-learning algorithm is a mathematical model that learns to find
patterns in the input that is fed to it. This input is referred to as training data.
In this part we need to insert some data manually to teach the programme how to
classify the data steadyingly. The most frequently mistakes people have been
making, for instance, is to consider that IPL and Laser hair removal is the same
method.
So, in this stage, we may teach the programme how to classify the data, show
which keywords are useful and which one of them are not- and afterwards, the
programme may finally provide us a system to eliminate the
wrong/unappropriated/useless keywords as well as to promote nice/handy
keywords by using various algorithm.
Even if you want to classify your services for instance, laser for armpit, laser for face
or laser for feet, this classification will ensure you deeper insight into certain
objectives.

14.
Test Data:
Once a machine learning algorithm learns the underlying patterns of the training
data, it needs to be tested on fresh data (or test data) that it has never seen before,
but which still belongs to the same distribution as the training data.
If our model performs well on the test data then it is considered as a ML model that
generalizes our dataset of interest.

15.
Artificial Intelligence in Call centres
AI software has been developed that can listen to calls and decipher their impact on the customer,
such as how the issue was resolved, whether the customer’s loyalty will increase in the future as a
result of the call, and what could have been done to help smooth the situation if the customer gets
upset.
For instance; Singapore bank POSB has launched online chat functionality that understands and reacts
to its customers’ spoken language.
It is the latest bank to harness artificial intelligence (AI) to automate customer interactions with the
POSB digibank Virtual Assistant, which is available via Facebook Messenger.
By analysing, interpreting and understanding high volumes of customer inquiries, the solution could
support up-selling or cross-selling of various products or services, while the RPA robots could auto-fill
the application form to save the customer time.
Today’s digitally connected, always-on consumers demand unprecedented levels of 24x7x365
customer service
Chatbots can help reduce customer service costs by up to 40%

16.
The vast amount of customer data and analytics being gathered means organizations have
the opportunity to understand each individual customer at a deeper level. For many
businesses, every customer touchpoint is captured digitally. With smart data analytics and
AI tools businesses can achieve a new standard of customer service, personalizing
communication and support based on the customer’s engagement to date. By creating a
more personally relevant experience for each customer, businesses can begin resolving
potential service issues or needs before they occur. After all, the better you know your
customer, the better you are able to anticipate their needs. With a comprehensive view of
all touchpoints, contact centres will be able to identify opportunities to proactively address
and/or eliminate reduce customer service issues.
• 57 percent of customer care executives consider call reduction their top priority for the
next five years.
• According to IBM, 70 percent of customers would prefer using messaging over voice for
customer service when given the choice.

17.
Voice is the most used communication channel for service. Voice, which 73% of customers use for
customer service, is still the most widely used channel. However, web self-service and digital channels
like chat and email are following close behind.
For example, according to George, "80% of the calls an airline receives to change a ticket do not result in
the ticket changing," because the person may not efficiently be made aware of all of the terms and
conditions involved, such as change fees or scheduling issues. The AI system can quickly provide this
pertinent and relevant information to the caller, without engaging the services of a live agent. "By using
bots, customer call volume can be reduced [significantly].“
RECOMMENDATION
By looking these info and examples, we can create our own AI for call centre department at least when
our employees are not at office.
Firstly, it can seamlessly give customers the right information they need at the right time by offering
self-service options, eliminating the need for a call to customer service.
Second, AI has the potential to give customer service representatives more information to help them
handle the complicated issues that self-service cannot resolve.
In addition, with AI technology, the more it gets used, the more it learns, meaning that it grows ever
more sophisticated. This capability makes it ideal for everyday IT processes like password resets.

18.
RECOMMENDATION
We should built a nice website which
demonstrate a whole body of human being.
Lets say a customer clicked on `chest` to see
the information that have is located, like how
much it will cost and how many treatments
she/he needs.
With this click, we can deduce that this
customer needs a treatment for this part and
we can directly target this customer via phone
calls, massages, e-mails.
Even if they do not click, it is important that
where they are holding the mouse on the
screen. In this way, we can guess their
intentions.
This Strategy is used by bigger companies like
amazon and e-bay, so why we not?