1.
Artificial Intelligence (AI): How
to position yourself for the
future
Folasade Adedeji

2.
Content
1. Introduction
2. Facts about AI
3. Machines getting smarter
4. Job landscape in 2022
5. What is AI
6. Applications of AI
7. How does AI work
8. Limitations
9. Careers in AI
10. The future with AI
11. What to do
12. Ways to succeed in ML career
13. Where to get skilled

3.
1. Bible Text
Jeremiah 29:11 NLT
For I know the plans I have for you, says the Lord. “They are plans for good and not for
disaster, to give you a future and a hope.

4.
Some facts about AI
University course enrolment in AI and ML is increasing all over the world.
Experts predict that by 2020, 85% of all customer service interactions will be handled
without the need for a human agent.’’ – Forbes
According to the world economic forum, 75 million jobs will be obsolete by the year
2022, but then, the advancement of robotics and artificial intelligence will make 58
million jobs more than it destroyed.

5.
Machines getting smarter
An algorithm was able to detect an Ebola outbreak more than a week before it was
announced by the World Health Organization.
Driverless cars are forecast to make up 75% of all traffic by 2040.
Human-robot marriages would be legal by the year 2050.’’ – CBC Radio

6.
What is AI - Definition
 Artificial intelligence (AI) is the ability of a computer program or a machine to think
and learn.
 It is also a field of study which tries to make computers "smart".
 They work on their own without being encoded with commands.
 AI uses algorithms to predict the future, while humans use our intuition to predict the
future. The algorithm is based on previous data, while the intuition is based on our
previous experience in life

7.
What contributes to AI
 Artificial intelligence is a science and technology based on disciplines such as:
 Computer Science, Biology, Psychology, Linguistics, Mathematics, and Engineering.
 A major thrust of AI is in the development of computer functions associated with
human intelligence, such as reasoning, learning, and problem solving. Out of the
following areas, one or multiple areas can contribute to build an intelligent system

8.
 Relationship of AI to
Other disciplines

9.
Applications of AI
AI has been dominant in various fields such as:
 Gaming : Playing games against the computer
 Natural Language Processing : Natural language processing (NLP) describes
the interaction between human language and computers, In any case, the
computer is able to identify the appropriate word, phrase, or response by using
context clues, the same way that any human would
 e.g chatbots, hiring tool, conversational search, Siri, Alexa

10.
Expert Systems
An automated reasoning system that attempts to mimic the performance of the human expert. It offers advice or make decisions.
Application in medical diagnosis.

11.
Speech recognition
A technology that allows spoken input into systems.
You talk to your computer, phone, or device and it uses what you said as input to
trigger some action.
The technology is being used to replace other methods of input like typing, clicking, or
selecting in other ways.
Applications: car Bluetooth system, digital assistance.

12.
Intelligent Robots
Intelligent robotics are the robots that functions as an intelligent machine,
that is, it can be programmed to take actions or make choices based on
input from sensors.
They have sensors to detect physical data from the real world such as light,
image courtesy of may field robotics

13.
 Today, many fields including, legal, finance, biotech, infotech, nanotech, energy,
healthcare, education etc. is wide open to revolutionary transformational
developments.
 AI will most likely, have a similar impact as the Internet had.

14.
Example of application of AI: Japanese
cucumber farmer use deep learning and
Tensorflow to sort cucumber

15.
How does AI work
AI works by combining large amounts of data with fast, iterative processing and
intelligent algorithms, allowing the software to learn automatically from patterns or
features in the data.
AI is a broad field of study that includes many theories, methods and technologies, as
well as the following major subfields:

16.
AI subfields
Machine Learning
Neural network,
Deep learning,
Cognitive
computing,
Computer vision,
Natural language
processing

17.
Using these technologies, computers can be trained to accomplish specific tasks by
processing large amounts of data and recognizing patterns in the data.

18.
Limitations
 AI thrives on data any inaccuracies in the data will be reflected in the results.
 AI systems are trained to do a clearly defined task. The system that detects fraud
cannot drive a car or give you legal advice. In fact, an AI system that detects health
care fraud cannot accurately detect tax fraud or warranty claims fraud.
 These systems are very specialized. They are focused on a single task and are far
from behaving like humans.

19.
Careers in AI
 Machine learning engineer: need the knowledge of modern programming
languages like Java, Python, Scala
 Data scientist: Need a knowledge of platforms and tools like Hive, Hadoop,
MapReduce, Pig, Spark. Programmming languages like Perl, Python, Scala, SQL
 Business intelligence developer: data warehousing, data mining, sql,
 Research scientists
 Big data engineers/architect:
 Solutions architect
 Algorithm developers

20.
The future with AI
 Some skills are going into extinct, AI is creating more jobs
 No permanent jobs , keep learning new skills
 A degree is no longer sufficient to be indispensable at work
 Creative thinkers, innovative and inventive workers will make the head way
 Digital and media literacy will be paramount
 Customer demands will always change

21.
What to do
You shouldn’t worry about AI replacing you. Instead,
 It’s time to rethink your position in the job market
 It’s time to question your current career path, and consider if it would be wise to
make a few changes.
 Keep yourself in the loop
 Follow the newest trends in your in your market
 Do some research on where your industry is heading. What’s the new and
upcoming trends within your industry?
 Find some industry leaders, and listen carefully to what they’ve got to say

22.
What to do
 Get accustomed with both new and disruptive technologies
 Can you apply it in your current role?
 Train yourself for both new and disruptive AI technologies
 Go after that new and disruptive AI job
 Refining the Soft Skills
 Understanding your customers through qualitative research
 Getting up to speed with analytics, coding, and math
 Adopting a growth mind set

23.
Ways to succeed in ML career
 Understand what machine learning is.
 Be curious.
 Translate business problems into mathematical terms
 Be a team player
 Learn Python and how to use machine learning libraries
 Take online courses or attend a data science bootcamp.

24.
Where to get skilled
 Google Machine Learning Crash Course
 Udacity
 Coursera
 EdX

25.
THANK YOU