Robotic Process Automation

(RPA)Harnessing the power of AI and ML

Barnabas Chirombo

Head: ACL Africa

Technological and assurance process changes

Environmental Changes

1. Fourth Industrial revolution

2. Data - is the key to the future

3. Know your clients so you can service

what they actually want, when they

want it, how they want it.

4. If you don’t adapt, someone else will

Enablers of the Data Analytics Revolution

1. Artificial Intelligence (AI)

2. Natural Language Processing

3. Information as a corporate asset

4. Smart devices that produce and

consume IoT data

5. Trust – digital ethics frameworks

Data bots• Internet Bot

• Definition - What does Internet Bot mean?

• A data bot, in its most generic sense, is software that performs an automated task over the data.

• More specifically, a bot is an automated application used to perform simple and repetitive tasks that would be time-consuming, mundane or impossible for a human to perform.

• Bots can be used for productive tasks, but they are also frequently used for malicious purposes.

Intelligent Virtual Assistants (IVA)/Robots

Artificial Intelligence

AI deals with the following issues-

•Reasoning and Problem Solving

•Knowledge representation

•Planning

•Learning

Natural Language Processing (NLP)

•Perception

•Motion and Manipulation

•Social Intelligence

•General Intelligence

Machine Learning

• “Hey Siri, what is Machine Learning?”

•Machine Learning is concerned with giving machines the ability

to learn by training algorithms on a huge amount of data. It

makes use of algorithms and statistical models to perform a task

without needing explicit instructions.

•There are three types of learning here:

•Supervised and semi-supervised learning

•Unsupervised learning

•Reinforcement learning

Machine Learning

Machine Learning often deals with the following issues:

• Collecting data

• Filtering data

• Analyzing data

• Training algorithms

• Testing algorithms

• Using algorithms for future predictions

• Common examples of this phenomenon are virtual personal assistants,

refined search engine results, image recognition, and product

recommendations.

Deep Learning

Deep Learning is an approach to Machine Learning; one that focuses on learning

data representations rather than on task-specific algorithms. It makes use of Deep

Neural Networks, which are inspired by the structure and function of the human

brain.

Data Science

•“The science and engineering of making intelligent machines, especially intelligent computer programs”.

•Intelligence distinguishes us from everything in the world.

•How about consciousness?

•Making computers understand, apply knowledge.

•Also, improve skills - significant role in our evolution.

Requirements for Career in AI

•Various levels of math, including probability, statistics,

algebra, calculus, logic, and algorithms.

•Bayesian networking or graphical modelling, including

neural nets.

•Physics, engineering, and robotics.

•Computer science, programming languages, and coding.

•Cognitive science theory.

•Read more about programming languages: R, Machine

Learning

Future of Artificial Intelligence

•Machines better than humans in translating languages;

•Running a truck;

•Working in the retail sector

•Competition and race for domination in the AI field

•In every sphere of life, AI is present.

•From 80’s to now, Artificial intelligence is becoming more

intelligent and accepted every day.

•Lots of opportunities for business.

Staying relevant to the AI revolution:

a. Keep a finger on the pulse

b. Piggyback on the innovators

c. Brainstorm potential uses with your team

d. Start small and focus on creating real value

e. Prepare the ground

f. Collaborate

Data Science

Data Science

Artificial Intelligence

Machine Learning

Deep

Learning