2. TABLE OF CONTENT
INTRO TO AI
EVOLUTION OF AI
● GPS
● ELIZA
FIELDS IN AI
● MACHINE LEARNING
● DATA SCIENCE
3. INTRO TO AI
At its simplest form, artificial
intelligence is a field, which combines
computer science and robust datasets,
to enable problem-solving.
It also encompasses sub-fields of
machine learning and deep learning,
which are frequently mentioned in
conjunction with artificial
intelligence.
These disciplines are
comprised of AI
algorithms which seek to
create expert systems
which make predictions
or classifications based on
input data.
4. EVOLUTION OF AI
AI has had a number of different
periods, distinguished by whether the
focus was on proving logical theorems
or trying to mimic human thought via
neurology.
Artificial intelligence dates back to
the late 1940s when computer
pioneers like Alan Turing and John
von Neumann first started examining
how machines could “think.”
5. EVOLUTION OF AI - GPS
A significant milestone in AI occurred
in 1956 when researchers proved that a
machine could solve any problem if it
were allowed to use an unlimited
amount of memory.
The result was a program called the
General Problem Solver (GPS).
6. EVOLUTION OF AI
A second major milestone came in
1965 with the development of
programs like Shakey the robot and
ELIZA, which automated simple
conversations between humans and
machines.
These early programs paved the way
for more advanced speech recognition
technology, eventually leading to Siri
and Alexa.
7. FIELDS OF AI
It’s essential to know that AI is not a
single field but a combination of
various fields.
Artificial Intelligence (AI) is the
general term for being able to make
computers do things that require
intelligence if done by humans. AI can
be broken down into two major fields,
Machine Learning (ML) and Neural
8. MACHINE LEARNING
Machine Learning (ML) makes
computers learn from data and
experience to improve their
performance on some tasks or
decision-making processes.
ML uses statistics and probability
theory for this purpose. Machine
learning uses algorithms to parse data,
learn from it, and make
determinations without explicit
programming.
10. DATA SCIENCE
Data Science is an interdisciplinary
field that focuses on extracting
knowledge from data sets which are
typically huge in amount. The field
encompasses analysis, preparing data
for analysis, and presenting findings
to inform high-level decisions in an
organization. As such, it incorporates
skills from computer science,
mathematics, statistics, information
visualization, graphic, and business.
11. DATA SCIENCE
● Retrieving data: Finding and getting access to the data needed in our project
is the next step. Mixing and merging data from as many data sources as
possible is what makes a data project great, so look as far as possible. This
data is either found within the company or retrieved from a third party. So,
here are a few ways to get ourselves some usable data: connecting to a
database, using API’s or looking for open data.
● Data preparation: The next data science step is the dreaded data preparation
process that typically takes up to 80% of the time dedicated to our data
project. Checking and remediating data errors, enriching the data with data
from other data sources, and transforming it into a suitable format for your
12. DATA SCIENCE
● Data exploration: Now that we have clean our data, it’s time to manipulate it
to get the most value out of it. Diving deeper into our data using descriptive
statistics and visual techniques is how we explore our data. One example of
that is to enrich our data by creating time-based features, such as: Extracting
date components (month, hour, day of the week, week of the year, etc.),
Calculating differences between date columns or Flagging national holidays.
Another way of enriching data is by joining datasets — essentially, retrieving
columns from one data-set or tab into a reference data-set.
13. DATA SCIENCE
● Presentation and automation: Presenting our results to the stakeholders and
industrializing our analysis process for repetitive reuse and integration with
other tools. When we are dealing with large volumes of data, visualization is
the best way to explore and communicate our findings and is the next phase of
our data analytics project.
● Data modeling: Using machine learning and statistical techniques is the step
to further achieve our project goal and predict future trends. By working with
clustering algorithms, we can build models to uncover trends in the data that
were not distinguishable in graphs and stats. These create groups of similar
14. AI: PRESENT
The recent advancements in AI have led to the emergence of a new type of system
called Generative Adversarial Networks (GANs), which generate realistic images,
text, or audio. Due to their remarkable capabilities, some people are concerned
that this technology could replace humans in the future.
GANs are just one example of how AI is changing our lives. This section explores
more current AI examples and its applications in software systems such as GPT3,
DALL.E, and virtual reality/augmented reality (VR/AR).
15. AI: PRESENT
AI-based software systems are comprised of many layers such as foundational
models, advanced algorithms, and automated reasoning tools. Some of the most
popular AI-based systems that use these layers include GPT3, DALL.E, AlphaGo,
RoBERTa, and many others.
DALL.E and GPT3 are large-scale models that have achieved remarkable results in
computer vision and natural language processing (NLP).
