Artificial Intelligence
Artificial Intelligence (AI) is a
field of study of computer science
that makes machines seem like as if they have human intelligence.
In short,
AI can be defined as “a machine with human thought process.”
A person called together a group of scientists and mathematicians to see if a
machine could learn just like a child does using trial and error to develop
formal reasoning and problem-solving skills.
An AI machine (or program) follows 3
basic steps viz.,
1. Learn
2. Act
3. Adapt
It then repeats these steps over and
over again and gets much smarter as more and more data are fed to it.
Types
of AI
1. Analytical AI
2. Human-inspired AI
3. Humanized AI
Machine
Learning
Types
of ML algorithms
1. Regulated and semi-managed learning
2. Unsupervised learning
3. Reinforcement learning
Applications of AI
1. Healthcare
2. Automotive
3. Government
4.
Video Games
5.
Advertising
6. Art
Questions on AI
Getting Started in AI
1. Newbie
2. Programmer
3. Data Scientists
Jobs in AI
2. Data Scientist
3. Business
Intelligence (BI) Developer
4. Research
Scientist
5. Big Data
Engineer/ Architect
1. Critical
Thinking
2. Creativity
3. Leadership
Artificial Intelligence can be
classified into 3 different types of AI systems viz.,
It has the characteristics of acquiring knowledge and processing
thoughts.
It has the ability to process emotions and feelings along with
logics.
It has logical reasoning, emotional feeling and social
intelligence.
Reasoning, planning, learning, natural language processing, perception and
the ability to move and manipulate objects are the traditional goals of AI
research. General intelligence is the long-term goals. Many
tools used in AI, are based on statistics, probability and economics such as search
and mathematical optimization, artificial neural networks,
and methods. The AI field expands to computer science, information engineering, mathematics, psychology, philosophy, and many more.
Machine learning
(ML) may be defined as a set of Artificial Intelligence that focuses
on the scientific study of applied mathematics models and algorithms
that pc systems use to effectively perform a
particular task while notusing explicit
instructions.
In short, ML means “training an AI”.
The algorithms differ with each other
on the basis of “type of data” and “type of task”.Regulated learning calculations are known to manufacture a scientific model of a lot of information that contains both the sources of info and the ideal yields, which prepares information, and comprises of a lot of preparing precedents.These
algorithms take a set of data that contains only inputs, and find structure in
the data, like grouping or clustering of data points and therefore learn from
test data that has not been labelled, classified or categorized. These
algorithms identify commonalities in the data and react based on the presence
or absence of such commonalities.Reinforcement
learning is generally focussing on how software agents ought to
take actions in an environment. They use dynamic
programming techniques.
AI is capable of doing any
intellectual task as long as it is given enough amount of data to consume and
produce the most probable outcome.
But, when an AI technique reaches
mainstream use and becomes a tool of daily routine, then it is neglected and no
longer considered as an artificial intelligence which is known as the AI effect. For e.g., an optical
character reader is now no longer considered as a unique helpful device as it
has become a general-purpose device. As we continue to make more advanced AI
machines (or software), the previous ones will fall to the prey to AI effect
and will sadly no longer be considered as an Artificial Intelligence device.
AI is being used in vehicles
(self-driving cars, drones), online assistants (Google Assistant, Siri, Alexa,
Cortana), art (making paintings and writing poetries), medical science, online
search engines (Google, Bing), playing games and quizzes.
AI can
correctly determine the accurate dose of drugs to be given to patients. It can
predict which combination of drugs will be most effective for a person. IBM Watson (AI computer of IBM) gained
worldwide recognition for successfully diagnosing a woman who was suffering
from Leukemia. In the years to come, AI will be making its place above highly
trained doctors and surgeons.
Today,
there are over 30 companies utilizing AI in their driverless cars, some of them
are tech giants such as Google, Apple, and Tesla. Recent developments have made the innovation of self-driving
trucks possible. Tesla has recently launched Model-T which is a driverless car
under the budget of $30,000.00 only which makes it viable to use for common
public too.
Applications of AI to the public sector are growing around
the world. It can be used for public
policy objectives (emergency services, health
and welfare), as well as assist the public to interact with government (virtual
assistants for public queries). AI can be used to
automate the repetitive paperwork at government offices and hence can help us
gain some more time for other policies.
In video games, it is generally used to generate dynamic
purposeful behaviour in non-player
characters (NPCs). Modern video games make the
use of Virtual Reality (VR) and Augmented Reality (AR) along with Artificial Intelligence
(AI) to produce real-life like scenes and realistic graphics.
Major publishers now use artificial intelligence (AI)
technology to post stories more effectively and generate higher volumes of
traffic, AI is being used as an effective tool to target specific audience for
a specific niche with appropriate and relevant online advertisements.
A highly trained AI is capable of creating a masterpiece much
more perfect than the original one. It can learn patterns and lines and
specific words and colours to be used for the artwork to be made. A perfectly
trained AI can do stuff far beyond the capabilities of an artist’s mind. It can
go places with amazing possibilities. Recent exhibitions showcasing the usage
of AI to produce art include the Google-sponsored benefit and auction at the
Gray Area Foundation in San Francisco, where artists experimented with the
deepdream algorithm. The exhibition named "Unhuman: Art in the Age of AI” which
took place in Los Angeles and Frankfurt in 2017.
As AI
is growing at such an exponential rate, it is obvious that some questions are
being raised to its capabilities and intentions, some of which are as follows.
1. Can a machine
solve any problem that a human being can solve using intelligence?
2. How can we
ensure that machines behave ethically and that they are used ethically?
Just the fact that five out of six people use AI services in one form or another every day
proves that this is a viable career option. AI has been the most preferred
career choice for a while now because of the growing adoption of the technology
across industries and the need for trained professionals to do the jobs created
by this growth. It is assumed that AI will create close to 2.3 million jobs by
2020. It is estimated that this technology will wipe out over 1.7 million
jobs, resulting in about half a million new jobs worldwide. AI also offers many
unique career opportunities. You should get your hands on this opportunity
before the time fades away.
There are different
paths for different types of professionals interested in learning AI.
If
you are new to the field of computer science, then firstly you should start
with mathematics and some basic coding experience (like C++ or Python), and
then move to the complex algorithms.
If
you are well-aware about the world of programming and have enough experience,
then you can move straight into the algorithms and start coding.
If
you are moving from a data analysis to machine learning, you must know
how to prepare data, as well as have good communication skills and business
knowledge, and be proficient at model building and visualization; all along
with great programming skills.
No matter where you are starting from, just remember that, “An
AI never stops learning, so you can’t stop learning either”.
If career in
artificial intelligence appeals you and you want a piece of the AI pie, then
what kind of jobs should you start looking for? What skills would you need to
get hired? Let’s take a closer look.
1. Machine
Learning Engineer
Role: Their role is at the heart of AI projects and is perfect for
those who hail from a background in applied research and data science. They are responsible for
building and managing platforms for machine learning projects.
Annual Average Salary: $114,856
Skills required: Java, Python
Role: Data scientists do the work of collecting, analysing, and
interpreting large, complex datasets by both machine learning and predictive
analytics. They also play a very important role in developing algorithms that
enable the collection and cleaning of data which help in its further analysis.
Annual Average Salary: $120,931
Skills Required: Hive, Hadoop, Perl, Python, Scala, SQL
Role: BI developers analyse complex data sets to identify
business and market trends. They are typically responsible for designing,
modelling, and maintaining complex data in highly accessible cloud-based data
platforms.
Annual Average Salary: $92,278
Skills Required: Data warehouse design, Data mining, SQL (Queries,
Server Integration Services, Server Reporting Services).
Role: Research Scientists are experts in multiple AI disciplines,
including applied mathematics, machine learning, deep learning, and
computational statistics.
Average Annual Salary: $99,809
Skills Required: Benchmarking, Parallel computing, Distributed
computing.
Annual Average Salary: $151,307
Skills Required: C++, Java, Python, Scala, Data mining, Data
visualization, Data migration.
Along with the above-mentioned
skills, an AI professional must have the following soft skills as he/she needs
to work in a team to complete an AI project.
Critical Thinking allows the employee to use AI to make meaningful
decisions. For example, a computer can model climate change, but it takes human
beings to devise and enact policies to stem it.
Creativity helps employees develop innovative solutions to problems. It
requires openness to innovation and mental flexibility. In a future when AI efficiency and
productivity are givens, the organizations that permit original ideas to flow
freely will distinguish themselves. Nick Seneca says, “Breakthrough creativity
is fundamentally organic, not algorithmic.”
Leadership skills help provide any employee with the ability to take
charge of a situation and ensure it is solved or brought to a successful
conclusion. It about “getting work done” through others. Today, if you are operating
in machine learning, you're possibly operating as a part of a team, and this
team would comprise people that have direct interaction with the business. Hence,
it directly implies that if you want to be successful as a machine learning
practitioner today, you must be ready and able to interact with the business
and be a team player.
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