· Handwriting Recognition − The handwriting recognition software
reads the text written on paper by a pen or on screen by a stylus. It can
recognize the shapes of the letters and convert it into editable text.
· Intelligent Robots − Robots are able to perform the tasks given by a human.
They have sensors to detect physical data from the real world such as light,
heat, temperature, movement, sound, bump, and pressure. They have efficient
processors, multiple sensors and huge memory, to exhibit intelligence. In
addition, they are capable of learning from their mistakes and they can adapt
to the new environment.
Various approaches to AI
Four approaches have been
followed. As one might expect, a tension exists between approaches centred on humans and approaches centered
around rationality. A Human centred approach must be an empirical science, involving hypothesis and experimental
confirmation. A rationalist approach involves
a combination of mathematics and engineering. People in each group sometimes
cast aspersions on work done in the
other groups, but the truth is that each direction has yielded valuable insights. Let us look at each in more detail
Acting humanly: The Turing
Test approach
The Turing Test, proposed by Alan Turing (1950),
was designed to provide a satisfactory operational definition of intelligence.
Turing defined intelligent behavior as the ability to achieve human-level performance in all cognitive tasks, sufficient to fool an interrogator. Roughly
speaking, the test he proposed
is that the computer
should be interrogated by a human via
a teletype, and passes the test if the interrogator cannot tell if there is a computer
or a human at the other end. Chapter 26
discusses the details of the test, and whether or not a computer is really intelligent if it passes. For now,
programming a computer to pass the test provides plenty to work on. The computer would need to possess the following
capabilities:
· natural
language processing to enable it to communicate successfully in English (or
some other human language);
· knowledge representation to store information provided before or during the interrogation;
· automated
reasoning to use the stored information to answer questions and to draw new conclusions;
· machine learning
to adapt to new
circumstances and to detect and extrapolate
patterns
Thinking humanly: If we are going to say
that a given program thinks like a human, we must have some way of determining how humans think. We need to get
inside the actual workings of human minds.
Thinking rationally: The Greek
philosopher Aristotle was one of the first to attempt to codify "right thinking," that is,
irrefutable reasoning processes. His famous syllogisms provided patterns for argument structures that always gave correct conclusions given correct premises.
Acting rationally: Acting rationally
means acting so as to achieve one's goals, given one's beliefs. An agent
is just something that perceives and acts. (This may be an unusual use of
the word, but you will get used to
it.) In this approach, AI is viewed as the study and construction of rational agents.
An AI engineer builds AI
models using machine learning algorithm and deep learning neural networks to draw business insights, which can be used to make
business decisions that affect the entire
organization. These engineers also create weak or strong AIs, depending on what
goals they want to achieve.
AI engineers have a sound
understanding of programming, software engineering, and data science. They use different tools and techniques so they can process data,
as well as develop and maintain AI systems.
Responsibilities of AI Engineer
As an AI engineeran AI engineer, you need
to perform certain tasks, such as develop, test, and deploy AI models through programming algorithms like random
forest, logistic regression, linear regression, and so on.
Responsibilities include:
· Convert the machine learning
models into application program interfaces (APIs)
so that other
applications can use it
· Build AI models from scratch and help the different components of the organization (such as product
managers and stakeholders)
understand what results they gain from the model
· Build data ingestion and data
transformation infrastructure
· Automate infrastructure that the data science team uses
· Perform statistical analysis and tune the results so that the organization can make better-
informed decisions
· Set up and manage AI development and product infrastructure
· Be a good team
player, as coordinating with others is a must
Skills Required to Become
an AI Engineer
Professionals who are
finding how to become an AI engineer should also know about the skills required
in this field. Some of them
include:
· Programming Skills
The first skill required
to become an AI engineer is programming. To become well-versed in AI, it’s crucial
to learn programming languages, such as python, R, java, and C++ to build and implement models.
· Linear Algebra,
Probability, and Statistics
To understand and implement
different AI models—such as Hidden Markov models, Naive Bayes, Gaussian mixture models, and linear
discriminant analysis—you must have detailed knowledge of linear
algebra, probability, and statistics.
· Spark and Big Data Technologies
AI engineers work with large
volumes of data, which could be streaming or real-time production- level data in terabytes or petabytes. For
such data, these engineers need to know about Spark and other big data technologies to make sense of it. Along
with Apache Spark, one can also use
other big data technologies,
such as hadoop,
Cassandra, and MongoDB. · Algorithms and Frameworks
Understanding how machine learning
algorithms like linear regression, KNN, Naive Bayes, Support
Vector Machine, and others work will help you implement machine learning models
with ease. Additionally, to build AI
models with unstructured data, you should understand deep learning algorithms (like a convolutional neural network, recurrent
neural network, and generative adversarial network) and implement them
using a framework. Some of the frameworks used in artificial intelligence are PyTorch, Theano, TensorFlow, and Caffe.
·Communication and Problem-solving Skills
AI engineers need to
communicate correctly to pitch their products and ideas to stakeholders. They should also have excellent problem-solving
skills to resolve obstacles for decision making and drawing helpful
business insights.
Let us explore the career
and roles in AI in the next section of the How to become an AI Engineer article.
· AI Engineer
Salary
According to Glassdoor, the
average annual salary of an AI engineer is $114,121
in the United States and ₹765,353
in India. The salary may differ in several organizations and with the knowledge and expertise, you bring to the table. ·Career in AI
Since several industries
around the world use AI to some degree or the other, including healthcare and education, there has been exponential
growth in the career opportunities within the field of AI. Some of these job
roles are:
·AI Developer
An AI developer works
closely with electrical engineers and develops software to create artificially intelligent robots.
·AI Architect
AI architects work closely
with clients to provide constructive business and system integration services.
They also create and maintain
the entire architecture.
·Machine Learning
Engineer
machine learning engineeres
build predictive models using vast volumes of
data. They have in-depth knowledge of machine learning
algorithms, deep learning
algorithms, and deep learning frameworks.· Data Scientists
Data scientists collect,
clean, analyze, and interpret large and complex
datasets by leveraging both machine learning and predictive analytics.
·Business Intelligence Developer
They're responsible for designing, modeling, and analyzing complex
data to identify
the business and market
trends.
What is Intelligence ?
The ability of a system
to calculate, reason, perceive relationships and analogies, learn from
experience, store and retrieve information from memory, solve problems,
comprehend complex ideas, use natural language fluently, classify, generalize,
and adapt new situations.
What
is Intelligence Composed of ?
The intelligence is intangible.
It is composed of −
- Reasoning
- Learning
- Problem Solving
- Perception
- Linguistic Intelligence
- Reasoning − It is the set of processes that enables us to provide basis for
judgement, making decisions, and predictio
· Learning − It is the activity of gaining knowledge or skill by
studying, practising, being taught, or experiencing something. Learning
enhances the awareness of the subjects of the study.
The ability of learning
is possessed by humans, some animals, and AI-enabled systems. Learning is
categorized as −
o Auditory Learning − It is learning by listening and hearing.
For example, students listening to recorded audio lectures.
o Episodic Learning − To learn by remembering sequences of
events that one has witnessed or experienced. This is linear and orderly.
o Motor Learning − It is learning by precise movement of
muscles. For example, picking objects, Writing, etc.
o Observational Learning − To learn by watching and imitating others.
For example, child tries to learn by mimicking her parent.
o Perceptual Learning − It is learning to recognize stimuli that
one has seen before. For example, identifying and classifying objects and
situations.
o Relational Learning − It involves learning to differentiate
among various stimuli on the basis of relational properties, rather than
absolute properties. For Example, Adding ‘little less’ salt at the time of
cooking potatoes that came up salty last time, when cooked with adding say a
tablespoon of salt.
o Spatial Learning − It is learning through visual stimuli
such as images, colors, maps, etc. For Example, A person can create roadmap in
mind before actually following the road.
o Stimulus-Response Learning − It is learning to perform a particular
behavior when a certain stimulus is present. For example, a dog raises its ear
on hearing doorbell.
·
Problem Solving − It is the process in which one perceives and tries to
arrive at a desired solution from a present situation by taking some path,
which is blocked by known or unknown hurdles.
Problem solving also
includes decision making, which is the process of selecting the
best suitable alternative out of multiple alternatives to reach the desired
goal are available.
·
Perception − It is the process of acquiring, interpreting, selecting,
and organizing sensory information.
Perception
presumes sensing. In humans, perception is aided by sensory organs.
In the domain of AI, perception mechanism puts the data acquired by the sensors
together in a meaningful manner.
·
Linguistic Intelligence − It is one’s ability to use, comprehend,
speak, and write the verbal and written language. It is important in
interpersonal communication.
Difference
between Human and Machine Intelligence
· Humans perceive by patterns whereas the machines perceive by set
of rules and data.
·Humans store and recall information by patterns, machines do it by
searching algorithms. For example, the number 40404040 is easy to remember,
store, and recall as its pattern is simple.
·Humans can figure out the complete object even if some part of it
is missing or distorted; whereas the machines cannot do it correctly.
The domain of artificial
intelligence is huge in breadth and width. While proceeding, we consider the
broadly common and prospering research areas in the domain of AI −
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