An
introduction to Artificial Intelligence
“AI
computers learn from experience. Computers are brilliant at
computational intelligence – remembering, calculating probability
and discerning patterns in datasets “
What
is an AI exactly?
Artificial
Intelligence (AI)
emphasizes on creation of intelligent machines that work and react as
humans. Computers with artificial
intelligence are
designed for activities such as; Speech recognition, Learning,
Planning and Problem solving. ... Knowledge engineering is a core
aspect of AI
research.
What
Is Intelligence?
All
but the simplest human
behaviours ascribed to intelligence, while even the most complicated insect
behaviour
is never taken as an indication of intelligence. What is the
difference? Consider the behaviour of the digger wasp,
Sphex
ichneumoneus.
When the female wasp returns to her burrow with food, she first
deposits it on the threshold ,
checks for intruders inside her burrow, and only then, if the coast
is clear, carries her food inside. The real nature of the wasp’s
instinctual behaviours revealed if the food is moved a few inches away from the entrance to
her burrow while she is inside: on emerging, she will repeat the
whole procedure as often as the food is displaced.
Intelligence—conspicuously absent in the case of Sphex —must
include the ability to adapt to new circumstances.
Psychologists
generally
do not characterize human
intelligence
by
just one trait but by the combination of many diverse abilities.
Research in AI has focused chiefly on the following components of
intelligence: learning, reasoning, problem solving, perception , and using language.
A
brief History of Artificial Intelligence
Artificial
intelligence is a buzzword today, although this term is not new. In
1956, a group of avant-garde experts from different backgrounds
decided to organize a summer research project on AI. Four bright
minds led the project; John McCarthy (Dartmouth College), Marvin
Minsky (Harvard University), Nathaniel Rochester (IBM), and Claude
Shannon (Bell Telephone Laboratories).
The
primary purpose of the research project was to tackle "every
aspect of learning or any other feature of intelligence that can in
principle be so precisely described, that a machine can be made to
simulate it."
The
proposal of the summits included
-
Automatic Computers
-
How Can a Computer Be Programmed to Use a Language?
-
Neuron Nets
-
Self-improvement
It
led to the idea that intelligent computers can be created. A new era
began, full of hope -Artificial
intelligence.
Type
of Artificial Intelligence :
Artificial
intelligence can be divided into three subfields:
-
Artificial intelligence
-
Machine learning
-
Deep learning
Machine
Learning
Deep
learning
AI
applications
Machine
learning is the art of study
of algorithms that
learn
from
examples
and
experiences.
Machine
learning is based on the idea that there exist
some
patterns
in
the data that were identified
and
used
for
future predictions.
The
difference from hardcoding rules is that the machine
learns on its own to find such rules.
Deep
learning is a sub-field of machine learning. Deep learning does not
mean the machine learns more in-depth knowledge; it means the machine
uses different layers to learn from the data. The depth of the model
is represented by the number of layers in the model.
For
instance, Google LeNet model for image recognition counts 22 layers.
In
deep learning, the learning phase is done through a neural network. A
neural network is an architecture where the layers are stacked on top
of each other.
AI
vs. Machine Learning :
Most
of our smartphone, daily device or even the internet uses Artificial
intelligence. Very often, AI
and machine learning are used interchangeably by big companies that
want to announce their latest innovation. However, Machine learning
and AI are different in some ways.
AI-
artificial intelligence- is the science of training machines to
perform human tasks. The term was invented in the 1950s when
scientists began exploring how computers could solve problems on
their own.
Artificial
Intelligence is a computer that is given human-like properties. Take
our brain; it works effortlessly and seamlessly to calculate the
world around us. Artificial Intelligence is the concept that a
computer can do the same. It can be said that AI is the large science
that mimics human aptitudes.
Machine
learning is a distinct subset of AI that trains a machine how to
learn. Machine learning models look for patterns in data and try to
conclude. In a nutshell, the machine does not need to be explicitly
programmed by people. The programmers give some examples, and the
computer is going to learn what to do from those samples.
Next
generation AI
The
creation of a more general purpose AI, being more versatile and
closer to the way human intelligence operates, goes hand in hand with
what some call the fourth industrial revolution: the coming together
of AI, blockchain, internet of things and 5G.
However,
bringing these technologies together requires more advances in cloud
storage, quantum computing and powerful computational algorithms.
There
are some who argue that truly general AI will always remain a
fiction. Yet AI innovation continues feverishly. Sub-disciplines of
semantics, contextual programming and heuristics are developing, and
human interaction is being enhanced through cyber psychology.
Artificial
intelligence has made its way into a number of areas. Here are six
examples.
-
AI in healthcare. The biggest bets are on improving patient outcomes and reducing costs. Companies are applying machine learning to make better and faster diagnoses than humans. One of the best known healthcare technologies is IBM Watson. It understands natural language and is capable of responding to questions asked of it. The system mines patient data and other available data sources to form a hypothesis, which it then presents with a confidence scoring schema. Other AI applications include chatbots, a computer program used online to answer questions and assist customers, to help schedule follow-up appointments or aid patients through the billing process, and virtual health assistants that provide basic medical feedback.
-
AI in business. Robotic process automation is being applied to highly repetitive tasks normally performed by humans. Machine learning algorithms are being integrated into analytics and CRM platforms to uncover information on how to better serve customers. Chat bots have been incorporated into websites to provide immediate service to customers. Automation of job positions has also become a talking point among academics and IT analysts.
-
AI in education. AI can automate grading, giving educators more time. AI can assess students and adapt to their needs, helping them work at their own pace. AI tutors can provide additional support to students, ensuring they stay on track. AI could change where and how students learn, perhaps even replacing some teachers.
-
AI in finance. AI in personal finance applications, such as Mint or Turbo Tax, is disrupting financial institutions. Applications such as these collect personal data and provide financial advice. Other programs, such as IBM Watson, have been applied to the process of buying a home. Today, software performs much of the trading on Wall Street.
-
AI in law. The discovery process, sifting through of documents, in law is often overwhelming for humans. Automating this process is a more efficient use of time. Startups are also building question-and-answer computer assistants that can sift programmed-to-answer questions by examining the taxonomy and ontology associated with a database.
-
AI in manufacturing. This is an area that has been at the forefront of incorporating robots into the workflow . Industrial robots used to perform single tasks and were separated from human workers, but as the technology advanced that changed.
Next Post : Machine Learning :)
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