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Summary introduction to AI

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Key Areas of AI: Machine Learning (ML): A subset of AI that allows systems to learn and improve from experience without being explicitly programmed. It uses algorithms and statistical models to find patterns in data. Natural Language Processing (NLP): This involves enabling machines to understand and respond to human language. Examples include chatbots, translation services, and voice assistants like Siri or Alexa. Computer Vision: This involves enabling machines to interpret and make decisions based on visual data from the world, such as recognizing objects or faces in images or video. Robotics: AI in robotics is about designing and building intelligent machines that can perform tasks autonomously or semi-autonomously. These systems often use AI to process sensory data and make decisions. Expert Systems: These are AI programs designed to mimic the decision-making abilities of a human expert in specific fields, such as medicine or finance. Types of AI: Narrow AI (Weak AI): This is AI designed to perform a narrow task, like facial recognition or internet searches. It's highly specialized and does not possess general intelligence or consciousness. General AI (Strong AI): This type of AI, which is still theoretical, would have the ability to understand, learn, and apply intelligence across a broad range of tasks, much like a human being. Applications of AI: Healthcare: AI helps in diagnosing diseases, drug discovery, and personalized treatment plans. Finance: AI is used for fraud detection, algorithmic trading, and customer service. Autonomous Vehicles: AI powers self-driving cars and trucks. Entertainment: AI recommends music, movies, and other media based on user preferences. Customer Service: AI-driven chatbots and virtual assistants help businesses respond to customer inquiries.

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A Brief Introduction to Artificial Intelligence
What is AI and how is it going to shape the future
By Dibbyo Saha, Undergraduate Student, Computer Science, Ryerson University

What is Artificial Intelligence?




Image by Gerd Altmann from Pixabay

Generally speaking, Artificial Intelligence is a computing concept that helps a
machine think and solve complex problems as we humans do with our intelligence.
For example, we perform a task, make mistakes and learn from our mistakes (At
least the wise ones of us do!). Likewise, an AI or Artificial Intelligence is supposed
to work on a problem, make some mistakes in solving the problem and learn from
the problems in a self-correcting manner as a part of its self-improvement. Or in
other words, think of this like playing a game of chess. Every bad move you make
reduces your chances of winning the game. So, every time you lose against your
friend, you try remembering the moves you made which you shouldn’t have and
apply that knowledge in your next game and so on. Eventually, you get better and
your precision, or in this case probability of winning or solving a problem

, improves by a noteworthy extent. AI is programmed to do something similar to
that!
Artificial Intelligence vs Traditional Robotics




Photo by Possessed Photography on Unsplash


When we hear the word “Robot”, an image of a metal box with creepy eyes and
speaking in a mechanical voice pops into our head. I mean that’s what we have
been watching in television for years, isn’t it? And to a certain degree we are right.
Traditional robotics has been perceived by pop culture as an arena that creates
humanlike machines to work for us as saviours and sometimes as super-villains
bringing in a cascade of tyranny into the human world. However, real life robots
aren’t as humanlike as we want them to be, yet. They are programmed in a specific
way to only execute tasks that it has been programmed to perform.

Imagine a self-driving car that has been designed to drive you on its own according
to where you instruct it to take you. Now for a traditional robot, the car is going to
go through the exact road that it was programmed to select for a certain destination
by its creators, possibly without the knowledge of traffic and cause accidents.
However, a human driver would have chosen the shortest path or check which
paths have the least traffic today and would be the most convenient path for that
particular destination. That is the exact humanlike creative thinking the traditional
robots lack! They are fixed in their own “not so smart” way and are largely
dependent on the program they are built on and the instructions that they are being
given. If a certain instruction doesn’t coincide with their program, the robot won’t
even be able to run, let alone going the extra step of being creative. This is the
limitation of traditional robots Artificial Intelligence is being developed to
overcome. Unlike the conventional “bips and bops”, a good AI will simulate the

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