As we all know that, Artificial Intelligence (AI) is a Buzzword in the Industry today and for a good reason. AI or Artificial Intelligence and Machine Learning have already made so much progress in the Technological field and become a hot topic in the tech industry. Perhaps more than our daily lives Artificial Intelligence (AI) is impacting the business world more. There was about $300 million in venture capital invested in AI startups in 2014, a 300% increase than a year before (Bloomberg). AI is everywhere, from gaming stations to maintaining complex information at work. Computer Engineers and Scientists are working hard to impart intelligent behavior in the machines making them think and respond to real-time situations. AI is transiting from just a research topic to the early stages of enterprise adoption. Tech giants like Google and Facebook have placed huge bets on Artificial Intelligence and Machine Learning and are already using them in their products. But this is just the beginning, over the next few years, we may see AI steadily glide into one product after another.
What is Artificial Intelligence(AI)?
AI is a technique that enables machines to mimic human behavior. Artificial Intelligence is the theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.
Artificial Intelligence is accomplished by studying how human brain thinks, learns, decide, and work while trying to solve a problem, and then using the outcomes of this study as a basis of developing intelligent software and systems.
Types Of Artificial Intelligence
Artificial Narrow Intelligence:
Commonly known as weak AI, Artificial Narrow Intelligence involves applying AI only to specific tasks.
The existing AI-based systems that claim to use “artificial intelligence” are actually operating as weak AI. Alexa is a good example of narrow intelligence. It operates within a limited predefined range of functions. Alexa has no genuine intelligence or self-awareness.
Google search engine, Sophia, self-driving cars, and even the famous AlphaGo, fall under the category of weak AI.
Artificial General Intelligence:
Commonly known as strong AI, Artificial General Intelligence involves machines that possess the ability to perform any intellectual task that a human being can.
As we see, machines don’t possess human-like abilities, they have a strong processing unit that can perform high-level computations but they’re not yet capable of thinking and reasoning like a human.
“Strong AI would take off on its own, and re-design itself at an ever-increasing rate. Humans, who are limited by slow biological evolution, couldn’t compete and would be superseded.”
Artificial Super Intelligence:
Artificial Super Intelligence is a term referring to the time when the capability of computers will surpass humans.
What is Machine Learning(ML)?
Machine learning is a subset of Artificial Intelligence (AI) which provides machines the ability to learn automatically by feeding it tons of data & allowing it to improve through experience. Thus, Machine Learning is a practice of getting Machines to solve problems by gaining the ability to think.
How does Machine Learning Work?
The Machine Learning algorithm is trained using a training data set to create a model. When new input data is introduced to the ML algorithm, it makes a prediction on the basis of the model.
The prediction is evaluated for accuracy and if the accuracy is acceptable, the Machine Learning algorithm is deployed. If the accuracy is not acceptable, the Machine Learning algorithm is trained again and again with an augmented training data set.
Types Of Machine Learning
A machine can learn to solve a problem by following any one of the following three approaches:
Supervised learning is a technique in which we teach or train the machine using data that is well labeled.
To understand Supervised Learning let’s consider an analogy. As a kid we all needed guidance to solve math problems. Our teachers helped us understand what addition is and how it is done.
Similarly, we can think of supervised learning as a type of Machine Learning that involves a guide. The labeled data set is the teacher that will train us to understand patterns in the data. The labeled data set is nothing but the training data set.
So, in the above image, we’re feeding the machine images of Tom and Jerry and the goal is for the machine to identify and classify the images into two groups (Tom images and Jerry images).
The training data set that is fed to the model is labeled, as in, we’re telling the machine, ‘this is how Tom looks and this is Jerry’. By doing so we’re training the machine by using labeled data. In Supervised Learning, there is a well-defined training phase done with the help of labeled data.
Unsupervised learning involves training by using unlabeled data and allowing the model to act on that information without guidance.
Think of unsupervised learning as a smart kid that learns without any guidance. In this type of Machine Learning, the model is not fed with labeled data, as in the model has no clue that ‘this image is Tom and this is Jerry’, it figures out patterns and the differences between Tom and Jerry on its own by taking in tons of data.
For example, it identifies prominent features of Tom such as pointy ears, bigger size, etc, to understand that this image is of type 1. Similarly, it finds such features in Jerry and knows that this image is of type 2.
Therefore, it classifies the images into two different classes without knowing who Tom is or Jerry is.
Reinforcement Learning is a part of Machine learning where an agent is put in an environment and he learns to behave in this environment by performing certain actions and observing the rewards which it gets from those actions.
To understand this let's imagine that we were dropped off at an isolated island! Initially, we all would be panic but as time passes by, we will learn how to live on the island. we will explore the environment, understand the climate condition, the type of food that grows there, the dangers of the island, etc.
This is exactly how Reinforcement Learning works, it involves an Agent (we, stuck on the island) that is put in an unknown environment (island), where he must learn by observing and performing actions that result in rewards.
Reinforcement Learning is mainly used in advanced Machine Learning areas such as self-driving cars, AplhaGo, etc.
What Problems Can Machine Learning Solve?
There are three main categories of problems that can be solved using Machine Learning:
In this type of problem, the output is a continuous quantity. For example, if we want to predict the speed of a car given the distance, it is a Regression problem. Regression problems can be solved by using Supervised Learning algorithms like Linear Regression.
In this type, the output is a categorical value. Classifying emails into two classes, spam and non-spam is a classification problem that can be solved by using Supervised Learning classification algorithms such as Support Vector Machines, Naive Bayes, Logistic Regression, K Nearest Neighbor, etc.
What is Clustering?
This type of problem involves assigning the input into two or more clusters based on feature similarity. For example, clustering viewers into similar groups based on their interests, age, geography, etc can be done by using Unsupervised Learning algorithms like K-Means Clustering.
Major Use Cases Of Artificial Intelligence
Artificial Intelligence is used almost everywhere today, in systems such as Mail spam filtering, Credit-Card fraud detection systems, Virtual Assistants, and so on.
Artificial Intelligence For Rescue Missions
What we majorly require is the use of Artificial Intelligence and technology to ensure that help arrives faster. We can start by developing systems that help first responders find victims of earthquakes, floods, and any other natural disasters.
Normally, responders need to examine aerial footage to determine where people could be stranded. However, examining a vast number of photos and drone footage is very time and labor-intensive.
This is a time-critical process and it might very well be the difference between life and death for the victims.
An Artificial Intelligence system developed at Texas A&M University permits computer programmers to write basic algorithms that can examine extensive footage and find missing people in under two hours.
Artificial Intelligence For Smart Agriculture
In my opinion, Neural networks work well to provide smart agricultural solutions.
Everything ranging from complete monitoring of the soil and crop yield to providing predictive analytic models to track and predict various factors and variables that could affect future yields.
For example, the Berlin-based agricultural tech startup PEAT has developed a deep learning algorithm-based application called Plantix which can identify defects and nutrient deficiencies in the soil.
Their algorithms correlate particular foliage patterns with certain soil defects, plant pests, and diseases.
Artificial Intelligence In Healthcare — Better Surgeries And Prosthetics
Well, one day we’re wondering — ‘What is Artificial Intelligence and later robots are ready to perform surgical procedures on us?
Robots today are machine learning-enabled tools that provide doctors with extended precision and control. These machines enable shortening the patients’ hospital stay, positively affecting the surgical experience and reducing medical costs all at once.
Similarly, mind-controlled robotic arms and brain chip implants have begun helping paralyzed patients regain mobility and sensations of touch.
Overall, Machine learning and Artificial Intelligence are helping improve patient experience on the whole.
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