Artificial Intelligence
Artificial intelligence (AI), is intelligence demonstrated by machines, unlike the natural intelligence displayed by humans and animals. Colloquially, the term “artificial intelligence” is often used to describe machines (or computers) that mimic “cognitive” functions that humans associate with the human mind, such as “learning” and “problem solving”.
Founding Father Of Artificial intelligence (AI)
John McCarthy was an American computer scientist and cognitive scientist. McCarthy was one of the founders of the discipline of artificial intelligence. He co-authored the document that coined the term “artificial intelligence”
Subsets Of Artificial intelligence (AI)
Machine Learning
Machine Learning is the field of study that gives computers the capability to learn without being explicitly programmed. As it is evident from the name, it gives the computer that makes it more similar to humans: The ability to learn. Machine learning is actively being used today, perhaps in many more places than one would expect.
Types In Machine Learning (ML)
Supervised Learning : Supervised learning as the name indicates the presence of a supervisor as a teacher. Basically supervised learning is a learning in which we teach or train the machine using data which is well labeled that means some data is already tagged with the correct answer. After that, the machine is provided with a new set of examples(data) so that supervised learning algorithm analyses the training data(set of training examples) and produces a correct outcome from labeled data.
Unsupervised Learning :Unsupervised learning is the training of machine using information that is neither classified nor labeled and allowing the algorithm to act on that information without guidance. Here the task of machine is to group unsorted information according to similarities, patterns and differences without any prior training of data.
Unlike supervised learning, no teacher is provided that means no training will be given to the machine. Therefore machine is restricted to find the hidden structure in unlabeled data by our-self.
Reinforcement learning : Reinforcement learning is an area of Machine Learning. It is about taking suitable action to maximize reward in a particular situation. It is employed by various software and machines to find the best possible behavior or path it should take in a specific situation. Reinforcement learning differs from the supervised learning in a way that in supervised learning the training data has the answer key with it so the model is trained with the correct answer itself whereas in reinforcement learning, there is no answer but the reinforcement agent decides what to do to perform the given task. In the absence of a training dataset, it is bound to learn from its experience.
Deep Learning
Deep learning is a particular kind of machine learning that achieves great power and flexibility by learning to represent the world as a nested hierarchy of concepts, with each concept defined in relation to simpler concepts, and more abstract representations computed in terms of less abstract ones.
Which means in an simpler way Deep learning is a branch of machine learning which is completely based on artificial neural networks, as neural network is going to mimic the human brain so deep learning is also a kind of mimic of human brain. In deep learning, we don’t need to explicitly program everything. The concept of deep learning is not new. It has been around for a couple of years now. It’s on hype nowadays because earlier we did not have that much processing power and a lot of data. As in the last 20 years, the processing power increases exponentially, deep learning and machine learning came in the picture.
Subsets & Applications :
1.Neural Networks
2.Computer Vision
3. Natural Language Processing
4. Sequence Models
5. Time Series Forecasting
Neural networks : They are artificial systems that were inspired by biological neural networks. These systems learn to perform tasks by being exposed to various datasets and examples without any task-specific rules. The idea is that the system generates identifying characteristics from the data they have been passed without being programmed with a pre-programmed understanding of these datasets.
Neural networks are based on computational models for threshold logic. Threshold logic is a combination of algorithms and mathematics. Neural networks are based either on the study of the brain or on the application of neural networks to artificial intelligence. The work has led to improvements in finite automata theory.
Types Of Neural Networks :
- Artificial Neural Network
- Convolutional Neural Network
- Recurrent Neural Network
There are many more types like Deep Neural Nets , mask R-CNN , LSTM’S many more these are primary ..
Applications of Artificial Intelligence(AI) , Machine Learning(ML) , Deep Learning (DL)
Well , You can say from Google Search to Self -Driving Vehicles everywhere AI is used in many ways as follows :
Word Prediction ( Google Search )
Face recognition (Mobiles & more places )
Weather Prediction ( Weather Apps)
Recommendation Systems ( Netflix , Prime, Spotify)
Voice Assistants ( Alexa, Siri ,Google )
Autonomous Vehicles ( Tesla, Merc , BMW , SpaceX )
Emoji Relation ( Example : happy (smiley emoji)( Whatsapp,Instagram))
Sentiment Analysis ( Whether the given things are positive or negative
Many more Applications to go .. AI is Future & Ruling the World
Quote By Famous Deep Learning Pioneer
Deep Learning is a superpower. With it you can make a computer see, synthesize novel art, translate languages, render a medical diagnosis, or build pieces of a car that can drive itself. If that isn’t a superpower, I don’t know what is.
— Andrew Ng
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