Artificial Intelligence Understanding, Lecture notes of Artificial Intelligence

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AI,ML,DL

understanding

-Prof Prema Kirubakaran

What is AI?

• (^) Artificial intelligence, or AI, describes when a machine mimics cognitive functions that humans associate with other human minds, such as learning and problem solving.
• (^) On an even more elementary level, AI can merely be a programmed rule that tells the machine to behave in a specific way in certain situations.
• (^) In other words, artificial intelligence can be nothing more than several if-else statements.

Machine Learning

Machine learning is a relatively old field and incorporates methods and algorithms that have been around for dozens of years, some of them since the 1960s. These classic algorithms include the Naïve Bayes classifier and support vector machines, both of which are often used in data classification. In addition to classification, there are also cluster analysis algorithms such as K-means and tree-based clustering. To reduce the dimensionality of data and gain more insight into its nature, machine learning uses methods such as principal component analysis and tSNE.

How Machine Learning Works: How Do We Minimize Error?

• (^) The training component of a machine learning model means the model tries to optimize along a certain dimension.
• (^) In other words, machine learning models try to minimize the error between their predictions and the actual ground truth values.

ML predictions

• (^) We can compare the model’s prediction with the ground truth value and adjust the parameters of the model so next time the error between these two values is smaller.
• (^) This process is repeated millions of times until the parameters of the model that determine the predictions are so good that the difference between the predictions of the model and the ground truth labels are as small as possible.

How Companies Are Using AI and Machine Learning Today

• (^) AI and machine learning can lead to a variety of automated tasks.
• (^) The technology affects virtually every industry — from IT security malware search, to weather forecasting, to stockbrokers looking for optimal trades.

Deep Learning

Deep learning is a subfield of artificial intelligence based on artificial neural networks. Since deep learning algorithms also require data in order to learn and solve problems, we can also call it a subfield of machine learning. The terms machine learning and deep learning are often treated as synonymous. However, these systems have different capabilities. Unlike machine learning, deep learning uses a multi-layered structure of algorithms called the neural network.

Schematic diagram of Neural

Network or NN

ANN’s

• (^) Artificial neural networks have unique capabilities that enable deep learning models to solve tasks that machine learning models could never solve.
• (^) All recent advances in intelligence are due to deep learning.
• (^) Without deep learning we would not have self-driving cars, chatbots or personal assistants like Alexa and Siri. Google Translate would remain primitive and Netflix would have no idea which movies or TV series to suggest.
• (^) We can even go so far as to say that the new industrial revolution is driven by artificial neural networks and deep learning. This is the best and closest approach to true machine intelligence we have so far because deep learning has two major advantages over machine learning.

DL

When it comes to deep learning models, we have artificial neural networks, which don’t require feature extraction. The layers are able to learn an implicit representation of the raw data on their own.

• (^) A deep learning model produces an abstract, compressed representation of the raw data over several layers of an artificial neural network.
• (^) We then use a compressed representation of the input data to produce the result. The result can be, for example, the classification of the input data into different classes.

• (^) During the training process, the neural network optimizes this step to obtain the best possible abstract representation of the input data.
• (^) Deep learning models require little to no manual effort to perform and optimize the feature extraction process.
• (^) In other words, feature extraction is built into the process that takes place within an artificial neural network without human input.