Now a days every business or startup wants to include artificial intelligence or machine learning in their business application and we have seen potential of artificial intelligence in business growth in last few years. But do we really understand in depth what is artificial intelligence and machine learning ? or difference between Artificial intelligence and machine learning ? let’s understand that in detail.
what is artificial intelligence?
Artificial intelligence is nothing but mimicking human brain and how it operates. without information our brain can not operate same applies to artificial intelligence as well. Our brain accumulates information, process that information and produce outcome similarly artificial intelligence do the same job but in artificial intelligence it happens billion times faster than out brain. This term ‘Artificial intelligence ‘ introduced by McCarthy, Marvin Minsky, Nathaniel Rochester, and Claude E. Shannon. This concept introduced few decades back but it’s applications what we are seeing right now since last few years in various domains and sectors.
Artificial intelligence in more precise way :
If we want to describe artificial intelligence in more tangible manner, it’s play of: ‘data/information + mathematics + algorithms written by engineer + software tools & frameworks developed by companies’ and this whole structure or architecture used in different business applications for different use cases for example prediction of outcome based on data that we feed to machine, algorithms that we write , machine learning model that we implement. most important thing to understand is more data we feed to the machine better the outcome we can expect.
What is machine learning and how it is different than artificial intelligence?
machine learning is subset of artificial intelligence. Artificial intelligence is broad term given initially just to make sense for normal people so that they can comprehend how machine(computers) going to mimic human brain. ML is a subset of AI that focuses on teaching machines to learn from data without being explicitly programmed. In machine learning , algorithm or set of procedure (recipe in our normal human language ) is applied to set of data to know patterns , trends or to make sense of data. in the end we get machine learning model which is outcome of applying algorithm on dataset which helps predict outcome , decision making , making sense of new data. In machine learning , human intervention is not required. Machine learn on it’s own which helps humans.
different machine learning techniques
Supervised machine learning technique: this machine learning technique need labeled data to know or understand patterns to predict outcome. In this technique algorithm learn from labeled data. Example : linear regression , logistic regression, decision trees.
Unsupervised machine learning technique: this technique can predict outcome without feeding unlabeled data by learning patterns , trends on it’s own and generate output. Example: K-means and clustering.
deep learning: In this machine learning technique, there are multiple layers of neural networks . deep learning algorithms can be used for both supervised and unsupervised machine learning. data goes through multiple layers before we see outcome in this machine learning technique. Examples: Image classification , speech recognition, natural language processing.
Different machine learning algorithms :
linear regression : A simple algorithm used to map the linear relationship between input features.
logistic regression: it’s extension of linear algorithm which used for classification of tasks.
decision tree algorithm: A supervised learning algorithm that is mainly used to solve classification problems.
support vector algorithm : A supervised learning algorithm that can be used for both classification and regression tasks.
k-nearest Neighbors: A simple algorithm which stores all available cases and classifies new cases based on similarity
Naïve bayes: Probabilistic algorithm which is based on Bayes’ theorem and used for classification tasks.
random forest: An ensemble learning method that constructs a multitude of decision trees at training time and outputs the class that is the mode of the classes (classification) or mean prediction (regression) of the individual trees.
Gradient Boosting Machines (GBM): it’s algorithm that builds series of decision tress which corrects error of previous one.
These are different algorithms of machine learning. Choice is depends on the problem you are trying to solve and complexity of data.
What are use cases or business applications of Artificial intelligence and machine learning?
building recommendation engine: with some input of initial database , algorithms used to recommend products or services to users based on their previous interactions and initial database. Example: Netflix
Image recognition: Deep learning algorithms can be used to classify images into different categories such as animals, objects, and people. For example, Google Photos uses deep learning to automatically tag and organize photos based on their content
Speech recognition: Deep learning algorithms can be used to recognize speech and convert it into text. This technology is used in virtual assistants like Siri and Alexa to understand voice commands
Natural language processing: Deep learning algorithms can be used to analyze and understand human language. This technology is used in chatbots, virtual assistants, and other applications that require natural language processing
self driving car: deep learning algorithm is used detect objects on the road used by self driving cars and use that information to take decision.
fraud detection: again , deep learning algorithm used to detect frauds by analyzing patterns in previous data to prevent frauds in future.
Medical uses: deep learning algorithm used to analyze images taken by MRI’s and X-ray to diagnose disease better.
What hold in future for humanity ? Will AI replace humans?
Definitely not:) AI is here to assist humans. future is promising for humans. In so many ways AI is going to make humans more productive , creative which is happening as we discuss this post:)
