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Role/Importance of Statistics in AI

Role/Importance of Statistics in AI

Role/Importance of Statistics in AI

The field of Artificial Intelligence deals with making predictions and finding patterns in structures of data to make those predictions. This helps the machine in carrying out various analytical tasks without human intervention. Statistics is a set of principles used to obtain information about data to make decisions. It makes various chunks of data have a relationship with each other and with itself. Hence, statistics play an important role in AI and any person working in the field of AI is well versed with the concepts of probability and statistics. Solving problems in AI requires finding out how data is distributed, information about dependent and independent variables, and so on.

How do statistics play a role in the field of AI?
Statistics serve as a foundation for analysis and dealing with data in data science. A lot of performance metrics used in machine learning algorithms like accuracy, precision, recall, f-score, root mean squared error, etc. use statistics as the base. These further help in the visual representation of data and performance algorithms used on it for better understanding. Statistics helps identify specific trivial patterns, outliers in the data, and metric summaries like median, mean, standard deviation, and so on.

10 ideas in statistics that form an important part of AI –

• Predictive Validation is a fundamental principle of statistics and machine learning. It is used to determine if the scores in one part of the experiment can accurately predict performance in another part of the experiment. It draws a relationship between two different parts of a measuring system in a predictive manner.
• Data visualization and exploration was an idea that helped to discover new and unexpected insights from data. The popular notion of statistics being used to confirm what we already know was refuted with this information and motivated discoveries in various branches of AI.
• Spline smoothing is a statistical approach for fitting nonparametric curves. It is a class of algorithms that can fit arbitrary smooth curves without overfitting outliers. Curve fitting used polynomials, exponentials, and other fixed forms. But with spline smoothing a lot of these methodologies were made simpler and more accessible.
• Bootstrapping is an approach to perform statistical inference without making any assumptions. Inferences cannot be made without assumptions, but bootstrapping made it possible for assumptions to come implicitly with the computational procedure, through resampling of data. This allowed simulations to replace mathematical analysis.
• Open-ended Bayesian models changed the existing models of statistics, which were all static. This Bayesian model gave rise to modern statistical analysis where problems were solved flexibly by calling libraries of distributions and transformations. The faster computations capabilities have revolutionized machine learning and statistics.
• Causal inference is the core of any problems, that deal not only with analysis and prediction in AI but also in determining things that could happen if some operation was not performed or performed differently. This idea identifies what questions can be reliably answered from a given experiment and makes algorithms more robust and effective.
• Regression is a commonly used algorithm of machine learning where an outcome variable is predicted from a set of inputs or features. There had to be a lot of inputs and their interactions to predict results, which made the algorithm statistically unstable. So, lasso was designed to make a more efficient algorithm for regularisation.
• Statistical graphics framework usually involves pie charts, histograms, and scatter plots. But a new framework was designed that abstractly explores how data and visualization are related. It served as an important step towards integrating exploratory data and analysis of models into the workflow of data science and AI.
• Prediction and inference feedback are popularly used in self-driving cars so that they can learn to drive with minimal assistance from a human. Generative adversarial networks or GANs enable reinforcement learning problems to be solved automatically. This forms a link between Artificial Intelligence and parallel processing. GANs essentially link prediction with generative models.
• Deep learning makes flexible and non-linear predictions using a large number of features. The building blocks of deep learning include logistic regression, multilevel structure, and Bayesian inference. It can solve many prediction problems, ranging from consumer behavior to image analysis. Such statistical algorithms are used to fit large models in real-time.

How to apply statistical thinking to AI problems?

• Clearly articulate the problem, define and describe the scope correctly.
• Translate the problem into a data science methodology by picking the right data model.
• Quality of data plays a very important role in the result. Spend time cleaning, understanding, and transforming your data.
• Descriptive statistics and graphs are the beginning of the workflow. It can spot unexpected trends which lead to biased learning.
• Trials and experiments should have a sound design and all variations of the parameter to be measured must be taken into account.
• Consider both explanatory variables and response variables to cover all possible outcomes.
• Use hypothesis testing and always include control groups to gain valuable insights.
• Validate the model to make it robust.
• Update models from time to time as parameters keep influencing outcomes in different ways.
• Finally, implement procedures to check whether the AI model is successfully contributing to the goals for which it was created.

AI involves transforming massive amounts of raw data into usable and actionable information. Any advanced computational process requires analytical skills. All such analytical skills are derived from the practices of statistics. Hence, to truly flourish in the ability to make machines intelligent and keep developing new tools for AI, development should be directed towards newer statistical methods and tools. These tools can create models that can handle the complex behavior of world systems and make machines that can assist human beings in a plethora of tasks.

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