# Artificial Intelligence Frontiers in Statistics: AI and statistics III

The age of spreadsheet is over.

A google search, a passport scan, your online shopping history, a tweet. All of these contain data that can be collected, analyzed, and monetized. Supercomputer and algorithms allow us to make sense of an increasingly large amount of information in real time.

In less than 10 years, CPUs are expected to reach the processing power of the human brain. With the rise of big data and fast computing power, many CEOs, CTOs, and decision makers of organizations are thinking of ways to innovate their company. When they want to launch a new product or service, they are looking to data analytics for insights on market, demand, the target demographic, etc. Artificial Intelligence and machine learning are being adopted into the enterprise at a rapid pace.

This trend is only likely to surge upward. In artificial intelligence, job openings are rising faster than job seekers.

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## Artificial Intelligence - foundations of computational agents -- References

Perhaps the most compelling aspect about Machine Learning is its seemingly limitless applicability. Machine Learning techniques are already being applied to critical areas within the Healthcare sphere, impacting everything from care variation reduction efforts to medical scan analysis. Our definition changed over time what AI exactly is. We have come a long way from the Smart Fellow Robot as seen in this footage below.

AI is just a computer that is able to mimic or simulate human thought or behavior. By allowing computers to learn how to solve problems on their own, machine learning has made a series of breakthroughs that once seemed nearly impossible. Machine learning is a branch of artificial intelligence where a class of data-driven algorithms enables software applications to become highly accurate in predicting outcomes without any need for explicit programming.

The basic premise here is to develop algorithms that can receive input data and leverage statistical models to predict an output while updating outputs as new data becomes available. The processes involved have a lot in common with predictive modeling and data mining.

This is because both approaches demand one to search through the data to identify patterns and adjust the program accordingly. Most of us have experienced machine learning in action in one form or another. If you have shopped on Amazon or watched something on Netflix, those personalized product or movie recommendations are machine learning in action.

Did you correctly predict the next word in the unrolled text sequence text RNN? How far did your latent distribution diverge from a unit Gaussian VAE? These questions tell you how well your representation function is working; more importantly, they define what it will learn to do. Optimization is the last piece of the puzzle.

Once you have the evaluation component, you can optimize the representation function in order to improve your evaluation metric. In neural networks, this usually means using some variant of stochastic gradient descent to update the weights and biases of your network according to some defined loss function. And voila! Borrowing statistical terms like logistic regression do give us useful vocabulary to discuss our model space, but they do not redefine them from problems of optimization to problems of data understanding.

Aside: The term artificial intelligence is stupid. In the 19th century, a mechanical calculator was considered intelligent link. I wish we could stop using such an empty, sensationalized term to refer to real technological techniques. Further defying the purported statistical nature of deep learning is, well, almost all of the internal workings of deep neural networks.

## No, Machine Learning is not just glorified Statistics

Fully connected nodes consist of weights and biases, sure, but what about convolutional layers? Rectifier activations? Batch normalization? Residual layers? Memory and attention mechanisms? Let me also point out the difference between deep nets and traditional statistical models by their scale. Deep neural networks are huge. How do you think your average academic advisor would respond to a student wanting to perform a multiple regression of over million variables? The idea is ludicrous.

I will remind you, however, that not only is deep learning more than previous techniques, it has enabled to us address an entirely new class of problems. Prior to , problems involving unstructured and semi-structured data were challenging, at best. This has yielded considerable progress in fields such as computer vision, natural language processing, speech transcription, and has enabled huge improvement in technologies like face recognition, autonomous vehicles, and conversational AI.

That said, it has made a significant contribution to our ability to attack problems with complex unstructured data. Many have interpreted this article as a diss on the field of statistics, or as a betrayal of my own superficial understanding of machine learning. In retrospect, I regret directing so much attention on the differences in the ML vs. Let me be clear: statistics and machine learning are not unrelated by any stretch. Machine learning absolutely utilizes and builds on concepts in statistics, and statisticians rightly make use of machine learning techniques in their work.

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The distinction between the two fields is unimportant, and something I should not have focused so heavily on. Recently, I have been focusing on the idea of Bayesian neural networks. These techniques give a principled approach to uncertainty quantification and yield better-regularized predictions.

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The fields are not mutually exclusive, but that does not make them the same, and it certainly does not make either without substance or value. A mathematician could point to a theoretical physicist working on Quantum field theory and rightly say that she is doing math, but she might take issue if the mathematician asserted that her field of physics was in fact nothing more than over-hyped math. Statistics is invaluable in machine learning research and many statisticians are at the forefront of that work. But ML has developed million parameter neural networks with residual connections and batch normalization, modern activations, dropout and numerous other techniques which have led to advances in several domains, particularly in sequential decision making and computational perception.

It has found and made use of incredibly efficient optimization algorithms, taking advantage of automatic differentiation and running in parallel on blindingly fast and cheap GPU technology. All of this is accessible to anyone with even basic programming abilities thanks to high-level, elegantly simple tensor manipulation software. Sign in. Get started. No, Machine Learning is not just glorified Statistics. Joe Davison Follow. Towards Data Science Sharing concepts, ideas, and codes.