How a regulated AI industry can lead to a sound human and machine relationship

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So we promise you, they will never ever, ever, ever turn evil — “The Mitchells vs the machines”

If you think I am speaking in my wild imagination on what artificially intelligent devices can do to us, you are wrong.


AI Alignment and Safety

It is not the technology at fault, but the intention

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Responsible AI, Ethical AI, AI for social good — I am sure you must have heard these terms at some point or the other, whether you are a Data Scientist or not.

“The development of full artificial intelligence could spell the end of the human race.”

And there my journey of understanding this critical aspect of the AI foundation started. …


A journey towards ethical AI

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If you also carry a vision of ensuring that the product you are working on follows all the written rules of “AI for good”, then you would have definitely encountered a situation where your data is biased.


Principles and benefits of going cloud-native

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Cloud-native has changed the dynamics of the software industry and how people think of deploying and operating software applications.

Let’s develop a little background on what is cloud computing — “It refers to the on-demand availability of resources like cloud-storage and computes power without the…


Time-varying data characteristics

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One of the most critical assumptions in ML data modeling is that the train and test dataset belong to similar distribution. This emphasizes the property of generalization of ML solution


Degrees of freedom, statistical significance, p-value

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You must be thinking why I am calling this article Binging with Statistics? Well, we all have been binging on Netflix, hot star, amazon prime and what not while struggling amid pandemic.


Sharing my solution to help you kickstart your hackathon journey

Source

I recently participated in MachineHack’s Buyer’s Time Prediction Challenge and would like to share my approach with you. So, let's get started with a quick outline:

  • Problem Statement
  • Data understanding
  • Solution: a) Target variable transformation, b) Outlier removal, c) Feature Engineering, and d) Modeling
  • Learning from peers

Problem Statement:


Sufficiency, Robustness, and more

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In this article, I will talk about the various properties of a statistic.


A quick aggregation of data quality checks

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Machine Learning practitioners spend a lot of their time with data as the data quality issues inherently deter the learning of any algorithm. It follows the “Garbage in, garbage out” principle, i.e. poor data quality can only result in poor learning. Good quality data is crucial to the success of any machine learning algorithm.


and the properties of an estimator

Source: Business vector created by jcomp

We often come across terms like a statistic and an estimator while working on statistical problems in the realm of Machine Learning. It is important to understand the difference between the two so that we know the context behind such terms in parameter learning. But, before understanding the difference between a statistic and an estimator, let’s first understand a few concepts on the properties of a statistical model.

Vidhi Chugh

Data Scientist

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