If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Over the past few weeks, a bunch of my friends started playing a game called Wordle. Soon, most of my chat rooms were filled with people sharing their Wordle results. I thought it would be a fun challenge to see what strategies an AI could use to solve the puzzle. This post is a deep dive into some of the strategies used to build an AI solver. At a high level, the game's objective is to guess a hidden five-letter word within six tries.
Uplift modelling is a predictive modelling technique that uses machine learning models to estimate the treatment's incremental effect at the user level. It's frequently used for personalizing product offerings, as well as targeting promotions and advertisements. In the context of causal inference, in this article, we will discuss the uplift modelling, its types of modelling and lastly, we will see how a Python-based package called CausalML can be used to address the causal inference. Following are the major points to be discussed in this article. Let's start the discussion by understanding the uplift modelling.
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Artificial intelligence (AI) algorithms can spot patterns that humans can't. But they still can't explain, say, what caused one object to collide with another. The concept of AI involves other subsets such as machine learning and deep learning. Many AI experts predict that machines may outperform humans at every task within 45 years. Self driving cars hurtling along the highway and weaving through traffic have less understanding of what might cause an accident than children who have just learned to walk.
Data can be broadly divided into continuous data, those that can take an infinite number of points within a given range such as distance or time, and categorical/discrete data, which contain a finite number of points or categories within a given group of data such as payment methods or customer complaints. We have already seen examples of applying regression to continuous prediction problems in the form of linear regression where we predicted sales, but in order to predict categorical outputs we can use logistic regression. While we are still using regression to predict outcomes, the main aim of logistic regression is to be able to predict which category and observation belongs to rather than an exact value. Examples of questions which this method can be used for include: "How likely is a person to suffer from a disease (outcome) given their age, sex, smoking status, etc (variables/features)?" "How likely is this email to be spam?" "Will a student pass a test given some predictors of performance?".
Rishabh Agarwal, Max Schwarzer, Pablo Samuel Castro, Aaron Courville and Marc G. Bellemare won an outstanding paper award at NeurIPS2021 for their paper Deep Reinforcement Learning at the Edge of the Statistical Precipice. In this blog post, Rishabh Agarwal and Pablo Samuel Castro explain this work. Reinforcement learning (RL) is an area of machine learning that focuses on learning from experiences to solve decision making tasks. While the field of RL has made great progress, resulting in impressive empirical results on complex tasks, such as playing video games, flying stratospheric balloons and designing hardware chips, it is becoming increasingly apparent that the current standards for empirical evaluation might give a false sense of fast scientific progress while slowing it down. To that end, in "Deep RL at the Edge of the Statistical Precipice", given as an oral presentation at NeurIPS 2021, we discuss how statistical uncertainty of results needs to be considered, especially when using only a few training runs, in order for evaluation in deep RL to be reliable.
As more and more businesses are facing credit card fraud and identity theft, the popularity of "fraud detection" is rising in Google Trends. Companies are looking for credit card fraud detection software that will help to eliminate this problem or at least reduce the possible dangers. Before looking at the SPD Group credit card fraud detection project, let's answer the most common questions: It is a set of activities undertaken to prevent money or property from being obtained through false pretenses. Models make predictions based on information about a transaction and some context (historical) information. To make the model more robust, we used only the most important features which were selected based on χ² (a chi-square is a test that measures how expectations compare to actual observed data) and recursive feature elimination techniques.
As a teacher of Data Science (Data Science for Internet of Things course at the University of Oxford), I am always fascinated in cross connection between concepts. To recap, Logistic regression is a binary classification method. It can be modelled as a function that can take in any number of inputs and constrain the output to be between 0 and 1. This means, we can think of Logistic Regression as a one-layer neural network. I hope you found this analysis useful as well.
Artificial intelligence (AI) isn't new to the world of stock picking, but it hasn't really been an option for retail investors. Traditionally, powerful artificial intelligence systems – and the high-octane brainpower needed to develop and operate them – have been available only to institutional investors. Financial technology company Danelfin, formerly known as Danel Capital, is trying to change all that. Danelfin has developed an analytics platform that harnesses the power of big data technology and machine learning. The goal is to level the playing field by giving regular investors access to institutional-level technology that helps them make smarter decisions with their tactical stock picks.
The NFL and Amazon Web Services (AWS) unveiled a new statistic this week to help rank the league's most prolific quarterbacks based on their decision-making. A few brilliant strokes of ingenuity, combined with a large dose of capitalism, made the e-retailer into the world's cloud services leader. The team behind Next Gen Stats (NGS) created the "Passing Score" statistic to assess whether a quarterback made the optimal decision after the play. The NFL said dozens of quarterback evaluation metrics exist but they all miss out on isolating the specific variables a quarterback must evaluate before a passing play. Josh Helmrich, NFL Director of strategy and business development, told ZDNet that AWS and the NFL worked together to combine 7 different machine learning models and several play variables to create the Next Gen Stats Passing Score.