Text provides a compelling example of unstructured data that can be used to motivate and explore classification problems. Challenges arise regarding the representation of features of text and student linkage between text representations as character strings and identification of features that embed connections with underlying phenomena. In order to observe how students reason with text data in scenarios designed to elicit certain aspects of the domain, we employed a task-based interview method using a structured protocol with six pairs of undergraduate students. Our goal was to shed light on students' understanding of text as data using a motivating task to classify headlines as "clickbait" or "news". Three types of features (function, content, and form) surfaced, the majority from the first scenario. Our analysis of the interviews indicates that this sequence of activities engaged the participants in thinking at both the human-perception level and the computer-extraction level and conceptualizing connections between them.

A machine learning algorithm written in Python was designed to predict which companies from the S&P 1500 index are likely to beat the S&P 500 index on a monthly basis. To do so, a random forest regression based algorithm, taking as input the financial ratios of all the constituents of the S&P 1500, was implemented. We will therefore skip step 1 in this article. Those with access to the datasets through the required subscriptions can instead refer to the complete notebook hosted on the following Github project: SP1500StockPicker. The random forest method is based on multiple decision trees.

Chooch is rapidly becoming the new standard in AI training, labeling, hashtags, and monetization for archived and live digital assets. Developers can use Chooch's general APIs or train Chooch for a mission-critical activity involving video and image content. And Chooch inferences and interprets content and the environment based on Chooch's trained perceptions of the physical world. Chooch can be used by plugging it into a platform. And it can be trained by simply providing a concept, pointing the platform into a direction, or feeding your own data.

Experiments at the Large Hadron Collider produce about a million gigabytes of data every second. Even after reduction and compression, the data amassed in just one hour at the LHC is similar to the data volume Facebook collects in an entire year. Luckily, particle physicists don't have to deal with all of that data all by themselves. They partner with a form of artificial intelligence that learns how to do complex analyses on its own, called machine learning. "Compared to a traditional computer algorithm that we design to do a specific analysis, we design a machine learning algorithm to figure out for itself how to do various analyses, potentially saving us countless man-hours of design and analysis work," says College of William & Mary physicist Alexander Radovic, who works on the NOvA neutrino experiment.

Artificial Intelligence (AI) has been with us for well over half a century, but confusion still exists regarding what it is and where it is best applied. This is especially true for the latest AI incarnation: "deep learning." Overall, AI is a group of different technologies created to automate tasks that are typically accomplished by humans. The first types of AI were expert systems had specific instruction sets or rules that encoded types of activities and decisions into software. Now deep learning neural networks are all the rage.