A professor says AI chatbot software, such as ChatGPT, could restructure postsecondary education by replacing some textbooks and promoting critical thinking. The release of the Amazon Kindle e-reader revolutionized the way books are read. Many avid readers read exclusively on the Kindle, while others only go with an old-fashioned book. Some choose to do a combination of both. There are pros and cons to both of these methods of reading.

Amazon's virtual shelves already feature books written by artificial intelligence. One startup believes that even titles written by humans would benefit from some AI, in the form of an accompanying chatbot primed to talk about a book's contents. YouAI, a startup that offers tools for building AI apps, recently developed an app it calls Book AI, which promises to "turn any book into an AI." It spins up a chatbot that knows everything about the book and can talk about it endlessly--like a bespectacled Terminator that just dropped in on your book club meeting. Dmitry Shapiro, YouAI's CEO, says he's talking with a number of publishers large and small about creating chatbots to accompany new releases.

Now that we've all had experience with large language models, their limitations are all too visible. But their prose doesn't explode in the mind like the words of Jennifer Egan, Emily St. John Mandel, or David Foster Wallace do. Yes they can make music. But Taylor Swift and Kendrick Lamar are sleeping very well at night. And they sure can summarize history speedily and neatly, but not with the perspicacity of Barbara Tuchman or Ron Chernow.

The Reading&Machine project exploits the support of digitalization to increase the attractiveness of libraries and improve the users' experience. The project implements an application that helps the users in their decision-making process, providing recommendation system (RecSys)-generated lists of books the users might be interested in, and showing them through an interactive Virtual Reality (VR)-based Graphical User Interface (GUI). In this paper, we focus on the design and testing of the recommendation system, employing data about all users' loans over the past 9 years from the network of libraries located in Turin, Italy. In addition, we use data collected by the Anobii online social community of readers, who share their feedback and additional information about books they read. Armed with this heterogeneous data, we build and evaluate Content Based (CB) and Collaborative Filtering (CF) approaches. Our results show that the CF outperforms the CB approach, improving by up to 47\% the relevant recommendations provided to a reader. However, the performance of the CB approach is heavily dependent on the number of books the reader has already read, and it can work even better than CF for users with a large history. Finally, our evaluations highlight that the performances of both approaches are significantly improved if the system integrates and leverages the information from the Anobii dataset, which allows us to include more user readings (for CF) and richer book metadata (for CB).

We investigate how well traditional fiction genres like Fantasy, Thriller, and Literature represent readers' preferences. Using user data from Goodreads we construct a book network where two books are strongly linked if the same people tend to read or enjoy them both. We then partition this network into communities of similar books and assign each a list of subjects from The Open Library to serve as a proxy for traditional genres. Our analysis reveals that the network communities correspond to existing combinations of traditional genres, but that the exact communities differ depending on whether we consider books that people read or books that people enjoy. In addition, we apply principal component analysis to the data and find that the variance in the book communities is best explained by two factors: the maturity/childishness and realism/fantastical nature of the books. We propose using this maturity-realism plane as a coarse classification tool for stories.

Arthur Samuel, an American pioneer in the field of computer gaming, artificial intelligence and machine learning defined Machine Learning as a "Field of study that gives computers the ability to learn without being explicitly programmed". There are computer programs that can teach themselves to grow and change when exposed to new data. Machine Learning focuses on such programs. Both search the data to look for patterns. Data mining applications extract data for human comprehension and machine learning mines that data to find out patterns.

Marley Passey feels a part while working on the CNC machine. One of the most prevalent ableist tropes is the conception that, because a person is disabled a certain way, they can't do certain activities. A Blind person can't see so they must be unable to read books or watch television, for instance. That we typically use our eyes to read books or watch TV is a signal that, without the sense of sight, the ability to do these things is seemingly impossible. That's a problem: more than ableist, this sentiment shows a profound ignorance of accessibility and assistive technologies--which, given technology's capabilities nowadays, can feel downright insulting.

Alexa wants to help your child learn how to read. With Amazon's new Reading Sidekick, kids can say "Alexa, let's read," to an Amazon Kids-enabled Echo device or the Amazon Kids app on a tablet and the artificial intelligence-powered assistant will take turns reading with them. An Amazon Kids subscription ($2.99 monthly) is required. Kids can choose from hundreds of physical and digital books that are supported, with more being added monthly. After asking Alexa to read with them, the AI assistant will ask how much do they want to read: a little, a lot, or taking turns.

Customers post online reviews at any time. With the timestamp of online reviews, they can be regarded as a flow of information. With this characteristic, designers can capture the changes in customer feedback to help set up product improvement strategies. Here we propose an approach for capturing changes of user expectation on product affordances based on the online reviews for two generations of products. First, the approach uses a rule-based natural language processing method to automatically identify and structure product affordances from review text. Then, inspired by the Kano model which classifies preferences of product attributes in five categories, conjoint analysis is used to quantitatively categorize the structured affordances. Finally, changes of user expectation can be found by applying the conjoint analysis on the online reviews posted for two successive generations of products. A case study based on the online reviews of Kindle e-readers downloaded from amazon.com shows that designers can use our proposed approach to evaluate their product improvement strategies for previous products and develop new product improvement strategies for future products.

One of the ways I continue my learning is reading. I read for 30 minutes before hitting the bed every day. This not only makes sure that I learn some thing daily, but also ends my day in a fulfilling manner. Over the years, I have read a variety of books on various subjects. In this article, I will share a list of 7 must read books, which I think should be present in every Analyst's bookshelf.