Media
How to Build a Recommender Engine for Medical Research Papers
In 2006, Netflix, which was then a DVD rental service, announced a data science competition for movie rating predictions. The company would offer a $1 million grand prize to the team that could improve their existing recommender system's prediction accuracy by 10%. The competition garnered much interest from researchers and engineers in both academia and industry. Within the first year of the competition, over 40,000 teams from more than 100 countries had entered the competition [1]. In June 2009, the prize was awarded to BellKor's Pragmatic Chaos, a team of AT&T engineers, who submitted the winning algorithm a few minutes earlier than the second-place team [2].
Everything You Need To Know About AI In Marketing
According to Alan Perlis, a leading computer scientist, "A year spent in Artificial intelligence is enough to make one believe in God". This is probably why the introduction of artificial intelligence in marketing (or AI in marketing) has been so well-received. While there is growing uncertainty surrounding the true intentions of AI and if it will replace human jobs, the critical role of artificial intelligence in marketing is one that will help industries and businesses alike. To better understand this bittersweet relationship, let's first start from the basics. Simply put, AI is the ability of a machine to think and learn.
Massive Machine Learning Study Demonstrates Gender Stereotyping And Sexist Language In Literature
An unsupervised machine learning study presented at the 2019 meeting of Association for Computational Linguistics--which examined 3.5M books published between 1900 and 2008--indicates that men are described based on their behavior, where women are described based on appearance. In specific, words like "beautiful" and "sexy" are two of the adjectives most frequently used to describe women, while common descriptors for men were "brave," "rational," and "righteous." The books, which amounted to approximately 11B words in sum, included a mix of fiction and non-fiction. "We are clearly able to see that the words used for women refer much more to their appearances than the words used to describe men," said University of Copenhagen computer scientist and assistant professor Isabelle Augenstein in a statement. "Thus, we have been able to confirm a widespread perception, only now at a statistical level."
Big Recsys Redux: Recs at Netflix
I wrote about recommender systems last week, but there is so much discussion around their effects right now in the mainstream tech press that they deserve a second issue. As a recap, I said that there were two things that made recommender systems super ineffective, and that YouTube, one of the premier companies tech using recommendations, suffers from both a lot of the first and a lot of the second. Recommender systems today have two huge problems that are leading companies (sometimes at enormous pressure from the public) to rethink how they're being used: technical bias, and business bias. The real problem is YouTube's business model. YouTube is THIRSTY for advertising money, at all times.
About AIVA
Aiva is an Artificial Intelligence (A.I.) capable of composing emotional soundtracks for films, video games, commercials and any type of entertainment content. She has been learning the art of music composition by reading through a large collection of music partitions, written by the greatest Composers (Mozart, Beethoven, Bach, ...) to create a mathematical model representation of what music is. This model is then used by Aiva to write completely unique music. Recently, Aiva became the first virtual artist to have her creations registered with an author's rights society (SACEM). This achievement does not mean that Aiva will replace musicians; we will continue to encourage collaborations between man and machine.