In 2019, using AI to evaluate artwork is finally more productive than foolish. We all hope that someday soon our Roomba will judge our living habits and give unsolicited advice on how we could spruce things up with a few pictures and some natural light. There is already an extensive amount of Deep Learning dedicated to photo recognition but a team in Croatia is adapting them for use on fine art. It makes sense that everything is geared toward cameras since most of us have a vast photographic portfolio but fine art takes longer to render. Even so, the collection on Wikiart.org is vast and already a hotbed for computer classification work, so they set to work there.
As they modify existing convolutional neural networks, they check themselves by comparing results with human ratings to keep what works and discard what flops. Fortunately, fine art has a lot of existing studies and commentary, whereas the majority of photographs in the public domain have nothing more than a file name and maybe some EXIF data. The difference here is that photograph-parsing AI can say, “That is a STOP sign,” while the fine art AI can say, “That is a memorable painting of a sign.” Continue reading “AI And Art Appreciation”
Beats and rhymes are life in the world of hip-hop. A rapper’s ability to seamlessly merge the two is the mark of a master wordsmith. Ranking a rapper’s contributions to hip-hop will forever remain subjective, however [Matt] sought to apply a more quantitative approach to the matter. He created an interactive data set containing all the lyrics from over 150 rappers in order to determine which rapper’s vocabulary was the largest. Now everyone can know definitively which rapper’s rhymes truly are “the freshest”.
The study encompasses hip-hop artists from the last thirty years, pitting recent hit-makers like Lil Uzi Vert against veteran artists like KRS-One. To ensure everything is on even playing field [Matt] limited the study to the first 35,000 lyrics of each artist including any material on a mixtape, EP, or full album release. Rappers’ vocabulary was then plotted according to the total number of unique words found in their lyrics (i.e.: “shorty” and the alternative spelling “shawty” were each considered to be unique words). Oddly enough, there were some notable exclusions from the list as artists like Chance the Rapper, Queen Latifah, and The Notorious B.I.G’s discography did not exceed the 35,000 lyrics mark.
When digging into the data, there was a downward trend in the vocabulary used amongst popular artists of the last decade. [Matt] attributed this trend to the fact that many of these artists have modeled their music to reflect the pop/rock music structure that makes use of simple, repetitive choruses. While others may attribute this downward trend to a general lack of talent when it comes to lyricism, however, it should be noted that the economics of music streaming platforms have had an effect on the average song length. Though whatever era of hip-hop you subscribe to, it is always interesting to see where your favorite emcees rank.
If you walk into a dog owner’s home that dog is probably going to make a beeline to see if you are a threat. If you walk into a cat owner’s home, you may see the cat wandering around, if it even chooses to grace you with its presence. For some people, a dog’s direct approach can be nerve-wracking, or even scary depending on their history and relative size of the dog. Still, these domestic animals are easy to empathize with especially if you or your family have a pet. They have faces which can convey curiosity or smug indifference but what if you were asked to judge the intent of something with no analogs to our own physical features like a face or limbs? That is what researchers at the IDC Herzliya in Israel and Cornell University in the US asked when they made the Greeting Machine to move a moon-like sphere around a planet-like sphere.
Participants were asked to gauge their feelings about the robot after watching the robot move in different patterns. It turns out that something as simple as a sphere tracing across the surface of another sphere can stir consistent and predictable emotions in people even though the shapes do not resemble a human, domestic pet, or anything but a snowman’s abdomen. This makes us think about how our own robots must be perceived by people who are not mired in circuits all day. Certainly, a robot jellyfish lazing about in the Atlantic must feel less threatening than a laser pointer with a taste for human eyeballs.
Continue reading “Robot’s Actions And Our Reactions”