The world of battle rap clashes with the cutting edge of artificial intelligence in unexpected ways. From dissecting lyrical battles to exploring facial recognition technology for cows, this analysis delves into contrasting realms, revealing insights into human judgment, technological advancement, and the stoicism of farm animals.
Battle Rap Breakdown: Cassidy vs. Goodz
The Cassidy vs. Goodz battle ignited discussions about judging criteria, crowd influence, and the relevance of lyrical content in contemporary battle rap.
Crowd Influence vs. Lyrical Substance
One observer notes that crowd reaction heavily influences the perceived outcome, suggesting a different result in a silent room or with a different audience. This raises questions about the verses' actual impact in 2019, implying a shift towards performance and crowd engagement over intricate lyricism.
Performance vs. Content
The analysis highlights a contrast between Goodz's strong performance and Cassidy's lyrical content. While Goodz excelled in performance aspects like crowd control, pacing, and confidence, Cassidy delivered more substantial lyrical material. This discrepancy complicates the determination of a winner, as judging often prioritizes performance over lyrical complexity.
Cassidy's verses included "3x and 6x multies" and some "light shit about Goodz swag being an illusion," while also making a "questionable choice" by "bringing up uterus infection". Conversely, Goodz's approach was described as relying on "trash angles," while Cassidy's angles were "non-existent".
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Pre-emptive Rebuttals and Accurate Angles
Both rappers attempted pre-emptive rebuttals, with Cassidy addressing potential criticisms related to "the r kelly shit, tranny shit, money shit etc." Cassidy used mostly accurate "angles"/pre-rebuttals, but didn't really cover them enough, usually just hovering over an angle for a bar or two.
Perceptions and Expectations
Cassidy's pre-battle promotion heightened expectations, leading some to anticipate a dominant performance. Despite this, opinions remained divided, with some viewers still favoring Cassidy, highlighting that "Cassidy worst bars are arguably better than much of what Goodz said," even though Goodz had some great bars, but they were few and far inbetween.
Ultimately, the Cassidy vs. Goodz battle exemplifies the subjective nature of battle rap judging, where crowd response, performance, and lyrical content all contribute to the overall perception of who won.
Facial Recognition for Cows: Cainthus and the Future of Agriculture
Switching gears from the battle rap arena, the focus shifts to an innovative application of facial recognition technology: monitoring cow behavior on farms. Cainthus, an artificial-intelligence startup, aims to revolutionize agriculture by using computer vision to track and analyze the behavior of individual cows.
Addressing Bullying and Improving Milk Production
Stephen Lawlor, a dairy farmer in Ireland, partnered with Cainthus to address issues such as bullying among cows. By monitoring cow behavior, Cainthus helps farmers identify and address problems that affect milk production. A mature lactating Holstein will eat well over a hundred pounds of grass and other feed in a day, and produce about nine gallons of milk. Immature cows yield less to begin with, and their output falls further if they have trouble reaching their food.
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How Cainthus Works
Cameras installed above feed areas and water troughs capture footage of cow behavior. The system analyzes this data to provide insights into each animal's health, eating habits, and social interactions. Cainthus’s chief financial officer is David Hunt’s fraternal twin, Ross. They’re thirty-six years old.
The Potential of AI in Agriculture
David Hunt believes that artificial intelligence can reduce the environmental impact of food production and make it more humane. By enabling farmers to monitor their herds more closely, Cainthus helps optimize feeding, detect illnesses early, and improve overall animal welfare.
From Cows to Humans: Ethical Considerations
The Hunts’ long-term ambitions don’t necessarily end at agriculture. Working with animals gives Cainthus a research advantage over facial-recognition companies focussing on people, he said, because cows don’t hide behind hats, sunglasses, or clothes, and they don’t object if you spy on them, and you can interfere at will with their behavior. David Hunt acknowledges the potential for misuse of facial recognition technology, emphasizing the importance of considering ethical implications. He said, “If you put it in the wrong hands, facial-recognition technology is a dangerous tool,”.
Overcoming Stoicism
Cows are slow-moving prey animals, and as a result they are incredibly stoic-because if they show pain they’re going to be killed first. Cows regard humans as threats-with good reason-and they are adept at concealing injuries and illnesses. “By the time we see cows in pain as we perceive it, they may have endured a lot already,” Kavanagh continued. “So, if we have a system that looks at them when they aren’t afraid, we may see the pain sooner.” David Hunt said, “One of the joys of facial recognition is that we can see cows’ natural behavior, instead of ‘Uh-oh, girls, calm down, don’t make eye contact with the predator.’ ”
The Science Behind Facial Recognition
The article further explores the science behind facial recognition technology, tracing its development from early computer science experiments to modern artificial neural networks.
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Pattern Recognition and Neural Networks
Facial recognition relies on pattern recognition, a skill that varies greatly among individuals. Computer scientists began trying to use a digital form of pattern recognition to identify faces in photographs. The first challenge was programming a computer simply to determine whether a given image contained a face. Almost all current facial-recognition systems employ what are known as artificial neural networks. They aren’t programmed, in the old-fashioned sense. If you’re using one to recognize faces, you don’t write lines of code related to things like hair color and nose length; instead, you “train” the neural network, by repeatedly giving it large numbers of labelled examples and counterexamples-“cow”; “not cow”-which it compares, beginning at the pixel level.
The Role of Databases and Deep Learning
The availability of large image databases and advancements in computing power have fueled the rapid progress of facial recognition technology. Enormous databases of useful images are available. He said, “Suppose it’s the nineteen-eighties and somebody comes to you from the future and says, ‘Here’s a neural-network design that will work great for face recognition. You just need a million pairs of faces, for training.’ And you would say, ‘That’s great, but I can’t get a million pairs of faces.’ Well, today you can just scrape them off the Internet.”
Applications and Limitations
Facial recognition technology has diverse applications, including security systems, medical diagnoses, and even sports broadcasting. However, the technology is not without its limitations, as demonstrated by I.B.M.’s experience retraining a system to recognize a specific athlete's gestures.