LISTEN: Kaytranda & Aminé Unleash Anticipated Collaborative Album, “KAYTRAMINÉ”
Kaytranada and Aminé have joined forces to deliver on their captivating new collab album, KAYTRAMINÉ. As you’ll hear below, the highly-anticipated project showcases the unique and genre-blending abilities of both artists; from Kaytranada’s signature production style and Aminé’s charismatic flow, the EP is a sonic journey that arrives just in time for summer. The duo effortlessly combines elements of hip-hop, R&B, and electronic music, pushing boundaries and creating a refreshing sound that defies categorization. Hear what we mean by streaming the album below and let us know your thoughts in the comments section as well.
KAYTRAMINÉ (Self Titled) | Stream
‘LISTEN: Kaytranda & Aminé Unleash Anticipated Collaborative Album, “KAYTRAMINÉ”
The post LISTEN: Kaytranda & Aminé Unleash Anticipated Collaborative Album, “KAYTRAMINÉ” appeared first on Run The Trap: The Best EDM, Hip Hop & Trap Music.
By: Max Chung
Title: LISTEN: Kaytranda & Aminé Unleash Anticipated Collaborative Album, “KAYTRAMINÉ”
Sourced From: runthetrap.com/2023/05/20/listen-kaytranda-amine-unleash-anticipated-collaborative-album-kaytramine/
Published Date: Sat, 20 May 2023 19:49:30 +0000
Did you miss our previous article…
How AI is helping historians better understand our past
It’s an evening in 1531, in the city of Venice. In a printer’s workshop, an apprentice labors over the layout of a page that’s destined for an astronomy textbook—a dense line of type and a woodblock illustration of a cherubic head observing shapes moving through the cosmos, representing a lunar eclipse.
Like all aspects of book production in the 16th century, it’s a time-consuming process, but one that allows knowledge to spread with unprecedented speed.
Five hundred years later, the production of information is a different beast entirely: terabytes of images, video, and text in torrents of digital data that circulate almost instantly and have to be analyzed nearly as quickly, allowing—and requiring—the training of machine-learning models to sort through the flow. This shift in the production of information has implications for the future of everything from art creation to drug development.
But those advances are also making it possible to look differently at data from the past. Historians have started using machine learning—deep neural networks in particular—to examine historical documents, including astronomical tables like those produced in Venice and other early modern cities, smudged by centuries spent in mildewed archives or distorted by the slip of a printer’s hand.
Historians say the application of modern computer science to the distant past helps draw connections across a broader swath of the historical record than would otherwise be possible, correcting distortions that come from analyzing history one document at a time. But it introduces distortions of its own, including the risk that machine learning will slip bias or outright falsifications into the historical record. All this adds up to a question for historians and others who, it’s often argued, understand the present by examining history: With machines set to play a greater role in the future, how much should we cede to them of the past?
Big data has come to the humanities throughinitiatives to digitize increasing numbers of historical documents, like the Library of Congress’s collection of millions of newspaper pages and the Finnish Archives’ court records dating back to the 19th century. For researchers, this is at once a problem and an opportunity: there is much more information, and often there has been no existing way to sift through it.
That challenge has been met with the development of computational tools that help scholars parse complexity. In 2009, Johannes Preiser-Kapeller, a professor at the Austrian Academy of Sciences, was examining a registry of decisions from the 14th-century Byzantine Church. Realizing that making sense of hundreds of documents would require a systematic digital survey of bishops’ relationships, Preiser-Kapeller built a database of individuals and used network analysis software to reconstruct their connections.
This reconstruction revealed hidden patterns of influence, leading Preiser-Kapeller to argue that the bishops who spoke the most in meetings weren’t the most influential; he’s since applied the technique to other networks, including the 14th-century Byzantian elite, uncovering ways in which its social fabric was sustained through the hidden contributions of women. “We were able to identify, to a certain extent, what was going on outside the official narrative,” he says.
Preiser-Kapeller’s work is but one example of this trend in scholarship. But until recently, machine learning has often been unable to draw conclusions from ever larger collections of text—not least because certain aspects of historical documents (in Preiser-Kapeller’s case, poorly handwritten Greek) made them indecipherable to machines. Now advances in deep learning have begun to address these limitations, using networks that mimic the human brain to pick out patterns in large and complicated data sets.
Nearly 800 years ago, the 13th-century astronomer Johannes de Sacrobosco published the Tractatus de sphaera, an introductory treatise on the geocentric cosmos. That treatise became required reading for early modern university students. It was the most widely distributed textbook on geocentric cosmology, enduring even after the Copernican revolution upended the geocentric view of the cosmos in the 16th century.
The treatise is also the star player in a digitized collection of 359 astronomy textbooks published between 1472 and 1650—76,000 pages, including tens of thousands of scientific illustrations and astronomical tables. In that comprehensive data set, Matteo Valleriani, a professor with the Max Planck Institute for the History of Science, saw an opportunity to trace the evolution of European knowledge toward a shared scientific worldview. But he realized that discerning the pattern required more than human capabilities. So Valleriani and a team of researchers at the Berlin Institute for the
By: Moira Donovan
Title: How AI is helping historians better understand our past
Sourced From: www.technologyreview.com/2023/04/11/1071104/ai-helping-historians-analyze-past/
Published Date: Tue, 11 Apr 2023 09:00:00 +0000
Look the part: The DMD Rivale retro full face helmet
Given the rise in popularity of modern classic motorcycles in recent years, it’s only natural that motorcycle gear and apparel would follow suit. Just like most major OEMs have at least a couple of retro-styled bikes in their catalogs, most major gear and helmet companies have at least a couple of retro-styled items to match those bikes. But the Italian company DMD does it differently.
DMD doesn’t just dabble in café racer gear… they live and breathe it. Case in point: the DMD Rivale retro full-face helmet.
Based in Bergamo, an impossibly picturesque city in Italy’s alpine Lombardy region, DMD specializes in helmets and apparel. Specifically, helmets and apparel that combine modern materials and manufacturing processes, with all the style and panache of yesteryear.
This family-owned enterprise traces its roots back to 1975 California. While traveling to the US, the patriarch of the family, Amilcare, decided to start a motorcycle gear import and distribution company.
By: Wesley Reyneke
Title: Look the part: The DMD Rivale retro full face helmet
Sourced From: www.bikeexif.com/dmd-rivale-retro-helmet
Published Date: Sat, 27 May 2023 17:01:29 +0000
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