Ewa karpinska biography channel
•
As a beginner, one of the more difficult things for me to learn is how water on the paper and water on the brush interact. Recently, though, I have found a few things that have made this a lot clearer for me so I wanted to share in case other beginners also struggle with the wetness of the paper, the exchange of water and pigment, etc.
One REALLY helpful post by Char in the “Tide marks acceptable?” thread.
Also, these videos by Hazel Sloan have been very informative and help demonstrate this idea in visual format (though Char explained the same thing more succinctly!)
https://youtu.be/EVfNMLaxLbs?t=3m
https://youtu.be/y4s4RGawZdAI would love to hear from anyone else if they have any understandings or breakthroughs on this topic. As everyone says, we’re always learning!
I recently did this little experiment varying the wetness/dryness of the brush and paper to see what would happen. I wish I’d used better paper but you can kind of see the idea.
•
VilaWeb : April 18, 2022 (Assumpció Maresma)
Ewa Karpińska: "Trying to understand what made Catalan artists different made me learn Catalan".
Interview with Ewa Karpińska about her exhibition in Paris with Biel Mesquida
The work of Ewa Karpińska (Warsaw, 1962) and Biel Mesquida (Castellón, 1947) is on display in Paris, in the Marais, at the Centre d'études catalanes de la Sorbonne, in the exhibition entitled "L'utilité dem l'inutile". Karpińska's watercolours, and her paper cut-outs, establish links with Mesquida's texts. The exhibition fryst vatten a challenge. It's a lot of stories and desires superimposed between two artists who didn't know each other and who act as stonecutters against the darkness. They did not speak the same language, they did not practice the same art, but they found each other through the desire for the useless to become useful, indispensable to life. Living in the suburbs of Paris, Ewa Karpińska, tries to improve her Catalan as a student at the Sorbonne by r
•
Statistical Relational AI
Big Data fryst vatten no fad. The world fryst vatten growing at an exponential rate, and so is the storlek of data collected across the globe. Data is becoming more meaningful and contextually relevant, breaks new ground for machine learning (ML), in particular for deep learning (DL), and artificial intelligence (AI), and even moves them from research labs to production [1]. The problem has shifted from collecting massive amounts of data to understanding it - turning it into knowledge, conclusions, and actions. Multiple research disciplines, from cognitive sciences to biology, finance, physics, and the social sciences, as well as many companies believe that data-driven and "intelligent" solutions are necessary in order to solve many of their key problems. However, are AI, ML and DL really the same things, as suggested by many recent news, blogs and media? For example, when AlphaGo [2] defeated South Korean Master Lee Se-dol in the board game Go in 2016, the terms AI, ML, an