- Jul 2023
Xu, ICCV, 2019 "Temporal Recurrent Networks for Online Action Detection"
arxiv: https://arxiv.org/abs/1811.07391 hypothesis: https://hyp.is/go?url=https%3A%2F%2Fopenaccess.thecvf.com%2Fcontent_ICCV_2019%2Fpapers%2FXu_Temporal_Recurrent_Networks_for_Online_Action_Detection_ICCV_2019_paper.pdf&group=world
- Jan 2023
human-devised measures of time hold within them powerful political and economic forces they track people within the patterns of activity they become habituated to machine time measured and parceled out by industrial society
!- comment : Deep conditioning
- Nov 2021
That's a picture of it in the background. And this organism has the special trick that we call "photosynthesis," the ability to go take energy from the sun and transform carbon dioxide into oxygen. And over the course of billions of years, so starting from two and a half billion years ago, little by little these bacteria spread across the planet 00:07:08 and converted all that carbon dioxide in the air into the oxygen that we now have. And it was a very slow process. First, they had to saturate the seas, then they had to saturate the oxygen that the earth would absorb, and only then, finally, could oxygen begin to build up in the atmosphere. So you see, just after about 900 million years ago, oxygen starts to build up in the atmosphere. And about 600 million years ago, something really amazing happens. 00:07:35 The ozone layer forms from the oxygen that has been released in the atmosphere. And it sounds like a small deal, like we talked about the ozone a couple decades ago, but it actually turns out that before the ozone layer existed, earth was not really able to sustain complex, multicellular life. We had single-celled organisms, we had a couple of simple, multicellular organisms, but we didn't really have anything like you or me. 00:07:59 And shortly after the ozone layer came into place, the earth was able to sustain complex multicellular life. There was a Cambrian explosion of life in the seas. And the first plants got onto land. In fact, there was actually no life on land ahead of that. Another way to see this is, this is kind of a chart of pretty much most of the animals that you guys are familiar with. 00:08:24 And right at the bottom in time is the formation of the ozone layer. Like nothing that you are familiar with today could exist without the contributions of these tiny organisms over those billions of years. And where are they now? Well actually, they never really left us. The direct descendants of the cyanobacteria were eventually captured by plants. And they're now called chloroplasts. 00:08:49 So this is a zoom-in of a plant leaf - and we probably ate some of these guys today - where tons of little chloroplasts are still trapped - contributing photosynthesis and making energy for the plants that continue to be the other half of our lungs on earth. And in this way, our breaths are very deeply united. Every out-breath is mirrored by the in-breath of a plant,
This would be nice to turn into a science lesson or to represent this in an experiential, participatory Deep Humanity BEing Journey. To do this, it would be important to elucidate the series of steps leading from one stated result to the next, showing how scientific methodology works to link the series of interconnected ideas together. Especially important is the science that glues everything together over deep time.
- Apr 2021
If we accept the idea that the entire surface of the earth is migratory, then why not landscapes in particular? A landscape — as a scene, landschap, ecosystem, and socio-political territory — is a material assembly of moving entities, a dynamic medium which changes in quality and structure through the aggregate movements or actions of the things that constitute it.
- Sep 2020
Lu, D. (n.d.). AI can edit video in real time to sync new audio to people’s lips. New Scientist. Retrieved September 14, 2020, from https://www.newscientist.com/article/2254326-ai-can-edit-video-in-real-time-to-sync-new-audio-to-peoples-lips/
- Apr 2020
Punn, N. S., Sonbhadra, S. K., & Agarwal, S. (2020). COVID-19 Epidemic Analysis using Machine Learning and Deep Learning Algorithms [Preprint]. Health Informatics. https://doi.org/10.1101/2020.04.08.20057679
- artificial intelligence
- machine learning
- data sharing
- Johns Hopkins
- deep learning