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fintech Twitter NodeXL SNA Map and Report for Monday, 13 June 2022 at 06:17 UTC

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The graph represents a network of 6,865 Twitter users whose recent tweets contained "fintech", or who were replied to or mentioned in those tweets, taken from a data set limited to a maximum of 18,000 tweets. The network was obtained from Twitter on Monday, 13 June 2022 at 09:58 UTC. The tweets in the network were tweeted over the 1-day, 13-hour, 8-minute period from Saturday, 11 June 2022 at 17:08 UTC to Monday, 13 June 2022 at 06:17 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods. These tweets may expand the complete time period of the data.


40+ Best Resources to Learn Tensorflow (YouTube, Courses, Books, etc)

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Do you want to learn Tensorflow and looking for the Best Resources to Learn Tensorflow?… If yes, you are in the right place. In this article, I have listed all the best resources to learn Tensorflow including Online Courses, Tutorials, Books, and YouTube Videos. So, give your few minutes and find out the best resources to learn Tensorflow. You can bookmark this article so that you can refer to this article later.


The Impact of Creative AI – FE News

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The UK government has highlighted Artificial Intelligence as one of the four'Grand Challenges' which will transform our future. However, what this transformation will look like is very much unknown, but we are standing on the edge of a technological revolution no one can truly comprehend. Humans generally have a tainted representation of AI in stories; AI is created to serve humans, but it becomes aware that we are irrelevant, and tries to destroy us. At SXSW 2018, Tesla's Elon Musk said the current state of AI regulation is "insane," calling the technology "more dangerous than nukes." But why are we so scared of AI, and how could it impact our jobs, or even our humanity?


smarthome_2022-05-27_20-09-36.xlsx

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The graph represents a network of 3,357 Twitter users whose tweets in the requested range contained "smarthome", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Saturday, 28 May 2022 at 03:34 UTC. The requested start date was Saturday, 28 May 2022 at 00:01 UTC and the maximum number of days (going backward) was 14. The maximum number of tweets collected was 7,500. The tweets in the network were tweeted over the 10-day, 8-hour, 57-minute period from Tuesday, 17 May 2022 at 09:45 UTC to Friday, 27 May 2022 at 18:42 UTC.


digitaltransformation_2022-06-08_04-54-41.xlsx

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The graph represents a network of 4,090 Twitter users whose tweets in the requested range contained "digitaltransformation", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Wednesday, 08 June 2022 at 12:06 UTC. The requested start date was Wednesday, 08 June 2022 at 00:01 UTC and the maximum number of tweets (going backward in time) was 7,500. The tweets in the network were tweeted over the 2-day, 7-hour, 41-minute period from Sunday, 05 June 2022 at 16:19 UTC to Wednesday, 08 June 2022 at 00:00 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.


GAN as a Face Renderer for 'Traditional' CGI

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Opinion When Generative Adversarial Networks (GANs) first demonstrated their capability to reproduce stunningly realistic 3D faces, the advent triggered a gold rush for the unmined potential of GANs to create temporally consistent video featuring human faces. Somewhere in the GAN's latent space, it seemed that there must be hidden order and rationality – a schema of nascent semantic logic, buried in the latent codes, that would allow a GAN to generate consistent multiple views and multiple interpretations (such as expression changes) of the same face – and subsequently offer a temporally-convincing deepfake video method that would blow autoencoders out of the water. High-resolution output would be trivial, compared to the slum-like low-res environments in which GPU constraints force DeepFaceLab and FaceSwap to operate, while the'swap zone' of a face (in autoencoder workflows) would become the'creation zone' of a GAN, informed by a handful of input images, or even just a single image. There would be no more mismatch between the'swap' and'host' faces, because the entirety of the image would be generated from scratch, including hair, jawlines, and the outermost extremities of the facial lineaments, which frequently prove a challenge for'traditional' autoencoder deepfakes. As it transpired, it was not going to be nearly that easy.


tensorflow_2022-06-01_03-28-01.xlsx

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The graph represents a network of 1,678 Twitter users whose tweets in the requested range contained "tensorflow", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Wednesday, 01 June 2022 at 10:33 UTC. The requested start date was Wednesday, 01 June 2022 at 00:01 UTC and the maximum number of days (going backward) was 14. The maximum number of tweets collected was 7,500. The tweets in the network were tweeted over the 2-day, 4-hour, 31-minute period from Sunday, 29 May 2022 at 14:20 UTC to Tuesday, 31 May 2022 at 18:51 UTC.


#selfdrivingcars_2022-06-01_05-29-21.xlsx

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The graph represents a network of 1,612 Twitter users whose tweets in the requested range contained "#selfdrivingcars", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Wednesday, 01 June 2022 at 12:38 UTC. The requested start date was Wednesday, 01 June 2022 at 00:01 UTC and the maximum number of tweets (going backward in time) was 7,500. The tweets in the network were tweeted over the 19-day, 11-hour, 44-minute period from Thursday, 12 May 2022 at 09:04 UTC to Tuesday, 31 May 2022 at 20:48 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.


Qualitative humanities research is crucial to AI

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"All research is qualitative; some is also quantitative" Harvard Social Scientist and Statistician Gary King Suppose you wanted to find out whether a machine learning system being adopted - to recruit candidates, lend money, or predict future criminality - exhibited racial bias. You might calculate model performance across groups with different races. But how was race categorised– through a census record, a police officer's guess, or by an annotator? Each possible answer raises another set of questions. Following the thread of any seemingly quantitative issue around AI ethics quickly leads to a host of qualitative questions.


kaggle_2022-05-28_21-18-40.xlsx

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The graph represents a network of 4,665 Twitter users whose tweets in the requested range contained "kaggle", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Sunday, 29 May 2022 at 04:24 UTC. The requested start date was Sunday, 29 May 2022 at 00:01 UTC and the maximum number of days (going backward) was 14. The maximum number of tweets collected was 7,500. The tweets in the network were tweeted over the 13-day, 2-hour, 40-minute period from Sunday, 15 May 2022 at 16:23 UTC to Saturday, 28 May 2022 at 19:03 UTC.