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Bot Hunting Is All About the Vibes

WIRED

Christopher Bouzy is trying to stay ahead of the bots. As the person behind Bot Sentinel, a popular bot-detection system, he and his team continuously update their machine learning models out of fear that they will get "stale." The task? Sorting 3.2 million tweets from suspended accounts into two folders: "Bot" or "Not." To detect bots, Bot Sentinel's models must first learn what problematic behavior is through exposure to data. And by providing the model with tweets in two distinct categories--bot or not a bot--Bouzy's model can calibrate itself and allegedly find the very essence of what, he thinks, makes a tweet problematic.


tech4good_2022-09-17_11-31-13.xlsx

#artificialintelligence

The graph represents a network of 2,089 Twitter users whose tweets in the requested range contained "tech4good", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Saturday, 17 September 2022 at 18:32 UTC. The requested start date was Saturday, 17 September 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, 15-hour, 51-minute period from Saturday, 03 September 2022 at 06:52 UTC to Friday, 16 September 2022 at 22:43 UTC.


#cloudcomputing_2022-09-19_08-00-01.xlsx

#artificialintelligence

The graph represents a network of 1,946 Twitter users whose tweets in the requested range contained "#cloudcomputing", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Monday, 19 September 2022 at 15:07 UTC. The requested start date was Monday, 19 September 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, 3-hour, 28-minute period from Thursday, 15 September 2022 at 22:50 UTC to Sunday, 18 September 2022 at 02:19 UTC.



#python Twitter NodeXL SNA Map and Report for keskiviikko, 21 syyskuuta 2022 at 16.03 UTC

#artificialintelligence

The graph represents a network of 3 602 Twitter users whose recent tweets contained "#python", 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 Wednesday, 21 September 2022 at 17:39 UTC. The tweets in the network were tweeted over the 13-hour, 45-minute period from Wednesday, 21 September 2022 at 02:18 UTC to Wednesday, 21 September 2022 at 16:03 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.


datamining_2022-09-18_23-45-00.xlsx

#artificialintelligence

The graph represents a network of 3,785 Twitter users whose tweets in the requested range contained "datamining", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Monday, 19 September 2022 at 06:55 UTC. The requested start date was Monday, 19 September 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, 31-minute period from Monday, 05 September 2022 at 00:29 UTC to Sunday, 18 September 2022 at 03:00 UTC.


#FinTech Twitter NodeXL SNA Map and Report for Saturday, 24 Sep 22 #SEOhashtag

#artificialintelligence

The graph represents a network of 7,262 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 Saturday, 24 September 2022 at 06:58 UTC. The tweets in the network were tweeted over the 1-day, 18-hour, 0-minute period from Thursday, 22 September 2022 at 10:58 UTC to Saturday, 24 September 2022 at 04:59 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.


TechScape: AI's dark arts come into their own

The Guardian

Programming a computer is, if you squint, a bit like magic. You have to learn the words to the spell to convince a carefully crafted lump of sand to do what you want. If you understand the rules deeply enough, you can chain together the spells to force the sand to do ever more complicated tasks. If your spell is long and well-crafted enough, you can even give the sand the illusion of sentience. That illusion of sentience is nowhere more strong than in the world of machine learning, where text generation engines like GPT-3 and LaMDA are able to hold convincing conversations, answer detailed questions, and perform moderately complex tasks based on just a written request.


InsurTech_2022-09-16_05-22-16.xlsx

#artificialintelligence

The graph represents a network of 2,027 Twitter users whose tweets in the requested range contained "InsurTech", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Friday, 16 September 2022 at 12:33 UTC. The requested start date was Friday, 16 September 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, 18-hour, 48-minute period from Tuesday, 13 September 2022 at 05:07 UTC to Thursday, 15 September 2022 at 23:56 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.


Algorithms Can Now Mimic Any Artist. Some Artists Hate It

#artificialintelligence

Swedish artist Simon Stålenhag is known for haunting paintings that blend natural landscapes with the eerie futurism of giant robots, mysterious industrial machines, and alien creatures. Earlier this week, Stålenhag appeared to experience some dystopian dread of his own when he found that artificial intelligence had been used to mimic his style. This content can also be viewed on the site it originates from. The act of AI imitation was performed by Andres Guadamuz, a reader in intellectual property law at the University of Sussex in the UK who has been studying legal issues around AI-generated art. He used a service called Midjourney to create images resembling Stålenhag's spooky style, and posted them to Twitter.