The graph represents a network of 2,201 Twitter users whose tweets in the requested range contained "#FinServ", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Sunday, 05 July 2020 at 02:54 UTC. The requested start date was Sunday, 05 July 2020 at 00:01 UTC and the maximum number of days (going backward) was 14. The maximum number of tweets collected was 5,000. The tweets in the network were tweeted over the 7-day, 12-hour, 36-minute period from Friday, 26 June 2020 at 18:21 UTC to Saturday, 04 July 2020 at 06:57 UTC.
Description -- This database, updated daily, contains ads that ran on Facebook and were submitted by thousands of ProPublica users from around the world. We asked our readers to install browser extensions that automatically collected advertisements on their Facebook pages and sent them to our servers. We then used a machine learning classifier to identify which ads were likely political and included them in this dataset.
Twitter just like every other social media platform is fast becoming a noisy place. Following the right set of people not only cleans up your feed but turns twitter into a very powerful source of dense information. There are 500 million tweets sent each day. In this information mayhem one must know whom to follow. Last year, in November a blog called IPFC online came up with a list of top 50 digital influencers on Twitter.
Google Dataset Search: Similar to how Google Scholar works, Dataset Search lets you find datasets wherever they're hosted, whether it's a publisher's site, a digital library, or an author's web page. It's a phenomenal dataset finder, and it contains over 25 million datasets. Kaggle: A data science site that contains a variety of externally contributed to exciting datasets. You can find all kinds of niche datasets in its master list, from ramen ratings to basketball data to and even Seattle pet licenses. Although the data sets are user-contributed and thus have varying levels of cleanliness, the vast majority are clean.
Every recession in the past and downturn in modern memory has led to the revolution of tech industry incumbents and the rise of new powers. The brutal recession of the early 1980s gave rise to the personal computer era. In the milder recession of the early 1990s, the federal government essentially handed the internet over to the private sector and laid the seeds for the dotcom boom that gave birth to Amazon, while Microsoft pulled off Windows' victory over Apple. The Great Recession of 2008-2009 hit the technology industry much less harshly than the rest of the economy. While the industry didn't experience any major setback, the downturn did propel the rise of social media in the form of Facebook and Twitter.
An operation on that scale is so big, writes Mr Levy, "that it can only be policed by algorithms or armies". In fact, Facebook uses both. Human moderators work alongside algorithms trained to spot posts that violate either an individual country's laws or the site's own policies. But algorithms have many advantages over their human counterparts. They do not sleep, or take holidays, or complain about their performance reviews.
In a far-reaching and candid interview after IBM's #THINK2020 digital event, Rob and I discussed his insights into what's happening with AI in the enterprise and where the biggest long-term impacts will be. Editorial comments are set off by square brackets [such as these.] There's one word that describes what I'm seeking here: insight. I am seeking your insights. I don't care about corporate secrets or numbers or competitive positioning. I've looked at your blog posts and reviews of your books.
In a change to the regular format, we're asking members to grab a glass of their favourite tipple and join us for an inspiring and informal evening webinar with last year's phenomenal BIMA Conference keynote speaker Nell Watson, Futurist and Technology Philosopher. In a conversation with bestselling author, technologist and and CEO of Vala Health Pete Trainor, she will share her views on the current situation we find ourselves in and what's next. Eleanor'Nell' Watson is a Machine Intelligence researcher who helped to pioneer Deep Machine Vision at her company QuantaCorp, which enables fast and accurate body measurement from just two photos. In sharing her knowledge as AI Faculty at Singularity University and author of Machine Intelligence courseware for O'Reilly Media, she realised the importance of protecting human rights and putting ethics, safety, and the values of human spirit into A.I. Nell serves as Chair & Vice-Chair respectively of the IEEE's ECPAIS Transparency Experts Focus Group, and P7001 Transparency of Autonomous Systems committee on AI Ethics & Safety, engineering credit score-like mechanisms to safeguard algorithmic trust.