- Versuchskaninchen kackt nach Impfung ab (Guinea pig falls after receiving the shot) (d)
- A New Satellite Can Peer Inside Buildings, Day or Night (Neuer Satellit mit einem Hochfequenz-Strahlenbeam/Radar kann durch Wolken, Nebel und in Gebäude sehen)
On Wednesday, Capella launched a platform allowing governmental or private customers to request images of anything in the world — a capability that will only get more powerful with the deployment of six additional satellites next year.
By contrast, Capella can peer right through cloud cover, and see just as well in the daylight as in total darkness. That’s because instead of optical imaging, it uses synthetic aperture radar, or SAR.
SAR works similarly to how dolphins and bats navigate using echolocation. The satellite beams down a powerful 9.65 GHz radio signal toward its target, and then collects and interprets the signal as it bounces back up into orbit. And because the satellite is sending down its own signal rather than passively capturing light, sometimes those signals can even penetrate right through a building’s wall, peering at the interior
- Another “Pre-Crime” AI System Claims It Can Predict Who Will Share Disinformation Before It’s Published (Weitere KI soll vorhersagen können, wer Desinformationen in asozialen Medien veröffentlichen oder teilen werde)
Uri Gal, Associate Professor in Business Information Systems, at the University of Sydney, Australia — noted that from what he has seen so far, these systems are “no better at telling the future than a crystal ball.”
University of Sheffield researchers have developed an artificial intelligence-based algorithm that can accurately predict (79.7 per cent) which Twitter users are likely to share content from unreliable news sources before they actually do it
Findings could help governments and social media companies such as Twitter and Facebook better understand user behaviour and help them design more effective models for tackling the spread of disinformation.
The researchers analysed over 1 million tweets from approximately 6,200 Twitter users by developing new natural language processing methods – ways to help computers process and understand huge amounts of language data.
They often posted tweets with words such as ‘liberal’, ‘government’, ‘media’, and their tweets often related to politics in the Middle East and Islam, with their tweets often mentioning ‘Islam’ or ‘Israel’.
In contrast […] this group of users often posted tweets with words such as ‘mood’. ‘wanna’, ‘gonna’, ‘I’ll’, ‘excited’, and ‘birthday’.
We also found that the correlation between the use of impolite language and the spread of unreliable content can be attributed to high online political hostility.
The study, Identifying Twitter users who repost unreliable news sources with linguistic information, is published in PeerJ. To access the paper in full, visit: https://doi.org/10.7717/peerj-cs.325 “
*siehe auch https://www.sheffield.ac.uk/news/ai-can-predict-twitter-users-likely-spread-disinformation-they-do-it – das passt Zwitscher (siehe dazu den gestrigen Eintrag unten) natürlich bestens in den Kram. (d)
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