Lucidea, provider of ArchivEra, CuadraSTAR SKCA and Eloquent Archives, will participate in the Archives & Records Association (ARA) annual conference on 28th and 30th August in Leeds. Visit Lucidea at Stand #5 in the Queens Hotel to see how easily their archival collections management solutions enable researchers and the public to connect with the historic materials archivists work hard to preserve. Lucidea's archives specialists will demonstrate the powerful and versatile capabilities of ArchivEra, CuadraSTAR SKCA, and Eloquent Archives that make them a valued technology partner in the archives community. ARA attendees will be first in the UK to see Lucidea's exciting new AI prototype for archives. With Artificial Intelligence (AI) integration now available in ArchivEra, Lucidea's clients will enjoy powerful automatic categorization functionality.

In 2009, Netflix was sued for releasing movie ratings data from half a million subscribers who were identified only by unique ID numbers. The video streaming service divulged this "anonymized" information to the public as part of its Netflix Prize contest, in which participants were asked to use the data to develop a better content recommendation algorithm. But researchers from the University of Texas showed that as few as six movie ratings could be used to identify users. A closet lesbian sued Netflix, saying her anonymity was compromised. The lawsuit was settled in 2010. The Netflix case reveals a problem about which the public is just starting to learn, but that data analysts and computer scientists have known for years. In anonymized datasets where distinguishing characteristics of a person such as name and address have been deleted, even a handful of seemingly innocuous information can lead to identification.

A humanoid robot sent by Russia's Vladimir Putin to the International Space Station (ISS) has failed to dock. A live stream of the six-foot Fedor robot - strapped in the pilot seat of the Soyuz spacecraft while wielding a Russia's white, blue and red flag - had to be interrupted with the humanoid still 100 meters away, news agency AFP reported. Fedor, an artificially intelligent robot known officially as Skybot F-850, was on an automated and unmanned mission to the space station, where he is supposed to help out crews for ten days. He is said to be the first humanoid sent to space by Russia. Having failed its landing at 05:30 BST on Saturday, Fedor will now have to wait until Monday to try reaching the ISS again.

A humanoid robot sent by Russia's Vladimir Putin to the International Space Station (ISS) has failed to dock. A live stream of the six-foot Fedor robot - strapped in the pilot seat of the Soyuz spacecraft while wielding a Russia's white, blue and red flag - had to be interrupted with the humanoid still 100 meters away, news agency AFP reported. Fedor, an artificially intelligent robot known officially as Skybot F-850, was on an automated and unmanned mission to the space station, where he is supposed to help out crews for ten days. He is said to be the first humanoid sent to space by Russia. Having failed its landing at 05:30 BST on Saturday, Fedor will now have to wait until Monday to try reaching the ISS again.

Consumers International has released a new study on consumer experiences of artificial intelligence (AI) and the ways in which AI enabled services shape their consumer journeys and experiences, and consumer outcomes. The report'Artificial Intelligence: consumer experiences in new technology' contains a summary of new findings about the consumer experience of AI from IPSOS Global's participatory research with families and individuals in India, Australia and Japan, and insights from interviews with expert stakeholders from the region. It also incorporates the results of the multi-stakeholder roundtable held in Singapore in March 2019, where consumer organisations, businesses, academics and regulators discussed how AI enabled technology can deliver the best possible outcomes for consumers, whilst recognising and mitigating against potential challenges and risks.

I'm working on a data labeling project to assist with the labeling and sharing of datasets. The goal is to minimize your time scraping the web for images and labeling data manually. Why let your side projects go to waste when you can share your datasets with others? I have included an AI powered annotation tool as well. Do check out https://hungryai.com/home and let me know what you think.

Adversarial Machine Learning – A research field that lies at the intersection of machine learning and computer security.

We've referred to machine learning before as the beginning of today's AI explosion. It's "simply" software that ingests data, learns from it, and can then …

Karen Roby: So your office turned to AI and machine learning to address the problem with potholes.