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New Google Maps feature will show routes to nearest public transport

Daily Mail - Science & tech

Google Maps is working on a new feature that will show you how to reach the nearest public transport connection, according to new leaked screenshots. The new Maps filter will let users choose what mode of transportation they will be using at the very beginning of their daily commute, the screenshots show. Once rolled out, the feature will allow commuters to work out their preferred route to various transport connections, such as the train station, when they return to the workplace after the coronavirus pandemic. The screenshots also reveal an option to get more accurate Uber fares using data from Google Maps and a slightly new design for the Maps interface. 'Google Maps is working on route options with "Connections to Public Transit", such as car and transit, bicycle and transit, auto rickshaw, ride service [and] motorcycle and transit,' said Jane Wong, a Hong Kong-based hacker, tech blogger and software engineer, who leaked the screenshots.


Not just nipples: how Facebook's AI struggles to detect misinformation

The Guardian

"It's much easier to build an AI system that can detect a nipple than it is to determine what is linguistically hate speech." The Facebook founder Mark Zuckerberg made that comment in 2018 when he was discussing how the company tackles content that is deemed inappropriate or, in Facebook terms, judged to be violating community standards. Facebook's artificial intelligence technology for identifying nudity gets it right more often than not. Between January and March this year, Facebook removed 39.5m pieces of content for adult nudity or sexual activity, and 99.2% of it was removed automatically, without a user reporting it. There were 2.5m appeals against removal and 613,000 pieces of content were restored. But it doesn't work every time, and the AI has problems with historical photos and paintings.


Big Tech's artificial intelligence aristocracy

#artificialintelligence

When he testified before Congress, Facebook CEO Mark ZuckerbergMark Elliot ZuckerbergHillicon Valley: Biden calls on Facebook to change political speech rules Dems demand hearings after Georgia election chaos Microsoft stops selling facial recognition tech to police The Hill's Campaign Report: Biden campaign goes on offensive against Facebook Biden campaign calls on Facebook to change political speech rules MORE loved to tell legislators that his team would "follow up with you" on that, or that his team is building AI tools for that. These AI tools would supposedly solve many content moderation problems, ranging from misinformation to terrorism to fake accounts. Today, you could add coronavirus misinformation to that list, but you could also ask if these AI tools have actually solved any of these problems (or if Zuckerberg's team ever did follow up). Many decisions today, such as ranking a website in search results, are made by algorithms. These algorithms are perceived as objective, mechanical and unbiased, while humans are perceived as subjective, fallible and full of bias. That model of the world mostly works -- at least until AI is added into the picture.


Facebook trains artificial intelligence on 'hateful memes'

#artificialintelligence

Facebook unveiled an initiative Tuesday to take on "hateful memes" by using artificial intelligence, backed by crowd sourcing, to identify maliciously motivated posts. The leading social network said it had already created a database of 10,000 memes -- images often blended with text to deliver a specific message -- as part of a ramped-up effort against hate speech. Facebook said it was releasing the database to researchers as part of a "hateful memes challenge" to develop improved algorithms to detect hate-driven visual messages, with a prize pool of $100,000. "These efforts will spur the broader AI research community to test new methods, compare their work, and benchmark their results in order to accelerate work on detecting multimodal hate speech," Facebook said in a blog post. Facebook's effort comes as it leans more heavily on AI to filter out objectionable content during the coronavirus pandemic that has sidelined most of its human moderators.


Pinterest adds new board features as revenue declines due to coronavirus

The Independent - Tech

The social media site Pinterest has rolled out new features to its app, making it easier for users to manage their boards by letting them add dates and notes to them. Pinterest is an app where users save images – called pins – to collections called boards, with a large base of fashion, travel, home decor and hobbyist users. Adding notes to boards will allow users to annotate things they've saved with personal information, such as adding ingredients to an image of a meal or creating to-do lists for crafts. Users can use dates to track timelines for projects, as well as letting users archive the boards afterwards. Pinterest's other major feature it's introducing is an improvement to its recommendation technology, suggesting sections to organise your boards topics.


COVID-19 response: Utopia Analytics offers help for major social media companies - Utopia Analytics

#artificialintelligence

All of the major social media companies and their parent corporations have issued a joint statement on their COVID-19 response efforts. They have also invited other companies to join them as they work to keep their communities healthy and safe. In the statement, the companies stressed their joint effort to combat fraud and misinformation about the virus, elevate authoritative content on their platforms, and share critical updates in coordination with the governments. The Finnish text analytics company Utopia Analytics is aware of the struggle that social media giants now face with content moderation. This is why Utopia Analytics is offering its Utopia AI Moderator service to one of these giants at cost for as long as the crisis lasts.


#digitaltransformation Twitter NodeXL SNA Map and Report for Saturday, 21 March 2020 at 08:38 UTC

#artificialintelligence

The graph represents a network of 9,647 Twitter users whose recent tweets contained "#digitaltransformation", 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, 21 March 2020 at 09:47 UTC. The tweets in the network were tweeted over the 4-day, 1-hour, 52-minute period from Tuesday, 17 March 2020 at 06:45 UTC to Saturday, 21 March 2020 at 08:38 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.


An Information Diffusion Approach to Rumor Propagation and Identification on Twitter

arXiv.org Machine Learning

With the increasing use of online social networks as a source of news and information, the propensity for a rumor to disseminate widely and quickly poses a great concern, especially in disaster situations where users do not have enough time to fact-check posts before making the informed decision to react to a post that appears to be credible. In this study, we explore the propagation pattern of rumors on Twitter by exploring the dynamics of microscopic-level misinformation spread, based on the latent message and user interaction attributes. We perform supervised learning for feature selection and prediction. Experimental results with real-world data sets give the models' prediction accuracy at about 90\% for the diffusion of both True and False topics. Our findings confirm that rumor cascades run deeper and that rumor masked as news, and messages that incite fear, will diffuse faster than other messages. We show that the models for True and False message propagation differ significantly, both in the prediction parameters and in the message features that govern the diffusion. Finally, we show that the diffusion pattern is an important metric in identifying the credibility of a tweet.


Artificial Intelligence for Social Good: A Survey

arXiv.org Artificial Intelligence

Its impact is drastic and real: Youtube's AIdriven recommendation system would present sports videos for days if one happens to watch a live baseball game on the platform [1]; email writing becomes much faster with machine learning (ML) based auto-completion [2]; many businesses have adopted natural language processing based chatbots as part of their customer services [3]. AI has also greatly advanced human capabilities in complex decision-making processes ranging from determining how to allocate security resources to protect airports [4] to games such as poker [5] and Go [6]. All such tangible and stunning progress suggests that an "AI summer" is happening. As some put it, "AI is the new electricity" [7]. Meanwhile, in the past decade, an emerging theme in the AI research community is the so-called "AI for social good" (AI4SG): researchers aim at developing AI methods and tools to address problems at the societal level and improve the wellbeing of the society.


Predicting Attributes of Nodes Using Network Structure

arXiv.org Machine Learning

In many graphs such as social networks, nodes have associated attributes representing their behavior. Predicting node attributes in such graphs is an important problem with applications in many domains like recommendation systems, privacy preservation, and targeted advertisement. Attributes values can be predicted by analyzing patterns and correlations among attributes and employing classification/regression algorithms. However, these approaches do not utilize readily available network topology information. In this regard, interconnections between different attributes of nodes can be exploited to improve the prediction accuracy. In this paper, we propose an approach to represent a node by a feature map with respect to an attribute $a_i$ (which is used as input for machine learning algorithms) using all attributes of neighbors to predict attributes values for $a_i$. We perform extensive experimentation on ten real-world datasets and show that the proposed feature map significantly improves the prediction accuracy as compared to baseline approaches on these datasets.