New York-based Blackbird.AI has closed a $10 million Series A as it prepares to launched the next version of its disinformation intelligence platform this fall. The Series A is led by Dorilton Ventures, along with new investors including Generation Ventures, Trousdale Ventures, StartFast Ventures and Richard Clarke, former chief counter-terrorism advisor for the National Security Council. Existing investor NetX also participated. Blackbird says it'll be used to scale up to meet demand in new and existing markets, including by expanding its team and spending more on product dev. The 2017-founded startup sells software as a service targeted at brands and enterprises managing risks related to malicious and manipulative information -- touting the notion of defending the "authenticity" of corporate marketing.
The tagline of Spanish fact-checking outlet Maldita puts readers at the centre of the team's journalistic work: the Spanish phrase "Hazte Maldito" (meaning "Be part of Maldita!") invites the public to send in potentially fake news items and ask questions about the virus. Before the pandemic, Maldita received about 200 messages a day on their WhatsApp number, occupying a full-time journalist. After the pandemic started in March 2020 in Europe, their daily messages increased to nearly 2,000. Maldita has launched a WhatsApp chatbot to automate and centralize their interactions with their community. After a user sends in a social media post to the WhatsApp number - either a photo, a video, a link, or a WhatsApp channel that's been sharing questionable content, the bot analyses the content.
Kind of a weird question, but this arose from my curiosity regarding the use of machine learning to answer "purely qualitative" questions. From my limited prior knowledge of machine learning, most every algorithm/strategy that I have read uses some sort combination of numerical parameters or categorical parameters to break a problem down into inputs/outputs. Solutions are also evaluated in terms of these parameters or with a right/wrong(thinking picture identification). For instance, how could inputs be selected when trying to decide on what meal to eat given a choice between 3? Or where to go on vacation next? How would solutions be evaluated and compared?
In this photo illustration Facebook logo can be seen, Kolkata, India, 28 February, 2020. Facebook ... [ ] Inc on Thursday announced its decision to cancel its annual developer conference due to Coronavirus outbreak according a news media report. Some crisis situations are caused by what people say or do. On occasion, a crisis--or an embarrassing incident--is caused by technology. The New York Times reported yesterday that, "Facebook users who recently watched a video from a British tabloid featuring Black men saw an automated prompt from the social network that asked if they would like to'keep seeing videos about Primates', causing the company to investigate and disable the artificial intelligence-powered feature that pushed the message. "This was clearly an unacceptable error and we disabled the entire topic recommendation feature as soon as we realized this was happening so we could investigate the cause and prevent this from happening again," Facebook spokeswoman Dani Lever said in a statement to USA Today. "As we have said, while we have made improvements to our AI, we know it's not perfect and we have more progress to make," she said. "We apologize to anyone who may have seen these offensive recommendations." This is not the first time that advanced technology has created an embarrassing situation for an organization. The Washington Post reported yesterday that "a judge ruled that Apple will have to continue fighting a lawsuit brought by users in federal court in California, alleging that the company's voice assistant Siri has improperly recorded private conversations." Last week at the Paralympics in Tokyo, Toyota self-driving pods injured a pedestrian. Reuters reported that, "In a YouTube video, Toyota Chief Executive Akio Toyoda apologized for the incident and said he offered to meet the person but was unable to do so.
How to Write a Blog Post in 10 Minutes or Less using A.I.: Writing blog posts can take up a significant amount of time. Whether you're creating a new blog post or editing an old one, it takes time to find the right words and phrases to use. To make the whole process a lot easier, you can use artificial intelligence (A.I.) to write your content. A.I. is actually a powerful tool that can save you a lot of time when creating content. If you're curious to know how A.I. works in blog post creation, keep reading.
Now that we have all the predictions, we can combine them into a single score. To do this, multiple passes are needed to save computational power and to apply rules, such as content type diversity (i.e., content type should be varied so that viewers don't see redundant content types, such as multiple videos, one after another), that depend on an initial ranking score. First, certain integrity processes are applied to every post. These are designed to determine which integrity detection measures, if any, need to be applied to the stories selected for ranking. Then, in pass 0, a lightweight model is run to select approximately 500 of the most relevant posts for Juan that are eligible for ranking.
Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Social media users branded a humorous wedding cake topper that highlighted a groom's love for video games as offensive, but the couple behind the requested figurines say that's not what they meant. Perla Blanco and Gerardo Martinez got married in late July after four years of dating. Their special day, which was celebrated in Monterrey, Mexico, with close family and friends, went viral on TikTok when their guest Leonardo Aceves shared a clip of their unique wedding cake.
The ubiquitous availability of computing devices and the widespread use of the internet have generated a large amount of data continuously. Therefore, the amount of available information on any given topic is far beyond humans' processing capacity to properly process, causing what is known as information overload. To efficiently cope with large amounts of information and generate content with significant value to users, we require identifying, merging and summarising information. Data summaries can help gather related information and collect it into a shorter format that enables answering complicated questions, gaining new insight and discovering conceptual boundaries. This thesis focuses on three main challenges to alleviate information overload using novel summarisation techniques. It further intends to facilitate the analysis of documents to support personalised information extraction. This thesis separates the research issues into four areas, covering (i) feature engineering in document summarisation, (ii) traditional static and inflexible summaries, (iii) traditional generic summarisation approaches, and (iv) the need for reference summaries. We propose novel approaches to tackle these challenges, by: i)enabling automatic intelligent feature engineering, ii) enabling flexible and interactive summarisation, iii) utilising intelligent and personalised summarisation approaches. The experimental results prove the efficiency of the proposed approaches compared to other state-of-the-art models. We further propose solutions to the information overload problem in different domains through summarisation, covering network traffic data, health data and business process data.
Once upon a time, updates of computer operating systems were of interest only to geeks. You may recall how Version 14.5 of iOS, which required users to opt in to tracking, had the online advertising racketeers in a tizzy while their stout ally, Facebook, stood up for them. Now, the forthcoming version of iOS has libertarians, privacy campaigners and "thin-end-of-the-wedge" worriers in a spin. It also has busy mainstream journalists struggling to find headline-friendly summaries of what Apple has in store for us. "Apple is prying into iPhones to find sexual predators, but privacy activists worry governments could weaponise the feature" was how the venerable Washington Post initially reported it.
A website that uses machine-learning to quickly turn innocuous photos of famous and everyday women into realistic deepfake nudes is racking up howls of outrage--and millions of page views. The year-old site has garnered more than 38 million hits since the start of 2021, The Huffington Post reported, with five million in June alone, according to BBC News. HuffPo declined to name the website, but the BBC identified it as Deepsukebe, with both outlets referring to language on the site claiming its mission is to'make all men's dreams come true.' On its now-suspended Twitter page, Deepsukebe referred to itself as an'AI-leveraged nudifier.' It claims it doesn't save the fake photos it generates, but an'incentive program' rewards posters who share links of their deepfakes. Users who get enough people to click on them can'nudify' more pictures faster.