Facebook's experiments in internet connectivity haven't always gone well. But its latest innovation seems genuinely cool. Facebook Connectivity announced Monday that it has developed a robot that can travel along power lines deploying a thin yet durable fiber-optic cable of Facebook's own creation. It claims that this system, which utilizes the electrical grid to build out internet infrastructure, will be cheaper than the existing methods of laying internet cables, particularly in developing countries. That contributes to Facebook Connectivity's overall goal of increasing internet access.
It's time to reset, re-create and collaborate on a new paradigm where Compassion and Kindness are the prevailing norms, one where technology is a tool for making humans more humane and creating an Abundant world for the majority. Join us to turn this vision into reality. Let's look into the'White Mirror' … Inspired by Black Mirror (Netflix series) - 'White Mirror' (holding name while we devise a suitable one) provides an immersive flash forward (glimpse/vision) of our Utopian future. In uncertain times (like now), technological disruption and impactful stories can change our mental worldview - our perceptions and eventually our reality. Black Mirror is a powerful show, depicting a dystopian future caused in part by misused evolving technologies.
You don't have to be a prophet to foresee that artificial intelligence will also play an essential role in the field of human resource management. It will have a decisive impact on the way we connect people in the future. Using human-machine partnerships to improve the process of connecting people to the right job is relatively new to how most organizations hire. While there are many favorable advancements and novel solutions that promote more inclusive hiring, there are several risks to consider. First and foremost, we must challenge the assumption that hiring managers know what constitutes an ideal employee.
The ethics of artificial intelligence is part of the ethics of technology specific to robots and other artificially intelligent beings. It is typically divided into robo-ethics, a concern with the moral behavior of humans as they design, construct, use and treat artificially intelligent beings, and machine ethics, which is concerned with the moral behavior of artificial moral agents (AMAs). Algorithmic bias describes systematic and repeatable errors in a computer system that create unfair outcomes, such as privileging one arbitrary group of users over others. Bias can emerge due to many factors, including but not limited to the design of the algorithm or the unintended or unanticipated use or decisions relating to the way data is coded, collected, selected, or used to train the algorithm. Algorithmic bias is found across platforms, including but not limited to search engine results and social media platforms, and can have impacts ranging from inadvertent privacy violations to reinforcing social biases of race, gender, sexuality, and ethnicity.
A Tinder user in Utah, Jade Goulart, decided recently to use her account to support Black Lives Matter. She added a petition for justice for Breonna Taylor to her bio and wrote, "Instant response if you sign this petition." Goulart said she also added something like, "You mean to tell me you aren't out protesting for human rights? "I felt like something was weird about that," Goulart told Mashable over Twitter DM. "So I looked it up and saw that Tinder had come out and said that they originally were banning accounts for promoting BLM because it was against the'promotional purposes' part of their terms." She read BBC's coverage from early June, in which Tinder explained users were banned for fundraising for Black Lives Matter and related causes because such promotion was against its Community Guidelines. The dating app quickly walked that back, days after people began posting about it on social media, saying it wouldn't ban users for such activity anymore. "We have voiced our support ...
We've reached an inflection point. As the global response to COVID-19 evolves, communities around the world have moved from an era of "remote everything" into a more hybrid model of work, learning, and life. And as we all scramble to keep up, the future of work and education is being shaped before our eyes. At Microsoft, we've spent the last few months learning from our customers and studying how they use our tools. We've also worked with experts across virtual reality, AI, and productivity research to help understand the future of work.
An audit commissioned by Facebook Inc. urged it to improve artificial intelligence-based tools it uses to help identify problematic content such as hate speech, showcasing the current limits of technology in policing the world's largest social media platform. The report, made public Wednesday, examined Facebook's approach to civil rights and criticized it as "too reactive and piecemeal," despite much-publicized investments in AI-based censors and human analysts trained to track down and remove harmful content.
Facebook researchers have developed what they claim is the largest automatic speech recognition (ASR) model of its kind -- a model that learned to understand words in 51 languages after training on over 16,000 hours of voice recordings. In a paper published on the preprint server Arxiv.org, the coauthors say the system, which contains around a billion parameters, improves speech recognition performance up to 28.8% on one benchmark compared with baselines. Designing a single model to recognize speech in multiple languages is desirable for several reasons. It simplifies the backend production pipeline, for one thing, and studies have shown training multilingual models on similar languages can decrease overall word error rate (WER). Facebook's model -- a so-called joint sequence-to-sequence (Seq2Seq) model -- was trained while sharing the parameters from an encoder, decoder, and token set across all languages. The encoder maps input audio sequences to intermediate representations while the decoder maps the representations to output text, and the token set simplifies the process of working with many languages by sampling sentences at different frequencies.