SAN FRANCISCO, Oct. 24, 2019 (GLOBE NEWSWIRE) -- MICRON INSIGHT -- Micron Technology, Inc. (Nasdaq: MU), today announced a powerful new set of high-performance hardware and software tools for deep learning applications with the acquisition of FWDNXT, a software and hardware startup. When combined with advanced Micron memory, FWDNXT's (pronounced "forward next") artificial intelligence (AI) hardware and software technology enables Micron to explore deep learning solutions required for data analytics, particularly in IoT and edge computing. With this acquisition, Micron is integrating compute, memory, tools and software into a comprehensive AI development platform. This platform in turn provides the key building blocks required to explore innovative memory optimized for AI workloads. "FWDNXT is an architecture designed to create fast-time-to-market edge AI solutions through an extremely easy to use software framework with broad modeling support and flexibility," said Micron Executive Vice President and Chief Business Officer Sumit Sadana.

The "basic level", according to experiments in cognitive psychology, is the level of abstraction in a hierarchy of concepts at which humans perform tasks quicker and with greater accuracy than at other levels. We argue that applications that use concept hierarchies - such as knowledge graphs, ontologies or taxonomies - could significantly improve their user interfaces if they `knew' which concepts are the basic level concepts. This paper examines to what extent the basic level can be learned from data. We test the utility of three types of concept features, that were inspired by the basic level theory: lexical features, structural features and frequency features. We evaluate our approach on WordNet, and create a training set of manually labelled examples that includes concepts from different domains. Our findings include that the basic level concepts can be accurately identified within one domain. Concepts that are difficult to label for humans are also harder to classify automatically. Our experiments provide insight into how classification performance across domains could be improved, which is necessary for identification of basic level concepts on a larger scale.

In April 2019, following the Easter Sunday bomb attacks, the Government of Sri Lanka had to shut down Facebook and YouTube for nine days to stop the spreading of hate speech and false news, posted mainly in the local languages Sinhala and Tamil. This came about simply because these social media platforms did not have the capability to detect and warn about the provocative content. India's Ministry of Human Resource Development (MHRD) wants lectures on Swayama and NPTELb--the online teaching platforms--to be translated into all Indian languages. Approximately 2.5 million students use the Swayam lectures on computer science alone. The lectures are in English, which students find difficult to understand.

Samuel Greengard is an author and journalist based in West Linn, OR, USA.

During the last months of my mother's life, as she ventured further from lucidity, she was visited by music. In collusion with her dementia, her hearing loss filled her consciousness with musical hallucinations. Sometimes welcome, more often not, her musical visitations were vivid, yet segmented and tattered. She would occasionally comment on the singers. On rare occasions she would identify the performer. Mitch Miller, who wrote oppressively cheerful arrangements of popular songs from the 1950s, seemed to command a prominent role in her hallucinations.

The year is 1989 and we're introduced to the World Wide Web. The Berlin Wall is coming down. The Exxon Valdez is spilling oil in Prince William Sound, Alaska. Students are calling for democracy and free speech in Tiananmen Square. Crockett and Tubbs are clearing the mean streets of Miami.

Detecting fake news is getting more difficult as more false information pours onto the internet ... [ ] every day, and from very influential sources. New papers from MIT explore how current methods are failing, and bring new weapons to the fight against fake news. Now is the time to make facts great again. "Fake news," the 2017 Collins word of the year, poses a serious threat to the values of honesty, truth, and accountability--values that purveyors of falsified information don't seem to hold too closely. Apart from the most obvious dangers of spreading false information (erosion of trust, political or national hostility, widespread uncertainty) the prevalence of AI systems on social media mean that unverified claims and slanderous falsehoods are picked up and distributed at eye-watering speeds.

What does a milkman and a copywriter have in common? I'm not a milkman nor have I lived in the 50s. I have been a copywriter for almost a year. And I can share a bit of how a copywriter's life goes in the modern century. Nowadays, most of our hours involves facing a computer screen, whether at home or at the office. We don't carry pens and paper as much as we used to, unless we need something more physical to mimic our vintage creative processes.

From September 23 to 28, 2019, the Markkula Center's Director of Technology Ethics, Brian Green, attended two Vatican-sponsored meetings on AI. AI and Faith, an organization seeking to promote conversations about AI with religious organizations, interviewed Dr. Green on his experience. This interview is cross-posted on the Markkula Center website from AI and Faith with permission. First, Brian, please summarize your background for us, especially as it relates to AI. What, particularly, has drawn you to the topic of ethical AI? I have been working on technology ethics issues, including AI, for about 15 years, starting with my graduate studies at the Graduate Theological Union in Berkeley, where I had a strong focus on biotechnology and ethics.

Businesses know that rich analytics capabilities are critical to remain competitive in today's reality. While Canadian executives may feel challenged by the immense amount of data they already have access to, they plan to continue investing in new and expanded data sources to enable them to better mitigate risk – and meet constantly shifting consumer expectations,