Misleading or false information has been creating chaos in some places around the world. To mitigate this issue, many researchers have proposed automated fact-checking methods to fight the spread of fake news. However, most methods cannot explain the reasoning behind their decisions, failing to build trust between machines and humans using such technology. Trust is essential for fact-checking to be applied in the real world. Here, we address fact-checking explainability through question answering. In particular, we propose generating questions and answers from claims and answering the same questions from evidence. We also propose an answer comparison model with an attention mechanism attached to each question. Leveraging question answering as a proxy, we break down automated fact-checking into several steps -- this separation aids models' explainability as it allows for more detailed analysis of their decision-making processes. Experimental results show that the proposed model can achieve state-of-the-art performance while providing reasonable explainable capabilities.

Brazil's House of Representatives has approved a bill that sets out legal regulations for artificial intelligence (AI). Bill No. 21/20 outlines guidelines to develop and utilize AI in Brazil. The bill will regulate transparency regarding the use of AI in the public sector, promote the creation of AI for the public sector, and require the "adoption of regulatory instruments that promote innovation." AI can predict and make decisions when implemented into computer systems and machines. The innovation in society and the regulations that have been introduced have been welcomed by the author of the project, Deputy Eduardo Bismarck (PDT-CA). He stated that "the time is now to outline principles: rights and duties and responsibilities" to account for this innovation already integrated into reality.

Google is running a pilot project in Israel that could use artificial intelligence to make traffic lights 10% to 20% more efficient for drivers. In a blog post touting the technology giant's current efforts in the field of environmentally sustainable solutions, Google and Alphabet CEO Sundar Pichai wrote: "We're finding ways to make routes more efficient, across an entire city, with early research into using artificial intelligence to optimize the efficiency of traffic lights. We've been piloting this research in Israel to predict traffic conditions and improve the timing of when traffic lights change. So far, we are seeing a 10%-20% reduction in fuel consumption and delay time at intersections. We're excited to expand these pilots to Rio de Janeiro and beyond."

I've always found the fact that the acronym for artificial intelligence in English, AI, is surprisingly similar to the first two characters for that word in both simplified Chinese -- '人工智能'. The first two characters together, 人工, mean'people' and'work' individually, but when put together mean'artificial' while '智能' means'intelligent.' This is quite a fascinating linguistic experiment, and it's interesting that the two most widely used languages in the world came up a similar acronym or character for one of the most important technologies ever invented by man. Perhaps there is some weird universal synergy going on or maybe there's an easy answer hidden somewhere deep within the linguistic annals of these two languages. Either way, this got me thinking about language.

The Cognitive/Artificial Intelligence Systems Market report includes a comprehensive analysis of the global market. This includes investigating past progress, on-going market scenarios, and future prospects. Accurate data on the products, strategies and market share of leading companies in this particular market are mentioned. This report provides a 360-degree overview of the global market's competitive landscape. The report further predicts the size and valuation of the global market during the forecast period.

For entrepreneurial MIT students looking to put their skills to work for a greater good, the Media Arts and Sciences class MAS.664 (AI for Impact) has been a destination point. With the onset of the pandemic, that goal came into even sharper focus. Just weeks before the campus shut down in 2020, a team of students from the class launched a project that would make significant strides toward an open-source platform to identify coronavirus exposures without compromising personal privacy. Their work was at the heart of Safe Paths, one of the earliest contact tracing apps in the United States. The students joined with volunteers from other universities, medical centers, and companies to publish their code, alongside a well-received white paper describing the privacy-preserving, decentralized protocol, all while working with organizations wishing to launch the app within their communities.

Abbreviated summaryMorita-Sherman et al. demonstrate how sophisticated research imaging can cross the divide to clinical practice. They show how quantitative MR

Dummy data is randomly generated data that can be substituted for live data. Whether you are a Developer, Software Engineer, or Data Scientist, sometimes you need dummy data to test what you have built, it can be a web app, mobile app, or machine learning model. If you are using python language, you can use a faker python package to create dummy data of any type, for example, dates, transactions, names, texts, time, and others. Faker is a simple python package that generates fake data with different data types. Faker package is heavily inspired by PHP Faker, Perl Faker, and by Ruby Faker.

Or maybe you're wondering why we should even care about trains and how they operate--what is this, the 1800s?--so let's back up a bit. If you think America is solely dependent on trucks to move freight, you might be suffering from tunnel vision: Trains account for a third of the ton-miles--that is, a ton of weight carried a mile--that freight travels in the U.S. every year. That's almost as much as is carried by trucks. The U.S. has the most extensive rail network of any country on earth by miles of track--yes, even bigger than China's--and it's currently facing some of the same snarls and congestion as seemingly every other part of the country's supply chains, on account of unprecedented activity at ports and record demand at some rail hubs. Trains might seem like a mature technology with little room for improvement or expansion, since adding new rail lines is prohibitively expensive, as battles over the cost of the expansion of Amtrak service have shown.

The Transform Technology Summits start October 13th with Low-Code/No Code: Enabling Enterprise Agility. This week, The European Parliament, the body responsible for adopting European Union (EU) legislation, passed a non-binding resolution calling for a ban on law enforcement use of facial recognition technology in public places. The resolution, which also proposes a moratorium on the deployment of predictive policing software, would restrict the use of remote biometric identification unless it's to fight "serious" crime, such as kidnapping and terrorism. The approach stands in contrast to that of U.S. agencies, which continue to embrace facial recognition even in light of studies showing the potential for ethnic, racial, and gender bias. A recent report from the U.S. Government Accountability Office found that 10 branches including the Departments of Agriculture, Commerce, Defense, and Homeland Security plan to expand their use of facial recognition between 2020 and 2023 as they implement as many as 17 different facial recognition systems.