In 2021, innovation is progressing at the speed of light. Deep learning and machine learning breakthroughs have allowed computers to process and interpret data in ways we could never have imagined. Most people today still correlate artificial intelligence (AI) with science fiction, but as AI advances and becomes much more prevalent in our everyday lives, this perception is fading. AI is now a well-known term for entrepreneurs and even for normal people. AI technology has been acknowledged as a modern phenomenon in mainstream culture. To be more specific, the modern scientific field of AI was established in 1956, but substantial progress was seen towards creating an AI system by making it a technical reality that took decades.

Last week, our trending stock lists collected a motley crew of companies ranging from biotech to regular tech to home entertainment tech. In general, there was just a lot of tech. For the week of May 16, many of those same stocks hit our trending roundup again – for good reason. From a 49 million square foot downgrade to a pilot program intended to put credit cards in the hands of the credit-less, here's an inside look at what's making the market pop. Q.ai runs daily factor models to get the most up-to-date reading on stocks and ETFs.

Artificial intelligence and machine learning are two expanding means logistics companies can use to gather and process tremendous amounts of data …

The 3rd edition of the Montreal AI Ethics Institute's The State of AI Ethics captures the most relevant developments in AI Ethics since October 2020. It aims to help anyone, from machine learning experts to human rights activists and policymakers, quickly digest and understand the field's ever-changing developments. Through research and article summaries, as well as expert commentary, this report distills the research and reporting surrounding various domains related to the ethics of AI, including: algorithmic injustice, discrimination, ethical AI, labor impacts, misinformation, privacy, risk and security, social media, and more. In addition, The State of AI Ethics includes exclusive content written by world-class AI Ethics experts from universities, research institutes, consulting firms, and governments. Unique to this report is "The Abuse and Misogynoir Playbook," written by Dr. Katlyn Tuner (Research Scientist, Space Enabled Research Group, MIT), Dr. Danielle Wood (Assistant Professor, Program in Media Arts and Sciences; Assistant Professor, Aeronautics and Astronautics; Lead, Space Enabled Research Group, MIT) and Dr. Catherine D'Ignazio (Assistant Professor, Urban Science and Planning; Director, Data + Feminism Lab, MIT). The piece (and accompanying infographic), is a deep-dive into the historical and systematic silencing, erasure, and revision of Black women's contributions to knowledge and scholarship in the United Stations, and globally. Exposing and countering this Playbook has become increasingly important following the firing of AI Ethics expert Dr. Timnit Gebru (and several of her supporters) at Google. This report should be used not only as a point of reference and insight on the latest thinking in the field of AI Ethics, but should also be used as a tool for introspection as we aim to foster a more nuanced conversation regarding the impacts of AI on the world.

The 4th edition of the Montreal AI Ethics Institute's The State of AI Ethics captures the most relevant developments in the field of AI Ethics since January 2021. This report aims to help anyone, from machine learning experts to human rights activists and policymakers, quickly digest and understand the ever-changing developments in the field. Through research and article summaries, as well as expert commentary, this report distills the research and reporting surrounding various domains related to the ethics of AI, with a particular focus on four key themes: Ethical AI, Fairness & Justice, Humans & Tech, and Privacy. In addition, The State of AI Ethics includes exclusive content written by world-class AI Ethics experts from universities, research institutes, consulting firms, and governments. Opening the report is a long-form piece by Edward Higgs (Professor of History, University of Essex) titled "AI and the Face: A Historian's View." In it, Higgs examines the unscientific history of facial analysis and how AI might be repeating some of those mistakes at scale. The report also features chapter introductions by Alexa Hagerty (Anthropologist, University of Cambridge), Marianna Ganapini (Faculty Director, Montreal AI Ethics Institute), Deborah G. Johnson (Emeritus Professor, Engineering and Society, University of Virginia), and Soraj Hongladarom (Professor of Philosophy and Director, Center for Science, Technology and Society, Chulalongkorn University in Bangkok). This report should be used not only as a point of reference and insight on the latest thinking in the field of AI Ethics, but should also be used as a tool for introspection as we aim to foster a more nuanced conversation regarding the impacts of AI on the world.

The proliferation of fake news, i.e., news intentionally spread for misinformation, poses a threat to individuals and society. Despite various fact-checking websites such as PolitiFact, robust detection techniques are required to deal with the increase in fake news. Several deep learning models show promising results for fake news classification, however, their black-box nature makes it difficult to explain their classification decisions and quality-assure the models. We here address this problem by proposing a novel interpretable fake news detection framework based on the recently introduced Tsetlin Machine (TM). In brief, we utilize the conjunctive clauses of the TM to capture lexical and semantic properties of both true and fake news text. Further, we use the clause ensembles to calculate the credibility of fake news. For evaluation, we conduct experiments on two publicly available datasets, PolitiFact and GossipCop, and demonstrate that the TM framework significantly outperforms previously published baselines by at least $5\%$ in terms of accuracy, with the added benefit of an interpretable logic-based representation. Further, our approach provides higher F1-score than BERT and XLNet, however, we obtain slightly lower accuracy. We finally present a case study on our model's explainability, demonstrating how it decomposes into meaningful words and their negations.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. An unidentified inspector who failed to discover a crack in the Hernando de Soto Bridge linking Arkansas and Tennessee that prompted the span's closure was fired Monday morning and may face charges, according to reports. Arkansas Department of Transportation Director Lorie Tudor said the inspector was fired after drone video showed the crack on the bridge spanning the Mississippi River in May 2019. "This is unacceptable," Tudor said at a news conference.

How to predict the sound produced by a tonewood block once carved into the shape of a violin plate? What is the best shape for the best sound? Artificial intelligence offers answers to these questions. These are the conclusions that researchers of the Musical Acoustics Lab of Politecnico di Milano presented in a study that was recently published in Scientific Reports. In the article "A Data-Driven Approach to Violinmaking," the Chilean physicist and luthier Sebastian Gonzalez (post-doc researcher) and the professional mandolin player Davide Salvi (Ph.D. student) show how a simple and effective neural network is able to predict the vibrational be-havior of violin plates.

According to McKinsey, "Research shows that 70 percent of complex, large-scale change programs don't reach their stated goals. Common pitfalls include a lack of employee engagement, inadequate management support, poor or nonexistent cross-functional collaboration, and a lack of accountability." Last year I was doing some spring cleaning and looking for space in my home office for a digital piano. As I pulled books from my bookcase, packing them into boxes to go into storage, I found my Blockbuster Video membership card. I'd tucked it inside a book as a bookmark.

Bias in artificial intelligence seems to be an unending and endemic problem. Arguments abound about whether problems arise because the data going into AI analysis is biased, because the designers building systems are biased, or because the designers simply never tested their products to detect problematic outcomes. What can be done to address these seemingly perpetual issues? Are there specific applications whose use of AI should be limited? And if a fundamental breakthrough in AI leads to an unintended and undesired outcome, should AI-based products be suspended?