With the rise of computational social science, many scholars utilize data analysis and natural language processing tools to analyze social media, news articles, and other accessible data sources for examining political and social discourse. Particularly, the study of the emergence of echo-chambers due to the dissemination of specific information has become a topic of interest in mixed methods research areas. In this paper, we analyze data collected from two news portals, Breitbart News (BN) and New York Times (NYT) to prove the hypothesis that the formation of echo-chambers can be partially explained on the level of an individual information consumption rather than a collective topology of individuals' social networks. Our research findings are presented through knowledge graphs, utilizing a dataset spanning 11.5 years gathered from BN and NYT media portals. We demonstrate that the application of knowledge representation techniques to the aforementioned news streams highlights, contrary to common assumptions, shows relative "internal" neutrality of both sources and polarizing attitude towards a small fraction of entities. Additionally, we argue that such characteristics in information sources lead to fundamental disparities in audience worldviews, potentially acting as a catalyst for the formation of echo-chambers.

The philosophical world is busy making plans to mark the 300th birthday next year of the German philosopher Immanuel Kant. Non-philosophers might be forgiven for wondering why they should care about the opinions of a man who lived before the onset of cars, computers, and climate change. But arguably the most important thinker of European modernity had insights that can still illuminate some of our most vexing problems. Take the wide-spread concerns about AI that have emerged full force with the development of generative language models like ChatGPT-4. Kant's understanding of the nature of human intelligence can help us work out what, if anything, we have to fear in the face of machines that write, reason, and create exponentially faster than we can. Specifically, Kant's philosophy tells us that our anxiety about machines making decisions for themselves rather than following the instructions of their creators is misplaced.

What is the difference between artificial intelligence and machine learning, and when would you use one over the other?

The 2nd 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 July 2020. 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, including: AI and society, bias and algorithmic justice, disinformation, humans and AI, labor impacts, privacy, risk, and future of AI ethics. 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. These experts include: Danit Gal (Tech Advisor, United Nations), Amba Kak (Director of Global Policy and Programs, NYU's AI Now Institute), Rumman Chowdhury (Global Lead for Responsible AI, Accenture), Brent Barron (Director of Strategic Projects and Knowledge Management, CIFAR), Adam Murray (U.S. Diplomat working on tech policy, Chair of the OECD Network on AI), Thomas Kochan (Professor, MIT Sloan School of Management), and Katya Klinova (AI and Economy Program Lead, Partnership on AI). 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.

Artificial intelligence (AI) has made spectacular progress in the last two decades. Computers can now diagnose medical images, predict customer behaviour, manage financial portfolios, compose poetry, and even generate art. The AI can do some of these things better than humans. As AI marches furiously towards becoming increasingly smart systems, an old philosophical question has returned to haunt us: Is human intelligence qualitatively different from artificial intelligence, or are their differences only quantitative? The revolution in AI is primarily powered by a class of algorithms called artificial neural networks. These algorithms process large quantities of data and extract statistical patterns from it.

Technological change is the only constant in today's business world, disrupting everything from large organizations to small start-ups. Disruption affects everyone, but will you be the disruptor or the disrupted? You must pay close attention to the Hard Trends shaping the future of your industry, your business, and the outside world to identify opportunities used to innovate and grow rapidly, additionally using those Hard Trends to solve any problems your organization and customers might have before they occur. The shared definition and understanding of the words we use is an issue in business. While several companies are on course to use artificial intelligence (AI), machine learning (ML), and deep learning (DL), others hardly understand the fundamental differences between these powerful technologies.

Technological change is the only constant in today's business world, disrupting everything from large organizations to small start-ups. Disruption affects everyone, but will you be the disruptor or the disrupted? You must pay close attention to the Hard Trends shaping the future of your industry, your business, and the outside world to identify opportunities used to innovate and grow rapidly, additionally using those Hard Trends to solve any problems your organization and customers might have before they occur. The shared definition and understanding of the words we use is an issue in business. While several companies are on course to use artificial intelligence (AI), machine learning (ML), and deep learning (DL), others hardly understand the fundamental differences between these powerful technologies. How can one be successful, much less disruptive, when they themselves do not differentiate between AI, ML, and DL? Recently, technology company Sage conducted surveys pertaining to AI and individuals' understanding of it.

Academic literature on machine learning modeling does not explicitly address how enterprises across industries can utilize ML algorithms. And many companies, even after investing in foundational ML tools, still often get puzzled when defining business use cases for their AI apps, customizing general purpose machine learning models for domain-specific tasks, converting business requirements into data requirements, etc. In this post, we'll talk about key differences between traditional enterprise software development and ML model building and offer some ML lifecycle management tips (chiefly concerning data preparation and feature engineering) for those seeking to harness AI. In traditional software development we write out explicit instructions for a computer to follow and, therefore, the applications we end up with are deterministic. In machine learning, which is probabilistic in nature, we rely on data to write our if-then statements.

The terms "artificial intelligence" and "automation" are often used interchangeably. They're short-hand for robots and other machines that allow us to operate more efficiently and effectively -- whether it's a mechanical construct piecing together a car or the signal that sets off the smoke alarm in an emergency. But there are some pretty big differences between automated systems and AI machines. It's like apples and oranges; DVD and VHS; 2001's HAL and that shonky computer Matthew Broderick uses to start World War III in WarGames. Just to make things confusing, though, it's entirely possible for automated machines to be AI-based (but we'll get to that shortly).

At the recent Forbes CIO Summit in Half Moon Bay, California, I had the opportunity to share the stage with about a dozen leading technology leaders from a variety of different companies. Included among them were CEOs, CIOs, and venture capitalists. After the event concluded, I reached out to a number of contacts of mine who were in the audience to gauge what they found most interesting, and the person who was mentioned more than any other was Kyle Nel, who is the Executive Director of Lowe's Innovation Labs at Lowe's Home Improvement. You might think to yourself, "'Innovation Labs' sounds interesting, but Lowe's? How innovative can that be?" Very innovative, as it turns out.