This analysis on: The Hustle newsletter (lead story), Hacker News, The Growth Newsletter (issue #021), Social Blade, Data Science Weekly newsletter (issue of Jul 09 2020), Tubefilter. Around 1.5 years ago, I did an analysis of YouTube trending videos in US. That analysis was performed on trending videos of some months in 2017 and 2018. The analysis received a lot of interest on Kaggle and Reddit; I also received some emails praising the work done. That was 1.5 years ago. Today, I present an improved and expanded version of that analysis. This analysis is more advanced and contains new interesting elements. In this analysis, All trending videos for the whole year of 2019 were analyzed (More than 70,000 videos). Titles, descriptions, thumbnails, tags, views, likes/dislikes, and comments were all analyzed to produce the results shown in this post. Continue reading to know more about the analysis and the data or you can jump directly to the results section. The main goal of the analysis is to find interesting facts and patterns by exploring the data and by using effective visualizations. If you don't have time to read the full analysis, here are some of the insights that were extracted from the data along with links to their sections in the analysis: YouTube, as you know, is the most popular and most used video platform in the world today.

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Tesla CEO Elon Musk says people who don’t believe artificial intelligence could be smarter than humans are simply ‘way dumber than they think they are’.  It’s not a difficult concept to grasp. As anyone who’s seen 2001: A Space Odyssey or any film from the Terminator franchise can attest, it’s not hard to imagine. Skynet’s …

Having covered some of our favourite AI books and AI podcasts in previous lists, this time we wanted to focus on audio books. Whilst a good book can't be beaten, many prefer to digest their information differently, seen by an increase in audio book sales in recent years. Algorithms to live by is an exploration into how computer algorithms can be applied to our everyday lives, helping to solve common decision-making problems and illuminate the workings of the human mind. In this book Brian explains the problems we face in every day life which could be solved through leveraging AI, machine processes and algorithms. This audiobook aims to teach its listeners a concept which they can, eventually after repetition, learn by heart, then allowing them to brainstorm the various opportunities for python and deep learning application.

In a study accepted to the upcoming 2020 European Conference on Computer Vision, MIT and MIT-IBM Watson AI Lab researchers describe an AI system -- Foley Music -- that can generate "plausible" music from silent videos of musicians playing instruments. They say it works on a variety of music performances and outperforms "several" existing systems in generating music that's pleasant to listen to. It's the researchers' belief an AI model that can infer music from body movements could serve as the foundation for a range of applications, from adding sound effects to videos automatically to creating immersive experiences in virtual reality. Studies from cognitive psychology suggest humans possess this skill -- even young children report that what they hear is influenced by the signals they receive from seeing a person speak, for example. Foley Music extracts 2D key points of people's bodies (25 total points) and fingers (21 points) from video frames as intermediate visual representations, which it uses to model body and hand movements.

Kevin Gray: What are recommender systems? Anna Farzindar: When you search for a product on Amazon, the algorithm suggests other items with the note "Recommended for you, Kevin" or "Customers who bought this item also bought…" Recommender systems predict the preference of the user for these items, which could be in form of a rating or response. When more data becomes available for a customer profile, the recommendations become more accurate. There are a variety of applications for recommendations including movies (e.g. Could you give us a brief history of how they came about?

A group of doctors and data scientists is calling on hospitals to create clinical departments devoted to artificial intelligence (AI) to harness the power of the technology to transform patient care. While there have been many predictions of AI's potential to benefit healthcare delivery – from helping doctors perform surgery to catching cancer earlier – the technology's benefits so far have been blunted by inconsistent implementation, the researchers say. They outline a plan to make hospitals "AI ready," in a way they say would enhance both patient care and medical research. UVA Health's David J. Stone, MD, and colleagues from several other major medical centers have outlined their plan in a new article in the scientific journal BMJ Health & Care Informatics that was highlighted in the July 22 issue of the STAT health news site's Healthtech newsletter. They begin by offering a frank assessment of the current integration of AI in healthcare: "The reality of the available evidence increasingly leaves little room for optimism," they write.