Ai, a company that wants to "disrupt media creation," has created a service called TokkingHeads that will take any still image and turn it into a moving, …

During the spring semester of my junior year in college, I had the opportunity to study abroad in Copenhagen, Denmark. I had never been to Europe before that, so I was incredibly excited to immerse myself into a new culture, meet new people, travel to new places, and, most important, encounter a new language. Now although English is not my native language (Vietnamese is), I have learned and spoken it since early childhood, making it second-nature. Danish, on the other hand, is an incredibly complicated language with a very different sentence and grammatical structure. Before my trip, I tried to learn a bit of Danish using the app Duolingo; however, I only got a hold of simple phrases such as Hello (Hej) and Good Morning (God Morgen).

Despite the hate it gets from musicians over the less-than-ideal music streaming rates, Spotify is here to stay. Hundreds of millions of people around the world use Spotify to listen to their music, and its unsurprising to see why. With an impressive catalog of over 50 million songs and podcast episodes (and 40,000 new ones being uploaded per day), the Swedish company shows no signs of slowing down.

Statistical literacy (assessing statistical statements, arguments and associations) is extremely important for producing and interpreting results from data analysis, yet it usually isn't a part of mainstream statistics education [1]. From the correlation-causation error to immortal time bias, there are many ways to invalidate your results. You can lessen the odds by following a few good practices. When you design your analysis, make sure you're asking the right question. This isn't always easy, as the German Federal Ministry of the Interior, Building and Home Affairs found out after publishing a 2018 press release concerning the "successful" use of facial recognition technology at train stations [2].

We present Affect2MM, a learning method for time-series emotion prediction for multimedia content. Our goal is to automatically capture the varying emotions depicted by characters in real-life human-centric situations and behaviors. We use the ideas from emotion causation theories to computationally model and determine the emotional state evoked in clips of movies. Affect2MM explicitly models the temporal causality using attention-based methods and Granger causality. We use a variety of components like facial features of actors involved, scene understanding, visual aesthetics, action/situation description, and movie script to obtain an affective-rich representation to understand and perceive the scene. We use an LSTM-based learning model for emotion perception. To evaluate our method, we analyze and compare our performance on three datasets, SENDv1, MovieGraphs, and the LIRIS-ACCEDE dataset, and observe an average of 10-15% increase in the performance over SOTA methods for all three datasets.

Do mass media influence people's opinion of other countries? Using BERT, a deep neural network-based natural language processing model, we analyze a large corpus of 267,907 China-related articles published by The New York Times since 1970. We then compare our output from The New York Times to a longitudinal data set constructed from 101 cross-sectional surveys of the American public's views on China. We find that the reporting of The New York Times on China in one year explains 54% of the variance in American public opinion on China in the next. Our result confirms hypothesized links between media and public opinion and helps shed light on how mass media can influence public opinion of foreign countries.

Let's create a movie recommender based on ratings. In this example we have a collection of movies, a bunch of users, and movie ratings from users that range from 1 to 5. These ratings are sparse because each user rates only a small percentage of the total movies, and they are biased because users' ratings are distributed differently. Our goal is to take any user ID and search for recommended movies for that user. We will use Pinecone to tie everything together and expose the recommender as a real-time service that will take any user ID and return relevant movie recommendations.

Game theory is known to be a useful tool in the study of Machine Learning (ML) and Artificial Intelligence (AI) Multi-Agent interactions.

The Pico4ML is intended for machine learning and artificial intelligence projects based around Tiny Machine Learning (TinyML).

According to a new report by KPMG, "Thriving in an AI World," industry leaders may be experiencing an effect that the company is calling "COVID-19 whiplash" after a year of highly accelerated technology adoption. Specifically, 55 percent of business leaders in industrial manufacturing and 49 percent in retail and tech told KPMG that "AI is moving faster than it should in their industry." Moreover, these concerns are exceedingly pronounced within the section of leaders from small companies at 63 percent, leaders with high levels of AI knowledge at 51 percent and by Gen Z and Millennial business leaders at 51 percent. Notably, many business leaders with interest in AI implementation said COVID-19 has influenced their company's plans to adopt the technology and contributed to a quicker pace of adoption, including 53 percent from retail, 57 percent from tech, 72 percent from industrial manufacturing, and 37 percent from healthcare and life sciences. "Just one year ago, [our report] signaled on all accounts that AI was starting to have real impact across industries, yet industry leaders told us that they felt it was not being implemented fast enough," said Traci Gusher, principal of artificial intelligence at KPMG. "Fast forward to today, industry leaders are telling us they are experiencing what we at KPMG are calling COVID-19 whiplash, with AI adoption literally skyrocketing as a result of the pandemic. Now, Industry leaders are saying it's moving too fast."