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].

Without further adieu, let's predict the 2020 winners! For each category, we predict the most likely winner along with other nominees sorted by decreasing scores. Keep in mind that these scores aren't supposed to add up to 100. Rather, they are "points" given to the nominee by the underlying Deepnet model on a scale of 0 to 100. Another way to look at this is that the model is telling us how a movie/artist with a given set of characteristics will do in a given award based on 19 years of historical data on that award AND independent of the other nominees for the same award this year.

Following the success of predicting 6 out of 6 for the Oscars last year, we have the bar set high for using Machine Learning to predict the 2019 Oscars winners. This year, however, the results are not as obvious. For some of the top categories, our projected results show ties for who gets to take home the coveted gold statuette. Nevertheless, we are excited to share our predictions and see how the Academy Awards pan out this Sunday! Once again, we apply the standard Machine Learning workflow of collecting and preparing a dataset, building and evaluating models, to ultimately make predictions.

On the surface, The Godfather, The Sixth Sense and Little Miss Sunshine appear to have little in common. But even though these films belong to different genres and have very different plots, technically they have the same "emotional arc" - a journey of highs and lows. Using artificial intelligence, we analysed more than 6,000 scripts from the past 80 years and discovered all films fall within six emotional arcs. These include the emotional rise of "rags to riches" films such as The Shawshank Redemption and the rise and fall of "man in a hole" films such as Who Framed Roger Rabbit. But which of these are the most successful, critically and commercially? Tragedies, which depict a continuing emotional fall, appear to receive the highest number of Oscar nominations per film.

Machine Learning is accelerating its transition from academia to industry. We see more and more media outlets reporting about it, but most of the time they exclusively focus on the final results and don't look into all the human-powered tasks that happen behind the scenes... the stuff that really makes the magic possible. So for most people, Machine Learning continues to be some sort of elusive magic. We were recently approached by One, the Vodafone-sponsored section of El Pais, to explain how Machine Learning works and, after giving it some thought, we decided to explain it using a simple example in a domain everyone is familiar with. As the 89th annual Academy of Motion Picture Arts and Sciences Award ceremony draws near and movie fans all over the world are getting ready for their office pools, we couldn't resist the temptation to take a stab at predicting the 2017 Oscars by applying some BigML-powered Machine Learning courtesy of our own Teresa Álvarez and Cándido Zuriaga.

Unlike robotic AIs that are being built to emulate the human brain, UNU works with existing human intelligence instead of replicating it. The platform, which is open to the public, allows a group of people to converge on an answer in real time. While a swarm of seven predicted the Oscars, the Derby decision came from 20 people. For the AMA at 1 p.m. EDT today, the group that will make political predictions is expected to range from 100 to 200 people. The participants will come from UNU's user base, which has already answered fantasy football and cooking queries.

Unlike robotic AIs that are being built to emulate the human brain, UNU works with existing human intelligence instead of replicating it. The platform, which is open to public, allows a group of people to come together and converge on an answer in real time. While a swarm of seven predicted the Oscars, the Derby decision came from 20 people. For the AMA at 1pm ET today, the group that will make political predictions is expected to range from 100 to 200 people. The participants will come from UNU's current user base that has already answered fantasy football and cooking queries.

Unlike robotic AIs that are being built to emulate the human brain, UNU works with existing human intelligence instead of replicating it. The platform, which is open to public, allows a group of people to come together and converge on an answer in real time. While a swarm of seven predicted the Oscars, the Derby decision came from 20 people. For the AMA at 1pm ET today, the group that will make political predictions is expected to range from 100 to 200 people. The participants will come from UNU's current user base that has already answered fantasy football and cooking queries.