Of primary importance in formulating a response to the increasing prevalence and power of artificial intelligence (AI) applications in society are questions of ontology. Questions such as: What "are" these systems? How are they to be regarded? How does an algorithm come to be regarded as an agent? We discuss three factors which hinder discussion and obscure attempts to form a clear ontology of AI: (1) the various and evolving definitions of AI, (2) the tendency for pre-existing technologies to be assimilated and regarded as "normal," and (3) the tendency of human beings to anthropomorphize. This list is not intended as exhaustive, nor is it seen to preclude entirely a clear ontology, however, these challenges are a necessary set of topics for consideration. Each of these factors is seen to present a 'moving target' for discussion, which poses a challenge for both technical specialists and non-practitioners of AI systems development (e.g., philosophers and theologians) to speak meaningfully given that the corpus of AI structures and capabilities evolves at a rapid pace. Finally, we present avenues for moving forward, including opportunities for collaborative synthesis for scholars in philosophy and science.

"How to Train Your Dragon: The Hidden World" breathed some fire into a slumping box office with a franchise-best $55.5 million debut over Oscar weekend. Writer-director Dean DeBlois' third and supposedly final installment in the "How to Train Your Dragon" series notched the best opening of the year in U.S. and Canadian theaters. Going into the weekend, overall ticket sales for 2019 were down 18 percent, according to Comscore, throwing cold water on the record box office of 2018. But as Hollywood was set to gather for the Academy Awards on Sunday, "The Hidden World" lent the industry some good news -- albeit not a hint at all of the magnitude of what that was in theaters last Oscar weekend when "Black Panther" was the top film. Made for $129 million, "The Hidden World" rode good reviews (91 percent fresh on Rotten Tomatoes) and warm audience reaction (an A CinemaScore) to exceed the $43.7 million opening of the 2010 original (which ultimately made $494.9 million worldwide) and the $49 million opening of the 2014 sequel (which amassed $621.5 million).

A recent decision by research lab OpenAI to limit the release of a new algorithm has caused controversy in the AI community. The nonprofit said it decided not to share the full version of the program, a text-generation algorithm named GPT-2, due to concerns over "malicious applications." But many AI researchers have criticized the decision, accusing the lab of exaggerating the danger posed by the work and inadvertently stoking "mass hysteria" about AI in the process. The debate has been wide-ranging and sometimes contentious. It even turned into a bit of a meme among AI researchers, who joked that they've had an amazing breakthrough in the lab, but the results were too dangerous to share at the moment.

ONCE THOUGHT OF AS A NICHE TOY for early adopters, drones can now be found buzzing over parks, in select cities, and are even being increasingly used for video production as the popularity of aerial photography soars. However, drones aren't only for fun and entertainment, and the high-pitched hum of their spinning propellers could replace the wail of ambulance sirens for global citizens as drones are put to work for humanitarian purposes. In March of 2017, DJI, the manufacturers of the most popular commercial drones, published a report about drones' life-saving capabilities, citing cases in which drones manned by volunteers or bystanders were used in emergency situations like floods and avalanches, resulting in 59 life-saving rescues in China, Canada, the U.S., and Turkey. Given that it takes 25 people 35 hours to search one square mile for missing persons, compared to the 30 minutes it takes a drone to cover the same area, regardless of treacherous conditions on the ground, drones are uniquely suited for search and rescue, even when piloted by hobbyists. Based on the increasing trend of drone use in the last 10 months covered by the report, DJI estimated that drones would be directly responsible for saving at least one person per week in the future.

Last week, The New York Times ran a story called "The Rise of the Robot Reporter." The article discussed how major news organizations are increasingly using artificial intelligence (AI) software in the creation of their stories. Bloomberg, the Associated Press, the Washington Post and Forbes were among the companies mentioned in the article. NY Times reporter Jaclyn Peiser noted that the intent behind this software was not to replace humans, but to take over mundane jobs such as transcribing interviews or identifying fake images. That would free human reporters to concentrate on other work.

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.

Speaking at a conference in Lisbon, Portugal shortly before his death, Stephen Hawking told attendees that the development of artificial intelligence might become the "worst event in the history of our civilization," and he had every reason for concern. Known as an artificial superintelligence (ASI) by AI researchers, ethicists, and others, it has the potential to become more powerful than anything this planet has ever seen and it poses what will likely be the final existential challenge humanity will ever face as a species. To better understand what concerned Stephen Hawking, Elon Musk, and many others, we need to deconstruct many of the popular culture depictions of AI. The reality is that AI has been with us for a while now, ever since computers were able to make decisions based on inputs and conditions. When we see a threatening AI system in the movies, it's the malevolence of the system, coupled with the power of a computer, that scares us.

I am a major sci fi fan. Well, at least I thought I was until I went to my first Star Trek convention in my 20s and realized that I was in the minority of people who did not speak Klingon or know episode numbers, titles or dates. Most recently, I have become inspired by Black Mirror, a show originally aired by the BBC and now offered on Netflix. The brainchild of Charlie Brooker, Black Mirror is the Twilight Zone for our times, giving us a glimpse as to how technology trajectories can be used to affect society in unintended ways in the coming decades. As Frederik Pohl used to say, 'A good science fiction story should be able to predict not the automobile but the traffic jam.' Metaphorically speaking, this show sure is predicting traffic jams.

There are eight films nominated to win Best Picture at the 2019 Academy Awards. Following an impressive 94 percent score in predicting last year's Oscars, a San Francisco-based technology firm, thinks it has correctly predicted the winner for Sunday night's show. Unanimous A.I. uses its so-called "swarm A.I." technology to create an artificial intelligence comprised of a hive-mind of dozens of movie experts. The firm regularly predicts the results for the World Cup, NFL games, politics, and even Game of Thrones plot lines. This week, it shared the results of its 2019 Oscar predictions with Inverse.

The other answer here just posted text from an article on Medium. It goes over the idea of Bayesian deep networks, and lists three ways of implementing a Bayesian approach to network parameters. The first is to use Monte Carlo -- which means you have to first sample the network parameters (weights and biases), and then sample the network outputs from the inputs. That will never work at scale; you can't train anything practical that way, too slow. The second approach is to use variational inference to approximately find the right weights; but you still have to sample the weights and average in order to get the mean and variance for the network outputs, which still slows down inference, without mentioning that variational inference is approximate and often very computationally expensive. The third approach is the one that was actually proposed, that is, to use DropOut, which is hardly Bayesian in the traditional sense, whatever theoretical justification may be offered.