Sad not to attend NeurIPS (previously NIPS) this year? Thus I have written this papers selection for the 2018 edition that I want to share with you! NeurIPS (Neural Information Systems Processing, previously called NIPS) is more popular than a Beyoncé concert. The biggest AI conference in the world sold out in just a few minutes this year. Moreover, the number of accepted papers this year breaks all records (more than one thousand).

Marvel's Avengers: Endgame recently dethroned Avatar as the highest grossing movie in history and while there was no doubt about this movie becoming very successful, I want to understand what makes any given movie a success. I am using data from The Movie Database provided through kaggle. The data set is split into a train and test set with the train set containing 3,000 movies and the test set comprising 4,398. There are 22 features in both the train and test set, including budget, genres, belongs_to_collection, runtime, keywords and more. The train data set also contains the target variable revenue.

We seek causes through science, religion, and in everyday life. We get excited when a big rock causes a big splash, and we get scared when it tumbles without a cause. But our causal cognition is usually biased. The 'why' is influenced by the 'who'. It is influenced by the 'self', and by 'others'. We share rituals, we watch action movies, and we influence each other to believe in the same causes. Human mind is packed with subjectivity because shared cognitive biases bring us together. But they also make us vulnerable. An artificial mind is deemed to be more objective than the human mind. After many years of science-fiction fantasies about even-minded androids, they are now sold as personal or expert assistants, as brand advocates, as policy or candidate supporters, as network influencers. Artificial agents have been stunningly successful in disseminating artificial causal beliefs among humans. As malicious artificial agents continue to manipulate human cognitive biases, and deceive human communities into ostensive but expansive causal illusions, the hope for defending us has been vested into developing benevolent artificial agents, tasked with preventing and mitigating cognitive distortions inflicted upon us by their malicious cousins. Can the distortions of human causal cognition be corrected on a more solid foundation of artificial causal cognition? In the present paper, we study a simple model of causal cognition, viewed as a quest for causal models. We show that, under very mild and hard to avoid assumptions, there are always self-confirming causal models, which perpetrate self-deception, and seem to preclude a royal road to objectivity.

With machine learning, Digital Domain can speed up the process while achieving an unprecedented level of photorealism in their characters.

The man dubbed the world's top quant has a message for Wall Street firms lavishing millions on machine–learning programs: Most of you are doing it …

The result is a newly-unveiled machine that they've dubbed Leonardo, … Part bipedal robot, part aerial drone, the hybridized machine was inspired by … and grow increasingly fast and stable as machine learning algorithms improve …

Before we look at what scale is required, and what IT infrastructure model is ideal, let's quickly define the stages of advanced AI and machine learning …

Instead of bowing to pressure, the daytime TV host takes on her critics; reaction on'The Five.' When I was growing up, there was a presence in my life, five days a week that sent me and others around the country, messages of kindness. That presence was Mr. Fred Rogers. Mr. Rogers extolled the virtues of being an individual and caring about others, offering everyone, regardless of their background and based on their humanity, an invitation to "be his neighbor." Our discourse in America has changed a lot in the time we have lost Mr. Rogers.