Joystick and AI to recognize targets.

Human intelligence and the biological brain have long served as an inspiration for the architecture and design of artificial intelligence machine learning.

Artificial Intelligence (AI) has found a new expression in the art of Dutch photographer and digital designer Bas Uterwijk, who publishes faces "recreated" by machine learning technology from a software called Artbreeder. The operation of the application is simple to understand: it compiles all known information about a person - not only their physical facial structure, but also geographical and temporal information (place of birth and period of life, for example) - to create a figure that is more approximate than their technical conclusions can draw. Result is obvious: ultra-realistic figures with iconic names in history, such as the first president of the United States, George Washington, or even more contemporary and well-known celebrities, such as the rock star David Bowie, who died in January 2016. There is even Mike Ehrmantraut, a character lived by actor Johnathan Banks in the Breaking Bad series. Certainly, one figure that draws a lot of attention in Uterwijk's Instagram is the "picture" of Jesus Christ.

Recent work has raised concerns on the risk of spurious correlations and unintended biases in statistical machine learning models that threaten model robustness and fairness. In this paper, we propose a simple and intuitive regularization approach to integrate causal knowledge during model training and build a robust and fair model by emphasizing causal features and de-emphasizing spurious features. Specifically, we first manually identify causal and spurious features with principles inspired from the counterfactual framework of causal inference. Then, we propose a regularization approach to penalize causal and spurious features separately. By adjusting the strength of the penalty for each type of feature, we build a predictive model that relies more on causal features and less on non-causal features. We conduct experiments to evaluate model robustness and fairness on three datasets with multiple metrics. Empirical results show that the new models built with causal awareness significantly improve model robustness with respect to counterfactual texts and model fairness with respect to sensitive attributes.

AI can help professionals in financial services recognize patterns, apply defined rules, and make better-informed decisions in both operations and relationship management. Companies are implementing several AI based tools within their technology stack to that end, including market analysis solutions and financial models, which can help game-out the potential of a certain investment. RPA (Robotic Process Automation) can speed up transaction driven tasks, handled by bots instead of human operators, which drastically improves workflows and limits bottlenecks. While we are far from the futuristic implementations of AI as depicted in Hollywood movies-- "iRobot," or "Terminator" to name a few--the realities of how the technology will influence our lives is very real. AI can help professionals in financial services recognize patterns, apply defined rules, and make better-informed decisions in both operations and relationship management.

Having inspired a huge subgenre of political comedy, Jon Stewart, who walked away from The Daily Show in 2015, has returned to television in a determined but defensive crouch. That he's both worried about and pre-emptively rebelling against criticism is evident in the extremely '90s credit sequence that introduces his new weekly Apple TV show, The Problem With Jon Stewart. Over grinding, Rage Against the Machine -style guitars, the credits cycle through unflattering potential titles like The Money Grab With Jon Stewart before landing on a title that both sets up the show's format--each weekly episode deals with a central problem, like "War" or "Freedom"--and preempts the title of skeptical think pieces. Stewart plays defense as host too, alluding early and often to how old he looks and to how little his audience is laughing. Concerns that The Problem's writing staff might be too white and male, like The Daily Show's, are staved off by literally showing us Stewart bantering with his staff, which is admirably diverse.

In the 1980s the fantasy genre achieved unprecedented popularity with the release of films such as Labyrinth, The NeverEnding Story, Ladyhawke, and Time Bandits. Science fiction author Matthew Kressel says he loves watching classic fantasy movies like Krull, in spite of the slow pacing and dated special effects. "I know it's really cheesy, and corny at parts, but there's something about the world of that film that draws me in every time," Kressel says in Episode 486 of the Geek's Guide to the Galaxy podcast. "I watched that movie with my cousin, who's no longer alive, and I have an emotional attachment to it. Every time I watch it, I'm back as a kid in that theater watching it." Humor writer Tom Gerencer says that for adults who grew up in the '80s, nothing can compare to the magic of watching Heavy Metal or Highlander.

Artificial intelligence technology has completed Beethoven's previously incomplete Tenth Symphony. Next month, 194 years after the composer's death, the work will be performed for the first time in Germany. Ludwig van Beethoven's final orchestral composition, Symphony No. 9 in D minor, was debuted in 1824. In the latter years of his life, Beethoven began work on what would have been his tenth symphony. However, due to ill health, he was only able to complete a few musical sketches before dying in 1827 at the age of 56.

When Ludwig van Beethoven died in 1827, he was three years removed from the completion of his Ninth Symphony, a work heralded by many as his magnum opus. He had started work on his 10th Symphony but, due to deteriorating health, wasn't able to make much headway: All he left behind were some musical sketches. Ever since then, Beethoven fans and musicologists have puzzled and lamented over what could have been. His notes teased at some magnificent reward, albeit one that seemed forever out of reach. Now, thanks to the work of a team of music historians, musicologists, composers and computer scientists, Beethoven's vision will come to life. I presided over the artificial intelligence side of the project, leading a group of scientists at the creative AI startup Playform AI that taught a machine both Beethoven's entire body of work and his creative process.

Pre-trained models learn contextualized word representations on large-scale text corpus through a self-supervised learning method, which has achieved promising performance after fine-tuning. These models, however, suffer from poor robustness and lack of interpretability. Pre-trained models with knowledge injection, which we call knowledge enhanced pre-trained models (KEPTMs), possess deep understanding and logical reasoning and introduce interpretability to some extent. In this survey, we provide a comprehensive overview of KEPTMs for natural language processing. We first introduce the progress of pre-trained models and knowledge representation learning. Then we systematically categorize existing KEPTMs from three different perspectives. Finally, we outline some potential directions of KEPTMs for future research.