Online recommendation is an essential functionality across a variety of services, including e-commerce and video streaming, where items to buy, watch, or read are suggested to users. Justifying recommendations, i.e., explaining why a user might like the recommended item, has been shown to improve user satisfaction and persuasiveness of the recommendation. In this paper, we develop a method for generating post-hoc justifications that can be applied to the output of any recommendation algorithm. Existing post-hoc methods are often limited in providing diverse justifications, as they either use only one of many available types of input data, or rely on the predefined templates. We address these limitations of earlier approaches by developing J-Recs, a method for producing concise and diverse justifications. J-Recs is a recommendation model-agnostic method that generates diverse justifications based on various types of product and user data (e.g., purchase history and product attributes). The challenge of jointly processing multiple types of data is addressed by designing a principled graph-based approach for justification generation. In addition to theoretical analysis, we present an extensive evaluation on synthetic and real-world data. Our results show that J-Recs satisfies desirable properties of justifications, and efficiently produces effective justifications, matching user preferences up to 20% more accurately than baselines.

Conversational Intelligence requires that a person engage on informational, personal and relational levels. Advances in Natural Language Understanding have helped recent chatbots succeed at dialog on the informational level. However, current techniques still lag for conversing with humans on a personal level and fully relating to them. The University of Michigan's submission to the Alexa Prize Grand Challenge 3, Audrey, is an open-domain conversational chat-bot that aims to engage customers on these levels through interest driven conversations guided by customers' personalities and emotions. Audrey is built from socially-aware models such as Emotion Detection and a Personal Understanding Module to grasp a deeper understanding of users' interests and desires. Our architecture interacts with customers using a hybrid approach balanced between knowledge-driven response generators and context-driven neural response generators to cater to all three levels of conversations. During the semi-finals period, we achieved an average cumulative rating of 3.25 on a 1-5 Likert scale.

Reinforcement learning is a promising method to accomplish robotic control tasks. The task of playing musical instruments is, however, largely unexplored because it involves the challenge of achieving sequential goals - melodies - that have a temporal dimension. In this paper, we address robotic musicianship by introducing a temporal extension to goal-conditioned reinforcement learning: Time-dependent goals. We demonstrate that these can be used to train a robotic musician to play the theremin instrument. We train the robotic agent in simulation and transfer the acquired policy to a real-world robotic thereminist. Supplemental video: https://youtu.be/jvC9mPzdQN4

Using artificial intelligence and machine learning helps firms more efficiently supervise and mitigate risk at scale, and will now enable them to analyze …

In this blog we shall discuss about a few problems in artificial intelligence and their python implementations. The problems discussed here appeared as programming assignments in the edX course CS50's Introduction to Artificial Intelligence with Python (HarvardX:CS50 AI). The problem statements are taken from the course itself. Write a program that determines how many "degrees of separation" apart two actors are. According to the Six Degrees of Kevin Bacon game, anyone in the Hollywood film industry can be connected to Kevin Bacon within six steps, where each step consists of finding a film that two actors both starred in.

Before color photography took off in the 1960s, most photographs existed in black and white. Today, we can appreciate photography from past decades and centuries, but our appreciation for them is limited to the range of colors in which they were developed. More so, photographs don't last forever: if not preserved properly, their images fade, and with it our clarity of their history and memory blurs. Of course, history is not black and white, and nor should personal and global histories fade from memory, especially not colorful moments in American history. Technical advances in artificial intelligence and image processing have created new possibilities for restoring dimensions of color and visuality to historical, photographic artifacts.

Artificial intelligence is an increasingly important element of science, medicine, and even the minutiae of our daily lives. Chatbots, digital assistants, and movie and music recommendations from streaming services all depend on "deep learning"--a process by which computer models are trained to recognize patterns in data. That training requires powerful computers and lots and lots of energy--and associated carbon emissions. One of the most elaborate deep learning models, designed to produce human-like language and known as GPT-3, requires an amount of energy equivalent to the yearly consumption of 126 Danish homes and creates a carbon footprint equivalent to traveling 700,000 kilometers by car for a single training session. Still, the computing power used in deep learning grew 300,000-fold between 2012 and 2018, and if that pace of growth continues it's not hard to see how artificial intelligence could have a major climate impact.

Warner Bros has unveiled the trailer for a comedy titled Superintelligence, the latest creation of actress Melissa Mccarthy and her director husband Ben Falcone (following Tammy, The Boss, Life of the Party). This is skipping theaters and debuting directly on HBO Max at the end of the month - which is probably for the best considering this looks awful. When an all-powerful Superintelligence (voiced by James Corden) chooses to study the most average person on Earth, the fate of the world hangs in the balance. As the A.I. decides to enslave, save or destroy humanity, it's up to Carol to prove that people are worth saving. This stars Melissa McCarthy, Bobby Cannavale, Brian Tyree Henry, and Jean Smart.

Artificial intelligence (AI) is being used to create new'deepfake' pop songs that sound like they're being performed by dead musicians, including Elvis Presley, Frank Sinatra, David Bowie and Michael Jackson. Jukebox, created by California-based company OpenAI, is a neural network that generates eerie approximates of pop songs in the style of multiple artists. The neural network generates music, including rudimentary singing complete with lyrics in English and a variety of instruments like guitar and piano. OpenAI has created a expansive library of new tracks, imitating a diverse selection of artists, including the Beatles, Nirvana, Katy Perry, Simon and Garfunkel, Stevie Wonder, Elton John and Ed Sheeran, as well as deceased heroes that almost appear to be brought back to life. Most of the samples have a bizarre, faraway quality to them, as if they're poorly produced demos from the 1950s that haven't seen the light of day until now.

Chris Johnston, Executive Director for Paerata Rise says "Our goal is to be one of the most desirable places to live in Auckland and becoming a Smart Village is an extension of this. It means we are able to offer the utmost connection to our residents through a private network, and the most cutting-edge technologies." "We are proud to be able to support the unique, first class community being built at Paerata Rise with the latest concepts in mobile network technology. While everyone has experienced the frustration of bad coverage, ultimately an excellent network should go unnoticed – instead allowing users' mobile applications, services, and other benefits to come to the fore," adds Ross Spearman, General Manager of Dense Air New Zealand. "The realities of smart villages and connected neighbourhoods are starting to emerge in the world around us, however the real'smarts' need to be built into the foundations of communities which is why we are executing this project at this stage of our development," says Johnston.