"But AI has no internal world and it has no need to create its desires or fears." So rather than letting AI take complete control, results seem to be far more fruitful when human artists work hand-in-hand with machines. Musician and University of Sussex lecturer Dr Alice Eldridge suggests that we should treat AI as "just another tool that we have designed, like the wheel, or the combustion engine". She has helped create a cello that uses a combination of acoustics, electrification and an adaptive algorithm that makes the instrument self-resonate; or essentially, play itself. "With a classical cello you have to bring the instrument alive with a bow; a feedback cello is already singing, your job as a performer is to shape the sound - it's more like a dance than'controlling' an instrument in the traditional way," she says.

Networks are powerful data structures, but are challenging to work with for conventional machine learning methods. Network Embedding (NE) methods attempt to resolve this by learning vector representations for the nodes, for subsequent use in downstream machine learning tasks. Link Prediction (LP) is one such downstream machine learning task that is an important use case and popular benchmark for NE methods. Unfortunately, while NE methods perform exceedingly well at this task, they are lacking in transparency as compared to simpler LP approaches. We introduce ExplaiNE, an approach to offer counterfactual explanations for NE-based LP methods, by identifying existing links in the network that explain the predicted links. ExplaiNE is applicable to a broad class of NE algorithms. An extensive empirical evaluation for the NE method `Conditional Network Embedding' in particular demonstrates its accuracy and scalability.

Most of the existing recommender systems use the ratings provided by users on individual items. An additional source of preference information is to use the ratings that users provide on sets of items. The advantages of using preferences on sets are two-fold. First, a rating provided on a set conveys some preference information about each of the set's items, which allows us to acquire a user's preferences for more items that the number of ratings that the user provided. Second, due to privacy concerns, users may not be willing to reveal their preferences on individual items explicitly but may be willing to provide a single rating to a set of items, since it provides some level of information hiding. This paper investigates two questions related to using set-level ratings in recommender systems. First, how users' item-level ratings relate to their set-level ratings. Second, how collaborative filtering-based models for item-level rating prediction can take advantage of such set-level ratings. We have collected set-level ratings from active users of Movielens on sets of movies that they have rated in the past. Our analysis of these ratings shows that though the majority of the users provide the average of the ratings on a set's constituent items as the rating on the set, there exists a significant number of users that tend to consistently either under- or over-rate the sets. We have developed collaborative filtering-based methods to explicitly model these user behaviors that can be used to recommend items to users. Experiments on real data and on synthetic data that resembles the under- or over-rating behavior in the real data, demonstrate that these models can recover the overall characteristics of the underlying data and predict the user's ratings on individual items.

Recommending new items to existing users has remained a challenging problem due to absence of user's past preferences for these items. The user personalized non-collaborative methods based on item features can be used to address this item cold-start problem. These methods rely on similarities between the target item and user's previous preferred items. While computing similarities based on item features, these methods overlook the interactions among the features of the items and consider them independently. Modeling interactions among features can be helpful as some features, when considered together, provide a stronger signal on the relevance of an item when compared to case where features are considered independently. To address this important issue, in this work we introduce the Feature-based factorized Bilinear Similarity Model (FBSM), which learns factorized bilinear similarity model for TOP-n recommendation of new items, given the information about items preferred by users in past as well as the features of these items. We carry out extensive empirical evaluations on benchmark datasets, and we find that the proposed FBSM approach improves upon traditional non-collaborative methods in terms of recommendation performance. Moreover, the proposed approach also learns insightful interactions among item features from data, which lead to deep understanding on how these interactions contribute to personalized recommendation.

Matching Amazon's new free music offer on Echo speakers, Google now offers owners of the Google Home speaker access to free tunes, via YouTube Music. There's a big difference: songs are sponsored on Amazon Echo speakers, while tunes for Google users are free. Additionally, they can be listened on both the Google Home speaker line, and any speaker that has the Google Assistant, Google's Siri/Alexa like helper. That includes the brands Sony, JBL, Harman and others. What you can't get is on-demand song selection, but instead playlists or radio stations created based on your requests.

I've said for years that Assassin's Creed is more impressive for its art nowadays than the games themselves, but still, who would've guessed that one day Assassin's Creed would be used to restore a priceless piece of architectural history? That news, plus a new Lego Star Wars, an Old Republic expansion and potential film adaptation, details for Netflix's Witcher series, a remake of cult classic shooter XIII, and more. This is gaming news for April 15 to 19. This week's first freebie is a big one, relatively speaking. You probably heard that Cathédrale Notre-Dame de Paris caught on fire this week.

Selfie and cartoon are two popular artistic forms that are widely presented in our daily life. Despite the great progress in image translation/stylization, few techniques focus specifically on selfie cartoonization, since cartoon images usually contain artistic abstraction (e.g., large smoothing areas) and exaggeration (e.g., large/delicate eyebrows). In this paper, we address this problem by proposing a selfie cartoonization Generative Adversarial Network (scGAN), which mainly uses an attentive adversarial network (AAN) to emphasize specific facial regions and ignore low-level details. More specifically, we first design a cycle-like architecture to enable training with unpaired data. Then we design three losses from different aspects. A total variation loss is used to highlight important edges and contents in cartoon portraits. An attentive cycle loss is added to lay more emphasis on delicate facial areas such as eyes. In addition, a perceptual loss is included to eliminate artifacts and improve robustness of our method. Experimental results show that our method is capable of generating different cartoon styles and outperforms a number of state-of-the-art methods.

Today's business environment is becoming increasingly competitive. As a result, sales organizations need deeper insights and access to data to stay ahead of the competition. One way sales teams are getting a competitive advantage is through artificial intelligence and machine learning (AI/ML). However, while many companies are looking into and adopting AI/ML technologies, success relies on more than just the algorithms within the tools. Organizations need the right data in order for AI/ML to "learn" to be truly effective.

Turns out after they've been trained on enormous datasets, algorithms can not only tell what a picture is such as knowing a cat is a cat but can also generate absolutely original images. The artificial intelligence that makes this possible has matured significantly in recent years and in some applications is very proficient, but in other ways, still has a long way to go. Artificial Intelligence Can Now Generate Amazing Images – What Does The Mean For Humans? It's taken two decades for computer scientists to train and develop machines that can "see" the world around them--another example of an everyday skill humans take for granted yet one that is quite challenging to train a machine to do. Facial recognition technology, used both in retail and security, is one way AI and its ability to "see" the world is starting to be commonplace. Retailers use facial recognition technology to better market and sell to their target audience.

AI is capable of making music, but does that make AI an artist? As AI begins to reshape how music is made, our legal systems are going to be confronted with some messy questions regarding authorship. Do AI algorithms create their own work, or is it the humans behind them? What happens if AI software trained solely on Beyoncé creates a track that sounds just like her? "I won't mince words," says Jonathan Bailey, CTO of iZotope. "This is a total legal clusterfuck."