"Machine Learning: Living in the Age of AI," examines the extraordinary ways in which people are interacting with AI today. Hobbyists and teenagers are now developing tech powered by machine learning and WIRED shows the impacts of AI on schoolchildren and farmers and senior citizens, as well as looking at the implications that rapidly accelerating technology can have. The film was directed by filmmaker Chris Cannucciari, produced by WIRED, and supported by McCann Worldgroup.

This article is reprinted by permission from NextAvenue.org. New technology devices and apps pop up as abundantly as summer weeds here in Silicon Valley. Chip-enhanced products offer to satisfy almost every need imaginable. Prompts from your smart refrigerator tell you to buy more milk. With a voice command, music plays to facilitate meditation, thanks to your smart -- always on -- helper who listens for your next query from a canister on your kitchen counter; you know, the one with a woman's voice and name.

Nowadays, users open multiple accounts on social media platforms and e-commerce sites, expressing their personal preferences on different domains. However, users' behaviors change across domains, depending on the content that users interact with, such as movies, music, clothing and retail products. In this paper, we propose an adaptive deep learning strategy for cross-domain recommendation, referred to as ADC. We design a neural architecture and formulate a cross-domain loss function, to compute the non-linearity in user preferences across domains and transfer the knowledge of users' multiple behaviors, accordingly. In addition, we introduce an efficient algorithm for cross-domain loss balancing which directly tunes gradient magnitudes and adapts the learning rates based on the domains' complexities/scales when training the model via backpropagation. In doing so, ADC controls and adjusts the contribution of each domain when optimizing the model parameters. Our experiments on six publicly available cross-domain recommendation tasks demonstrate the effectiveness of the proposed ADC model over other state-of-the-art methods. Furthermore, we study the effect of the proposed adaptive deep learning strategy and show that ADC can well balance the impact of the domains with different complexities.

Until recently, artificial intelligence was a thing for science fiction movies and books. However, we are now in the midst of an ever-changing tech world where we are advancing faster than ever before. The future of AI is unknown, but that doesn't stop people from contemplating. Here are some of the best quotes about the future of AI. "Success in creating AI would be the biggest event in human history. Unfortunately, it might also be the last, unless we learn how to avoid the risks."

We introduce DrummerNet, a drum transcription system that is trained in an unsupervised manner. DrummerNet does not require any ground-truth transcription and, with the data-scalability of deep neural networks, learns from a large unlabeled dataset. In DrummerNet, the target drum signal is first passed to a (trainable) transcriber, then reconstructed in a (fixed) synthesizer according to the transcription estimate. By training the system to minimize the distance between the input and the output audio signals, the transcriber learns to transcribe without ground truth transcription. Our experiment shows that DrummerNet performs favorably compared to many other recent drum transcription systems, both supervised and unsupervised.

Two of the most prominent public knowledge graphs, DBpedia [16] and YAGO [18], build rich taxonomies using Wikipedia's infoboxes and category graph, respectively. They describe more than five million entities and contain multiple hundred millions of triples [27]. When it comes to relation assertions (RAs), however, we observe - even for basic properties - a rather low coverage: More than 50% of the 1.35 million persons in DBpedia have no birthplace assigned; even more than 80% of birthplaces are missing in YAGO. At the same time, type assertions (TAs) are not present as well for many instances - for example, there are about half a million persons in DBpedia not explicitly typed as such [23]. Missing knowledge in Wikipedia-based knowledge graphs can be attributed to absent information in Wikipedia, but also to the extraction procedures of knowledge graphs. DBpedia uses infobox mappings to extract RAs for individual instances, but it does not explicate any information implicitly encoded in categories. YAGO uses manually defined patterns to assign RAs to entities of matching categories. For example, they extract a person's year of birth by

Artificial intelligence is all the buzz. A recent article by Jon Schuppe of NBC news asks if we are ready for police to use facial recognition software to track our every move. Well, ready or not, it's already starting to happen. The Maryland Image Repository System was used to identify the Annapolis Capital Gazette shooter. The shooter's image was captured on a security camera, and software was used to match that image to the repository, which includes driver's license photos as well as state and federal mug shots.

For decades the concept of artificial intelligence (AI) has required creative interpretations of scientific theory to capture people's attention. Whether it was the overwhelmingly capable character Data from "Star Trek," the humorously unnerved C-3PO from "Star Wars," or any other sci-fi AI manifestation, the fact still stands that intriguing forms of artificial intelligence have primarily been relegated to the entertainment world until only recently. However, the lack of capable forms of AI in everyday life has changed. With each passing year, increasingly complex artificial intelligence models are seeing the light of day. While they may not look like sophisticated androids at this point, many of these are the germs from which more advanced models will ultimately evolve.

One of the fundamental questions of cultural evolutionary research is how individual-level processes scale up to generate population-level patterns. Previous studies in music have revealed that frequency-based bias (e.g. conformity and novelty) drives large-scale cultural diversity in different ways across domains and levels of analysis. Music sampling is an ideal research model for this process because samples are known to be culturally transmitted between collaborating artists, and sampling events are reliably documented in online databases. The aim of the current study was to determine whether frequency-based bias has played a role in the cultural transmission of music sampling traditions, using a longitudinal dataset of sampling events across three decades. Firstly, we assessed whether turn-over rates of popular samples differ from those expected under neutral evolution. Next, we used agent-based simulations in an approximate Bayesian computation framework to infer what level of frequency-based bias likely generated the observed data. Despite anecdotal evidence of novelty bias, we found that sampling patterns at the population-level are most consistent with conformity bias.

We hear the term "digital transformation" all the time, and probably too often in my humble opinion. But what does it really mean and why does it appear to be a never-ending struggle that permeates our existence? Is it a task condemning us to repeat pushing a digital boulder up a mountain like Sisyphus, only to see it roll down and start all over again? Digital transformation is a term used by many and understood by few. So, in my usual manner (I am consistent if nothing else) I will also include another less used, but possible more descriptive term - digital disruption.