Yury leads a team comprising UX and visual designers at one of the largest Russian Internet companies, Mail.Ru Group. Upgrade your inbox and get our editors' picks twice a month. Digital products are getting more and more complex. In this article, Yury Vetrov explains why we need to support more platforms, tweak usage scenarios for more user segments, and hypothesize more. I've been following the idea of algorithm-driven design for several years now and have collected some practical examples. The tools of the approach can help us to construct a UI, prepare assets and content, and personalize the user experience. The information, though, has always been scarce and hasn't been systematic. However, in 2016, the technological foundations of these tools became easily accessible, and the design community got interested in algorithms, neural networks and artificial intelligence (AI). Now is the time to rethink the modern role of the designer.

While artificial intelligence researcher Yves Bergquist believes we're "far away" from AI being able to take over for screenwriters, he said Tuesday at The Grill 2018 that could still have tremendous impact on the kinds of stories that get made in Hollywood. That's in part due to how AI applications could help process "low level data" for studios that could help them think beyond their assumptions about what audiences will or will not embrace. Also Read: Why eSports Is'Growing Like Wildfire,' From Activision Blizzard to Echo Fox "If you look at the mathematical structure of narrative that's been told in film or TV, you see there are not that many unique stories," he said at the event, held at the SLS Hotel Beverly Hills. "I think what's going on is creators are censoring themselves and telling the stories that are tried and true over and over and over again." "My hunch is that there are many, many, many more stories that are possible, desirable, that can be told," he added.

The CEO of a Cambridge-based Artificial Intelligence startup is helping to fashion pan-European governance and regulation of the rocketing technology segment. Dr Loubna Bouarfa of OKRA Technologies is one of 52 thought leaders on the advisory brains trust – the EU High-Level Expert Group on AI – and spent a week at a Brussels summit. Discussions were held in the European Parliament covering topics from programming bias, data security, error accountability and responsible workflows. As the founder and CEO of OKRA, a St John's Innovation Centre company, Dr Bouarfa represents AI practitioners and the healthcare space in particular. She said: "It is an honour to represent the innovative AI startup ecosystem, balancing an operational mindset with my ethical convictions. In my work at OKRA and beyond, ethical considerations around responsibility, transparency and accountability are always top of mind."

NEW YORK (FOX5NY.COM) - A singer and YouTube creator is making music history. Taryn Southern's video for the song "Break Free" has racked up more than 2 million views. She used artificial intelligence software to co-write and co-produce the song, which features passionate vocals and a pulsating beat. So why stop at one song? So Taryn just released what is said to be the first album co-written and co-produced by artificial intelligence.

It's software, let's be clear on that, it's just an application. Sorry to disappoint you but it's not a physical network of brains floating in slimy green liquid, or an army of cognitively connected robots, it's just an application. In fact, it's often just part of another application, such as Snapchat. A neural network is just a particular type of application that is very good at taking a bunch of data and giving you an answer to a question. One of the most commonly used examples is of course identifying photos with cats in them.

BootstrapLabs, a leading venture capital firm focusing on Applied AI and based in Silicon Valley, announced today the 4th annual Applied Artificial Intelligence Conference will be held in San Francisco on April 18, 2019. The BootstrapLabs Applied Artificial Intelligence Conference (#AAI19) has grown to become one of the top AI events in the U.S. Mentioned in publications such as Forbes, TechCrunch and Fortune, and selected by Business Insider as one of the top 20 AI conferences to attend, the exclusive conference has brought together over 2,000 professionals and is considered by many AI leaders to be the best forum to learn, network and discover the latest Applied AI technologies. The one-day conference will focus on the latest and future impacts of AI applications across a breadth of sectors including Transportation, Logistics, Healthcare, Energy, FinTech, the Future of Work, IoT and Cybersecurity. The conference will take place in downtown San Francisco and will feature keynotes from Artificial Intelligence leaders who will share practical wisdom and methodologies on how to take advantage of AI's powerful potential. Many CTOs, CIOs and AI/ML experts from top tech companies like Facebook, Amazon, Uber, MasterCard, Unity, Airbnb, Linkedin, Intel, Samsung, Tesla, JP Morgan, Comcast, Microsoft, IBM, Walmart and many others, including top AI founders and government officials, have been participating during past conference editions.

ABSTRACT We address the problem of disambiguating large scale catalogs through the definition of an unknown artist clustering task. We explore the use of metric learning techniques to learn artist embeddings directly from audio, and using a dedicated homonym artists dataset, we compare our method with a recent approach that learn similar embeddings using artist classifiers. While both systems have the ability to disambiguate unknown artists relying exclusively on audio, we show that our system is more suitable in the case when enough audio data is available for each artist in the train dataset. We also propose a new negative sampling method for metric learning that takes advantage of side information such as music genre during the learning phase and shows promising results for the artist clustering task. 1. INTRODUCTION 1.1 Motivation With contemporary online music catalogs typically proposing dozens of millions of recordings, a major problem is the lack of an universal and reliable mean to identify music artists. Contrarily to albums' and tracks' ISRC As a direct consequence, the name of an artist remains its defacto identifier in practice although it results in common ambiguity issues. For example, name artist collisions (e.g. Bill Evans is the name of a jazz pianist but also the name of a jazz saxophonist and the name of a blackgrass banjo player) or artist aliases (e.g. Youssou N'Dour vs. Youssou Ndour, Simon & Garfunkel vs Paul Simon and Art Garfunkel, Cat Stevens vs Yusuf Islam) are usual.

Many types of data can be represented as graphs. Edges may correspond to actual links in the data (e.g., users connected by some social network) or to levels of similarity induced from the data (e.g., users having liked a large common set of movies). The resulting graph is typically sparse in the sense that the number of edges is much lower than the total number of node pairs, which makes the data hard to exploit. A standard approach to the analysis of sparse graphs consists in embedding the graph in some vectorial space of low dimension, typically much smaller than the number of nodes [15, 19, 4]. Each node is represented by some vector in the embedding space so that close nodes in the graph (linked either directly or through many short paths in the graph) tend to be represented by close vectors in terms of the Euclidian distance.

ABSTRACT We explore active learning (AL) utterance selection for improving the accuracy of new underrepresented domains in a natural language understanding (NLU) system. Moreover, we propose an AL algorithm called Majority-CRF that uses an ensemble of classification and sequence labeling models to guide utterance selection for annotation. Experiments with three domains show that Majority-CRF achieves 6.6%-9% relative error rate reduction compared to random sampling with the same annotation budget, and statistically significant improvements compared to other AL approaches. Additionally, case studies with human-in-the-loop AL on six new domains show 4.6%-9% improvement on an existing NLU system. Index Terms-- Active Learning, Domain Expansion, Natural Language Understanding, Intelligent Virtual Assistants 1. INTRODUCTION Intelligent virtual assistants (IVA) with natural language understanding (NLU), such as Amazon Alexa, Apple Siri, Google Assistant, and Microsoft Cortana, are becoming increasingly popular. For IVA, NLU is a distinct component of spoken language understanding (SLU) [1], in conjunction with automatic speech recognition (ASR) and dialog management (DM). ASR produces a token sequence from speech, which is passed to NLU for both classifying the action or "intent" that the user wants to invoke (e.g.

Holden Foreman '21 is a Managing Editor for the News section. He hails from the city of St. Louis, Missouri and is studying electrical engineering, computer science and economics.