This publication draws on a wide range of expertise to illuminate the year ahead. Even so, all our contributors have one thing in common: they are human. But advances in technology mean it is now possible to ask an artificial intelligence (AI) for its views on the coming year. We asked an AI called GPT-2, created by OpenAI, a research outfit. GPT-2 is an "unsupervised language model" trained using 40 gigabytes of text from the internet.

With a sharp rise in fluency and users of "Hinglish" in linguistically diverse country, India, it has increasingly become important to analyze social content written in this language in platforms such as Twitter, Reddit, Facebook. This project focuses on using deep learning techniques to tackle a classification problem in categorizing social content written in Hindi-English into Abusive, Hate-Inducing and Not offensive categories. We utilize bi-directional sequence models with easy text augmentation techniques such as synonym replacement, random insertion, random swap, and random deletion to produce a state of the art classifier that outperforms the previous work done on analyzing this dataset.

Just from looking at the picture, I believe that W could be describing the weights from a final output layer, which is pretty standard to apply before a soft max. You might need to look at the code of the paper if it exists to double check, but I don't think the W relates to the W in the gated conv layers.

AI is being increasingly used to make important decisions. Many AI experts (including Jeff Dean, head of AI at Google, and Andrew Ng, founder of Coursera and deeplearning.ai) I am an AI researcher, and I'm worried about some of the societal impacts that we're already seeing. At the end, I'll briefly share some positive ways that we can try to address these. Before we dive in, I need to clarify one point that is important to understand: algorithms (and the complex systems they are a part of) can make mistakes. These mistakes come from a variety of sources: bugs in the code, inaccurate or biased data, approximations we have to make (e.g.

The topic du jour for tech regulation is not as you might expect, data, but rather a sexy new topic of interest to policymakers – artificial intelligence (AI). It has all the glamour of Hollywood movies, all the fear that propels despots to power, and it comes complete with simple sentences and graphics that make it a Trumpian communicator's dream. We get tired of hearing it, but it's so true: technology is changing rapidly. The speed of change continues to accelerate and, let's face it, regulators and policymakers do a poor job of understanding technology, much less creating effective regulation for it. In the chaos however, there are repetitive patterns.

Artificial Intelligence is seen by many as a great transformative tech. These questions make people shift from thinking purely about the functional capabilities to the ethics behind creating such powerful and potentially life-consequential technologies. As such, it makes sense to spend time considering what we want these systems to do and make sure we address ethical questions now so that we build these systems with the common good of humanity in mind. Will AI replace human workers? The most immediate concern for many is that AI-enabled systems will replace workers across a wide range of industries.

AI generating music is pretty commonplace now. Apps such as Mubert even let you play generative music on your phone. Now, Sony has just developed an AI that adds kick-drum beats to pre-existing songs, and make them more catchy. The AI adds "musically plausible" kick drum beats invariant to tempo and time-shift. The research team used 665 pop, rock, and hip-hop tracks to train the model where rhythm instruments including bass, kick and snare are available as separate 44.1kHz audio tracks.

Recommender Systems (RS) have became a popular research topic and, since 2016, Deep Learning methods and techniques have been increasingly explored in this area. News RS are aimed to personalize users experiences and help them discover relevant articles from a large and dynamic search space. The main contribution of this research was named CHAMELEON, a Deep Learning meta-architecture designed to tackle the specific challenges of news recommendation. It consists of a modular reference architecture which can be instantiated using different neural building blocks. As information about users' past interactions is scarce in the news domain, the user context can be leveraged to deal with the user cold-start problem. Articles' content is also important to tackle the item cold-start problem. Additionally, the temporal decay of items (articles) relevance is very accelerated in the news domain. Furthermore, external breaking events may temporally attract global readership attention, a phenomenon generally known as concept drift in machine learning. All those characteristics are explicitly modeled on this research by a contextual hybrid session-based recommendation approach using Recurrent Neural Networks. The task addressed by this research is session-based news recommendation, i.e., next-click prediction using only information available in the current user session. A method is proposed for a realistic temporal offline evaluation of such task, replaying the stream of user clicks and fresh articles being continuously published in a news portal. Experiments performed with two large datasets have shown the effectiveness of the CHAMELEON for news recommendation on many quality factors such as accuracy, item coverage, novelty, and reduced item cold-start problem, when compared to other traditional and state-of-the-art session-based recommendation algorithms.

One of the most popular movie franchises of our time is the "Terminator" series, launched back in the early 1980s and featuring six-time Mr. America bodybuilder Arnold Schwarzenegger as a futuristic humanoid killing machine As noted by Great Power War, the backstory to the film is that the creation of the nearly-invincible cyborg Terminators stemmed from a "SkyNet" computer system that controlled U.S. nuclear weapons and "got smart," eventually seeing all humans as its enemy. So, in one fell swoop, the system launched its missiles at pre-programmed targets, which, of course, invited a second-strike counter-launch and created a nuclear holocaust that nearly destroyed all of humankind. While the Terminator series never really identified the'smart' SkyNet computer system as having artificial intelligence, some years later after AI became more of a thing it was understood that's the kind of system the fictional SkyNet operated. The "machine-learning" aspect of AI is how SkyNet "got smart" one day and launched the nuclear payloads it controlled. But the Terminator series are just movies, right?

I like to watch rugby, even though I know very little about it. They rightfully believe they're talking to people who watch rugby a lot, so they feel no need to address me, personally, with rugby-for-dummies spiels that might give me an appreciation for the game. But emerging technology could soon solve my problem. Some companies are working on AI that will generate custom sports commentary, which means I could potentially tune into a streaming rugby game and listen to a human-sounding, AI-driven robot commentator that already understands my level of rugby savvy. Maybe my robot commentator will patiently explain the difference between a blood bin and a tight head.