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.

Online grocers are going all out to attract modern shoppers, and helping them in this endeavour are new-age technologies. Bigbasket is leveraging AI, ML, and analytics to streamline logistics and enhance customer experience. Founded in 2011, Bigbasket claims to be India's largest online food and grocery store. It has over 18,000 products and more than 1000 brands in its catalogue. "We are an e-commerce business where data is the new driving fuel. Data is collected from transactions, customer preference, shopping behaviour, etc to build a variety of algorithms (statistical algorithm, Deep Learning algorithm and ML algorithm). All these algorithms are used for different use cases," says Subramanian M S, Head of Analytics, Bigbasket.

While the foundations for deep learning sate to the 1980s, researchers George Dahl and Abdel-rahman Mohamed broke new ground in 2010 when they developed advanced deep learning speech recognition tools. This paved the way for more deep learning advances focusing on anything from facial recognition to machine translation. In 2011 a question-answering computer system developed by IBM's DeepQA project made headlines when it outplayed Brad Rutter and Ken Jennings, two of the most successful contestants to take part in the popular American game show Jeopardy! Artificial intelligence took another stride forward in October 2011 when Apple launched Siri, it's signature personal assistant. From reciting the weather forecast to plotting a route on Google Maps, Siri is now used by hundreds of millions of people around the world.

Here's everything our readers were most obsessed with in 2019. Purchases you make through our links may earn us a commission. As we head into the new year, we think it's fun to look at all the wonderful products that we bought in 2019. This year brought some incredible releases like Disney, Apple AirPods Pro, and the all-new Kindle--and honestly, some of these things were apart of what really make the year great. So we decided to roundup 100 of the most popular products that people bought over and over again. Whether it was a massive sale (looking at you Black Friday) or one of the hottest product people couldn't stop talking about (*cough* weighted blankets *cough*), our readers found something that caught their eyes. From robot vacuums to wireless headphones to streaming services, these are the most popular products that people couldn't stop buying in 2019. Everyone become obsessed with Disney in 2019. Although it was just released in November, the new streaming service Disney became the most popular product of the year. With it came nostalgia for the Disney classics, new original shows and movies, and plenty of Baby Yoda content. Seriously, if you're a fan of Marvel, Disney Princesses, Star Wars, Pixar, and all things Disney, you might want to consider following suit and getting a subscription for yourself. We still love the tried-and-true Instant Pot Duo. It's no surprise here--our readers were all about that Instant Pot life this year. The Duo 6 Quart, a.k.a. the most popular model out there, was far and away the biggest seller this year, and in no small part because of the deals that ran on it during Prime Day and Black Friday, respectively. If you were one of the lucky ducks who nabbed it when it was just $50, good on you. But you can still get it for a pretty good price right now, too. Nobody really wants to vacuum, but they also don't want to spend a fortune on a robot vacuum to do their dirty work. That's why the Eufy 11S was so popular this year. It's the best affordable robot vacuum we've ever tested because it balances great cleaning powering and a reasonable price. Our readers loved scooping it up--especially when it was on sale as it is right now.