Bank of America is rolling out the first widely available AI-driven virtual assistant of its kind in financial services, Erica, to its 25 million mobile clients. This latest innovation reflects the bank's continued investment in digital capabilities as part of its high-tech, high-touch client experience, including the Digital Mortgage Experience, mobile car shopping tool, Merrill Edge Guided Investing, and Business Advantage. "Everything we do is based on what we hear from our clients: how they want to interact with us and how we can make their financial lives better," said Michelle Moore, head of digital banking at Bank of America. "Erica delivers on this in many ways, from making it easy for clients to find what they are looking for to providing new and interactive ways to do their banking using voice, text or gesture. Through Erica, we are also delivering personalized solutions at scale by providing insights, such as how you can improve your credit score or create a budget."

JOURNALISTS at The Boston Globe searched for patterns in public records to uncover priests in the Catholic church who had sexually abused children. Now, researchers think artificial intelligence could do the same job faster, more accurately and on a much wider scale. The Boston Globe investigation, depicted in the film Spotlight, involved looking for clues like priests suddenly going on sick leave or moving around a lot. To continue reading this premium article, subscribe for unlimited access. Existing subscribers, please log in with your email address to link your account access.

For the better part of a decade, retail has looked to "personalization" as its primary marketing strategy in an effort to combat a rapidly deteriorating landscape. It's time to admit it's not working. True personalization, a 1:1 marketing experience between the brand and the consumer, is incredibly difficult, if not impossible with the current tools at a marketing team's disposal. In fact, it would probably take upward of 10,000 marketing managers to pull such a strategy off. Artificial intelligence and machine learning, on the other hand, could handle it in mere minutes.

With the advent of the smartphone we have witnessed one of the most powerful tools that mankind has ever seen. However, one of the lesser understood and talked about phenomena accompanying the smartphone is the rise of the image sensor. By integrating progressively more and more powerful image sensors into smartphones, we have taken photography and the information age to the next level. Today I wanted to share some thoughts on the image sensor--what all it has enabled, and where it might be heading in the future. Without this technology, we wouldn't have Facebook live or the countless videos that give people access to what's really happening on the ground.

According to data published by Grand View Research, the global artificial intelligence market size is projected to reach $35.87 billion by 2025, while growing at a CAGR of 57.2 percent. The report indicates that rapid improvements in fast information storage capacity, high computing power, and parallelization are some of the factors that are contributing to the rapid innovations of robotics and artificial intelligence technology. AI becomes prominent in end-use industries such as automotive and healthcare. In addition, there is strong demand for understanding and analyzing visual contents and gaining meaningful insights, which is also expected to provide strength to the market over the forecast period. A report by the McKinsey Global Institute surveyed companies about their use of AI.

If this sounded a bit dramatic, great. It's supposed to be to get your marketing team on its toes and prepared to embrace AI-powered marketing tools. Artificially intelligent systems constantly work on the background of popular products and services such as Netflix, Amazon, and, naturally, Google. In the past few years, though, AI has paved its way deeper into marketing, helping brands to enhance every step of the customer journey. Moreover, tools previously available to enterprise level companies have become affordable and accessible to medium- and small-sized businesses.

An attendee inspects a P20 Pro smartphone, manufactured by Huawei Technologies Co., during its unveiling in Paris, France, on Tuesday, March 21, 2018. AI is rapidly changing the way we think about photography. Just a couple of years from now, most advances in the photo space will be AI-centric, and not optics or sensor-centric as before. The advancement in photography technology will, for the first time ever, be untethered from physics, and will create a whole new way of thinking about photography. This is how it's going to happen.

Perhaps the greatest outrage in modern robotics is the continued non-existence of the robot housekeeper. Is it really so much to ask for a robot that sweeps and mops and brings you pills on platters, like Rosie from The Jetsons? Actually, it kind of is a lot to ask: A robot that can do even the simplest of chores (save for vacuuming), like setting a table, is a huge challenge because such tasks require both dexterity and planning. But the scientists at the MIT Computer Science and Artificial Intelligence Laboratory are working toward a world where robots make our coffee and set our tables. And that research is happening inside a simulation. Because if we want the machines to run our homes instead of leveling them, we have to train them up right.

They don't enslave us, but they do decide what news stories you see, Amazon products you're offered, Netflix shows to watch next. Most mornings, I ask Google, "What's the fastest route to work?" Based on traffic, it sends me along one of two highways. Both are six lanes of hot concrete, billboards, McDonalds and brake lights. Google knows which is faster. There's a third highway to work.

Music generation research has grown in popularity over the past decade, thanks to the deep learning revolution that has redefined the landscape of artificial intelligence. In this paper, we propose a novel approach to music generation inspired by musical segment concatenation methods and hash learning algorithms. Given a segment of music, we use a deep recurrent neural network and ranking-based hash learning to assign a forward hash code to the segment to retrieve candidate segments for continuation with matching backward hash codes. The proposed method is thus called Deep Segment Hash Learning (DSHL). To the best of our knowledge, DSHL is the first end-to-end segment hash learning method for music generation, and the first to use pair-wise training with segments of music. We demonstrate that this method is capable of generating music which is both original and enjoyable, and that DSHL offers a promising new direction for music generation research.