When Ludwig von Beethoven died in 1827, he was three years removed from the completion of his Ninth Symphony, a work heralded by many as his magnum opus. He had started work on his 10th Symphony but, due to deteriorating health, wasn't able to make much headway: All he left behind were some musical sketches. Ever since then, Beethoven fans and musicologists have puzzled and lamented over what could have been. His notes teased at some magnificent reward, albeit one that seemed forever out of reach. Now, thanks to the work of a team of music historians, musicologists, composers, and computer scientists, Beethoven's vision will come to life.

When Ludwig von Beethoven died in 1827, he was three years removed from the completion of his Ninth Symphony, a work heralded by many as his magnum opus. He had started work on his 10th Symphony but, due to deteriorating health, wasn't able to make much headway: All he left behind were some musical sketches. Ever since then, Beethoven fans and musicologists have puzzled and lamented over what could have been. His notes teased at some magnificent reward, albeit one that seemed forever out of reach. Now, thanks to the work of a team of music historians, musicologists, composers and computer scientists, Beethoven's vision will come to life.

When Ludwig von Beethoven died in 1827, he was three years removed from the completion of his Ninth Symphony, a work heralded by many as his magnum opus. He had started work on his Tenth Symphony but, due to deteriorating health, wasn't able to make much headway: All he left behind were some musical sketches. Ever since then, Beethoven fans and musicologists have puzzled and lamented over what could have been. His notes teased at some magnificent reward, albeit one that seemed forever out of reach. Now, thanks to the work of a team of music historians, musicologists, composers and computer scientists, Beethoven's vision will come to life. I presided over the artificial intelligence side of the project, leading a group of scientists at the creative A.I. startup Playform AI that taught a machine both Beethoven's entire body of work and his creative process.

Have you ever noticed that, after 6 p.m. on weekdays, you tend to listen to harmony-laden, lo-fi, guitar-based songs with medium-to-fast-paced rhythms and a strong backbeat -- but you'll skip ones that are too distorted? As opposed to weekend mornings, when you follow up a local news podcast with slower piano tracks sung by a solo female vocalist, with strings and horns, angular melodies, multiple sections (but no solos) and a touch of melancholy throughout? Chances are, you've never thought about your listening choices in such a detailed way. But Pandora's musicologists and scientists have, and that's how -- with the help of artificial intelligence, machine learning and the analysis of the listening habits of its more than 65 million monthly users -- it knows which song you'll want to hear next. "We treat every individual very specially, and focus on contextual recommendations to understand what you like, what you listen to," says Oscar Celma, Pandora's vice president of data science, of how the company maps the DNA of every piece of audio in Pandora's millions-wide song library and compares that with explicit and implicit user preference feedback to yield bespoke programming.

Researchers at the University of Pompeu Fabra, Cardiff University and the Technical University of Madrid used machine-learning algorithms to discover new things about the history of music. One of the main tasks of musicology researchers is to develop and validate musical hypotheses, after studying historical documents and other available information. Many historical documents have now been digitized and can be accessed and browsed on a computer, making it easy for researchers to access them online. However, basic search engines operate at an "exact text string matching" level, and hence do not always capture the underlying meaning in the content. In a recently published study, music data science researcher Sergio Oramas and his colleagues tested natural language processing (NLP) approaches that could make the most out of archived historical documents, helping scientists to uncover new hypotheses and identifying interesting patterns in available data.

From its first incarnation in 2000, to its online launch in 2005, up through today, Pandora [Music] set-out to differentiate itself -- a music discovery service hand-built on a scientific and proprietary matching engine. In 2000, 80% of the music industry's revenues came from less than 3% of the releases [2]. Tim Westergren, a musician and composer, saw an untapped market opportunity to bridge this gap -- changing the music industry paradigm and dynamics between artists and consumers. Tim saw an opportunity to match undiscovered artists and their music to listeners who would enjoy their sound. Matching would create value for the artists, listeners and the intermediary facilitating this process.

Apple wrapped up WWDC on Monday by showing off the HomePod, its brand new smart speaker. The stout, cylindrical gadget packs in seven tweeters, six microphones, and a four-inch woofer that delivers impressive sound. The HomePod is very much a speaker, but for Apple, it also represents something more: a way to bring Siri into your home at a time when virtual assistants are smarter than ever. The question is whether the HomePod brings anything new to the table. Like the Amazon Echo or Google Home, Apple's HomePod provides a new shell for its omnipresent virtual assistant. Phil Schiller, Apple's head of marketing, claimed that while most companies choose to focus on either sound or smarts, the HomePod brings both in spades.

Sarah Young listens to Pandora constantly. From the moment the 27-year-old hairstylist wakes up in the morning, she's tuned into one of the service's infinite personalized radio stations. When she's finished getting ready, she flips her laptop shut and heads into work, where an iPad streams Pandora all day. On her way to and from the salon, she listens to the "'90s new wave" station on her phone. Lately, she's noticed more repetition. "One time, they played this Portishead song five times in three hours," says Young. "Things seem to get more repetitive in the mid-afternoon." Young doesn't know it, but she's a lab rat.