Will there be a time in the not-so-distant future when people won't need to learn a second language -- instead relying on machine translation powered by artificial intelligence to interpret real-time conversations? This is a key question, and a fear, for many businesses in the language industry. But panelists at a recent symposium on how the "internet of things" will affect media outlets and education, co-hosted by English language school chain Aeon Corp. and The Japan Times, agreed that AI and other technologies will never be able to completely replace humans. For those who want to study a second language, the goal is not simply to become fluent in the language, said Aeon President Yoshikazu Miyake. "It's to become confident enough to enjoy communication in a foreign language," Miyake said.

Being able to predict whether a song can be a hit has impor- tant applications in the music industry. Although it is true that the popularity of a song can be greatly affected by exter- nal factors such as social and commercial influences, to which degree audio features computed from musical signals (whom we regard as internal factors) can predict song popularity is an interesting research question on its own. Motivated by the recent success of deep learning techniques, we attempt to ex- tend previous work on hit song prediction by jointly learning the audio features and prediction models using deep learning. Specifically, we experiment with a convolutional neural net- work model that takes the primitive mel-spectrogram as the input for feature learning, a more advanced JYnet model that uses an external song dataset for supervised pre-training and auto-tagging, and the combination of these two models. We also consider the inception model to characterize audio infor- mation in different scales. Our experiments suggest that deep structures are indeed more accurate than shallow structures in predicting the popularity of either Chinese or Western Pop songs in Taiwan. We also use the tags predicted by JYnet to gain insights into the result of different models.

This paper introduces a new large-scale music dataset, MusicNet, to serve as a source of supervision and evaluation of machine learning methods for music research. MusicNet consists of hundreds of freely-licensed classical music recordings by 10 composers, written for 11 instruments, together with instrument/note annotations resulting in over 1 million temporal labels on 34 hours of chamber music performances under various studio and microphone conditions. The paper defines a multi-label classification task to predict notes in musical recordings, along with an evaluation protocol, and benchmarks several machine learning architectures for this task: i) learning from spectrogram features; ii) end-to-end learning with a neural net; iii) end-to-end learning with a convolutional neural net. These experiments show that end-to-end models trained for note prediction learn frequency selective filters as a low-level representation of audio.

Apple has one big reason to move to a homegrown GPU: It wants full control over the hardware and software in its devices. The device maker is apparently developing its own GPU from scratch after dumping Imagination Technologies' PowerVR architecture, which is being used in the iPhone 7. The smartphone runs on the PowerVR A10 Fusion chip. It's not certain when Apple's homegrown GPU will appear in devices, and the company didn't respond to request for comment. Apple has made graphics improvement a priority in its iPhone and iPad models, so users should get better gaming experiences. The homegrown GPU could also boost artificial intelligence capabilities on Apple's devices, and also bring on board features like image recognition.

Today in Entertainment: '1984' screens today nationwide; 'Scarlett & Emma & Tilda & Matt' whitewashing T-shirt goes viral' Comedy Central announces new late-night series starring Jordan Klepper '1984' in 2017: Movie based on George Orwell's classic screens today '13 Reasons Why' star and Asian American comics take on Hollywood whitewashing with viral T-shirt Everybody loves'The Walking Dead's' Sonequa Martin-Green '1984' in 2017: Movie based on George Orwell's classic screens today '13 Reasons Why' star and Asian American comics take on Hollywood whitewashing with viral T-shirt Everybody loves'The Walking Dead's' Sonequa Martin-Green'Invader Zim' is returning to Nickelodeon as a TV movie Get ready for new "Invader Zim." After more than 10 years off the air, Nickelodeon's animated alien bent on conquering Earth is set for a comeback. The network has given the green light to a brand new "Invader Zim" TV movie from series creator Jhonen Vasquez that will include original cast members Richard Horvitz, Rikke Simons, Andy Berman and Melissa Fahn reprising their roles. "What makes this announcement extra thrilling is the adventure that Jhonen has created for Zim," said Chris Viscardi -- Nickelodeon's senior vice president of content development and production, animation -- in a press release. "I can promise you that it is as wonderfully absurd and strangely heartfelt as any fan of the original series could hope for, and kids seeing it for the first time will love it too."

Artificial intelligence can have all the same information as people, but it can address the volume of news and decipher validity without getting tired,

Research commissioned by Google has found that young people consider three of the company's products among the top ten "coolest" brands in the world. According to the search giant's'It's Lit: A guide to what teenagers think is cool' study, Google-owned YouTube is the world's coolest brand, with Google in third place and Chrome – a web browser – in tenth, ahead of the likes of Apple and Spotify. "For Generation Z, what's cool is also a representation of their values, their expectations of themselves, their peers, and the brands they hold in the highest regard," notes Google. "To Gen Z, Google search and that's part of makes it cool," reads the report. The research also found that Google is more popular amongst teenagers than Twitter, with 42.2% of the study's participants said to be on the platform and 35.4% on Twitter.

A weathercat." -- Poncho asked me where I was, so it could tell me the local weather forecast. I could have shared my location using Messenger's built-in location-sharing feature, but that would take me through three in-app menu screens, and wasn't the point of chatbots to chat?

Google is soothing its agitated advertisers with more powerful AI tools. The search giant came under fire recently when top brands claimed ads were appearing on YouTube propaganda vids. Understandably not wanting to fund extremism, even inadvertently, several of the companies pulled their ads. Gary Vaynerchuk was so impressed with TNW Conference 2016 he paused mid-talk to applaud us. Google is now ramping up the power of its AI to combat this.

We introduce LAMP: the Linear Additive Markov Process. Transitions in LAMP may be influenced by states visited in the distant history of the process, but unlike higher-order Markov processes, LAMP retains an efficient parameterization. LAMP also allows the specific dependence on history to be learned efficiently from data. We characterize some theoretical properties of LAMP, including its steady-state and mixing time. We then give an algorithm based on alternating minimization to learn LAMP models from data. Finally, we perform a series of real-world experiments to show that LAMP is more powerful than first-order Markov processes, and even holds its own against deep sequential models (LSTMs) with a negligible increase in parameter complexity.