In a lot of ways, smart speakers are the ideal home stereo for the streaming music generation. Telling an Amazon Echo to play whatever song was on your mind for the first time was a bit of a revelation. And music remains one of the most-used and most crucial features of any smart speaker. The only problem is lots of them sound terrible. Sonos, Google, Apple and Amazon all have smart speakers where music quality is paramount, but a $200 Echo Studio or Sonos one is a tough sell next to a $50 Echo Dot or Nest Mini.

The music you listen to can indicate your personality type, a study claims, with country fans more extroverted and blues lovers more emotionally stable. Spotify asked 5,808 volunteers to complete a personality test that rates them on openness, conscientiousness, extroversion, agreeableness and emotional stability. It then looked at their musical history and found that the songs people listened to could predict their personality type with'moderate to high accuracy'. For example, people who like Aretha Franklin and soul music generally tend to be more agreeable, and lovers of folk music are more likely to be open. Spotify was this week granted a patent for technology that uses personality types to'personalise user experience' by changing the tone of voice used in spoken messages delivered within the service.

At its best, the Chromecast with Google TV represents how streaming is supposed to work. You shouldn't have to sift through a dozen apps--Netflix, Amazon Prime, Hulu, Disney, HBO Max, and so on--just to figure out what to watch. Everything should instead be accessible from one menu that acts as a universal guide to streaming. Google's new $50 4K streaming dongle tries to deliver on that ideal. But because the new Chromecast often works so well, it's all the more glaring when it doesn't.

You may not realize it, but artificial intelligence (AI) is already enhancing our lives in a multitude of ways. AI systems already man our call centers, drive our cars, and take orders through kiosks at local fast food restaurants. In the days ahead, AI and machine learning will become a more prominent fixture, disrupting industries and extracting tediousness from our everyday lives. As we hand over larger chunks of our lives to the machines, we need to lift the hood to see what kind of ethics are driving them, and who is defining the rules of the road. Many CIOs have begun experimenting with AI in areas that may not be very visible to end users, such as automating warehouses.

The collaborative efforts are the key to accelerate technology diffusion by promoting innovations that democratise the access of new technologies, enhancing research and development in AI that address the issues of data protection, transparency and accountability so that it gains public trust and encourages greater investment.

Task-oriented dialogue systems help users accomplish tasks such as booking a movie ticket and ordering food via conversation. Generative models parameterized by a deep neural network are widely used for next turn response generation in such systems. It is natural for users of the system to want to accomplish multiple tasks within the same conversation, but the ability of generative models to compose multiple tasks is not well studied. In this work, we begin by studying the effect of training human-human task-oriented dialogues towards improving the ability to compose multiple tasks on Transformer generative models. To that end, we propose and explore two solutions: (1) creating synthetic multiple task dialogue data for training from human-human single task dialogue and (2) forcing the encoder representation to be invariant to single and multiple task dialogues using an auxiliary loss. The results from our experiments highlight the difficulty of even the sophisticated variant of transformer model in learning to compose multiple tasks from single task dialogues.

Procedural content generation in video games has a long history. Existing procedural content generation methods, such as search-based, solver-based, rule-based and grammar-based methods have been applied to various content types such as levels, maps, character models, and textures. A research field centered on content generation in games has existed for more than a decade. More recently, deep learning has powered a remarkable range of inventions in content production, which are applicable to games. While some cutting-edge deep learning methods are applied on their own, others are applied in combination with more traditional methods, or in an interactive setting. This article surveys the various deep learning methods that have been applied to generate game content directly or indirectly, discusses deep learning methods that could be used for content generation purposes but are rarely used today, and envisages some limitations and potential future directions of deep learning for procedural content generation.

Two main approaches for evaluating the quality of machine-generated rationales are: 1) using human rationales as a gold standard; and 2) automated metrics based on how rationales affect model behavior. An open question, however, is how human rationales fare with these automatic metrics. Analyzing a variety of datasets and models, we find that human rationales do not necessarily perform well on these metrics. To unpack this finding, we propose improved metrics to account for model-dependent baseline performance. We then propose two methods to further characterize rationale quality, one based on model retraining and one on using "fidelity curves" to reveal properties such as irrelevance and redundancy. Our work leads to actionable suggestions for evaluating and characterizing rationales.

Google Assistant can do a lot of things on our phones, but it's not so great at interacting with our Android apps. Sure you can ask Spotify to play a song or send a text with Telegram, but for the most part, apps and Assistant are mutually exclusive. As part of its Google Assistant Developer Day Thursday, Google has announced that a handful of "your favorite Android apps" have begun working with Assistant voice commands, including Discord, Etsy, MyFitnessPal, Mint, Nike Adapt, Postmates, Snapchat, Spotify, Twitter, and Walmart. As long as the corresponding app is set up on your phone, all you need to do is summon Assistant and ask to do something within an app, like ordering pizza on Postmates or checking an order status on Walmart. Google is calling the new feature App Actions, and they're similar to Alexa Actions.

Busted: A bot powered by OpenAI's powerful GPT-3 language model has been unmasked after a week of posting comments on Reddit. Under the username /u/thegentlemetre, the bot was interacting with people on /r/AskReddit, a popular forum for general chat with 30 million users. It was posting in bursts of roughly once a minute. Fooled ya--again: It's not the first time GPT-3 has fooled people into thinking what it writes comes from a human. In August a college student published a blog post that hit the top spot on Hacker News and led a handful of people to subscribe.