Today's newsletter comes with a more accurate prediction of the big Samsung event -- even if there's probably already another Galaxy device leaked before it starts -- and 100 percent more working links. After all the teases and photos, there shouldn't be many surprises, but if you want to know exactly what the next Galaxy Fold and Galaxy Note are like, then you'll find out in a few hours. With 57.5 million customers from Disney, 8.5 million from ESPN (up from 2.5 million a year ago) and 35.5 million from Hulu (up from 27.9 million), Disney now counts over 100 million direct customers. However, it's bringing in less money per user than other streamers, due to discounts, all while the pandemic has closed movie theaters and kept people away from theme parks. Disney did manage a hit when it released Hamilton direct to Disney, and it's following up with something bigger.

The AI Quantum Processor 8K combines the advantages of existing machine learning capabilities with new deep learning technologies to analyze …

A Seoul National University Master's student and developer has trained a face generating model to transfer normal face photographs into cartoon images in the distinctive style of Lee Mal-nyeon. The student (GitHub user name: bryandlee) used webcomics images by South Korean cartoonist Lee Mal-nyeon (이말년) as input data, building a dataset of malnyun cartoon faces then testing popular deep generative models on it. By combining a pretrained face generating model with special training techniques, they were able to train a generator at 256 256 resolution in just 10 hours on a single RTX 2080ti GPU, using only 500 manually annotated images. Since the cascade classifier for human faces provided in OpenCV-- a library of programming functions mainly aimed at real-time computer vision -- did not work well on the cartoon domain, the student manually annotated 500 input cartoon face images. The student incorporated FreezeD, a simple yet effective baseline for transfer learning of GANs proposed earlier this year by KAIST (Korea Advanced Institute of Science and Technology) and POSTECH ( Pohang University of Science and Technology) researchers to reduce the burden of heavy data and computational resources when training GANs. The developer tested the idea of freezing the early layers of the generator in transfer learning settings on the proposed FreezeG (freezing generator) and found that "it worked pretty well."

With the emergence of conversational artificial intelligence (AI) agents, it is important to understand the mechanisms that influence users' experiences of these agents. We study a common tool in the designer's toolkit: conceptual metaphors. Metaphors can present an agent as akin to a wry teenager, a toddler, or an experienced butler. How might a choice of metaphor influence our experience of the AI agent? Sampling metaphors along the dimensions of warmth and competence---defined by psychological theories as the primary axes of variation for human social perception---we perform a study (N=260) where we manipulate the metaphor, but not the behavior, of a Wizard-of-Oz conversational agent. Following the experience, participants are surveyed about their intention to use the agent, their desire to cooperate with the agent, and the agent's usability. Contrary to the current tendency of designers to use high competence metaphors to describe AI products, we find that metaphors that signal low competence lead to better evaluations of the agent than metaphors that signal high competence. This effect persists despite both high and low competence agents featuring human-level performance and the wizards being blind to condition. A second study confirms that intention to adopt decreases rapidly as competence projected by the metaphor increases. In a third study, we assess effects of metaphor choices on potential users' desire to try out the system and find that users are drawn to systems that project higher competence and warmth. These results suggest that projecting competence may help attract new users, but those users may discard the agent unless it can quickly correct with a lower competence metaphor. We close with a retrospective analysis that finds similar patterns between metaphors and user attitudes towards past conversational agents such as Xiaoice, Replika, Woebot, Mitsuku, and Tay.

Although widely adopted, existing approaches for fine-tuning pre-trained language models have been shown to be unstable across hyper-parameter settings, motivating recent work on trust region methods. In this paper, we present a simplified and efficient method rooted in trust region theory that replaces previously used adversarial objectives with parametric noise (sampling from either a normal or uniform distribution), thereby discouraging representation change during fine-tuning when possible without hurting performance. We also introduce a new analysis to motivate the use of trust region methods more generally, by studying representational collapse; the degradation of generalizable representations from pre-trained models as they are fine-tuned for a specific end task. Extensive experiments show that our fine-tuning method matches or exceeds the performance of previous trust region methods on a range of understanding and generation tasks (including DailyMail/CNN, Gigaword, Reddit TIFU, and the GLUE benchmark), while also being much faster. We also show that it is less prone to representation collapse; the pre-trained models maintain more generalizable representations every time they are fine-tuned.

In this paper, we present MusPy, an open source Python library for symbolic music generation. MusPy provides easy-to-use tools for essential components in a music generation system, including dataset management, data I/O, data preprocessing and model evaluation. In order to showcase its potential, we present statistical analysis of the eleven datasets currently supported by MusPy. Moreover, we conduct a cross-dataset generalizability experiment by training an autoregressive model on each dataset and measuring held-out likelihood on the others---a process which is made easier by MusPy's dataset management system. The results provide a map of domain overlap between various commonly used datasets and show that some datasets contain more representative cross-genre samples than others. Along with the dataset analysis, these results might serve as a guide for choosing datasets in future research. Source code and documentation are available at https://github.com/salu133445/muspy .

Artificial intelligence (A.I.) will one day integrate into human lives. While some of us are worried this form of technology will take over many jobs, it's also being used to enhance currently existing technology. But for those of you that still don't understand the potential of what AI can do, here are some tools and games you can try on your web browser. Sometimes when you're playing the piano, you might wish that you could perform a duet with someone else, only to realize that the people around you are tone deaf and should never touch a musical instrument. The good news is there's a program called A.I. Duet that can listen to the notes you're playing and try to follow up with its own tunes. It works best when you're playing a real song and not just some random notes.

Despite being a childless, science-fiction-loving grad student with nothing but time on my hands back in 2008, I somehow missed Terminator: The Sarah Connor Chronicles when it was on TV. Created by Josh Friedman, The Sarah Connor Chronicles was canceled after two seasons and 31 episodes, despite mostly-positive critical reception. Binging it under pandemic conditions, as I have been recently, has been unexpectedly cathartic. This is a show about people living in a sunny, beautiful, Southern Californian present day while haunted by the knowledge that a grim future might be coming, unless they change it by their actions. It's also about parenting under stress and feeling constantly under siege by inescapable circumstance, which--well, if that's too real, you can always focus on the nifty killer cyborgs instead.

Being the hot new trend for the youth and teenagers while prompting raised eyebrows from the adult section of the populace, TikTok, the video-sharing app which lets it's users create and share 15-second videos on an array of topics, has taken the digital world by storm and became the social network hub for amateur music videos. Tik Tok is an app which enables its users, largely the youth, to fulfil and satisfy their fun buried desires by creating an assortment of videos ranging from various fun challenges to dance to magic tricks to funny videos. What started as an app acclaimed and known for its lip-syncing feature, restricted to various areas of Asia, soon gave way to the merry world of dancing, gymnastics, cheerleading, parkour, and comedy, now finding its place and creating a buzz in various parts of the world. The app is known widely for its act-out memes, accompanied by music and other sound clips which get incessantly reproduced and remixed among its users. It's got a varied collection of tunes be it pop, rap, R&B, electro, and DJ tracks, which accompany its 15-second video clips, backed by a wide mixture of effects and filters.

Tony Harrington logs in to find out.