Generative AI is absolutely everywhere right now, so it's no surprise to see Spotify putting it to use in its latest feature, simply called "DJ." It's a new way to immediately start a personalized selection of music playing that combines Spotify's well-known personalization tools that you can find in playlists like Discover Weekly as well as the content that populates your home screen with some AI tricks. I got early access to DJ and have been playing with it for the last day to see how Spotify's latest take on personalized music works, but the feature is available as of today in beta for all premium subscribers in the US and Canada. While Spotify has loads of personalized playlists for users, I've found that the app lacks a simple way to tell it to just play some music you like. On Apple Music, for example, I can ask Siri to play music I like and it'll start a personalized radio station based on music I've played alongside some things it thinks I'll enjoy but haven't played before. It's a reliable way to jump right into my collection.

According to a report from Reuters, ChatGPT is listed as the author or co-author of at least 200 books on Amazon's Kindle Store. However, the number of bot-written books is likely higher than that since Amazon's policies don't require authors to disclose their use of AI. Brett Schickler published on the Kindle Store a children's book written and illustrated by AI. Although Schickler says the book has earned him less than $100 since its January release, he only spent a few hours creating it with ChatGPT prompts like "write a story about a dad teaching his son about financial literacy." Science-fiction publication Clarkesworld Magazine has temporarily halted short-story submissions after receiving a flood of articles suspected of using AI without disclosure, which was reported by PCMag.

This was a joint briefing with The Alan Turing Institute. ChatGPT is a language model developed by OpenAI that generates human-like text through deep learning. These language models are trained on a massive corpus of text from the internet and can respond to a wide range of questions and prompts with remarkable fluency and accuracy, making them a popular tool with a variety of applications, with ChatGPT amassing over 100 million users since its launch in November 2022. However, the use of large language models also raises important questions about the limitations and ethical considerations of relying on AI-generated text. There are limitations to the information they can provide and the accuracy of their answers.

This week, Dana and Stephen are joined by Jamelle Bouie as they begin by discussing Marc Maron's new HBO comedy special From Bleak to Dark. Then they review the Oscar-nominated Polish film EO. Then, Slate writer Dan Kois joins to talk about his article on the importance of hanging out. In Slate Plus they talk about this article and the Bing Chatbot. Dana: A YouTube user named "nobody."

Snakes can traverse almost all types of environments by bending their elongate bodies in 3-D to interact with the terrain. Similarly, a snake robot is a promising platform to perform critical tasks in various environments. Understanding how 3-D body bending effectively interacts with the terrain for propulsion and stability can not only inform how snakes traverse natural environments, but also allow snake robots to achieve similar performance. How snakes and snake robots move on flat surfaces has been understood well. However, such ideal terrain is rare in natural environments and little was understood about how to generate propulsion and maintain stability in 3-D terrain, except for some studies on arboreal snake locomotion and on robots using geometric planning. To bridge the knowledge gap, we integrated animal experiments and robotic studies in three representative environments: a large smooth step, an uneven arena of blocks of large height variation, and large bumps. We discovered that vertical body bending induces stability challenges but can generate large propulsion. When traversing a large smooth step, a snake robot is challenged by roll instability that increases with the amplitude of vertical bending. The instability can be reduced by body compliance that statistically improves body-terrain contact. Despite this, vertical body bending can potentially allow snakes to push against terrain for propulsion, as demonstrated by corn snakes traversing an uneven arena. A snake robot can generate large propulsion like this if contact is well maintained. Contact feedback control can help accommodate perturbations such as novel terrain geometry or excessive external forces by improving contact. Our findings provide insights into how snakes and snake robots can use vertical body bending for efficient and versatile traversal of the 3-D world stably.

Data scarcity is a problem that occurs in languages and tasks where we do not have large amounts of labeled data but want to use state-of-the-art models. Such models are often deep learning models that require a significant amount of data to train. Acquiring data for various machine learning problems is accompanied by high labeling costs. Data augmentation is a low-cost approach for tackling data scarcity. This paper gives an overview of current state-of-the-art data augmentation methods used for natural language processing, with an emphasis on methods for neural and transformer-based models. Furthermore, it discusses the practical challenges of data augmentation, possible mitigations, and directions for future research.

While ML generates much economic value, many of us have problematic relationships with social media and other ML-powered applications. One reason is that ML often optimizes for what we want in the moment, which is easy to quantify but at odds with what is known scientifically about human flourishing. Thus, through its impoverished models of us, ML currently falls far short of its exciting potential, which is for it to help us to reach ours. While there is no consensus on defining human flourishing, from diverse perspectives across psychology, philosophy, and spiritual traditions, love is understood to be one of its primary catalysts. Motivated by this view, this paper explores whether there is a useful conception of love fitting for machines to embody, as historically it has been generative to explore whether a nebulous concept, such as life or intelligence, can be thoughtfully abstracted and reimagined, as in the fields of machine intelligence or artificial life. This paper forwards a candidate conception of machine love, inspired in particular by work in positive psychology and psychotherapy: to provide unconditional support enabling humans to autonomously pursue their own growth and development. Through proof of concept experiments, this paper aims to highlight the need for richer models of human flourishing in ML, provide an example framework through which positive psychology can be combined with ML to realize a rough conception of machine love, and demonstrate that current language models begin to enable embodying qualitative humanistic principles. The conclusion is that though at present ML may often serve to addict, distract, or divide us, an alternative path may be opening up: We may align ML to support our growth, through it helping us to align ourselves towards our highest aspirations.

More than one hundred benchmarks have been developed to test the commonsense knowledge and commonsense reasoning abilities of artificial intelligence (AI) systems. However, these benchmarks are often flawed and many aspects of common sense remain untested. Consequently, we do not currently have any reliable way of measuring to what extent existing AI systems have achieved these abilities. This paper surveys the development and uses of AI commonsense benchmarks. We discuss the nature of common sense; the role of common sense in AI; the goals served by constructing commonsense benchmarks; and desirable features of commonsense benchmarks. We analyze the common flaws in benchmarks, and we argue that it is worthwhile to invest the work needed ensure that benchmark examples are consistently high quality. We survey the various methods of constructing commonsense benchmarks. We enumerate 139 commonsense benchmarks that have been developed: 102 text-based, 18 image-based, 12 video based, and 7 simulated physical environments. We discuss the gaps in the existing benchmarks and aspects of commonsense reasoning that are not addressed in any existing benchmark. We conclude with a number of recommendations for future development of commonsense AI benchmarks.

This paper proposes singing voice synthesis (SVS) based on frame-level sequence-to-sequence models considering vocal timing deviation. In SVS, it is essential to synchronize the timing of singing with temporal structures represented by scores, taking into account that there are differences between actual vocal timing and note start timing. In many SVS systems including our previous work, phoneme-level score features are converted into frame-level ones on the basis of phoneme boundaries obtained by external aligners to take into account vocal timing deviations. Therefore, the sound quality is affected by the aligner accuracy in this system. To alleviate this problem, we introduce an attention mechanism with frame-level features. In the proposed system, the attention mechanism absorbs alignment errors in phoneme boundaries. Additionally, we evaluate the system with pseudo-phoneme-boundaries defined by heuristic rules based on musical scores when there is no aligner. The experimental results show the effectiveness of the proposed system.

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