Recent controversies related to topics such as fake news, privacy, and algorithmic bias have prompted increased public scrutiny of digital technologies and soul-searching among many of the people associated with their development. In response, the tech industry, academia, civil society, and governments have rapidly increased their attention to "ethics" in the design and use of digital technologies ("tech ethics"). Yet almost as quickly as ethics discourse has proliferated across the world of digital technologies, the limitations of these approaches have also become apparent: tech ethics is vague and toothless, is subsumed into corporate logics and incentives, and has a myopic focus on individual engineers and technology design rather than on the structures and cultures of technology production. As a result of these limitations, many have grown skeptical of tech ethics and its proponents, charging them with "ethics-washing": promoting ethics research and discourse to defuse criticism and government regulation without committing to ethical behavior. By looking at how ethics has been taken up in both science and business in superficial and depoliticizing ways, I recast tech ethics as a terrain of contestation where the central fault line is not whether it is desirable to be ethical, but what "ethics" entails and who gets to define it. This framing highlights the significant limits of current approaches to tech ethics and the importance of studying the formulation and real-world effects of tech ethics. In order to identify and develop more rigorous strategies for reforming digital technologies and the social relations that they mediate, I describe a sociotechnical approach to tech ethics, one that reflexively applies many of tech ethics' own lessons regarding digital technologies to tech ethics itself.

There are concerns that the ability of language models (LMs) to generate high quality synthetic text can be misused to launch spam, disinformation, or propaganda. Therefore, the research community is actively working on developing approaches to detect whether a given text is organic or synthetic. While this is a useful first step, it is important to be able to further fingerprint the author LM to attribute its origin. Prior work on fingerprinting LMs is limited to attributing synthetic text generated by a handful (usually < 10) of pre-trained LMs. However, LMs such as GPT2 are commonly fine-tuned in a myriad of ways (e.g., on a domain-specific text corpus) before being used to generate synthetic text. It is challenging to fingerprinting fine-tuned LMs because the universe of fine-tuned LMs is much larger in realistic scenarios. To address this challenge, we study the problem of large-scale fingerprinting of fine-tuned LMs in the wild. Using a real-world dataset of synthetic text generated by 108 different fine-tuned LMs, we conduct comprehensive experiments to demonstrate the limitations of existing fingerprinting approaches. Our results show that fine-tuning itself is the most effective in attributing the synthetic text generated by fine-tuned LMs.

In recent years, chatbots have been empowered to engage in social conversations with humans and have the potential to elicit people to disclose their personal experiences, opinions, and emotions. However, how and to what extent people respond to chabots' self-disclosure remain less known. In this work, we designed a social chatbot with three self-disclosure levels that conducted small talks and provided relevant recommendations to people. 372 MTurk participants were randomized to one of the four groups with different self-disclosure levels to converse with the chatbot on two topics, movies, and COVID-19. We found that people's self-disclosure level was strongly reciprocal to a chatbot's self-disclosure level. Chatbots' self-disclosure also positively impacted engagement and users' perception of the bot and led to a more effective recommendation such that participants enjoyed and agreed more with the recommendations.

A sememe is defined as the minimum semantic unit in linguistics. Sememe knowledge bases (SKBs), which comprise words annotated with sememes, enable sememes to be applied to natural language processing. So far a large body of research has showcased the unique advantages and effectiveness of SKBs in various tasks. However, most languages have no SKBs, and manual construction of SKBs is time-consuming and labor-intensive. To tackle this challenge, we propose a simple and fully automatic method of building an SKB via an existing dictionary. We use this method to build an English SKB and a French SKB, and conduct comprehensive evaluations from both intrinsic and extrinsic perspectives. Experimental results demonstrate that the automatically built English SKB is even superior to HowNet, the most widely used SKB that takes decades to build manually. And both the English and French SKBs can bring obvious performance enhancement in multiple downstream tasks. All the code and data of this paper (except the copyrighted dictionaries) can be obtained at https://github.com/thunlp/DictSKB.

Ars is excited to be hosting this online debut of Sunspring, a short science fiction film that's not entirely what it seems. You know it's the future because H (played with neurotic gravity by Silicon Valley's Thomas Middleditch) is wearing a shiny gold jacket, H2 (Elisabeth Gray) is playing with computers, and C (Humphrey Ker) announces that he has to "go to the skull" before sticking his face into a bunch of green lights. It sounds like your typical sci-fi B-movie, complete with an incoherent plot. Except Sunspring isn't the product of Hollywood hacks--it was written entirely by an AI. To be specific, it was authored by a recurrent neural network called long short-term memory, or LSTM for short.

The Rewired investment fund focuses on enabling the technologies of the AI revolution by investing across five verticals including machine learning, robotics, bionics, sensors, mapping and localisation. Backed by technology investors including Tej Kohli, we believe that AI has the potential to positively impact every segment of economic activity and to improve every aspect of human life. In 2017 Ambassador, Elle King, and Wiz Khalifa performed together on Alex Da Kid's single, ''Not Easy'' which ranked highly in the iTunes chart. The tune was created using IBM's Watson, which scanned the most popular songs from the last five years as well as analysing news, media, reports, movie scripts and social media posts. The results helped the artists to understanding the "emotional temperature" of people at that specific time.

In today's technologically-enhanced world, just knowing how to play a guitar isn't quite enough (though it is cool). To really get the most out of an electric axe, you gotta know a thing or two about sound theory, and that includes the proper use of an effects pedal to get all those cool wah-wahs squeedly-dees. To properly use an effects pedal, it needs to be carefully programmed ahead of time, then manually activated with one's foot at the proper part of a song. If you're not much of a multitasker, perhaps you need a little assistant to handle the modulation while you focus on playing the actual guitar. Programmer and guitarist Keith Bloemer developed his latest pet project with that precise goal in mind: a Raspberry Pi-powered AI guitar effects pedal.

Apple, Microsoft, Sony and Google have all tried to create a "Netflix for games", offering unlimited access to a library of titles for a flat monthly fee. But a growing number of reports suggest they may be about to face stiff competition from the streaming company itself. Netflix has been approaching senior game industry executives about joining it to lead the creation of a subscription games service, according to reports from the tech news site the Information and Reuters. Video games are not completely alien to the streaming service. It has licensed some of its in-house properties, including Stranger Things and The Dark Crystal, to developers to create tie-in games in the past.

Last week, we published a guest blog by our dear Vibhav Mithal, which describes a'Trust and Ethical AI.' What began as a discussion on the topic "Business Rational for Ethical AI" prompted our Head of Community to wonder what the business justification could be for developing ethical AI systems. We all know that AI is everywhere – just think of Alexa, Netflix, YouTube, Spotify, and any other search engine you may use. It could be a part of our professional lives, homes, cities, and so on. I mean, we'll be surrounded by more than 125 billion IoT devices by 2030. As for 5G IoT devices, 5G connections will increase to 3.3 billion in 2030, with no remaining 3G connections in the market and just 120 million 2G connections by 2030.

Robotics, digital trickery, trackless rides -- modern theme parks are full of technological innovations. Rolly Crump, the 91-year-old designer who helped shape It's a Small World, the Enchanted Tiki Room and the Haunted Mansion at Disneyland, has his share of myth-making tales as well. He's one of the few surviving ex-Disney staffers who not only knew Walt Disney but also enjoyed a somewhat close relationship with him. When it comes to the creative process, he can be blunt -- myth-shattering, if you will. Consider this Crump insight: Sometimes the best theme park rides are built on lots of beer, probably even more marijuana and large purchases of pantyhose. Now, Crump's influence can be seen in a new ride at Knott's Berry Farm that's based on an old ride at Knott's Berry Farm. Knott's Bear-y Tales: Return to the Fair is an adorable, video-game like animated romp with cartoon critters and lots of pies -- a respectful and nostalgic 2021 endeavor that livens up the park by celebrating its history.