Existing foundation models are trained on copyrighted material. Deploying these models can pose both legal and ethical risks when data creators fail to receive appropriate attribution or compensation. In the United States and several other countries, copyrighted content may be used to build foundation models without incurring liability due to the fair use doctrine. However, there is a caveat: If the model produces output that is similar to copyrighted data, particularly in scenarios that affect the market of that data, fair use may no longer apply to the output of the model. In this work, we emphasize that fair use is not guaranteed, and additional work may be necessary to keep model development and deployment squarely in the realm of fair use. First, we survey the potential risks of developing and deploying foundation models based on copyrighted content. We review relevant U.S. case law, drawing parallels to existing and potential applications for generating text, source code, and visual art. Experiments confirm that popular foundation models can generate content considerably similar to copyrighted material. Second, we discuss technical mitigations that can help foundation models stay in line with fair use. We argue that more research is needed to align mitigation strategies with the current state of the law. Lastly, we suggest that the law and technical mitigations should co-evolve. For example, coupled with other policy mechanisms, the law could more explicitly consider safe harbors when strong technical tools are used to mitigate infringement harms. This co-evolution may help strike a balance between intellectual property and innovation, which speaks to the original goal of fair use. But we emphasize that the strategies we describe here are not a panacea and more work is needed to develop policies that address the potential harms of foundation models.

COVID-19 misinformation on social media platforms such as twitter is a threat to effective pandemic management. Prior works on tweet COVID-19 misinformation negates the role of semantic features common to twitter such as charged emotions. Thus, we present a novel COVID-19 misinformation model, which uses both a tweet emotion encoder and COVID-19 misinformation encoder to predict whether a tweet contains COVID-19 misinformation. Our emotion encoder was fine-tuned on a novel annotated dataset and our COVID-19 misinformation encoder was fine-tuned on a subset of the COVID-HeRA dataset. Experimental results show superior results using the combination of emotion and misinformation encoders as opposed to a misinformation classifier alone. Furthermore, extensive result analysis was conducted, highlighting low quality labels and mismatched label distributions as key limitations to our study.

Human-robot interaction relies on a noise-robust audio processing module capable of estimating target speech from audio recordings impacted by environmental noise, as well as self-induced noise, so-called ego-noise. While external ambient noise sources vary from environment to environment, ego-noise is mainly caused by the internal motors and joints of a robot. Ego-noise and environmental noise reduction are often decoupled, i.e., ego-noise reduction is performed without considering environmental noise. Recently, a variational autoencoder (VAE)-based speech model has been combined with a fully adaptive non-negative matrix factorization (NMF) noise model to recover clean speech under different environmental noise disturbances. However, its enhancement performance is limited in adverse acoustic scenarios involving, e.g. ego-noise. In this paper, we propose a multichannel partially adaptive scheme to jointly model ego-noise and environmental noise utilizing the VAE-NMF framework, where we take advantage of spatially and spectrally structured characteristics of ego-noise by pre-training the ego-noise model, while retaining the ability to adapt to unknown environmental noise. Experimental results show that our proposed approach outperforms the methods based on a completely fixed scheme and a fully adaptive scheme when ego-noise and environmental noise are present simultaneously.

Attention mechanisms have played a crucial role in the development of complex architectures such as Transformers in natural language processing. However, Transformers remain hard to interpret and are considered as black-boxes. This paper aims to assess how attention coefficients from Transformers can help in providing interpretability. A new attention-based interpretability method called CLaSsification-Attention (CLS-A) is proposed. CLS-A computes an interpretability score for each word based on the attention coefficient distribution related to the part specific to the classification task within the Transformer architecture. A human-grounded experiment is conducted to evaluate and compare CLS-A to other interpretability methods. The experimental protocol relies on the capacity of an interpretability method to provide explanation in line with human reasoning. Experiment design includes measuring reaction times and correct response rates by human subjects. CLS-A performs comparably to usual interpretability methods regarding average participant reaction time and accuracy. The lower computational cost of CLS-A compared to other interpretability methods and its availability by design within the classifier make it particularly interesting. Data analysis also highlights the link between the probability score of a classifier prediction and adequate explanations. Finally, our work confirms the relevancy of the use of CLS-A and shows to which extent self-attention contains rich information to explain Transformer classifiers.

Song translation requires both translation of lyrics and alignment of music notes so that the resulting verse can be sung to the accompanying melody, which is a challenging problem that has attracted some interests in different aspects of the translation process. In this paper, we propose Lyrics-Melody Translation with Adaptive Grouping (LTAG), a holistic solution to automatic song translation by jointly modeling lyrics translation and lyrics-melody alignment. It is a novel encoder-decoder framework that can simultaneously translate the source lyrics and determine the number of aligned notes at each decoding step through an adaptive note grouping module. To address data scarcity, we commissioned a small amount of training data annotated specifically for this task and used large amounts of augmented data through back-translation. Experiments conducted on an English-Chinese song translation data set show the effectiveness of our model in both automatic and human evaluation.

As an AI art critic, I've witnessed countless innovations and shifts in the world of art and photography. But few have been as groundbreaking as the recent advancements in AI-generated photography. Today, I want to share a simple few-step technique that will allow you to create professional headshots of yourself, your loved ones, and acquaintances without needing a photographer or dressing up. The answer is simple: efficiency, affordability, and flexibility. With platforms like AI SuitUp, StudioShot, and Purrfect AI, the world of professional photography has been revolutionized.

The recent surge in interest in new AI applications in 2023 has been nothing short of extraordinary. From ChatGPT to a growing list of other new apps, our technology and business worlds are rapidly evolving before our eyes in many exciting ways. As a curious technologist, I am fascinated by these new trends, and I wrote this primer on the topic back in January: "ChatGPT: Hopes, Dreams, Cheating and Cybersecurity." I have received many questions about the use of ChatGPT to generate content, and this YouTube video addressed the question: "Is It Plagiarism to Use ChatGPT in Your Published Works?" But as an author, blogger and creator of original content, I have other concerns that are growing just as fast as the new technology is being deployed.

A suspected drunk driver crashed into a man's spare bedroom in the early morning hours of Jan. 19 in Austin, Texas. Over a million hobby drones are registered in the U.S. You may never know when you're being watched. Check out my guide to avoiding drone surveillance. We wrote this after one hovered over my pool while I was swimming.

Meredith Broussard is a data journalist and academic whose research focuses on bias in artificial intelligence (AI). She has been in the vanguard of raising awareness and sounding the alarm about unchecked AI. Her previous book, Artificial Unintelligence (2018), coined the term "technochauvinism" to describe the blind belief in the superiority of tech solutions to solve our problems. She appeared in the Netflix documentary Coded Bias (2020), which explores how algorithms encode and propagate discrimination. Her new book is More Than a Glitch: Confronting Race, Gender and Ability Bias in Tech.

One afternoon an email arrives that threatens to end my career. Or at the very least, it makes me think seriously about what the end of my career might look like. It comes from a woman in Ely called Camden Woollven who has an interest in my restaurant reviews, a taste for the absurd and perhaps just a little too much time on her hands. Woollven works in the tech sector and has long been fascinated by OpenAI, a company founded in 2015, with investment from among others Elon Musk, to develop user-friendly applications involving artificial intelligence. In November last year, after $10bn worth of investment from Microsoft, OpenAI released ChatGPT3, a tool which has been trained on a vast array of data and allows us to commission articles and have human-like text conversations with a chatbot.