"Joan Is Awful," the first episode of the new season of Black Mirror, was inspired by The Dropout. Specifically, as creator Charlie Brooker told WIRED this week, it was sparked by seeing the deluge of content surrounding Elizabeth Holmes and the Theranos scandal--a book, a TV show, a movie, around 800 podcasts--and thinking about how weird it must have been for people to see themselves portrayed by Hollywood actors in events that "feel like they happened about 10 minutes ago." The content machine does seem to be catching up to people's real lives: The gap between a huge financial scandal and the announcement of the inevitable Michael Lewis book about it used to be years or months; now it's days. In the UK, Sky produced a dramatization of the British government's handling of the pandemic while Covid-19 was still tearing through communities. The logical conclusion of that narrowing of the gap between inspiration and dramatization--through Black Mirror's twisted, worst-case-scenario lens--is what viewers see in "Joan Is Awful": personalized content, generated by artificial intelligence, using deepfakes of famous actors as stand-ins for regular people.

Alec Robbins spent weeks thinking about anuses. It was fall 2022, and Robbins, a cartoonist and game designer by trade, had been given a curious task: design an egg character for a video game to be played on Netflix's sketch comedy series I Think You Should Leave. And there was one part of the character that had to be just right for the gag to land. In the bit, show creator Tim Robinson was going to be playing an office worker goofing around on a (fake) vintage Mac game where the goal was to feed eggs to a bigger egg. Land enough eggs in his mouth and the anthropomorphized egg would reward the triumphant player with a peep show. Thus, Robbins' butthole had to look like a real prize.

There's no question that artificial intelligence is changing our lives. A bot that sounds almost human can author your emails, teach you a new language, book your trip,s or even be your friend. Check out direct links to try those out here. One woman I spoke with on my national radio show even married her AI companion. No kidding, she says he's the perfect partner.

Fox News correspondent Grady Trimble has the latest on fears the technology will spiral out of control on'Special Report.' Artificial intelligence will likely not reach human-like cognition unless the programs are connected to robots and designed with evolutionary principles, researchers in the U.K. found. Revolutionary AI platforms that mimic human conversation, such as the wildly popular ChatGPT, will never reach human-like cognition despite their large their neural networks and the massive datasets they are trained on if they remain disembodied and only appear on computer screens, researchers at the University of Sheffield reported in a new study. ChatGPT, a chatbot that can simulate conversations with human users who provide prompts to the AI platform, learns in a similar way to human children through supervised and unsupervised learning. Unsupervised learning entails the system learning through trial and error, such as a human telling the chatbot an answer to a prompt was wrong and building off of that information.

FOX Business correspondent Lydia Hu has the latest on jobs at risk as AI further develops on "America's Newsroom." Lightning Motorcycles is speeding up the design of its bikes with the help of artificial intelligence. The San Jose, California-based, electric motorcycle builder has started using new computer-aided design software from AutoCAD that leverages generative AI to develop complex components in a fraction of the time it would take engineers to do it using standard methods. "It really allows our engineers to draw on a much greater database of ideas," Lightning Motors Corp. CEO JoJo Hatfield told Fox News Digital. "Where we would typically be limited by the engineers' experience, we can now draw on the generative design software's database of options."

This article describes and evaluates a new online AI-creativity course. The course is based around three near-state-of-the-art AI models combined into a pop song generating system. A fine-tuned GPT-2 model writes lyrics, Music-VAE composes musical scores and instrumentation and Diffsinger synthesises a singing voice. We explain the decisions made in designing the course which is based on Piagetian, constructivist 'learning-by-doing'. We present details of the five-week course design with learning objectives, technical concepts, and creative and technical activities. We explain how we overcame technical challenges to build a complete pop song generator system, consisting of Python scripts, pre-trained models, and Javascript code that runs in a dockerised Linux container via a web-based IDE. A quantitative analysis of student activity provides evidence on engagement and a benchmark for future improvements. A qualitative analysis of a workshop with experts validated the overall course design, it suggested the need for a stronger creative brief and ethical and legal content.

Biographical event detection is a relevant task for the exploration and comparison of the ways in which people's lives are told and represented. In this sense, it may support several applications in digital humanities and in works aimed at exploring bias about minoritized groups. Despite that, there are no corpora and models specifically designed for this task. In this paper we fill this gap by presenting a new corpus annotated for biographical event detection. The corpus, which includes 20 Wikipedia biographies, was compared with five existing corpora to train a model for the biographical event detection task. The model was able to detect all mentions of the target-entity in a biography with an F-score of 0.808 and the entity-related events with an F-score of 0.859. Finally, the model was used for performing an analysis of biases about women and non-Western people in Wikipedia biographies.

Speaker diarization (SD) is typically used with an automatic speech recognition (ASR) system to ascribe speaker labels to recognized words. The conventional approach reconciles outputs from independently optimized ASR and SD systems, where the SD system typically uses only acoustic information to identify the speakers in the audio stream. This approach can lead to speaker errors especially around speaker turns and regions of speaker overlap. In this paper, we propose a novel second-pass speaker error correction system using lexical information, leveraging the power of modern language models (LMs). Our experiments across multiple telephony datasets show that our approach is both effective and robust. Training and tuning only on the Fisher dataset, this error correction approach leads to relative word-level diarization error rate (WDER) reductions of 15-30% on three telephony datasets: RT03-CTS, Callhome American English and held-out portions of Fisher.

Multiple measures, such as WEAT or MAC, attempt to quantify the magnitude of bias present in word embeddings in terms of a single-number metric. However, such metrics and the related statistical significance calculations rely on treating pre-averaged data as individual data points and employing bootstrapping techniques with low sample sizes. We show that similar results can be easily obtained using such methods even if the data are generated by a null model lacking the intended bias. Consequently, we argue that this approach generates false confidence. To address this issue, we propose a Bayesian alternative: hierarchical Bayesian modeling, which enables a more uncertainty-sensitive inspection of bias in word embeddings at different levels of granularity. To showcase our method, we apply it to Religion, Gender, and Race word lists from the original research, together with our control neutral word lists. We deploy the method using Google, Glove, and Reddit embeddings. Further, we utilize our approach to evaluate a debiasing technique applied to Reddit word embedding. Our findings reveal a more complex landscape than suggested by the proponents of single-number metrics. The datasets and source code for the paper are publicly available.

Abstract: Cover song identification (CSI) focuses on finding the same music with different versions in reference anchors given a query track. In this paper, we propose a novel system named CoverHunter that overcomes the shortcomings of existing detection schemes by exploring richer features with refined attention and alignments. CoverHunter contains three key modules: 1) A convolution-augmented transformer (i.e., Conformer) structure that captures both local and global feature interactions in contrast to previous methods mainly relying on convolutional neural networks; 2) An attention-based time pooling module that further exploits the attention in the time dimension; 3) A novel coarse-to-fine training scheme that first trains a network to roughly align the song chunks and then refines the network by training on the aligned chunks. At the same time, we also summarize some important training tricks used in our system that help achieve better results. Experiments on several standard CSI datasets show that our method significantly improves over state-of-the-art methods with an embedding size of 128 (2.3% on SHS100K-TEST and 17.7% on DaTacos).