With the advent of the ability of artificial intelligence ("AI") to alter an individual's voice and image (whether in deepfakes or expressly fictional works), it is critical to determine who – if anyone – owns the right to do so, particularly when the voice or image is clearly identified with a fictional character from an existing film. This issue is highlighted by the recent license by James Earl Jones (the voice of Darth Vader) of his voice to an AI company. While articles state that the license of his voice was for use by Disney (the owner of the Star Wars franchise), the transaction raises the following questions: (a) could anyone use his voice without permission and (b) could James Earl Jones have licensed his voice to third parties for use in other films, particularly if used in the distinctive manner of Darth Vader? This article will refer to the individual whose voice or image is at issue as the "Individual," the licensee of AI rights as the "AI Licensee," the new AI work incorporating the voice or image as the "AI Work," and any prior work that the voice or image is taken from, or resembles elements of, as the "Prior Work." Let's first deal with the right of publicity.

People have been afraid that AI will make humans obsolete since its introduction to the workforce. We began to see AI take over jobs and cause layoffs in certain industries like the automotive industry. Although this only fuelled the anti-AI firestorm, it may have been a mistake. According to current trends, AI seems more likely to create jobs than take over. We keep you informed about current trends in the job marketplace.

Hyderabad-headquartered Meslova designs, develops and delivers domain-specific products and applications using artificial intelligence to some of the largest governments and enterprises. This MoU aims at leveraging the complementary strengths and capabilities of BEL and Meslova, a statement said.

Find open roles in Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), Computer Vision (CV), Data Engineering, Data Analytics, Big Data, and Data Science in general, filtered by job title or popular skill, toolset and products used.

Recent work on large language models relies on the intuition that most natural language processing tasks can be described via natural language instructions. Language models trained on these instructions show strong zero-shot performance on several standard datasets. However, these models even though impressive still perform poorly on a wide range of tasks outside of their respective training and evaluation sets. To address this limitation, we argue that a model should be able to keep extending its knowledge and abilities, without forgetting previous skills. In spite of the limited success of Continual Learning we show that Language Models can be continual learners. We empirically investigate the reason for this success and conclude that Continual Learning emerges from self-supervision pre-training. Our resulting model Continual-T0 (CT0) is able to learn diverse new tasks, while still maintaining good performance on previous tasks, spanning remarkably through 70 datasets in total. Finally, we show that CT0 is able to combine instructions in ways it was never trained for, demonstrating some compositionality.

Recommendation systems have illustrated the significant progress made in characterizing users' preferences based on their past behaviors. Despite the effectiveness of recommending accurately, there exist several factors that are essential but unexplored for evaluating various facets of recommendation systems, e.g., fairness, diversity, and limited resources. To address these issues, we propose Track2Vec, a GPU-free customizable-driven framework for fairness music recommendation. In order to take both accuracy and fairness into account, our solution consists of three modules, a customized fairness-aware groups for modeling different features based on configurable settings, a track representation learning module for learning better user embedding, and an ensemble module for ranking the recommendation results from different track representation learning modules. Moreover, inspired by TF-IDF which has been widely used in natural language processing, we introduce a metric called Miss Rate - Inverse Ground Truth Frequency (MR-ITF) to measure the fairness. Extensive experiments demonstrate that our model achieves a 4th price ranking in a GPU-free environment on the leaderboard in the EvalRS @ CIKM 2022 challenge, which is superior to the official baseline by about 200% in terms of the official scores. In addition, the ablation study illustrates the necessity of ensembling each group to acquire both accurate and fair recommendations.

Building automatic speech recognition (ASR) systems is a challenging task, especially for under-resourced languages that need to construct corpora nearly from scratch and lack sufficient training data. It has emerged that several African indigenous languages, including Kiswahili, are technologically under-resourced. ASR systems are crucial, particularly for the hearing-impaired persons who can benefit from having transcripts in their native languages. However, the absence of transcribed speech datasets has complicated efforts to develop ASR models for these indigenous languages. This paper explores the transcription process and the development of a Kiswahili speech corpus, which includes both read-out texts and spontaneous speech data from native Kiswahili speakers. The study also discusses the vowels and consonants in Kiswahili and provides an updated Kiswahili phoneme dictionary for the ASR model that was created using the CMU Sphinx speech recognition toolbox, an open-source speech recognition toolkit. The ASR model was trained using an extended phonetic set that yielded a WER and SER of 18.87% and 49.5%, respectively, an improved performance than previous similar research for under-resourced languages.

We explore the use of a neural network inspired by predictive coding for modeling human music perception. This network was developed based on the computational neuroscience theory of recurrent interactions in the hierarchical visual cortex. When trained with video data using self-supervised learning, the model manifests behaviors consistent with human visual illusions. Here, we adapt this network to model the hierarchical auditory system and investigate whether it will make similar choices to humans regarding the musicality of a set of random pitch sequences. When the model is trained with a large corpus of instrumental classical music and popular melodies rendered as mel spectrograms, it exhibits greater prediction errors for random pitch sequences that are rated less musical by human subjects. We found that the prediction error depends on the amount of information regarding the subsequent note, the pitch interval, and the temporal context. Our findings suggest that predictability is correlated with human perception of musicality and that a predictive coding neural network trained on music can be used to characterize the features and motifs contributing to human perception of music.

Avacapo simplifies the process of working with digital characters and replaces several tools for 3D modeling and animation. The platform allows you to create a ready-made video with characters, as well as specialized animation files for their further use in video games or animated films.

Last August, an image generated via artificial intelligence took the Colorado State Fair annual art competition prize for digital art/digitally manipulated photography. The entrant, Jason M. Allen, created the piece using a digital image creation program dubbed Midjourney. Similar to tools like DALL-E2 and Stable Diffusion, Midjourney allows users to enter descriptive text that will, with the help of AI, generate art--though not everyone agrees it should be called that. The prize's announcement prompted discussions and generated some backlash from artists and critics, some of whom posed a question: Did Allen cheat by using AI to create his piece, titled Théâtre D'opéra Spatial? The prize also reignited the heated debate about whether machines will lead to the demise of visual art, a form of expression often considered--at least heretofore--as a high and singularly human achievement.