YouTube has sidestepped controversies surrounding the use of artificial intelligence (AI) to generate new music with a new tool that clones singers' voices. The new feature, called Dream Track, is available in YouTube Shorts – the Google-owned platform's answer to TikTok that lets users post short videos. Users can enter a prompt about what sort of music style they want – such as'upbeat' or'ballad' – and select the artist they want the AI to imitate. Nine artists have allowed their voice to be copied for the tool, including Alec Benjamin, Charlie Puth, Charli XCX, John Legend, Sia and Troye Sivan. YouTube posted a short clip of what the clone version of US singer Charlie Puth sounds like – and it's impressively close to the real thing.

During the pandemic, Tokyo's bustling Meiji University campus stood still. My students were confined to their homes, appearing only as small figures on my screen during Zoom lectures on human-computer interaction. I spent the days in my lab, looking for ways to pass the time. On a particularly bland day in 2020, I was reminiscing about how, before the pandemic, Tokyo used to be packed with people who had flown across the world to enjoy the exciting food scene. But now restaurants were empty and people longed for foods they once relished.

OpenAI, whose generative AI products initially raised fears of widespread cheating on homework, is now exploring how it can get its popular ChatGPT chatbot into classrooms, according to a senior executive. OpenAI's chief operating officer, Brad Lightcap, said at a conference in San Francisco that the company will form a team to explore educational applications of a technology that has threatened to upend industries, stoked new legislation and become a popular learning tool. "Most teachers are trying to figure out ways to incorporate (ChatGPT) into the curriculum and into the way they teach," Lightcap said at the INSEAD Americas Conference last week. "We at OpenAI are trying to help them think through the problem and we probably next year will establish a team with the sole intent of doing that."

Leveraging massive knowledge and learning schemes from large language models (LLMs), recent machine learning models show notable successes in building generalist agents that exhibit the capability of general-purpose task solving in diverse domains, including natural language processing, computer vision, and robotics. However, a significant challenge remains as these models exhibit limited ability in understanding and interacting with the 3D world. We argue this limitation significantly hinders the current models from performing real-world tasks and further achieving general intelligence. To this end, we introduce an embodied multi-modal and multi-task generalist agent that excels in perceiving, grounding, reasoning, planning, and acting in the 3D world. Our proposed agent, referred to as LEO, is trained with shared LLM-based model architectures, objectives, and weights in two stages: (i) 3D vision-language alignment and (ii) 3D vision-language-action instruction tuning. To facilitate the training, we meticulously curate and generate an extensive dataset comprising object-level and scene-level multi-modal tasks with exceeding scale and complexity, necessitating a deep understanding of and interaction with the 3D world. Through rigorous experiments, we demonstrate LEO's remarkable proficiency across a wide spectrum of tasks, including 3D captioning, question answering, embodied reasoning, embodied navigation, and robotic manipulation. Our ablation results further provide valuable insights for the development of future embodied generalist agents.

Generative AI has significantly reduced the entry barrier to the domain of AI owing to the ease of use and core capabilities of automation, translation, and intelligent actions in our day to day lives. Currently, Large language models (LLMs) that power such chatbots are being utilized primarily for their automation capabilities for software monitoring, report generation etc. and for specific personalized question answering capabilities, on a limited scope and scale. One major limitation of the currently evolving family of LLMs is 'hallucinations', wherein inaccurate responses are reported as factual. Hallucinations are primarily caused by biased training data, ambiguous prompts and inaccurate LLM parameters, and they majorly occur while combining mathematical facts with language-based context. Thus, monitoring and controlling for hallucinations becomes necessary when designing solutions that are meant for decision makers. In this work we present the three major stages in the journey of designing hallucination-minimized LLM-based solutions that are specialized for the decision makers of the financial domain, namely: prototyping, scaling and LLM evolution using human feedback. These three stages and the novel data to answer generation modules presented in this work are necessary to ensure that the Generative AI chatbots, autonomous reports and alerts are reliable and high-quality to aid key decision-making processes.

Recommender systems are widely used in various online services, with embedding-based models being particularly popular due to their expressiveness in representing complex signals. However, these models often lack interpretability, making them less reliable and transparent for both users and developers. With the emergence of large language models (LLMs), we find that their capabilities in language expression, knowledge-aware reasoning, and instruction following are exceptionally powerful. Based on this, we propose a new model interpretation approach for recommender systems, by using LLMs as surrogate models and learn to mimic and comprehend target recommender models. Specifically, we introduce three alignment methods: behavior alignment, intention alignment, and hybrid alignment. Behavior alignment operates in the language space, representing user preferences and item information as text to learn the recommendation model's behavior; intention alignment works in the latent space of the recommendation model, using user and item representations to understand the model's behavior; hybrid alignment combines both language and latent spaces for alignment training. To demonstrate the effectiveness of our methods, we conduct evaluation from two perspectives: alignment effect, and explanation generation ability on three public datasets. Experimental results indicate that our approach effectively enables LLMs to comprehend the patterns of recommendation models and generate highly credible recommendation explanations.

To engage human users in meaningful conversation, open-domain dialogue agents are required to generate diverse and contextually coherent dialogue. Despite recent advancements, which can be attributed to the usage of pretrained language models, the generation of diverse and coherent dialogue remains an open research problem. A popular approach to address this issue involves the adaptation of variational frameworks. However, while these approaches successfully improve diversity, they tend to compromise on contextual coherence. Hence, we propose the Bayesian Open-domain Dialogue with Empirical Bayes (BODEB) framework, an empirical bayes framework for constructing an Bayesian open-domain dialogue agent by leveraging pretrained parameters to inform the prior and posterior parameter distributions. Empirical results show that BODEB achieves better results in terms of both diversity and coherence compared to variational frameworks.

The recent success of large language models gives new urgency to the question of how model performance should be evaluated. In many tasks, models can be evaluated for the accuracy of their outputs. However, models can also be evaluated along other important dimensions. For example, we can assess models for the transparency or interpretability of their judgments (Creel 2020; Vredenburgh 2022). We can also assess models for the presence of problematic biases (Kelly 2023; Johnson 2020). Most work on biases in large language models focuses on a conception of bias closely tied to unfairness, especially as affecting marginalized social groups. However, recent work has alleged that large language models also show a number of classic cognitive biases familiar from work in the psychology of reasoning, behavioral economics, and judgment and decisionmaking (Dasgupta et al. 2022; Lin and Ng 2023; Jones and Steinhardt 2022). This development is exciting because it raises the possibility of using cognitive bias as a novel metric by which to evaluate the performance of large language models.

The task of assigning internationally accepted commodity codes (aka HS codes) to traded goods is a critical function of customs offices. Like court decisions made by judges, this task follows the doctrine of precedent and can be nontrivial even for experienced officers. Together with the Korea Customs Service (KCS), we propose a first-ever explainable decision supporting model that suggests the most likely subheadings (i.e., the first six digits) of the HS code. The model also provides reasoning for its suggestion in the form of a document that is interpretable by customs officers. We evaluated the model using 5,000 cases that recently received a classification request. The results showed that the top-3 suggestions made by our model had an accuracy of 93.9\% when classifying 925 challenging subheadings. A user study with 32 customs experts further confirmed that our algorithmic suggestions accompanied by explainable reasonings, can substantially reduce the time and effort taken by customs officers for classification reviews.

Model interpretability and model editing are crucial goals in the age of large language models. Interestingly, there exists a link between these two goals: if a method is able to systematically edit model behavior with regard to a human concept of interest, this editor method can help make internal representations more interpretable by pointing towards relevant representations and systematically manipulating them.