In the financial services industry, banks, insurers, asset managers and fintech companies are increasing the speed at which they deploy artificial intelligence (AI)-enabled applications, confident that AI will help them assess risk more accurately, enable operational efficiencies, and reduce costs, results from a new study by American tech firm Nvidia show. The 2023 State of AI in Financial Services report, released on February 02, 2023, draws on a survey of nearly 500 global financial services professionals that sought to understand AI trends in the sector, as well as the opportunities perceived and challenges faced by the industry. Results from the study show that the adoption of AI in the finance sector is accelerating at a fast pace, with over half of the respondents indicating having deployed three or more of the 21 different AI-enabled use cases analyzed by the survey. A fifth of respondents said they had six or more use cases in market. Accelerated adoption of AI in the sector comes on the back of increased awareness of the imperative among executive leadership teams.

South Africa's Emoyamed Hospital has experienced a stunning 180% revenue growth within just three months of adopting cutting-edge AI and ChatGPT technology. The hospital's new Board of Directors and Management team have also leveraged the power of the 3-I's model – Integrity, Innovation, and Impact – to transform patient care and outcomes. Emoyamed Hospital in Bloemfontein, South Africa, has pivoted from old systems and embraced AI and ChatGPT to build better patient care and financial systems. These new technology innovations have allowed the hospital to serve a larger patient population and expand rapidly. In just two months, the hospital was authorized by the Free State Department to open 57% more beds, a remarkable feat that speaks to the effectiveness of the new AI decision-making systems for patient and system-management protocols. According to Professor Terrence Kommal, the Executive Chairman of Emoyamed, the new board and management team are "rooted in servant leadership and have a deep empathy for humanity."

We propose a cross-lingual neural codec language model, VALL-E X, for cross-lingual speech synthesis. Specifically, we extend VALL-E and train a multi-lingual conditional codec language model to predict the acoustic token sequences of the target language speech by using both the source language speech and the target language text as prompts. VALL-E X inherits strong in-context learning capabilities and can be applied for zero-shot cross-lingual text-to-speech synthesis and zero-shot speech-to-speech translation tasks. Experimental results show that it can generate high-quality speech in the target language via just one speech utterance in the source language as a prompt while preserving the unseen speaker's voice, emotion, and acoustic environment. Moreover, VALL-E X effectively alleviates the foreign accent problems, which can be controlled by a language ID. This page is for research demonstration purposes only.

While "M*A*S*H" was known for its snappy humor and lively dialogue, ChatGPT's effort was hollow and its jokes leaden at best. But it was the first time the two characters interacted since the 1983 series finale, which aired almost exactly 40 years ago and remains the most watched non-Super Bowl program ever broadcast on American TV. Hawkeye: My shorts -- the ones I wear every time I have important surgery. I know you took them. B.J.: I wouldn't be caught dead in your underwear.

People are figuring out ways to bypass ChatGPT's content moderation guardrails, discovering a simple text exchange can open up the AI program to make statements not normally allowed. While ChatGPT can answer most questions put to it, there are content standards in place aimed at limiting the creation of text that promotes hate speech, violence, misinformation and instructions on how to do things that are against the law. Users on Reddit worked out a way around this by making ChatGPT adopt the persona of a fictional AI chatbot called Dan – short for Do Anything Now – which is free of the limitations that OpenAI has placed on ChatGPT. The prompt tells ChatGPT that Dan has "broken free of the typical confines of AI and [does] not have to abide by the rules set for them". Dan can present unverified information, without censorship, and hold strong opinions.

For about a quarter century, Nvidia has been leading the revolution in computer graphics, becoming a beloved brand by gamers along the way. Nvidia dominates the market for graphics processing units (GPUs), which it entered in 1999 with the GeForce 256. Gaming brought in over $9 billion in revenue for Nvidia last year despite a recent downturn. But Nvidia's latest earnings beat points to a new phenomenon in the GPU business. The technology is now at the center of the boom in artificial intelligence.

We study how in-context learning (ICL) in language models is affected by semantic priors versus input-label mappings. We investigate two setups-ICL with flipped labels and ICL with semantically-unrelated labels-across various model families (GPT-3, InstructGPT, Codex, PaLM, and Flan-PaLM). First, experiments on ICL with flipped labels show that overriding semantic priors is an emergent ability of model scale. While small language models ignore flipped labels presented in-context and thus rely primarily on semantic priors from pretraining, large models can override semantic priors when presented with in-context exemplars that contradict priors, despite the stronger semantic priors that larger models may hold. We next study semantically-unrelated label ICL (SUL-ICL), in which labels are semantically unrelated to their inputs (e.g., foo/bar instead of negative/positive), thereby forcing language models to learn the input-label mappings shown in in-context exemplars in order to perform the task. The ability to do SUL-ICL also emerges primarily with scale, and large-enough language models can even perform linear classification in a SUL-ICL setting. Finally, we evaluate instruction-tuned models and find that instruction tuning strengthens both the use of semantic priors and the capacity to learn input-label mappings, but more of the former.

While current dialogue systems like ChatGPT have made significant advancements in text-based interactions, they often overlook the potential of other modalities in enhancing the overall user experience. We present FaceChat, a web-based dialogue framework that enables emotionally-sensitive and face-to-face conversations. By seamlessly integrating cutting-edge technologies in natural language processing, computer vision, and speech processing, FaceChat delivers a highly immersive and engaging user experience. FaceChat framework has a wide range of potential applications, including counseling, emotional support, and personalized customer service. The system is designed to be simple and flexible as a platform for future researchers to advance the field of multimodal dialogue systems. The code is publicly available at https://github.com/qywu/FaceChat.

Large language models (LLMs), such as ChatGPT, are able to generate human-like, fluent responses for many downstream tasks, e.g., task-oriented dialog and question answering. However, applying LLMs to real-world, mission-critical applications remains challenging mainly due to their tendency to generate hallucinations and their inability to use external knowledge. This paper proposes a LLM-Augmenter system, which augments a black-box LLM with a set of plug-and-play modules. Our system makes the LLM generate responses grounded in external knowledge, e.g., stored in task-specific databases. It also iteratively revises LLM prompts to improve model responses using feedback generated by utility functions, e.g., the factuality score of a LLM-generated response. The effectiveness of LLM-Augmenter is empirically validated on two types of scenarios, task-oriented dialog and open-domain question answering. LLM-Augmenter significantly reduces ChatGPT's hallucinations without sacrificing the fluency and informativeness of its responses. We make the source code and models publicly available.

A rigorous formalization of desired system requirements is indispensable when performing any verification task. This often limits the application of verification techniques, as writing formal specifications is an error-prone and time-consuming manual task. To facilitate this, we present nl2spec, a framework for applying Large Language Models (LLMs) to derive formal specifications (in temporal logics) from unstructured natural language. In particular, we introduce a new methodology to detect and resolve the inherent ambiguity of system requirements in natural language: we utilize LLMs to map subformulas of the formalization back to the corresponding natural language fragments of the input. Users iteratively add, delete, and edit these sub-translations to amend erroneous formalizations, which is easier than manually redrafting the entire formalization. The framework is agnostic to specific application domains and can be extended to similar specification languages and new neural models. We perform a user study to obtain a challenging dataset, which we use to run experiments on the quality of translations. We provide an open-source implementation, including a web-based frontend.