Pretrained large language models (LLMs) have emerged as a driving force in the evolution of AI systems, and the global race is on to make such models even more powerful. Promising research directions for improving LLMs include model-specific fine-tuning and task-specific prompt engineering. Both of these approaches however have their…

Countless studies have been conducted on non-verbal human communication through pose, appearance, facial expression, hand gestures, etc., aka body language. Non-verbal signals can carry rich information with regard to human expression, and effectively capturing, interpreting and generating such signals can significantly improve the realism of digital humans (avatars) in telepresence…

In the new paper ViperGPT: Visual Inference via Python Execution for Reasoning, a Columbia University research team presents ViperGPT, a framework for solving complex visual queries by integrating code-generation models into vision via a Python interpreter. The proposed approach requires no additional training and achieves state-of-the-art results.

Today’s large language models (LLMs) have moved beyond simple language tasks, demonstrating impressive in-context question-answering capabilities informed by novel user-initiated prompting techniques. …

The ability of graph transformers (GT) to model long-range interactions has improved expressivity and made such architectures a promising alternative to traditional graph neural network (GNN) approaches. The GT downside is their poor scalability, which renders them prohibitively expensive when dealing with large and complex graphs. In the new paper…

ChatGPT’s humanlike conversational competency has taken AI to new levels — might the generative large language model’s next evolutionary leap equip it with similar skills in the visual medium? In the new paper Visual ChatGPT: Talking, Drawing and Editing with Visual Foundation Models, a Microsoft Research Asia team presents Visual…

robotic. The leveraging of deep neural networks in recent years has dramatically transformed TTS, enabling conditioning on factors such as stress and intonation to achieve higher quality and much more humanlike results. Contemporary TTS models however still perform best when dealing with a specific speaker in a specific language. Cross-lingual…

Google’s error-marred unveiling of its Bard chatbot in Paris last month was disappointing, to say the least — but don’t count the tech giant out of the AI language model race just yet. …

Diffusion-based AI models continue to wow the world with remarkable image, audio, and video generation capabilities. This performance however comes at a price, as diffusion models’ iterative sampling process that progressively removes noise to generate high-quality outputs typically requires 10–2000 times more compute than conventional single-step generative models such as…