That would have been nice from the start. Several LG smart TV owners, including some staff, were surprised to find what looked like suddenly installed on their devices earlier this week. After all the raised eyebrows, a representative from LG has reached out to say that the company will take steps to allow users to delete the shortcut icon if they wish. According to the spokesperson, the Copilot icon is a shortcut for launching the AI chatbot in the TV's web browser rather than an application embedded in the appliance. We've asked for more specifics about when people will be able to get rid of the Copilot prompt, but have not received a response at this time.

Diffusion language models have recently emerged as a competitive alternative to autoregressive language models. Beyond next-token generation, they are more efficient and flexible by enabling parallel and any-order token generation. However, despite empirical successes, their computational power and fundamental limitations remain poorly understood. In this paper, we formally study whether non-autoregressive generation in Masked Diffusion Models (MDM) enables solving problems beyond the reach of Auto-Regressive Models (ARM). Our results show that MDM with sufficiently large context length is computationally universal with decoding steps matching the optimal parallel time complexity in PRAM. However, when controlling for other factors, MDM's flexibility to generate in any-order does not expand what ARM can already solve. To address this, we propose a new form of generation called any-process generation, which extends MDM with capabilities to remask, insert and delete tokens, allowing self-correction, length-variable editing, and adaptive parallelism. Theoretically and empirically, we demonstrate these capabilities enable scalability to significantly harder reasoning problems that are otherwise intractable for ARM and vanilla MDM. Additionally, they prove essential for generation tasks where objects naturally evolve through non-sequential processes, crucial for extending current LLMs beyond natural language to domains such as coding and science.

AI for search has arrived, and it can be useful, in moderation. Breakthroughs, discoveries, and DIY tips sent every weekday. AI has made its way into nearly all of the apps and websites we use regularly, whether you like it or not. From editing images to planning trips, or doing anything else on our digital devices, AI is now more likely to show up. That extends to web searches as well.

Neural Information Processing Systems http://nips.cc/

Editing complex real-world sound scenes is difficult because individual sound sources overlap in time. Generative models can fill-in missing or corrupted details based on their strong prior understanding of the data domain. We present a system for editing individual sound events within complex scenes able to delete, insert, and enhance individual sound events based on textual edit descriptions (e.g., ``enhance Door'') and a graphical representation of the event timing derived from an ``event roll'' transcription. We present an encoder-decoder transformer working on SoundStream representations, trained on synthetic (input, desired output) audio example pairs formed by adding isolated sound events to dense, real-world backgrounds. Evaluation reveals the importance of each part of the edit descriptions -- action, class, timing. Our work demonstrates ``recomposition'' is an important and practical application.

We demonstrate that this is due to attention weights that lose fidelity with longer sequences, particularly when the input numbers are numerically similar.