Sam Altman returns as CEO, OpenAI has a new initial board
Mira Murati as CTO, Greg Brockman returns as President. Read messages from CEO Sam Altman and board chair Bret Taylor.
Mira Murati as CTO, Greg Brockman returns as President. Read messages from CEO Sam Altman and board chair Bret Taylor.
One of the critical challenges in model-based reinforcement learning (MBRL) is managing imperfect dynamics models. This limitation of MBRL becomes particularly evident in complex environments, where the ability to forecast accurate models is crucial yet difficult, often leading to suboptimal policy learning. The challenge is achieving accurate predictions and ensuring these models can adapt and…
Powered by wellows.com Welcome Interested in sponsorship opportunities? Join the AI conversation and transform your advertising strategy with AI weekly sponsorship aiweekly.co In the News OpenAI’s new for-profit plan leaves many unanswered questions OpenAI has abandoned its controversial restructuring plan. In a dramatic reversal, the company said Monday it would no longer try to separate…
We’re developing a blueprint for evaluating the risk that a large language model (LLM) could aid someone in creating a biological threat. In an evaluation involving both biology experts and students, we found that GPT-4 provides at most a mild uplift in biological threat creation accuracy. While this uplift is not large enough to be conclusive,…
Deep convolutional neural networks (DCNNs) have been a game-changer for several computer vision tasks. These include object identification, object recognition, image segmentation, and edge detection. The ever-growing size and power consumption of DNNs have been key to enabling much of this advancement. Embedded, wearable, and Internet of Things (IoT) devices, which have restricted computing resources…
Machine translation, a crucial aspect of Natural Language Processing, has significantly increased. Yet, a primary challenge persists: producing translations beyond mere adequacy to reach near perfection. Traditional methods, while effective, often need to be improved by their reliance on large datasets and supervised fine-tuning (SFT), leading to limitations in the quality of the output. Recent…
Model Predictive Control (MPC) has become a key technology in a number of fields, including power systems, robotics, transportation, and process control. Sampling-based MPC has shown effectiveness in applications such as path planning and control, and it is useful as a subroutine in Model-Based Reinforcement Learning (MBRL), all because of its versatility and parallelizability, Despite…