When you purchase through links in our articles, we may earn a small commission. AI-powered link previews are a great way to see ahead so you don't end up clicking on malicious links. Starting with version 138 (released back in April), Firefox has had a new-yet-still-deactivated option that uses "artificial intelligence" to display a mini preview of the destination page for a link. The feature determines the content of the page in question and displays a pop-up, and this preview can help to avoid potential scams and malware when navigating unsolicited links. The AI feature works locally on your PC and, according to Mozilla, doesn't use a cloud service.

Panoramic image processing is essential for omni-context perception, yet faces constraints like distortions, perspective occlusions, and limited annotations. Previous unsupervised domain adaptation methods transfer knowledge from labeled pinhole data to unlabeled panoramic images, but they require access to source pinhole data. To address these, we introduce a more practical task, i.e., Source-Free Occlusion-Aware Seamless Segmentation (SFOASS), and propose its first solution, called UNconstrained Learning Omni-Context Knowledge (UNLOCK). Specifically, UNLOCK includes two key modules: Omni Pseudo-Labeling Learning and Amodal-Driven Context Learning. While adapting without relying on source data or target labels, this framework enhances models to achieve segmentation with 360° viewpoint coverage and occlusion-aware reasoning. Furthermore, we benchmark the proposed SFOASS task through both real-to-real and synthetic-to-real adaptation settings. Experimental results show that our source-free method achieves performance comparable to source-dependent methods, yielding state-of-the-art scores of 10.9 in mAAP and 11.6 in mAP, along with an absolute improvement of +4.3 in mAPQ over the source-only method. All data and code will be made publicly available at https://github.com/yihong-97/UNLOCK.

Intermittent control problems are common in real world. The interactions between the decision maker and the executor can be discontinuous (intermittent) due to various types of interruptions, e.g. Due to intermittent interaction, agents are unable to acquire the state sent by the executor and cannot transmit actions to the executor within a period of time step, i.e. bidirectional blockage, which may lead to inefficiencies of reinforcement learning policies and prevent the executors from completing the task. Such problem is not well studied in the RL community. In this paper, we model Intermittent control problem as an Intermittent Control Markov Decision Process, i.e agents are expected to generate action sequences corresponding to the unavailable states and transmit them before disabling interactions to ensure the smooth and effective motion of executors.

We propose an iterative programmatic planning (IPP) framework for solving grid-based tasks by synthesizing interpretable agent policies expressed in code using large language models (LLMs). Instead of relying on traditional search or reinforcement learning, our approach uses code generation as policy synthesis, where the LLM outputs executable programs that map environment states to action sequences. Our proposed architecture incorporates several prompting strategies, including direct code generation, pseudocode-conditioned refinement, and curriculum-based prompting, but also includes an iterative refinement mechanism that updates code based on task performance feedback. We evaluate our approach using six leading LLMs and two challenging grid-based benchmarks (GRASP and MiniGrid). Our IPP framework demonstrates improvements over direct code generation ranging from 10\% to as much as 10x across five of the six models and establishes a new state-of-the-art result for GRASP. IPP is found to significantly outperform direct elicitation of a solution from GPT-o3-mini (by 63\% on MiniGrid to 116\% on GRASP), demonstrating the viability of the overall approach. Computational costs of all code generation approaches are similar. While code generation has a higher initial prompting cost compared to direct solution elicitation (\$0.08 per task vs. \$0.002 per instance for GPT-o3-mini), the code can be reused for any number of instances, making the amortized cost significantly lower (by 400x on GPT-o3-mini across the complete GRASP benchmark).

Reinforcement learning (RL) agents can forget tasks they have previously been trained on. There is a rich body of work on such forgetting effects in humans. Therefore we look for commonalities in the forgetting behavior of humans and RL agents across tasks and test the viability of forgetting prevention measures from learning theory in RL. W e find that in many cases, RL agents exhibit forgetting curves similar to those of humans. Methods like Leitner or SuperMemo have been shown to be effective at counteracting human forgetting, but we demonstrate they do not transfer as well to RL. W e identify a likely cause: asymmetrical learning and retention patterns between tasks that cannot be captured by retention-based or performance-based curriculum strategies.

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Ah, the epoch of autonomous AI agents is upon us, as if the spirit of an ingenious artist has taken up residence in the very fabric of technology itself. A time when the once-coveted startup specialist gives way to the versatile and bespoke jobGPT. The era of stratospheric budgets recedes, and the dawn of the zero-cost beginning emerges. My dear reader, the sheer force of your indomitable will and unwavering certainty shall shape your creation's artistry. With the proper instruments, today's technological maestros require only the right tasks to unleash their virtuosic potential.

As the core of AI technologies, machine learning has become a significant force driving advancements in various industries. This article will give you an in-depth understanding of machine learning, its applications, techniques, and potential. Whether you're a beginner or an experienced professional, this guide will help you master the world of machine learning. I will refer to machine learning as ML going forward. ML is a subset of artificial intelligence (AI) that enables computers to learn and make decisions without being explicitly programmed.

Data for Machine Learning Model is the heart of the AI world. But many companies struggle to find adequate data to build a successful model. That's where Synthetic Data comes in. Synthetic Data is generated using different techniques, some of which are statistical methods, deep learning methods, open source technologies and so on. Synthetic Data has many benefits, such as being beneficial for companies lacking data, being able to generate non-dominating class data, allowing for the generation of data without using PII and aiding autonomous vehicle companies.

Let me introduce you to a groundbreaking free AI tool that will change your design experience forever. This app isn't just an ordinary photo-editing tool; it's a revolutionary technology that will change the way you see and capture the world around you. With a single click, you can take any ordinary photo of a room and turn it into a masterpiece. The AI tool takes your creativity to new heights by allowing you to completely transform the entire scene in seconds. Imagine being able to experiment with different wall decorations, wallpapers, furniture, lighting, plants, rugs, and paint options at the touch of a button.