Guillaume Broche collects the award for game of the year at 2025's The Game awards. Guillaume Broche collects the award for game of the year at 2025's The Game awards. Alien: Rogue Incursion Arken Age Ghost Town Marvel's Deadpool VR The Midnight Walk - WINNER

Graph Neural Networks (GNNs) became useful for learning on non-Euclidean data. However, their best performance depends on choosing the right model architecture and the training objective, also called the loss function. Researchers have studied these parts separately, but a large-scale evaluation has not looked at how GNN models and many loss functions work together across different tasks. To fix this, we ran a thorough study - it included seven well-known GNN architectures. We also used a large group of 30 single plus mixed loss functions. The study looked at both inductive and transductive settings. Our evaluation spanned three distinct real-world datasets, assessing performance in both inductive and transductive settings using 21 comprehensive evaluation metrics. From these extensive results (detailed in supplementary information 1 \& 2), we meticulously analyzed the top ten model-loss combinations for each metric based on their average rank. Our findings reveal that, especially for the inductive case: 1) Hybrid loss functions generally yield superior and more robust performance compared to single loss functions, indicating the benefit of multi-objective optimization. 2) The GIN architecture always showed the highest-level average performance, especially with Cross-Entropy loss. 3) Although some combinations had overall lower average ranks, models such as GAT, particularly with certain hybrid losses, demonstrated incredible specialized strengths, maximizing the most top-1 results among the individual metrics, emphasizing subtle strengths for particular task demands. 4) On the other hand, the MPNN architecture typically lagged behind the scenarios it was tested against.

Virtual film production requires intricate decision-making processes, including scriptwriting, virtual cinematography, and precise actor positioning and actions. Motivated by recent advances in automated decision-making with language agent-based societies, this paper introduces FilmAgent, a novel LLM-based multi-agent collaborative framework for end-to-end film automation in our constructed 3D virtual spaces. FilmAgent simulates various crew roles, including directors, screenwriters, actors, and cinematographers, and covers key stages of a film production workflow: (1) idea development transforms brainstormed ideas into structured story outlines; (2) scriptwriting elaborates on dialogue and character actions for each scene; (3) cinematography determines the camera setups for each shot. A team of agents collaborates through iterative feedback and revisions, thereby verifying intermediate scripts and reducing hallucinations. We evaluate the generated videos on 15 ideas and 4 key aspects. Human evaluation shows that FilmAgent outperforms all baselines across all aspects and scores 3.98 out of 5 on average, showing the feasibility of multi-agent collaboration in filmmaking. Further analysis reveals that FilmAgent, despite using the less advanced GPT-4o model, surpasses the single-agent o1, showing the advantage of a well-coordinated multi-agent system. Lastly, we discuss the complementary strengths and weaknesses of OpenAI's text-to-video model Sora and our FilmAgent in filmmaking.

Video surveillance has long been a necessary tool for law enforcement to keep communities safe and reduce crime. Yet sifting through hundreds of hours of footage can be time-consuming and slow down investigations. Video content analytics make this footage significantly more valuable by extracting, identifying and classifying video metadata, making the footage searchable, actionable and quantifiable. The ability to efficiently review, analyze, and respond to events captured by video surveillance has revolutionized law enforcement operations. Video analytics help these agencies accelerate investigations, attain situational awareness, and derive operational intelligence.

Our editors have been hard at work combing through Amazon's Prime Day deals, as well as competing deals from other major online retailers. The result is a compilation of the best deals out there in all the popular tech categories: smart home; TV and audio; PC parts; laptops--you name it. We'll be updating our recommendations as new deals surface, so bookmark this page and check back regularly. See our complete list of the best Prime Day deals on TVs and soundbars. See our complete list of the best Prime Day deals on robot vacuums and air purifiers.

Have you heard about the latest Natural Language Processing framework that was released recently? There has been a remarkable rise in the amount of research and breakthroughs happening in NLP in the last couple of years. I can trace this recent rise to one (seismic) paper – "Attention is All You Need" by Google AI in June 2017. This breakthrough has spawned so many new and exciting NLP libraries that enable us to work with text in ways that were previously limited to our imagination (or Hollywood). We can see a similar pattern when we expand the search to include the entire globe!

When I published the article "Understanding Blockchain" many of you wrote me to ask me if I could make one dedicated to Artificial Intelligence. The truth is that I hadn't had time to get on with it and before sharing anything, I wanted to finish some courses in order to add value to the recommendations. The problem with Artificial Intelligence is that it's much more fragmented, both technologically and in use cases, than Blockchain, making it a real challenge to condense all the information and share it meaningfully. Likewise, I have tried to make an effort in the summary of key concepts and in the compilation of interesting sources and resources, I hope it helps you as well as it did to me! Let's start with a little history. The timeline you see is taken from this article and it shows the most important milestones of Artificial Intelligence.

The project was started in 2007 by David Cournapeau as a Google Summer of Code project, and since then many volunteers have contributed. It is currently maintained by a team of volunteers. For running the examples Matplotlib 1.1.1 is required. CBLAS exists in many implementations; see Linear algebra libraries for known issues. The documentation includes more detailed installation instructions.

Here comes the most important group of articles published on AV. These articles explain who can become an analyst, how to become an analyst and how to sharpen your analytical thinking.