Author(s): Towards AI Editorial Team Originally published on Towards AI. Good morning, AI enthusiasts! This week from the AI community: Open‑Sora 2.0 shows what open-source video generation can do on a tight budget. We also cover JAX’s growing role in high-performance computing, how inverse neural networks rethink input-output mapping, and where LLMs are still falling short in real-world orgs. As always, we’ve got fresh community builds, collab opportunities, and a meme to wrap it all up. Enjoy the read! What’s AI Weekly Open-Sora really caught my attention; it is a fully open-source video generator. They managed to train an end-to-end video generator with just 200,000$. Okay, 200,000$ is a lot of money, but it’s quite low compared to what OpenAI’s Sora or other state-of-the-art video generation models cost. So this week, I am diving into how Open‑Sora 2.0 is built and trained. The training pipeline is not just divided into two stages, but into three distinct stages, each carefully optimized to save compute, reduce cost, and deliver state‑of‑the‑art performance. Read why this matters in the article or watch the video on YouTube. — Louis-François Bouchard, Towards AI Co-founder & Head of Community We’ve got a new guest post out this week — this time with Rami’s Data Newsletter (aka Rami Krispin) — diving into something that doesn’t always get the hype it deserves: LLM data prep. Everyone talks about fine-tuning and model choice, but none of that matters if your data is a mess. In this piece, we explore practical ways to define data standards, ethically scrape and clean your datasets, and cut out the noise — whether you’re pretraining from scratch or fine-tuning a base model. If you’re working on LLMs, this is one of those foundations that’s easy to overlook but hard to ignore. 👉 Read the post here! Data Preparation for LLM: The Key To Better Model Performance Using high-quality data, ethical scraping, and data pre-processing to build reliable LLMs ramikrispin.substack.com Learn AI Together Community section! Featured Community post from the Discord Jonnyhightop has built OneOver, a complete AI workstation. It provides access to multiple powerful AI models through a single, intuitive interface. Users can simultaneously compare up to 3 AI models to find the best for each task, generate and compare images with the advanced Image Studio, and access text generation with specialized prompts and shortcuts. Test out the platform here and support a fellow community member. If you have any questions or feedback, share them in the thread! AI poll of the week! Most of you don’t feel OpenAI leads the LLM race anymore, so what other models do you use and for which tasks? Tell us in the thread! Collaboration Opportunities The Learn AI Together Discord community is flooding with collaboration opportunities. If you are excited to dive into applied AI, want a study partner, or even want to find a partner for your passion project, join the collaboration channel! Keep an eye on this section, too — we share cool opportunities every week! 1. Nericarcasci is working on LEO, a Python-based tool that acts like a conductor for AI. It currently uses local LLMs via Ollama and can suggest commands from natural language input. They are looking for an enthusiast who can take it further. If this sounds fun, reach out in the thread! 2. Robert2405 is looking for an accountability partner to study together. If you think this would help you as well, connect in the thread! 3. Bunnyfuwho has created a custom AI framework with a persistent persona across any interaction, a dynamic moral and ethical framework. They are looking for people who can test it and give feedback. If you think you can help, get the framework in the thread! Meme of the week! Meme shared by ghost_in_the_machine TAI Curated Section Article of the week Beyond Simple Inversion: Building and Applying Inverse Neural Networks By Shenggang Li This blog explores Inverse Neural Networks (INNs) as a method for determining system inputs (x) given observed outputs (y), particularly for complex, multi-valued, or noisy scenarios where traditional inversion fails. INNs utilize paired forward and inverse models trained with cycle consistency loss and regularization constraints (like range limits or smoothness priors) to reconstruct plausible inputs. The discussion in the blog included training strategies, using latent noise to find multiple solutions, and comparing MLP performance against the improved accuracy of Kolmogorov–Arnold Networks (KANs). It concludes with case studies demonstrating INN’s capabilities and suggesting promising future directions. Our must-read articles 1. JAX: The Hidden Gem of AI Research and High-Performance Computing By Harshit Kandoi This article examines JAX, a high-performance numerical computing library from Google Research, highlighting its advantages over TensorFlow and PyTorch. JAX excels in speed and scalability due to its just-in-time (JIT) compilation via XLA, automatic differentiation, and vectorization capabilities. It’s particularly suited for AI research, HPC, and scientific computing, offering features like seamless multi-GPU/TPU support and a NumPy-like API. While JAX faces challenges like a steeper learning curve and a less mature ecosystem than established frameworks, its unique strengths make it a valuable tool for researchers and those working on computationally demanding projects. 2. Manus AI — Does it Live Up to the Hype? By Thomas Reid The rise of autonomous AI agents is generating significant interest. This article reviews Manus, an autonomous AI agent capable of handling various tasks independently. The author tested Manus by requesting a travel itinerary from Edinburgh to Cusco, Peru. While Manus successfully generated a basic itinerary, it struggled with accessing real-time flight and hotel pricing data, providing only estimates and some inaccurate cost information. Although not fully autonomous in this instance, Manus offered a useful starting point for further research. 3. In-Depth Comparison Between KAN and MLPs By Fabio Yáñez Romero This article compares Kolmogorov-Arnold Networks (KANs) and Multilayer Perceptrons (MLPs), highlighting their mathematical foundations and practical applications in deep learning. KANs, based on the Kolmogorov-Arnold Representation Theorem, decompose multivariate functions into sums of univariate functions, offering advantages in interpretability and explainability due to their traceable, individual variable transformations. Conversely, MLPs, while benefiting from the Universal Approximation Theorem, present challenges in interpretability because of their complex, interconnected weight structures. While KANs show promise in symbolic learning and offer dynamic activation functions, they suffer from training instability, architectural complexity, and scalability issues compared to more established MLPs. 4. 10 Ways LLMs Can Fail Your Organization By Gary George This blog examines ten common ways Large Language Models (LLMs) can fail in organizational settings, illustrating each with real-world examples. These failures range from generating false information (“hallucinations”) and misinterpreting user queries to exhibiting bias, producing incoherent responses, and displaying inappropriate tones. It also highlights issues with data retrieval, straying from prompts, providing incomplete answers, and susceptibility to user manipulation. The author advocates for proactive risk mitigation strategies, including using LLM analytics and observability tools, to ensure reliable and trustworthy AI interactions. If you are interested in publishing with Towards AI, check our guidelines and sign up. We will publish your work to our network if it meets our editorial policies and standards. Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor. Published via Towards AI