Original): Towards the AI editorial team
Originally published in the direction of artificial intelligence.
Good morning, AI enthusiasts!
This week, the AI: Open-Sora 2.0 shows what Open Source video can generate with a strict budget. We also include the growing role of Jax in high performance calculations, how the opposite neural networks are thinking about input mapping and where LLM still does not have real orgs. As always, we have fresh community compilations, cooperation opportunities and a meme to end it all. Enjoy reading!
What is a week AI
Open-Sora really caught my attention; It is a fully open source video generator. They managed to train a comprehensive video generator with just $ 200,000. Okay, $ 200,000 is a lot of money, but it is quite low compared to what Sorai cost or other most modern video generation models. This week I dive in the construction and trained Open -Sora 2.0. The training pipeline is not only divided into two stages, but into three separate stages, each carefully optimized to save calculations, reduce costs and provide state efficiency. Read why it matters in the article Or Watch the video on YouTube.
-Louis-François Bouchard, towards the co-founder of AI and the head of the community
This week we have a new post of guests – this time with Rami Data Bulletin (Aka Arms Krispin) – diving in something that does not always get the noise that deserves: LLM DATA PREP. Everyone talks about refining and choosing a model, but nothing matters if your data is a mess.
In this song, we examine the practical ways to define data standards, ethical scrape and cleaning data sets, and cutting noise-not meaninglessly from whether you simplify from scratch or refine the basic model. If you work on LLM, this is one of those foundations that can be easily overlooked, but it's difficult to ignore.
Preparation of data for LLM: The key to better performance of the model
Use of high quality data, ethical scraping and data processing for the construction of reliable LLMS
ramikrispin.substack.com
Learn a community section!
Presented post from disagreement
JonnyHightt He built Oneover, a complete AI work station. It provides access to many powerful AI models through one intuitive interface. Users can simultaneously compare to 3 AI models to find the best for each task, generate and compare images with Advanced Image Studio, and gain access to generating text with specialized prompts and shortcuts. Test the platform here and support another member of the community. If you have any questions or opinions, Share them in the thread!
Ai Poll week!
Most of you no longer think that OpenAI is running the LLM race, so what other models do you use and for what tasks? Tell us in the thread!
Opportunities for cooperation
The community of science and together is flooding with the possibilities of cooperation. If you are excited to immerse yourself in the artificial intelligence, you want a learning partner and even want to find a partner for your passion project, Join the cooperation channel! Also have an eye on this section – we share cool possibilities every week!
1. Nericarcasci He works on Leo, a tool based on Python, which acts like a conductor AI. Currently, it uses local LLMS via Ollama and may suggest commands from the natural language entry. They are looking for enthusiasts that can go further. If it sounds funny Reach in the thread!
2. Robert2405 He is looking for a partner responsible for learning together. If you think it will help you too Connect in the thread!
3. Bunnyfuwho He created a non -standard AI frame with a permanent personality in every interaction, dynamic moral and ethical frames. They are looking for people who can test it and provide feedback. If you think you can help Get the framework in the thread!
Meme of the week!
Meme made available by Ghost_in_the_machine
TAI section
Article
Apart from simple inversion: building and using reverse neural networks By Shenggang Li
This blog studies the opposite neural networks (Inns) as a method of determining the system input data (X) provided for observed results (s), especially in the case of complex, multi -level or noisy scenarios in which traditional inversion fails. Inns use paired models forward and reverse trained with limitations of loss of cycle consistency and regulatory limitations (such as scope limits or priority) to reconstruct likely inputs. The discussion on the blog included training strategies, using a latent noise to find many solutions and compare MLP performance with better accuracy of the Kolmogorowa network – Arnold (Kans). Summarizes cases of Inn skills and suggest promising future fields.
Ours must read articles
1. JAX: Hidden AI gem and high -performance calculations by Harshit Kandoi
In this article, he analyzes JAX, a high -performance numerical computing library from Google Research, emphasizing its advantages in relation to tensorflow and question. JAX is distinguished by speed and scale due to its compilation Just-in-Time (JIT) via XLA, automatic differentiation and vectorization possibilities. It is especially suitable for AI, HPC and Computing Scientific research, offering functions such as trouble-free multi-gp/tpu and API support reminiscent of NumPy. While Jax faces challenges, such as a more steep learning curve and a less mature ecosystem than a fixed frame, its unique strengths make it a valuable tool for scientists and people working on projects requiring calculation.
2. Manus AI – Does it live with noise? By Thomas Reid
The increase in autonomous AI agents generates significant interest. This article has reviewed Manus, an autonomous AI agent capable of independent operation of various tasks. The author tested Manus, asking for a travel plan from Edinburgh to Cusco, Peru. While Manus successfully generated a basic travel plan, he fought access to data on flight and hotels in real time, providing only estimates and some inaccurate costs. Although in this case it is not fully autonomous, Manus offered a useful starting point for further research.
3. In -depth comparison between KAN and MLPS Fabio Yáñez Romero
This article compares the Kolmogorov-Arnold (Kans) and multilayer perceprons (MLPS) networks, emphasizing their mathematical foundations and practical applications in deep learning. Cans, based on the claim of the Kolmogorowa-Arnold representation, distribute multidimensional functions into the sums of single-legged functions, offering advantages in interpretation and the possibility of explaining due to their terror, individual variable transformations. And vice versa, MLP, using the universal claim of approximately, pose challenges in the interpretation due to their complex, mutual related weight structures. While cans are promised in symbolic learning and offers dynamic activation functions, they suffer from training instability, architectural complexity and scalability problems compared to the more determined MLP.
4. 10 ways in which LLM can shock your organization according to Gary George
This blog analyzes ten common ways in which large language models (LLMS) can cope in organizational conditions, illustrating each of the actual examples. These failures include generating false information (“hallucinations”) and incorrectly interpretative user queries to show bias, create inconsistent answers and display the wrong tones. It also emphasizes problems with data search, departing from prompts, giving incomplete answers and susceptibility to manipulation of users. The author is in favor of proactive risk reducing strategies, including the use of analytical tools and LLM observation to ensure reliable and trustworthy AI interactions.
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