A closer look at ‘marriage’ of artificial intelligence models – ryan

“Ha” said that his team focused on forming “collective intelligence capable of continuing even if one of the” models stopped.

The “Sakana Ai” approach includes combining the advantages of different models to achieve greater flexibility and sustainability, and this approach has attracted the support of the American Nafidia Giant Company, as well as Japanese banks and other companies looking to adopt advanced sedimentary artificial intelligence solutions quickly. The company seeks to inspire nature, as organisms from ants cooperate to humans to solve problems, which was reflected in its name “Sakana”, which means “fish” in Japanese.

The “Sakana AI” approach reflects the philosophy of “combining models” aimed at improving accuracy, enhancing durability, improving resource use, and enhancing generalization. The integration does not mean merely the random combination of “models”, but rather related to the creation of an integrated entity that benefits from the individual strengths of each model, just as the two different characters in marriage to each other learn to achieve better integration.

What is “Mix models”?

“Merving models” refers to the process of collecting several “models” “automatic learning”, whether they are “large linguistic models” (“” LLM “) or specialized, to provide better performance in various applications. This technique includes what is sometimes known as “Models Assembly” (“Model Ensaming”) or “Assembly of weights” (“Model Agreegeon”). The main goal is to enhance the capabilities of each model by addressing and exploiting individual complications when needed, thus improving the final results through multiple areas.

Benefits of “Mix models”

Improving accuracy:
The integration benefits from the strengths of each model, which enhances accuracy in various tasks. For example, in linguistic translation, a coach model can be combined on translation from English to Chinese with another model of translation from Chinese to Japanese, which reduces errors and enhances the quality of multi -language translation. As for summarizing the texts, the merging of “models” specialized in various fields such as news and social media and “magazines” provides accurate and comprehensive summaries, capturing the fine details of each type of content.

Increased durability:
The integration can improve the durability of “models” when dealing with various data. In the analysis of feelings, the combination of “models” trained on articles, publications on social media and product reviews leads to more reliable expectations. As for the “Chatter” robots (“” “” Tributs “), it can provide accurate and consistent responses regardless of the type of inquiry, if” models “specialized in technical support, complaints management and product information are merged.

Improving resource use:
The “Mixing Models” allows more efficient use of computer resources, where “models” can be integrated into different languages ​​into one model, such as merging “trained” models on English, Japanese, Spanish and French, to reduce the need for separate “models”, thus reducing energy consumption and increasing sustainability.

Techniques “Mix models”

There are many methods to collect “models”:

“Liner Mirg”): The weighted average is used to control the contribution of each model in the final model.

SLERP (“Ambassador Lynir Intercision”): It maintains the engineering properties of the phrase, and collects two models at a time with the possibility of creating multiple hierarchical installations.

“Task Victor Algoreths”): Trends in Ozan space to improve tasks, and can be modified and integrated to improve performance in several tasks. It includes techniques such as “Task Aristotette”, “Tayez” (“Tarim, Elce Sain & Merg”), and “Dar” Droub and Riscal)).

“Frankenge”: Merging several “models” specialized for creating a single -based model and improvement on specific data sets.

“Model Mix” applications

Applications include “natural language” processing such as translation, summary of texts, emotional analysis, as well as support for self -driving vehicles and “robots”, and improve the accuracy of “computer vision” in identifying images and detecting medical things and applications.

Merging models allows for resolution enhancement by taking advantage of the strengths of each model, such as improving multi -language translations or summarizing the content from various sources with high accuracy. It also enhances durability through various data collections, such as combining feelings analysis or chatting robots trained into multiple data sets to provide reliable and consistent performance. It achieves more efficient resource use by integrating specialized models into multiple languages ​​within one model, which reduces the need for separate models and reduces energy consumption.

Applications include “natural language” processing such as translation, summary of texts, and emotional analysis, as well as self -driving and robots, where compact models can make better decisions by combining several experiences, and computer vision that improves the accuracy of images recognition and detection of things and face recognition, including advanced medical applications.

Future challenges

Despite the advantages, this technology faces challenges such as the compatibility of the structure, the variation of performance between “models”, the dangers of excess or lack of allocation, and the complexity of the compact “models” and the difficulty of their interpretation, which requires accurate tests to ensure performance.

Last May, “Sakana AI” announced a long -term partnership with the Japanese “MUFG” bank to develop “artificial intelligence” systems for banks, while “Ha” focuses on maintaining a small and specialized research team, with the expansion of the branch that supports the deployment of “artificial intelligence” solutions in the public sector and private companies.

With the increasing demand for specialized “models”, it seems that “combining models” is the future of developing “artificial intelligence”, providing more intelligent and flexible tools, similar to human relations in their ability to learn, adapt and integrate.