ERNIE: A Breakthrough in Multimodal AI

Baidu has launched its new model, ERNIE, which is described as a revolution in the world of multimodal artificial intelligence, outperforming models like GPT and Gemini in certain tests. This model is specifically designed to target data often overlooked by text-focused models.

Challenges in Processing Complex Data

Many companies face significant challenges in extracting valuable information from non-textual data such as engineering drawings, factory floor video footage, medical scans, and logistical dashboards. This is where the ERNIE-4.5-VL-28B-A3B-Thinking model comes in, designed to be the optimal solution for these challenges.

What interests company engineers is not only the model’s multimodal capabilities but also its unique architecture. It is described as a “lightweight” model, activating only three billion parameters during operation. This strategy aims to reduce the high inference costs that often hinder AI scaling projects.

Model Superiority in Complex Visual Data Analytics

The ERNIE model proves its efficiency in processing dense non-textual data. For example, it can interpret a “peak time reminder” chart to find the best visiting times, a challenge reflecting resource scheduling issues in logistics or retail.

The model also shows exceptional capability in technical fields, such as solving electrical circuits using Ohm’s and Kirchhoff’s laws. The future of this model could assist in verifying designs or explaining complex drawings to new employees.

From Perception to Automation: A Paradigm Shift in AI

One of the main challenges facing AI in organizations is transitioning from perception to automation. The ERNIE 4.5 model claims to address this challenge by integrating visual guidance with tool usage.

The model can perform tasks such as finding all people wearing suits in an image and returning their coordinates in JSON format, facilitating visual inspection in production lines or reviewing site images for safety compliance purposes.

Enhancing Business Intelligence with Multimodal AI

Baidu’s new model also targets company video archives, from training sessions to security footage. It can extract all on-screen text and link it to precise timestamps.

The model also demonstrates temporal awareness, capable of finding specific scenes (such as those filmed on a bridge) by analyzing visual evidence. The clear goal is to make large video libraries searchable, allowing employees to find the exact moment a particular topic was discussed in a two-hour seminar.

Conclusion

The advancements brought by the ERNIE model to the AI world are a significant step towards the future, where these models can see, read, and make decisions in specific business contexts. Although the hardware requirements to run these models may pose a barrier for some, the potential benefits make it essential for major companies to weigh this investment against the expected gains in efficiency and analytical capability.