Liquid’s Block-Based AI Models Boost Complexity and Transparency
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Liquid is changing how AI models are built and explained. Their new approach uses blocks to make models more complex. These blocks are parts of the models that can work together in new ways. This helps build better language and vision models. But it isn't just the models that stand out; it's how they handle the complexity. They use a system called the Liquid DevKit to manage everything.
The DevKit is not just for building models, though. It helps train them, too. Training models is like teaching them to understand and make decisions. The DevKit makes this process more scalable. This means it can handle much larger models by spreading work across many computers. For example, a model with 12 billion parts can be managed by distributing its tasks. This is done using a simple command that splits the model's work among different computers.
Another impressive feature is how they explain what the model does. When a model makes a prediction, it can seem like a mystery. But Liquid's models come with a function called "do explain." This function shows why a model made a certain decision. It examines what part of the input data led to the model's conclusion. This transparency is important because it helps people trust and understand AI better.
Liquid provides a fascinating example of their work with vision-language models. These models can look at an image and a text description or question. Then, they give a response based on what they see. In one case, the model was shown a picture of a cat and an insect. It correctly identified both, even though the insect was tiny and hard to see.
By using the "do explain" function, the team could see how the model focused on different parts of the image. When asked about the image, the model's attention moved from the cat to the insect and finally to the ground. This step-by-step explanation revealed the model's thought process.
Liquid's approach with blocks and the DevKit shows a new way to build and understand AI. It combines flexibility, scalability, and clarity in AI model development. With these tools, AI systems become not just smarter but also more transparent and trustworthy. As AI continues to grow, methods like these might become the standard for creating and using intelligent systems.