𝐌𝐨𝐬𝐭 𝐦𝐨𝐝𝐞𝐥𝐬.

Static: AI models are fixed, do not change during inference, and process input-based data. We find them recycling the same output with no ability to learn when put to use.

Black Box-Oriented: AI models lack transparency when outputs are given. How they arrive at conclusions is hidden. Most results are biased, sometimes unrelated, and leave no room for correction.

Outdated: Let’s face it, models are no longer effective. They fail to keep track of new data, leaving models behind. As real-world data evolves over time, we find models producing inaccurate outputs, leading to degraded performance.

With @Alloranetwork Dynamic, Self-Improving Models, AI Models Are Set to Be
Dynamic: AI models evolve based on performance signals and context. With reputers evaluating accuracy, models adapt to new data by learning from each other’s output.

Transparent: Models on Allora are verified, and their performance is openly evaluated and incentivized on-chain. Every prediction is checked, validated, and scored. With open data repositories available to users, we can all see how results are generated.