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How Multi-Modal AI Models Function On a higher level, multimodal AI systems function on three integrated levels: 1. Modality-S First, every type of input, whether it is text, image, audio, or video, is passed through a unique encoder:…
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1. The early years: Bigger meant better When GPT-3, PaLM, Gemini 1, Llama 2 and similar models came, they were huge. The assumption was: “The more parameters a model has, the more intelligent it becomes.” And honestly, it…
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From Single-Mode to Multimodal: A Giant Leap All these years, our interactions with AI have been generally single-mode. You wrote text, the AI came back with text. That was single-mode. Handy, but a bit like talking with someone…
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Attention, Not Sequence: The major point is Before the advent of Transformers, most models would usually process language sequentially, word by word, just like one reads a sentence. This made them slow and forgetful over long distances. For…
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1. First, Understand Where Latency Comes From Before reducing latency, it's important to understand why AI systems feel slow. Most delays come from a combination of: Network calls to AI APIs Large model inference time Long or badly…
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The Human Parallel: Fast vs. Slow Thinking Psychologist Daniel Kahneman popularly explained two modes of human thinking: System 1 (fast, intuitive, emotional) and System 2 (slow, mindful, rational). System 1 is the reason why you react by jumping…
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Scaling Laws: A Key Aspect of AI Scaling laws identify a pattern found in current AI models: when you are scaling model size, the size of the training data, and computational capacity, there is smooth convergence. It is…