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Multimodal AI

Models that read, see, and listen at once.

Part 1

Judging the Wine with Every Sense at Once

how multimodal AI combines text, image, audio, and video understanding into one system, the way a sommelier judges a wine through sight, smell, and taste together.

Part 2

What Each Sense Actually Reports

what text, images, audio, and video each individually contribute to a multimodal system's understanding, before any of it gets combined.

Part 3

Before the Palate Could Be Trained

how AI systems handled different input types before multimodal fusion became genuinely practical, and why combining separate models never worked as well.

Part 4

Reading the Label Before the First Sip

how the text modality grounds and contextualizes a multimodal system's understanding of everything else it processes.

Part 5

The Color in the Glass

what the image modality specifically contributes to a multimodal system, and the kinds of visual understanding that text alone can't replicate.

Part 6

The Aroma Before the Taste

what the audio modality contributes to multimodal AI, and why tone, emphasis, and timing carry information a transcript alone loses.

Part 7

Tasting Notes That Move

what the video modality adds beyond a single still image: motion, sequence, and change over time.

Part 8

Blending the Notes Into One Judgment

how multimodal fusion actually combines separate modalities into one unified representation a model can reason over.

Part 9

When the Nose and the Palate Disagree

how multimodal systems handle genuine conflicts between what different modalities seem to indicate.

Part 10

A Vocabulary for Describing What's Tasted

how a shared embedding space lets a multimodal model compare and relate concepts across genuinely different modalities.

Part 11

Training the Palate

what it takes to train a multimodal model, and why paired, genuinely aligned data across modalities is the hardest part of the process.

Part 12

The Blind Tasting Test

how evaluating a multimodal model requires testing genuine cross-modal reasoning, not just each modality's capability in isolation.

Part 13

A Sommelier Who's Never Tasted That Region's Wine

the real limitations multimodal models still face with unfamiliar domains, and why cross-modal generalization isn't automatic.

Part 14

Describing a Wine to Someone Who Can't Taste It

how multimodal models generate output in one modality from input in another, like captioning images or generating images from text.

Part 15

The Full Tasting Menu

how multimodal capability extends agentic AI systems, letting an agent perceive and act on images, audio, and video, not just text.

Part 16

Cost of a Full Tasting

why multimodal models generally cost more to run than single-modality ones, and how to think about that tradeoff deliberately.

Part 17

The Sommelier's Certification

how trust and reliability in multimodal outputs get established through rigorous, ongoing verification, not assumed from a model's general capability.

Part 18

When to Order the Full Tasting vs. Just a Glass

a practical decision framework for choosing when a task genuinely needs multimodal capability, versus when a single modality is enough.

Part 19

The Tasting Room's Ongoing Operations

what it takes to run a multimodal AI system reliably in production over time, extending the operational discipline covered elsewhere in this content library.

Part 20

The Full Tasting Menu, Reassembled

reassembling every course covered across this series into the complete picture of how multimodal AI genuinely combines text, image, audio, and video.