AI models becoming multimodal
What "Multimodal AI" Actually Means — A Quick Refresher Historically, AI models like early ChatGPT or even GPT-3 were text-only: they could read and write words but not literally see or hear the world. Now, with multimodal models (like OpenAI's GPT-5, Google's Gemini 2.5, Anthropic's Claude 4, and MRead more
What “Multimodal AI” Actually Means — A Quick Refresher
Historically, AI models like early ChatGPT or even GPT-3 were text-only: they could read and write words but not literally see or hear the world.
Now, with multimodal models (like OpenAI’s GPT-5, Google’s Gemini 2.5, Anthropic’s Claude 4, and Meta’s LLaVA-based research models), AI can read and write across senses — text, image, audio, and even video — just like a human.
I mean, instead of typing, you can:
- Talk to AI orally.
- Show it photos or documents, and it can describe, analyze, or modify them.
- Play a video clip, and it can summarize or detect scenes, emotions, or actions.
- Put all of these together simultaneously, such as playing a cooking video and instructing it to list the ingredients or write a social media caption.
It’s not one upgrade — it’s a paradigm shift.
From “Typing Commands” to “Conversational Companionship”
Reflect on how you used to communicate with computers:
You typed, clicked, scrolled. It was transactional.
And now, with multimodal AI, you can simply talk in everyday fashion — as if talking to another human being. You can point what you mean instead of typing it out. This is making AI less like programmatic software and more like a co-actor.
For example:
- A pupil can display a photo of a math problem, and the AI sees it, explains the process, and even reads the explanation aloud.
- A traveler can point their camera at a sign and have the AI translate it automatically and read it out loud.
- A designer can sketch a rough logo, explain their concept, and get refined, color-corrected variations in return — in seconds.
The emotional connection has shifted: AI is more human-like, more empathetic, and more accessible. It’s no longer a “text box” — it’s becoming a friend who shares the same perspective as us.
Revolutionizing How We Work and Create
1. For Creators
Multimodal AI is democratizing creativity.
Photographers, filmmakers, and musicians can now rapidly test ideas in seconds:
- Upload a video and instruct, “Make this cinematic like a Wes Anderson movie.”
- Hum a tune, and the AI generates a full instrumental piece of music.
- Write a description of a scene, and it builds corresponding images, lines of dialogue, and sound effects.
This is not replacing creativity — it’s augmenting it. Artists spend less time on technicalities and more on imagination and storytelling.
2. For Businesses
- Customer support organizations use AI that can see what the customer is looking at — studying screenshots or product photos to spot problems faster.
- In online shopping, multimodal systems receive visual requests (“Find me a shirt like this but blue”), improving product discovery.
And even for healthcare, doctors are starting to use multimodal systems that combine text recordings with scans, voice notes, and patient videos to make more complete diagnoses.
3. For Accessibility
This may be the most beautiful change.
Multimodal AI closes accessibility divides:
- To the blind, AI can describe pictures and describe scenes out loud.
- To the deaf, it can interpret and understand emotions through voices.
- To the differently learning, it can interpret lessons into images, stories, or sounds according to how they learn best.
Technology becomes more human and inclusive — less how to learn to conform to the machine and more how the machine will learn to conform to us.
The Human Side: Emotional & Behavioral Shifts
- As AI systems become multimodal, the human experience with technology becomes more rich and deep.
- When you see AI respond to what you say or show, you get a sense of connection and trust that typing could never create.
It has both potential and danger:
- Potential: Improved communication, empathetic interfaces, and AI that can really “understand” your meaning — not merely your words.
- Danger: Over-reliance or emotional dependency on AI companions that are perceived as human but don’t have real emotion or morality.
That is why companies today are not just investing in capability, but in ethics and emotional design — ensuring multimodal AIs are transparent and responsive to human values.
What’s Next — Beyond 2025
We are now entering the “ambient AI era,” when technology will:
- Listen when you speak,
- Watch when you demonstrate,
- Respond when you point,
- and sense what you want — across devices and platforms.
- Imagine yourself walking into your kitchen and saying
- Teach me to cook pasta with what’s in my fridge,”
and your AI assistant looks at your smart fridge camera in real time, suggests a recipe, and demonstrates a video tutorial — all in real time.
Interfaces are gone here. Human-computer interaction is spontaneous conversation — with tone, images, and shared understanding.
The Humanized Takeaway
- Multimodal AI is not only making machines more intelligent; it’s also making us more intelligent.
- It’s closing the divide between the digital and the physical, between looking and understanding, between ordering and gossiping.
Short:
- Technology is finally figuring out how to talk human.
And with that, our relationship with AI will be less about controlling a tool — and more about collaborating with a partner that watches, listens, and creates with us.
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1. What Does "Multimodal" Actually Mean? "Multimodal AI" is just a fancy way of saying that the model is designed to handle lots of different kinds of input and output. You could, for instance: Upload a photo of a broken engine and say, "What's going on here?" Send an audio message and have it tranRead more
1. What Does “Multimodal” Actually Mean?
“Multimodal AI” is just a fancy way of saying that the model is designed to handle lots of different kinds of input and output.
You could, for instance:
It’s almost like AI developed new “senses,” so it could visually perceive, hear, and speak instead of reading.
2. How Did We Get Here?
The path to multimodality started when scientists understood that human intelligence is not textual — humans experience the world in image, sound, and feeling. Then, engineers began to train artificial intelligence on hybrid datasets — images with text, video with subtitles, audio clips with captions.
Neural networks have developed over time to:
These advances resulted in models that translate the world as a whole in, non-linguistic fashion.
3. The Magic Under the Hood — How Multimodal Models Work
It’s centered around something known as a shared embedding space.
Conceptualize it as an enormous mental canvas surface upon which words and pictures, and sounds all co-reside in the same space of meaning.
This is basically how it works in a grossly oversimplified nutshell:
So when you tell it, “Describe what’s going on in this video,” the model puts together:
That’s what AI does: deep, context-sensitive understanding across modes.
4. Multimodal AI Applications in the Real World in 2025
Now, multimodal AI is all around us — transforming life in quiet ways.
a. Learning
Students watch video lectures, and AI automatically summarizes lectures, highlights key points, and even creates quizzes. Teachers utilize it to build interactive multimedia learning environments.
b. Medicine
Physicians can input medical scans, lab work, and patient history into a single system. The AI cross-matches all of it to help make diagnoses — catching what human doctors may miss.
c. Work and Productivity
You have a meeting and AI provides a transcript, highlights key decisions, and suggests follow-up emails — all from sound, text, and context.
d. Creativity and Design
Multimodal AI is employed by marketers and artists to generate campaign imagery from text inputs, animate them, and even write music — all based on one idea.
e. Accessibility
For visually and hearing impaired individuals, multimodal AI will read images out or translate speech into text in real-time — bridging communication gaps.
5. Top Multimodal Models of 2025
Model Modalities Supported Unique Strengths:
GPT-5 (OpenAI)Text, image, soundDeep reasoning with image & sound processing. Gemini 2 (Google DeepMind)Text, image, video, code. Real-time video insight, together with YouTube & WorkspaceClaude 3.5 (Anthropic)Text, imageEmpathetic contextual and ethical multimodal reasoningMistral Large + Vision Add-ons. Text, image. ixa. Open-source multimodal business capability LLaMA 3 + SeamlessM4TText, image, speechSpeech translation and understanding in multiple languages
These models aren’t observing things happen — they’re making things happen. An input such as “Design a future city and tell its history” would now produce both the image and the words, simultaneously in harmony.
6. Why Multimodality Feels So Human
When you communicate with a multimodal AI, it’s no longer writing in a box. You can tell, show, and hear. The dialogue is richer, more realistic — like describing something to your friend who understands you.
That’s what’s changing the AI experience from being interacted with to being collaborated with.
You’re not providing instructions — you’re co-creating.
7. The Challenges: Why It’s Still Hard
Despite the progress, multimodal AI has its downsides:
Researchers are working day and night to develop transparent reasoning and edge processing (executing AI on devices themselves) to circumvent8. The Future: AI That “Perceives” Like Us
AI will be well on its way to real-time multimodal interaction by the end of 2025 — picture your assistant scanning your space with smart glasses, hearing your tone of voice, and reacting to what it senses.
Multimodal AI will more and more:
In effect, AI is no longer so much a text reader but rather a perceiver of the world.
Final Thought
The more senses that AI can learn from, the more human it will become — not replacing us, but complementing what we can do, learn, create, and connect.
Over the next few years, “show, don’t tell” will not only be a rule of storytelling, but how we’re going to talk to AI itself.
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