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daniyasiddiquiEditor’s Choice
Asked: 28/12/2025In:

multimodal AI models work

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  1. daniyasiddiqui
    daniyasiddiqui Editor’s Choice

    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: Text is represented in numerical form to convey grammar and meaniRead more

    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:

    • Text is represented in numerical form to convey grammar and meaning.
    • Pictures are converted into visual properties like shapes, textures, and spatial arrangements.
    • The audio feature set includes tone, pitch, and timing.

    These are the types of encoders that take unprocessed data and turn it into mathematical representations that the model can process.

    2. Shared

    After encoding, the information from the various modalities is then projected or mapped to a common representation space. The model is able to connect concepts across representations.

    For instance:

    • The word “cat” is associated with pictures of cats.
    • The wail of the siren is closely associated with the picture of an ambulance or fire truck.
    • A medical report corresponds to the X-ray image of the condition.

    Such a shared space is essential to the model, as it allows the model to make connections between the meaning of different data types rather than simply handling them as separate inputs.

    3. Cross-Modal Reasoning and Generation

    The last stage of the process is cross-modal reasoning on the part of the model; hence, it uses multiple inputs to come up with outputs or decisions. It may involve:

    • Image question answering in natural language.
    • Production of video subtitles.
    • Comparing medical images with patient data.
    • The interpretation of oral instructions and generating pictorial or textual information.

    Instead, state-of-the-art multi-modal models utilize sophisticated attention mechanisms that highlight the relevant areas of the inputs during the process of reasoning.

    Importance of Multimodal AI Models

    1. They Reflect Real-World Complexity

    “The real world is multimodal.” This is because health and medical informatics, travel, and even human communication are all multimodal. This makes it easier for AI to handle information in such a way that it is processed in a way that human beings also do.

    2. Increased Accuracy and Contextual Understanding

    A single data source may be restrictive or inaccurate. Multimodal models utilize multiple inputs, making it less ambiguous and accurate than relying on one data source. For example, analyzing images and text information together is more accurate than analyzing only images or text information while diagnosing.

    3. More Natural Human AI Interaction

    Multimodal AIs allow more intuitive ways of communication, like talking while pointing at an object, as well as uploading an image file and then posing questions about it. As a result, AIs become more inclusive, user-friendly, and accessible, even to people who are not technologically savvy.

    4. Wider Industry Applications

    Multimodal models are creating a paradigm shift in the following:

    • Healthcare: Integration of lab results, images, and patient history for decision-making.
    • Learning is more effectively done by computer interaction, such as using text, pictures
    • Smart cities involve video interpretation, sensors, and reports to analyze traffic and security issues.
    • E-Governance: Integration of document processing, scanned inputs, voice recording, and dashboards to provide better services.

    5. Foundation for Advanced AI Capabilities

    Multimodal AI is only a stepping stone towards more complex models, such as autonomous agents, and decision-making systems in real time. Models which possess the ability to see, listen, read, and reason simultaneously are far closer to full-fledged intelligence as opposed to models based on single modalities.

    Issues and Concerns

    Although they promise much, multimodal models of AI remain difficult to develop and resource-heavy. They demand extensive data and alignment of the modalities, and robust protection against problems of bias and trust. Nevertheless, work continues to increase efficiency and trustworthiness.

    Conclusion

    Multimodal AI models are a major milestone in the field of artificial intelligence. Through the incorporation of various forms of knowledge in a single concept, these models bring AI a step closer to human-style perception and cognition. While the relevance of these models mostly revolves around their effectiveness, they play a crucial part in making AI systems more relevant and real-world.

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daniyasiddiquiEditor’s Choice
Asked: 12/11/2025In:

tokenization and positional encoding ...

deeplearningllmsnlppositionalencodingtokenizationtransformers
  1. daniyasiddiqui
    daniyasiddiqui Editor’s Choice

    The World of Tokens Humans read sentences as words and meanings. Consider it like breaking down a sentence into manageable bits, which the AI then knows how to turn into numbers. “AI is amazing” might turn into tokens: → [“AI”, “ is”, “ amazing”] Or sometimes even smaller: [“A”, “I”, “ is”, “ ama”,Read more

    The World of Tokens

    • Humans read sentences as words and meanings.
    • Consider it like breaking down a sentence into manageable bits, which the AI then knows how to turn into numbers.
    • “AI is amazing” might turn into tokens: → [“AI”, “ is”, “ amazing”]
    • Or sometimes even smaller: [“A”, “I”, “ is”, “ ama”, “zing”]
    • Thus, each token is a small unit of meaning: either a word, part of a word, or even punctuation, depending on how the tokenizer was trained.
    • Similarly, LLMs can’t understand sentences until they first convert text into numerical form because AI models only work with numbers, that is, mathematical vectors.

    Each token gets a unique ID number, and these numbers are turned into embeddings, or mathematical representations of meaning.

     But There’s a Problem Order Matters!

    Let’s say we have two sentences:

    • “The dog chased the cat.”
    • “The cat chased the dog.”

    They use the same words, but the order completely changes the meaning!

    A regular bag of tokens doesn’t tell the AI which word came first or last.

    That would be like giving somebody pieces of the puzzle and not indicating how to lay them out; they’d never see the picture.

    So, how does the AI discern the word order?

    An Easy Analogy: Music Notes

    Imagine a song.

    Each of them, separately, is just a sound.

    Now, imagine if you played them out of order the music would make no sense!

    Positional encoding is like the sheet music, which tells the AI where each note (token) belongs in the rhythm of the sentence.

    Position Selection – How the Model Uses These Positions

    Once tokens are labeled with their positions, the model combines both:

    • What the word means – token embedding
    • Where the word appears – positional encoding

    These two signals together permit the AI to:

    • Recognize relations between words: “who did what to whom”.
    • Predict the next word, based on both meaning and position.

     Why This Is Crucial for Understanding and Creativity

    • Without tokenization, the model couldn’t read or understand words.
    • Without positional encoding, the model couldn’t understand context or meaning.

    Put together, they represent the basis for how LLMs understand and generate human-like language.

    In stories,

    • they help the AI track who said what and when.
    • In poetry or dialogue, they serve to provide rhythm, tone, and even logic.

    This is why models like GPT or Gemini can write essays, summarize books, translate languages, and even generate code-because they “see” text as an organized pattern of meaning and order, not just random strings of words.

     How Modern LLMs Improve on This

    Earlier models had fixed positional encodings meaning they could handle only limited context (like 512 or 1024 tokens).

    But newer models (like GPT-4, Claude 3, Gemini 2.0, etc.) use rotary or relative positional embeddings, which allow them to process tens of thousands of tokens  entire books or multi-page documents while still understanding how each sentence relates to the others.

    That’s why you can now paste a 100-page report or a long conversation, and the model still “remembers” what came before.

    Bringing It All Together

    •  A Simple Story Tokenization is teaching it what words are, like: “These are letters, this is a word, this group means something.”
    • Positional encoding teaches it how to follow the order, “This comes first, this comes next, and that’s the conclusion.”
    • Now it’s able to read a book, understand the story, and write one back to you-not because it feels emotions.

    but because it knows how meaning changes with position and context.

     Final Thoughts

    If you think of an LLM as a brain, then:

    • Tokenization is like its eyes and ears, how it perceives words and converts them into signals.
    • Positional encoding is to the transformer like its sense of time and sequence how it knows what came first, next, and last.

    Together, they make language models capable of something almost magical  understanding human thought patterns through math and structure.

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daniyasiddiquiEditor’s Choice
Asked: 09/11/2025In:

the difference between traditional AI ...

artificialintelligencedeeplearninggenerativeailargelanguagemodelsllmsmachinelearning
  1. daniyasiddiqui
    daniyasiddiqui Editor’s Choice

    The Big Picture Consider traditional AI/ML as systems learning patterns for predictions, whereas generative AI/LLMs learn representations of the world with which to generate novel things: text, images, code, music, or even steps in reasoning. In short: Traditional AI/ML → Predicts. Generative AI/LLMRead more

    The Big Picture

    Consider traditional AI/ML as systems learning patterns for predictions, whereas generative AI/LLMs learn representations of the world with which to generate novel things: text, images, code, music, or even steps in reasoning.

    In short:

    • Traditional AI/ML → Predicts.
    • Generative AI/LLMs → create and comprehend.

     Traditional AI/ Machine Learning — The Foundation

    1. Purpose

    Traditional AI and ML are mainly discriminative, meaning they classify, forecast, or rank things based on existing data.

    For example:

    • Predict whether an email is spam or not.
    • Detect a tumor in an MRI scan.
    • Estimate tomorrow’s temperature.
    • Recommend the product that a user is most likely to buy.

    Focus is placed on structured outputs obtained from structured or semi-structured data.

    2. How It Works

    Traditional ML follows a well-defined process:

    • Collect and clean labeled data (inputs + correct outputs).
    • Feature selection selects features-the variables that truly count.
    • Train a model, such as logistic regression, random forest, SVM, or gradient boosting.
    • Optimize metrics, whether accuracy, precision, recall, F1 score, RMSE, etc.
    • Deploy and monitor for prediction quality.

    Each model is purpose-built, meaning you train one model per task.
    If you want to perform five tasks, say, detect fraud, recommend movies, predict churn, forecast demand, and classify sentiment, you build five different models.

    3. Examples of Traditional AI

    Application           Example              Type

    Classification, Span detection, image recognition, Supervised

    Forecasting Sales prediction, stock movement, and Regression

    Clustering\tMarket segmentation\tUnsupervised

    Recommendation: Product/content suggestions, collaborative filtering

    Optimization, Route planning, inventory control, Reinforcement learning (early)

    Many of them are narrow, specialized models that call for domain-specific expertise.

    Generative AI and Large Language Models: The Revolution

    1. Purpose

    Generative AI, particularly LLMs such as GPT, Claude, Gemini, and LLaMA, shifts from analysis to creation. It creates new content with a human look and feel.

    They can:

    • Generate text, code, stories, summaries, answers, and explanations.
    • Translation across languages and modalities, such as text → image, image → text, etc.
    • Reason across diverse tasks without explicit reprogramming.

    They’re multi-purpose, context-aware, and creative.

    2. How It Works

    LLMs have been constructed using deep neural networks, especially the Transformer architecture introduced in 2017 by Google.

    Unlike traditional ML:

    • They train on massive unstructured data: books, articles, code, and websites.
    • They learn the patterns of language and thought, not explicit labels.
    • They predict the next token in a sequence, be it a word or a subword, and through this, they learn grammar, logic, facts, and how to reason implicitly.

    These are pre-trained on enormous corpora and then fine-tuned for specific tasks like chatting, coding, summarizing, etc.

    3. Example

    Let’s compare directly:

    Task, Traditional ML, Generative AI LLM

    Spam Detection Classifies a message as spam/not spam. Can write a realistic spam email or explain why it’s spam.

    Sentiment Analysis outputs “positive” or “negative.” Write a movie review, adjust the tone, or rewrite it neutrally.

    Translation rule-based/ statistical models, understand contextual meaning and idioms like a human.

    Chatbots: Pre-programmed, single responses, Conversational, contextually aware responses

    Data Science Predicts outcomes, generates insights, explains data, and even writes code.

    Key Differences — Side by Side

    Aspect 　　 　　Traditional AI/ML 　　 　　Generative AI/LLMs

    Objective – Predict or Classify from data; Create something entirely new

    Data Structured (tables, numeric), Unstructured (text, images, audio, code)

    Training Approach ×Task-specific ×General pretraining, fine-tuning later

    Architecture: Linear models, decision trees, CNNs, RNNs, Transformers, attention mechanisms

    Interpretability Easier to explain Harder to interpret (“black box”)

    Adaptability needs to be retrained for new tasks reachable via few-shot prompting

    Output Type: Fixed labels or numbers, Free-form text, code, media

    Human Interaction LinearGradientInput → OutputConversational, Iterative, Contextual

    Compute Scale\tRelatively small\tExtremely large (billions of parameters)

    Why Generative AI Feels “Intelligent”

    Generative models learn latent representations, meaning abstract relationships between concepts, not just statistical correlations.

    That’s why an LLM can:

    • Write a poem in Shakespearean style.
    • Debug your Python code.
    • Explain a legal clause.
    • Create an email based on mood and tone.

    Traditional AI could never do all that in one model; it would have to be dozens of specialized systems.

    Large language models are foundation models: enormous generalists that can be fine-tuned for many different applications.

    The Trade-offs

    Advantages      of Generative AI Bring        , But Be Careful About

    Creativity ↓ can produce human-like contextual output, can hallucinate, or generate false facts

    Efficiency: Handles many tasks with one model. Extremely resource-hungry compute, energy

    Accessibility: Anyone can prompt it – no coding required. Hard to control or explain inner reasoning

    Generalization Works across domains. May reflect biases or ethical issues in training data

    Traditional AI models are narrow but stable; LLMs are powerful but unpredictable.

    A Human Analogy

    Think of traditional AI as akin to a specialist, a person who can do one job extremely well if properly trained, whether that be an accountant or a radiologist.

    Think of Generative AI/LLMs as a curious polymath, someone who has read everything, can discuss anything, yet often makes confident mistakes.

    Both are valuable; it depends on the problem.

    Earth Impact

    • Traditional AI powers what is under the hood: credit scoring, demand forecasting, route optimization, and disease detection.
    • Generative AI powers human interfaces, including chatbots, writing assistants, code copilots, content creation, education tools, and creative design.

    Together, they are transformational.

    For example, in healthcare, traditional AI might analyze X-rays, while generative AI can explain the results to a doctor or patient in plain language.

     The Future — Convergence

    The future is hybrid AI:

    • Employ traditional models for accurate, data-driven predictions.
    • Use LLMs for reasoning, summarizing, and interacting with humans.
    • Connect both with APIs, agents, and workflow automation.

    This is where industries are going: “AI systems of systems” that put together prediction and generation, analytics and conversation, data science and storytelling.

    In a Nutshell,

    Dimension\tTraditional AI / ML\tGenerative AI / LLMs

    Core Idea: Learn patterns to predict outcomes. Learn representations to generate new content. Task Focus Narrow, single-purpose Broad, multi-purpose Input Labeled, structured data High-volume, unstructured data Example Predict loan default Write a financial summary Strengths\tAccuracy, control\tCreativity, adaptability Limitation Limited scope Risk of hallucination, bias.

    Human Takeaway

    Traditional AI taught machines how to think statistically. Generative AI is teaching them how to communicate, create, and reason like humans. Both are part of the same evolutionary journey-from automation to augmentation-where AI doesn’t just do work but helps us imagine new possibilities.

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mohdanasMost Helpful
Asked: 05/11/2025In:

a Transformer architecture

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  1. daniyasiddiqui
    daniyasiddiqui Editor’s Choice

    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 example, in a long sentence like. "The book, suggested by tRead more

    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 example, in a long sentence like.

    • “The book, suggested by this professor who was speaking at the conference, was quite interesting.”
    • Earlier models often lost track of who or what the sentence was about because information from earlier words would fade as new ones arrived.
    • This was solved with Transformers, which utilize a mechanism called self-attention; it enables the model to view all words simultaneously and select those most relevant to each other.

    Now, imagine reading that sentence but not word by word; in an instant, one can see the whole sentence-your brain can connect “book” directly to “fascinating” and understand what is meant clearly. That’s what self-attention does for machines.

    How It Works (in Simple Terms)

    The Transformer model consists of two main blocks:

    • Encoder: This reads and understands the input for translation, summarization, and so on.
    • Decoder: This predicts or generates the next part of the output for text generation.

    Within these blocks are several layers comprising:

    • Self-Attention Mechanism: It enables each word to attend to every other word to capture the context.
    • Feed-Forward Neural Networks: These process the contextualized information.
    • Normalization and Residual Connections: These stabilize training, and information flows efficiently.

    With many layers stacked, Transformers are deep and powerful, able to learn very rich patterns in text, code, images, or even sound.

    Why It’s Foundational for Generative Models

    Generative models, including ChatGPT, GPT-5, Claude, Gemini, and LLaMA, are all based on Transformer architecture. Here is why it is so foundational:

    1. Parallel Processing = Massive Speed and Scale

    Unlike RNNs, which process a single token at a time, Transformers process whole sequences in parallel. That made it possible to train on huge datasets using modern GPUs and accelerated the whole field of generative AI.

    2. Long-Term Comprehension

    Transformers do not “forget” what happened earlier in a sentence or paragraph. The attention mechanism lets them weigh relationships between any two points in text, resulting in a deep understanding of context, tone, and semantics so crucial for generating coherent long-form text.

    3. Transfer Learning and Pretraining

    Transformers enabled the concept of pretraining + fine-tuning.

    Take GPT models, for example: They first undergo training on massive text corpora (books, websites, research papers) to learn to understand general language. They are then fine-tuned with targeted tasks in mind, such as question-answering, summarization, or conversation.

    Modularity made them very versatile.

    4. Multimodality

    But transformers are not limited to text. The same architecture underlies Vision Transformers, or ViT, for image understanding; Audio Transformers for speech; and even multimodal models that mix and match text, image, video, and code, such as GPT-4V and Gemini.

    That universality comes from the Transformer being able to process sequences of tokens, whether those are words, pixels, sounds, or any kind of data representation.

    5. Scalability and Emergent Intelligence

    This is the magic that happens when you scale up Transformers, with more parameters, more training data, and more compute: emergent behavior.

    Models now begin to exhibit reasoning skills, creativity, translation, coding, and even abstract thinking that they were never taught. This scaling law forms one of the biggest discoveries of modern AI research.

    Earth Impact

    Because of Transformers:

    • It can write essays, poems, and even code.
    • Google Translate became dramatically more accurate.
    • Stable Diffusion and DALL-E generate photorealistic images influenced by words.
    • AlphaFold can predict 3D protein structures from genetic sequences.
    • Search engines and recommendation systems understand the user’s intent more than ever before.

    Or in other words, the Transformer turned AI from a niche area of research into a mainstream, world-changing technology.

     A Simple Analogy

    Think of the old assembly line where each worker passed a note down the line slow, and he’d lost some of the detail.

    Think of a modern sort of control room, Transformer, where every worker can view all the notes at one time, compare them, and decide on what is important; that is the attention mechanism. It understands more and is quicker, capable of grasping complex relationships in an instant.

    Transformers Glimpse into the Future

    Transformers are still evolving. Research is pushing its boundaries through:

    • Sparse and efficient attention mechanisms for handling very long documents.
    • Retrieval-augmented models, such as ChatGPT with memory or web access.
    • Mixture of Experts architectures to make models more efficient.
    • Neuromorphic and adaptive computation for reasoning and personalization.

    The Transformer is more than just a model; it is the blueprint for scaling up intelligence. It has redefined how machines learn, reason, and create, and in all likelihood, this is going to remain at the heart of AI innovation for many years ahead.

    In brief,

    What matters about the Transformer architecture is that it taught machines how to pay attention to weigh, relate, and understand information holistically. That single idea opened the door to generative AI-making systems like ChatGPT possible. It’s not just a technical leap; it is a conceptual revolution in how we teach machines to think.

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daniyasiddiquiEditor’s Choice
Asked: 16/10/2025In:

AI models becoming multimodal

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  1. daniyasiddiqui
    daniyasiddiqui Editor’s Choice

     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:

    • Upload a photo of a broken engine and say, “What’s going on here?”
    • Send an audio message and have it translated, interpreted, and summarized.
    • Display a chart or a movie, and the AI can tell you what is going on inside it.
    • Request the AI to design a presentation in images, words, and charts.

    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:

    • Merge multiple streams of data (e.g., words + pixels + sound waves)
    • Make meaning consistent across modes (the word “dog” and the image of a dog become one “idea”)
    • Make new things out of multimodal combinations (e.g., telling what’s going on in an image in words)

    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:

    • There are some encoders to which separate kinds of input are broken up and treated separately (words get a text encoder, pictures get a vision encoder, etc.).
    • These encoders take in information and convert it into some common “lingua franca” — math vectors.
    • One of the ways the engine works is by translating each of those vectors and combining them into smart, cross-modal output.

    So when you tell it, “Describe what’s going on in this video,” the model puts together:

    • The visual stream (frames, colors, things)
    • The audio stream (words, tone, ambient noise)
    • The language stream (your query and its answer)

    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:

    • Data bias: The AI can misinterpret cultures or images unless the training data is rich.
    • Computation cost: Resources are consumed by multimodal models — enormous processing and power are required to train them.
    • Interpretability: It is hard to know why the model linked a visual sign with a textual sign.
    • Privacy concerns: Processing videos and personal media introduces new ethical concerns.

    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:

    • Interprets facial expressions and emotional cues
    • Synthesizes sensor data from wearables
    • Creates fully interactive 3D simulations or videos
    • Works in collaboration with humans in design, healthcare, and learning

    In effect, AI is no longer so much a text reader but rather a perceiver of the world.

     Final Thought

    • Multimodality is not a technical achievement — it’s human.
    • It’s machines learning to value the richness of our world: sight, sound, emotion, and meaning.

    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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daniyasiddiquiEditor’s Choice
Asked: 01/10/2025In:

multimodal AI and traditional AI mode

aiexplainedaivstraditionalmodelsartificialintelligencedeeplearningmachinelearningmultimodalai
  1. daniyasiddiqui
    daniyasiddiqui Editor’s Choice

    What is "Multimodal AI," and How Does it Differ from Classic AI Models? Artificial Intelligence has been moving at lightening speed, but one of the greatest advancements has been the emergence of multimodal AI. Simply put, multimodal AI is akin to endowing a machine with sight, hearing, reading, andRead more

    What is “Multimodal AI,” and How Does it Differ from Classic AI Models?

    Artificial Intelligence has been moving at lightening speed, but one of the greatest advancements has been the emergence of multimodal AI. Simply put, multimodal AI is akin to endowing a machine with sight, hearing, reading, and even responding in a manner that weaves together all of those senses in a single coherent response—just like humans.

     Classic AI: One Track Mind

    Classic AI models were typically constructed to deal with only one kind of data at a time:

    • A text model could read and write only text.
    • An image recognition model could only recognize images.
    • A speech recognition model could only recognize audio.

    This made them very strong in a single lane, but could not merge various forms of input by themselves. Like, an old-fashioned AI would say you what is in a photo (e.g., “this is a cat”), but it wouldn’t be able to hear you ask about the cat and then respond back with a description—all in one shot.

     Welcome Multimodal AI: The Human-Like Merge

    Multimodal AI topples those walls. It can process multiple information modes simultaneously—text, images, audio, video, and sometimes even sensory input such as gestures or environmental signals.

    For instance:

    You can display a picture of your refrigerator and type in: “What recipe can I prepare using these ingredients?” The AI can “look” at the ingredients and respond in text afterwards.

    • You might write a scene in words, and it will create an image or video to match.
    • You might upload an audio recording, and it may transcribe it, examine the speaker’s tone, and suggest a response—all in the same exchange.
    • This capability gets us so much closer to the way we, as humans, experience the world. We don’t simply experience life in words—we experience it through sight, sound, and language all at once.

     Key Differences at a Glance

    Input Diversity

    • Traditional AI behavior → one input (text-only, image-only).
    • Multimodal AI behavior → more than one input (text + image + audio, etc.).

    Contextual Comprehension

    • Traditional AI behavior → performs poorly when context spans different types of information.
    • Multimodal AI behavior → combines sources of information to build richer, more human-like understanding.

    Functional Applications

    • Traditional AI behavior → chatbots, spam filters, simple image recognition.
    • Multimodal AI → medical diagnosis (scans + patient records), creative tools (text-to-image/video/music), accessibility aids (describing scenes to visually impaired).

    Why This Matters for the Future

    Multimodal AI isn’t just about making cooler apps. It’s about making AI more natural and useful in daily Consider:

    • Education → Teachers might use AI to teach a science conceplife.  with text, diagrams, and spoken examples in one fluent lesson.
    • Healthcare → A physician would upload an MRI scan, patient history, and lab work, and the AI would put them together to make recommendations of possible diagnoses.
    • Accessibility → Individuals with disabilities would gain from AI that “sees” and “speaks,” advancing digital life to be more inclusive.

     The Human Angle

    The most dramatic change is this: multimodal AI doesn’t feel so much like a “tool” anymore, but rather more like a collaborator. Rather than switching between multiple apps (one for speech-to-text, one for image edit, one for writing), you might have one AI partner who gets you across all formats.

    Of course, this power raises important questions about ethics, privacy, and misuse. If an AI can watch, listen, and talk all at once, who controls what it does with that information? That’s the conversation society is only just beginning to have.

    Briefly: Classic AI was similar to a specialist. Multimodal AI is similar to a balanced generalist—capable of seeing, hearing, talking, and reasoning between various kinds of input, getting us one step closer to human-level intelligence.

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