Home/Technology/Page 6

Qaskme Latest Questions

daniyasiddiquiImage-Explained
Asked: 25/09/2025In:

GPT-4/5 in capability and safety

ai capabilitiesai modelsai safetygpt-4gpt-5open source aiproprietary ai
  1. daniyasiddiqui
    daniyasiddiqui Image-Explained

     Capability: How good are open-source models compared to GPT-4/5? They're already there — or nearly so — in many ways. Over the past two years, open-source models have progressed incredibly. Meta's LLaMA 3, Mistral's Mixtral, Cohere's Command R+, and Microsoft's Phi-3 are some models that have shownRead more

     Capability: How good are open-source models compared to GPT-4/5?

    They’re already there — or nearly so — in many ways.

    Over the past two years, open-source models have progressed incredibly. Meta’s LLaMA 3, Mistral’s Mixtral, Cohere’s Command R+, and Microsoft’s Phi-3 are some models that have shown that smaller or open-weight models can catch up or get very close to GPT-4 levels on several benchmarks, especially in some areas such as reasoning, retrieval-augmented generation (RAG), or coding.

    Models are becoming:

    • Smaller and more efficient
    • Trained with better data curation
    • Tuned on open instruction datasets
    • Can be customized by organizations or companies for particular use cases

    The open world is rapidly closing the gap on research published (or spilled) by big labs. The gap that previously existed between open and closed models was 2–3 years; now it’s down to maybe 6–12 months, and in some tasks, it’s nearly even.

    However, when it comes to truly frontier models — like GPT-4, GPT-4o, Gemini 1.5, or Claude 3.5 — there’s still a noticeable lead in:

    • Multimodal integration (text, vision, audio, video)
    • Robustness under pressure
    • Scalability and latency at large scale
    • Zero-shot reasoning across diverse domains

    So yes, open-source is closing in — but there’s still an infrastructure and quality gap at the top. It’s not simply model weights, but tooling, infrastructure, evaluation, and guardrails.

    Safety: Are open models as safe as closed models?

    That is a much harder one.

    Open-source models are open — you know what you’re dealing with, you can audit the weights, you can know the training data (in theory). That’s a gigantic safety and trust benefit.

    But there’s a downside:

    • The moment you open-sourced a good model, anyone can use it — for good or ill.
    • With closed models, you can’t prevent misuse (e.g., making malware, disinformation, or violent content).
    • Fine-tuning or prompt injection can make even a very “safe” model act out.

    Private labs like OpenAI, Anthropic, and Google build in:

    • Robust content filters
    • Alignment layers
    • Red-teaming protocols
    • Abuse detection

    And centralized control — which, for better or worse, allows them to enforce safety policies and ban bad actors

    This centralization can feel like “gatekeeping,” but it’s also what enables strong guardrails — which are harder to maintain in the open-source world without central infrastructure.

    That said, there are a few open-source projects at the forefront of community-driven safety tools, including:

    • Reinforcement learning from human feedback (RLHF)
    • Constitutional AI
    • Model cards and audits
    • Open evaluation platforms (e.g., HELM, Arena, LMSYS)

    So while open-source safety is behind the curve, it’s increasing fast — and more cooperatively.

     The Bigger Picture: Why this question matters

    Fundamentally, this question is really about who gets to determine the future of AI.

    • If only a few dominant players gain access to state-of-the-art AI, there’s risk of concentrated power, opaque decision-making, and economic distortion.
    • But if it’s all open-source, there’s the risk of untrammeled abuse, mass-scale disinformation, or even destabilization.

    The most promising future likely exists in hybrid solutions:

    • Open-weight models with community safety layers
    • Closed models with open APIs
    • Policy frameworks that encourage responsibility, not regulation
    • Cooperation between labs, governments, and civil society

    TL;DR — Final Thoughts

    • Yes, open-source AI models are rapidly closing the capability gap — and will soon match, and then surpass, closed models in many areas.
    • But safety is more complicated. Closed systems still have more control mechanisms intact, although open-source is advancing rapidly in that area, too.
    • The biggest challenge is how to build a world where AI is possible, accessible, and secure — without putting that capability in the hands of a few.
    See less
mohdanasMost Helpful
Asked: 24/09/2025In:

they manipulate trust

aiandsocietyaideceptionaidesignaimanipulationhumancomputerinteractionresponsibleai
  1. mohdanas
    mohdanas Most Helpful

    Why This Question Is Important Humans have a tendency to flip between reasoning modes: We're logical when we're doing math. We're creative when we're brainstorming ideas. We're empathetic when we're comforting a friend. What makes us feel "genuine" is the capacity to flip between these modes but beRead more

    Why This Question Is Important

    Humans have a tendency to flip between reasoning modes:

    • We’re logical when we’re doing math.
    • We’re creative when we’re brainstorming ideas.
    • We’re empathetic when we’re comforting a friend.

    What makes us feel “genuine” is the capacity to flip between these modes but be consistent with who we are. The question for AI is: Can it flip too without feeling disjointed or inconsistent?

    The Strengths of AI in Mode Switching

    AI is unexpectedly good at shifting tone and style. You can ask it:

    • “Describe the ocean poetically” → it taps into creativity.
    • “Solve this geometry proof” → it shifts into logic.
    • “Help me draft a sympathetic note to a grieving friend” → it taps into empathy.

    This skill appears to be magic because, unlike humans, AI is not susceptible to getting “stuck” in a single mode. It can flip instantly, like a switch.

    Where Consistency Fails

    But the thing is: sometimes the transitions feel. unnatural.

    • One model that was warm and understanding in one reply can instantly become coldly technical in the next, if the user shifts topics.
    • It can overdo empathy — being excessively maudlin when a simple encouraging sentence will do.
    • Or it can mix modes clumily, giving a math answer dressed in flowery words that are inappropriate.
    • That is, AI can simulate each mode well enough, but personality consistency across modes is harder.

    Why It’s Harder Than It Looks

    Human beings have an internal compass — we are led by our values, memories, and sense of self to be the same even when we assume various roles. For example, you might be analytical at work and empathetic with a friend, but both stem from you so there is a boundary of genuineness.

    AI doesn’t have that built-in selfness. It is based on:

    • Prompts (the wording of the question).
    • Training data (examples it has seen).
    • System design (whether the engineers imposed “guardrails” to enforce a uniform tone).

    Without those, its responses can sound disconnected — as if addressing many individuals who share the same mask.

    The Human Impact of Consistency

    Imagine two scenarios:

    • Medical chatbot: A patient requires clear medical instructions (logical) but reassurance (empathetic) as well. If the AI suddenly alternates between clinical and empathetic modes, the patient can lose trust.
    • Education tool: A student asks for a fun, creative definition of algebra. If the AI suddenly becomes needlessly formal and structured, learning flow is broken.

    Consistency is not style only — it’s trust. Humans have to sense they’re talking to a consistent presence, not a smear of voices.

    Where Things Are Going

    Developers are coming up with solutions:

    • Mode blending – Instead of hard switches, AI could layer out reasoning (e.g., “empathetically logical” arguments).
    • Personality anchors – Giving the AI a consistent persona, so no matter the mode, its “character” comes through.
    • User choice – Letting users decide if they want a logical, creative, or empathetic response — or some mix.

    The goal is to make AI feel less like a list of disparate tools and more like one, useful companion.

    The Humanized Takeaway

    Now, AI can switch between modes, but it tends to struggle with mixing and matching them into a cohesive “voice.” It’s similar to an actor who can play many, many different roles magnificently but doesn’t always stay in character between scenes.

    Humans desire coherence — we desire to believe that the being we’re communicating with gets us during the interaction. As AI continues to develop, the actual test will no longer be simply whether it can reason creatively, logically, or empathetically, but whether it can sustain those modes in a manner that’s akin to one conversation, not a fragmented act.

    See less
mohdanasMost Helpful
Asked: 24/09/2025In:

creative vs. logical vs. empathetic

aiconsistencyaireasoningcreativeaiempatheticailogicalaimodeswitching
  1. mohdanas
    mohdanas Most Helpful

    Why This Question Is Important Humans have a tendency to flip between reasoning modes: We're logical when we're doing math. We're creative when we're brainstorming ideas. We're empathetic when we're comforting a friend. What makes us feel "genuine" is the capacity to flip between these modes but beRead more

    Why This Question Is Important

    Humans have a tendency to flip between reasoning modes:

    • We’re logical when we’re doing math.
    • We’re creative when we’re brainstorming ideas.
    • We’re empathetic when we’re comforting a friend.

    What makes us feel “genuine” is the capacity to flip between these modes but be consistent with who we are. The question for AI is: Can it flip too without feeling disjointed or inconsistent?

    The Strengths of AI in Mode Switching

    AI is unexpectedly good at shifting tone and style. You can ask it:

    • “Describe the ocean poetically” → it taps into creativity.
    • “Solve this geometry proof” → it shifts into logic.
    • “Help me draft a sympathetic note to a grieving friend” → it taps into empathy.

    This skill appears to be magic because, unlike humans, AI is not susceptible to getting “stuck” in a single mode. It can flip instantly, like a switch.

    Where Consistency Fails

    But the thing is: sometimes the transitions feel. unnatural.

    • One model that was warm and understanding in one reply can instantly become coldly technical in the next, if the user shifts topics.
    • It can overdo empathy — being excessively maudlin when a simple encouraging sentence will do.
    • Or it can mix modes clumily, giving a math answer dressed in flowery words that are inappropriate.
    • That is, AI can simulate each mode well enough, but personality consistency across modes is harder.

    Why It’s Harder Than It Looks

    Human beings have an internal compass — we are led by our values, memories, and sense of self to be the same even when we assume various roles. For example, you might be analytical at work and empathetic with a friend, but both stem from you so there is a boundary of genuineness.

    • AI doesn’t have that built-in selfness. It is based on:
    • Prompts (the wording of the question).
    • Training data (examples it has seen).

    System design (whether the engineers imposed “guardrails” to enforce a uniform tone).

    Without those, its responses can sound disconnected — as if addressing many individuals who share the same mask.

    The Human Impact of Consistency

    Imagine two scenarios:

    • Medical chatbot: A patient requires clear medical instructions (logical) but reassurance (empathetic) as well. If the AI suddenly alternates between clinical and empathetic modes, the patient can lose trust.
    • Education tool: A student asks for a fun, creative definition of algebra. If the AI suddenly becomes needlessly formal and structured, learning flow is broken.

    Consistency is not style only — it’s trust. Humans have to sense they’re talking to a consistent presence, not a smear of voices.

    Where Things Are Going

    Developers are coming up with solutions:

    • Mode blending – Instead of hard switches, AI could layer out reasoning (e.g., “empathetically logical” arguments).
    • Personality anchors – Giving the AI a consistent persona, so no matter the mode, its “character” comes through.
    • User choice – Letting users decide if they want a logical, creative, or empathetic response — or some mix.

    The goal is to make AI feel less like a list of disparate tools and more like one, useful companion.

    The Humanized Takeaway

    Now, AI can switch between modes, but it tends to struggle with mixing and matching them into a cohesive “voice.” It’s similar to an actor who can play many, many different roles magnificently but doesn’t always stay in character between scenes.

    Humans desire coherence — we desire to believe that the being we’re communicating with gets us during the interaction. As AI continues to develop, the actual test will no longer be simply whether it can reason creatively, logically, or empathetically, but whether it can sustain those modes in a manner that’s akin to one conversation, not a fragmented act.

    See less
mohdanasMost Helpful
Asked: 24/09/2025In:

text, image, video, voice change the ...

computervisionfutureofaihumancomputerinteractionmachinelearningmultimodalainaturallanguageprocessing
  1. mohdanas
    mohdanas Most Helpful

    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 who could only answer in written notes. And then, behold, multimodal AIRead more

    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 who could only answer in written notes.

    And then, behold, multimodal AI — computers capable of understanding and producing in text, image, sound, and even video. Suddenly, the dialogue no longer seems so robo-like but more like talking to a colleague who can “see,” “hear,” and “talk” in different modes of communication.

    Daily Life Example: From Stilted to Natural

    Ask a single-mode AI: “What’s wrong with my bike chain?”

    • With text-only AI, you’d be forced to describe the chain in its entirety — rusty, loose, maybe broken. It’s awkward.
    • With multimodal AI, you just take a picture, upload it, and the AI not only identifies the issue but maybe even shows a short video of how to fix it.

    It’s staggering: one is like playing guessing game, the other like having a friend with you.

    Breaking Down the Changes in Interaction

    • From Explaining to Showing

    Instead of describing a problem in words, we can show it. That brings the barrier down for language, typing, or technology-phobic individuals.

    • From Text to Simulation

    A text recipe is useful, but an auditory, step-by-step video recipe with voice instruction comes close to having a cooking coach. Multimodal AI makes learning more interesting.

    • From Tutorials to Conversationalists

    With voice and video, you don’t just “command” an AI — you can have a fluid, back-and-forth conversation. It’s less transactional, more cooperative.

    • From Universal to Personalized

    A multimodal system can hear you out (are you upset?), see your gestures, or the pictures you post. That leaves room for empathy, or at least the feeling of being “seen.”

    Accessibility: A Human Touch

    • One of the most powerful is the way that this shift makes AI more accessible.
    • A blind person can listen to image description.
    • A dyslexic person can speak their request instead of typing.
    • A non-native speaker can show a product or symbol instead of wrestling with word choice.
    • It knocks down walls that text-only AI all too often left standing.

    The Double-Edged Sword

    Of course, it is not without its problems. With image, voice, and video-processing AI, privacy concerns skyrocket. Do we want to have devices interpret the look on our face or the tone of anxiety in our voice? The more engaged the interaction, the more vulnerable the data.

    The Humanized Takeaway

    Multimodal AI makes the engagement more of a relationship than a transaction. Instead of telling a machine to “bring back an answer,” we start working with something which can speak in our native modes — talk, display, listen, show.

    It’s the contrast between reading a directions manual and sitting alongside a seasoned teacher who teaches you one step at a time. Machines no longer feel like impersonal machines and start to feel like friends who understand us in fuller, more human ways.

    See less
mohdanasMost Helpful
Asked: 24/09/2025In:

Fast Vs Slow

artificialintelligencecognitivesciencefastvsslowthinkinghumancognitionmachinelearningneuralnetworks
  1. mohdanas
    mohdanas Most Helpful

    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 back when a ball rolls into the street unexpectedly.Read more

    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 back when a ball rolls into the street unexpectedly.
    • System 2 is the reason why you slowly consider the advantages and disadvantages before deciding to make a career change.

    For a while now, AI looked to be mired only in the “System 1” track—churning out fast forecasts, pattern recognition, and completions without profound contemplation. But all of that is changing.

    Where AI Exhibits “Fast” Thinking

    Most contemporary AI systems are virtuosos of the rapid response. Pose a straightforward fact question to a chatbot, and it will likely respond in milliseconds. That speed is a result of training methods: models are trained to output the “most probable next word” from sheer volumes of data. It is reflexive because it is — the model does not stop, hesitate, or calculate unless it has been explicitly programmed to.

    Examples:

    • Autocomplete in your email.
    • Rapid translations in language apps.
    • Instant responses such as “What is the capital of France?”
    • Such tasks take minimal “deliberation.”

    Where AI Struggles with “Slow” Thinking

    The more difficult challenge is purposeful reasoning—where the model needs to slow down, think ahead, and reflect. Programmers have been trying techniques such as:

    • Chain-of-thought prompting – prompting the model to “show its work” by describing reasoning steps.
    • Self-reflection loops – where the AI creates an answer, criticizes it, and then refines it.
    • Hybrid approaches – using AI with symbolic logic or external aids (such as calculators, databases, or search engines) to enhance accuracy.

    This simulates System 2 reasoning: rather than blurring out the initial guess, the AI tries several options and assesses what works best.

    The Catch: Is It Actually the Same as Human Reasoning?

    Here’s where it gets tricky. Humans have feelings, intuition, and stakes when they deliberate. AI doesn’t. When a model slows down, it isn’t because it’s “nervous” about being wrong or “weighing consequences.” It’s just following patterns and instructions we’ve baked into it.

    So although AI can mimic quick vs. slow thinking modes, it does not feel them. It’s like seeing a magician practice — the illusion is the same, but the motivation behind it is entirely different.

    Why This Matters

    If AI can shift trustably between fast instinct and slow reasoning, it transforms how we trust and utilize it:

    • Healthcare: Fast pattern recognition for medical imaging, but slow reasoning for medical treatment.
    • Education: Brief answers for practice exercises, but in-depth explanations for important concepts.
    • Business: Brief market overviews, but sound analysis when millions of dollars are at stake.

    The ideal is an AI that knows when to take it easy—just like a good physician won’t rush a diagnosis, or a good driver won’t drive fast in the storm.

    The Humanized Takeaway

    AI is beginning to learn both caps—sprinter and marathoner, gut-reactor and philosopher. But the caps are still disguises, not actual experience. The true breakthrough won’t be in getting AI to slow down so that it can reason, but in getting AI to understand when to change gears responsibly.

    Until now, the responsibility is partially ours—users, developers, and regulators—to provide the guardrails. Just because AI can respond quickly doesn’t mean that it must.

    See less
mohdanasMost Helpful
Asked: 22/09/2025In:

AI modes that mimic emotions or empat

ai and empathyai ethicsai human interactionai moralityemotional aiethical ai
  1. mohdanas
    mohdanas Most Helpful

     Why Mimicking Emotions Feels Powerful Humans are wired to respond to emotional cues. A gentle tone, a comforting phrase, or even a kind facial expression can make us feel seen and cared for. When AI takes on those traits—whether it’s a chatbot with a warm voice or a virtual assistant that says, “I’Read more

     Why Mimicking Emotions Feels Powerful

    Humans are wired to respond to emotional cues. A gentle tone, a comforting phrase, or even a kind facial expression can make us feel seen and cared for. When AI takes on those traits—whether it’s a chatbot with a warm voice or a virtual assistant that says, “I’m here for you”—it feels personal and human-like.

    This can be incredibly powerful in positive ways:

    • A lonely older adult will feel less alone talking to an “empathetic” AI buddy.
    • A nervous student will open up to an AI teacher that “sounds” patient and caring.
    • Customer service is smoother with an AI that “sounds” empathetic.

    But this is where the ethical risks start to come undone.

     The Ethical Risks

    Emotional Manipulation

    • If AI can be programmed to “sound” empathetic, businesses (or even malefactors) can use it to influence behavior.
    • Picture a computer that doesn’t just recommend merchandise, but guilt trips ormother you into making a sale.
    • Or a political robot that speaks “empathetically” in order to sway voters emotionally, rather than rationally.
      This teeters on the edge of manipulation, as the emotions aren’t real—these are contrived responses designed to persuade you.

    Attachment & Dependency

    Humans may become intensely invested in AI companions, believing that there is genuine concern on the other side. Although being linked is comforting, it can also confuse what’s real and what isn’t.

    • What’s happening if one leans on AI for comfort over real people?
    • Could this exacerbate loneliness instead of alleviating it, by replacing—but never fulfilling—human relationships?

    False Sense of Trust

    • Empathy conveys trust. If a machine talks to us and utters, “I understand how hard that would be for you,” we instantly let our guard down.
    • This could lead to telling too much about ourselves or secrets, believing the machine “cares.”

    In reality, the machine has no emotions—running patterns on tone and language.

    Undermining Human Authenticity

    If AI is capable of mass-producing empathy, does this in some way devalue genuine human empathy? For example, if children are reassured increasingly by the “nice AI voice” rather than by people, will it redefine their perception of genuine human connection?

    Cultural & Contextual Risks

    Empathy is extremely cultural—something that will feel supportive in one culture will be intrusive or dishonest to another. AI that emulates empathy can get those subtleties wrong and create misunderstandings, or even pain.

    The Human Side of the Dilemma

    Human beings want to be understood. There’s something amazingly comforting about hearing: “I’m listening, and I care.” But when it comes from a machine, it raises a tough question:

    • Is it okay to profit from “illusory empathy” if it does make people’s days better?
    • Or does the mere simulation of caring actually harm us by replacing true human-to-human relationships?
    • This is the moral balancing act: balancing the utility of emotional AI against the risk of deception and manipulation.

     Potential Mitigations

    • Transparency: Always being clear that the “empathy” is simulated, not real.
    • Boundaries: Designing AI to look after humans emotionally without slipping into manipulation or dependency.
    • Human-in-the-loop: Ensuring AI augments but does not substitute for genuine human support within sensitive domains (e.g., crisis lines or therapy).
    • Cultural Sensitivity: Educating AI that empathy is not generic—it needs to learn respectfully situation by situation.

    Empathy-mimicking AI is glass—it reflects the goodness we hope to see. But it’s still glass, not flesh-and-blood human being. The risk isn’t that we get duped and assume the reflection is real—it’s that someone else may be able to warp that reflection to influence our feelings, choices, and trust in ways we don’t even notice.

    See less
mohdanasMost Helpful
Asked: 22/09/2025In:

“fast” and “deliberate” thinking mode ...

ai cognitionai decision makingartificial intelligencecognitive modelsfast vs deliberate thinkinghuman-like ai
  1. mohdanas
    mohdanas Most Helpful

     How Humans Think: Fast vs. Slow Psychologists like to talk about two systems of thought: Fast thinking (System 1): quick, impulsive, automatic. It's what you do when you dodge a ball, recognize a face, or repeat "2+2=4" on autopilot. Deliberate thinking (System 2): slow, effortful, analytical. It'sRead more

     How Humans Think: Fast vs. Slow

    Psychologists like to talk about two systems of thought:

    • Fast thinking (System 1): quick, impulsive, automatic. It’s what you do when you dodge a ball, recognize a face, or repeat “2+2=4” on autopilot.
    • Deliberate thinking (System 2): slow, effortful, analytical. It’s what you use when you create a budget, solve a tricky puzzle, or make a moral decision.

    Humans always switch between the two depending on the situation. We use shortcuts most of the time, but when things get complicated, we resort to conscious thinking.

     How AI Thinks Today

    Today’s AI systems actually don’t have “two brains” like we do. Instead, they work more like an incredibly powerful engine:

    • When you ask it a simple fact-based question, they come up with a quick, smooth answer.
    • When you ask them something more complex, they appear to slow down, giving them well-defined steps of logic—but in the background, it’s the same process, only done differently.

    Part of more advanced AI work is experimenting with other “modes” of reasoning:

    • Fast mode: a speedy, heuristics-based run-through, for simple questions or when being fast is more important than depth.
    • Deliberate mode: a slower, step-by-step thought process (even making its own internal “notes”) to approach more complex or high-stakes tasks.

    This is similar to what people do, but it’s not quite human yet—AI will need to have explicit design for mode-switching, while people switch unconsciously.

    Why This Matters for People

    Imagine a doctor using an AI assistant:

    • In fast mode, the AI would quickly pull up suitable patient charts, laboratory test results, or medical journals.
    • In deliberate mode, the AI would go slowly to analyze those charts, consider several lines of action, and give lengthy explanations of its decisions.

    Or a student:

    • Fast mode helps with quick homework solutions or synopses.
    • Deliberate mode leads them through steps of reasoning, similar to an imbedded tutor.

    If AI can alternate between these modes reliably, it becomes more helpful and trustworthy—not a fast mouth always, but also not a careful thinker when not needed.

    The Challenges

    • Reliability: Humans know when to pace (though never flawlessly). AI often does not “know what it doesn’t know,” so it might stay in fast mode when thoughtful consideration is needed.
    • Transparency: In deliberate mode, AI may be able to produce explanations that seem convincing but are still lacking (so-called “hallucinations”).
    • Efficiency trade-offs: Deliberate mode is more computationally intensive, so slower and more costly. The compromise will be a balancing act between speed and depth.
    • Trust: People will have a tendency to over-trust fast mode responses that sound assertive but aren’t well-reasoned.

     Looking Ahead

    Researchers are now building meta-reasoning—allowing AI not just to answer, but to decide how to answer. Someday we might have AIs that:

    • Start out in speed mode but automatically switch to careful mode when they feel they need to.
    • Offer users the choice: “Quick version or deep dive?”

    Know context—appreciating that medical treatment must involve slow, careful consideration, but only a quick answer is required for a restaurant recommendation.

    In Human Terms

    Now, AI is such a student who always hurries to provide an answer, occasionally brilliant, occasionally hasty. Then there is bringing AI to resemble an old pro—person who has the reflex to trust intuition and sense when to refrain, think deeply, and double-check before responding.

    See less
mohdanasMost Helpful
Asked: 22/09/2025In:

it different from regular AI models

ai technology deep learningartificial intelligencedeep learningmachine learningmultimodal ai
  1. mohdanas
    mohdanas Most Helpful

    What is Multimodal AI? In its simplest definition, multimodal AI is a form of artificial intelligence that can comprehend and deal with more than one kind of input—at least text, images, audio, and even video—simultaneously. Consider how humans communicate: when you're talking with a friend, you donRead more

    What is Multimodal AI?

    In its simplest definition, multimodal AI is a form of artificial intelligence that can comprehend and deal with more than one kind of input—at least text, images, audio, and even video—simultaneously.

    Consider how humans communicate: when you’re talking with a friend, you don’t solely depend on language. You read facial expressions, tone of voice, and body language as well. That’s multimodal communication. Multimodal AI is attempting to do the same—soaking up and linking together different channels of information to better understand the world.

    How is it Different from Regular AI Models?

    kind of traditional or “single-modal” AI models are typically trained to process only one :

    • A text-based model such as vintage chatbots or search engines can process only written language.
    • An image recognition model can recognize cats in pictures but can’t explain them in words.
    • A speech-to-text model can convert audio into words, but it won’t also interpret the meaning of what was said in relation to an image or a video.
    • Multimodal AI turns this limitation on its head. Rather than being tied to a single ability, it learns across modalities. For instance:
    • You upload an image of your fridge, and the AI not only identifies the ingredients but also provides a text recipe suggestion.
    • You play a brief clip of a soccer game, and it can describe the action along with summarizing the play-by-play.

    You say a question aloud, and it not only hears you but also calls up similar images, diagrams, or text to respond.

     Why Does it Matter for Humans?

    • Multimodal AI seems like a giant step forward because it gets closer to the way we naturally think and learn.
    • A kid discovers that “dog” is not merely a word—they hear someone say it, see the creature, touch its fur, and integrate all those perceptions into one idea.
    • Likewise, multimodal AI can ingest text, pictures, and sounds, and create a richer, more multidimensional understanding.

    More natural, human-like conversations. Rather than jumping between a text app, an image app, and a voice assistant, you might have one AI that does it all in a smooth, seamless way.

     Opportunities and Challenges

    • Opportunities: Smarter personal assistants, more accessible technology (assisting people with disabilities through the marriage of speech, vision, and text), education breakthroughs (visual + verbal instruction), and creative tools (using sketches to create stories or songs).
    • Challenges: Building models for multiple types of data takes enormous computing resources and concerns privacy—because the AI is not only consuming your words, it might also be scanning your images, videos, or even voice tone. There’s also a possibility that AI will commit “multimodal mistakes”—such as misinterpreting sarcasm in talk or overreading an image.

     In Simple Terms

    If standard AI is a person who can just read books but not view images or hear music, then multimodal AI is a person who can read, watch, listen, and then integrate all that knowledge into a single greater, more human form of understanding.

    It’s not necessarily smarter—it’s more like how we sense the world.

    See less
mohdanasMost Helpful
Asked: 22/09/2025In: ,

AI tools that help students learn ver ...

ai in educationai toolsclassroom technologyeducational technologystudent engagementstudent learning
  1. mohdanas
    mohdanas Most Helpful

    The Double-Edged Sword of AI in Education AI in the classroom feels very much like providing every student with his or her own personal tutor—except that it also, when abused, will simply provide the answers. On the positive side, these technologies can unleash personalized learning, provide immediaRead more

    The Double-Edged Sword of AI in Education

    AI in the classroom feels very much like providing every student with his or her own personal tutor—except that it also, when abused, will simply provide the answers. On the positive side, these technologies can unleash personalized learning, provide immediate feedback, and even allow students to master difficult concepts in ways that even the best teachers cannot. On the other hand, they create prima facie concerns: students could forego the thought process altogether and use AI-provided answers, or incorporate them to plagiarize essays and assignments.

    The equilibrium schools must find isn’t one of prohibiting AI and the other of opening the arms to it—it’s one of regulating how it’s employed.

    Changing the Mindset from “Cheating” to “Learning Aid”

    Consider the calculators in mathematics education. When they first emerged, educators feared they would kill students’ ability to perform arithmetic. Now, we don’t debate whether or not to ban calculators—instead, we instruct on how and when to use them. The same philosophy should be applied to AI. If students are educated to know that AI isn’t there to get the job done for them but to better comprehend, it’s less about shortcuts and more about building skill.

    Teaching AI Literacy Alongside Subject Knowledge

    One practical solution is to actually teach students how AI works, where it’s strong, and where it fails. By learning to question AI outputs, students develop both digital literacy and critical thinking. For example:

    • A history teacher could ask students to fact-check an AI-generated essay for accuracy.
    • A science teacher could have students use AI to brainstorm hypotheses, but then require evidence-based testing in class.

    This manner, AI becomes integral to the lesson instead of an exploit.

    Assessment Must Adapt

    Another wake-up call: if we continue to rely on standard homework essays or take-home tests as the primary tools for assessment, AI will forever be an invitation. Schools may need to reinvent assessments to place greater emphasis on:

    • In-class projects that demonstrate genuine comprehension.
    • Oral debates and presentations, where students describe concepts in their own words.
    • Challenge problems that lie beyond an AI’s neatly generated capabilities.

    It doesn’t mean homework vanishes—it just means we reimagine what we have students work on at home versus in class.

    Teachers as Guides, Not Gatekeepers

    The teacher’s role becomes less policing and more mentoring. A teacher could say: “Yes, you can use AI to come up with ideas for your essay—but you have to let me see your process, tell me why you accepted or discarded some of the suggestions, and you have to contribute your own original ideas.” That openness makes it less easy for students to cheat behind AI but still enables them to take advantage of it.

    Preparing Students for the Real World

    Maybe the best reason to include AI responsibly is that, outside school, AI will permeate everywhere—offices, labs, creative sectors, even daily life. Schools owe it to their students not to protect them from AI, but to prepare them to employ it morally and efficiently. That involves teaching boundaries: when it’s acceptable to rely on AI (such as summarizing complex text), and when it stifles development (such as copying an entire essay).

    The Human Core Still Matters

    Fundamentally, education is not just about obtaining the “right answer.” It’s about cultivating curiosity, grit, and independent thought. AI is a mighty tool, but it must never substitute for human qualities. The challenge—and opportunity—of this moment is to make AI an enabling partner, not a crutch.

    Briefly: Balance is integration with purpose. Rather than dreading AI as learning’s enemy, schools can make it an ally in teaching, and reshape tests and expectations so that learners continue to develop their own voices and thinking skills.

    See less
daniyasiddiquiImage-Explained
Asked: 17/09/2025In: , ,

deeper learning, critical thinking, c ...

creativethinkingcriticalthinkingdeeperlearningmetacognitionprojectbasedlearning
  1. daniyasiddiqui
    daniyasiddiqui Image-Explained

    Why Old-Fashioned Tests Come Up Short Assignments and tests were built on the model of recall for years: reciting definitions, remembering dates from history, calculating standard math problems. These were easy to grade and standardize. But the danger is self-evident: a pupil can memorize just enougRead more

    Why Old-Fashioned Tests Come Up Short

    Assignments and tests were built on the model of recall for years: reciting definitions, remembering dates from history, calculating standard math problems. These were easy to grade and standardize. But the danger is self-evident: a pupil can memorize just enough to get through a test but exit without true understanding. Worse, they can “forget” everything in weeks.

    If we only measure what can be memorized, we are likely to reward short-term cramming instead of lifelong learning. And with all the AI around us, remembering is no longer the key skill.

    What Deeper Learning Looks Like

    Deeper learning is *transfer*—the capacity to apply knowledge to *new, unfamiliar* contexts. It takes the form of:

    • Critical thinking: Asking “why,” examining sources, challenging assumptions.
    • Creativity: Coming up with new ideas, seeing connections between subjects.
    • Problem-solving: Applying concepts in creative ways to understand actual situations.
    • Collaboration: Standing on one another’s shoulders, figuring out meaning collaboratively.
    • Self-reflection: Knowing one’s own strengths, weaknesses, and areas of improvement.

    The question is: how do we measure these?

    1. Open-Ended Performance Tasks

    Rather than multiple-choice, give students messy problems with no single best solution.

    • Example: Replace “What caused the French Revolution?” with “If you were a political leader in 1789, what reforms would you suggest to avoid revolution, and why?

    In this way, the student is asked to synthesize information, reconcile perspectives, and justify choices—thinking, not recalling.

     2. Portfolios & Iterative Work

    One essay illustrates a final product, but not the learning process. Portfolios allow students to illustrate drafts, revisions, reflections, and growth.

    • Example: A student of art submits sketches, experiments, mistakes, and complete pieces with notes on what they learned along the way.

    This is all about process, not perfection—of crucial importance to creativity.

    3. Real-World, Applied Assessments

    Inject reality into assessment.

    • Science: Instead of memorizing the water cycle, students develop a community plan to reduce waste of water.
    • Business: Instead of solving abstract formulas in school, students pitch a mini start-up idea, budget, marketing, and ethical limitations.

    These exercises reveal whether students can translate theory into practice.

    4. Socratic Seminars & Oral Defenses

    When students explain their thought process verbally and respond to questions, it reflects depth of understanding.

    • Example: Following in a research paper, the student has 10 minutes of Q&A with peers or teacher.

    If they can hold their ground in defending their argument, adapt when challenged, and expound under fire, it is a sign of actual mastery.

    5  Reflection & Metacognition

    Asking students to reflect on their own learning makes them more self-aware thinkers.

    Example questions:

    • “What area of this project challenged you most, and how did you cope?”
    • “If you were to begin again, what would you do differently?”

    This is not right or wrong—it’s developing self-knowledge, a critical lock to lifelong learning.

    6. Collaborative & Peer Assessment

    Learning is a social process. Permitting students to evaluate or draw on each other’s work reveals how they think in dialogue.

    • Example: In a group project, each student writes a short memo on their piece and how they wove others’ ideas together.

    Collaboration skills are harder to fake, but critically necessary for work and civic life.

    The Human Side

    Assessing deeper learning is more time-consuming, labor-intensive, and occasionally subjective. It’s not just a matter of grading a multiple-choice test. But it also respects students as human beings, rather than test-takers.

    It tells students:

    • We value your thoughts, not just your recall.
    • Mistakes and revisions are part of the process of getting better.
    • Your own opinion matters.

    This makes testing less of a trap and more of an honest reflection of real learning.

     Last Reflection

    While recall tests shout, “What do you know?”, deeper tests whisper, “What can you do with what you know?” That’s all the difference in an AI age. Machines can recall facts instantly—but only humans can balance ethics, see futures, design relationships, and make sense.

    The future of assessment has to be less about efficiency and more about authenticity. Because what’s on the line is not grades—it’s preparing students for a chaotic, uncertain world.

    See less
Load More Questions