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

AI models truly understand emotions a ...

affective computingai limitationsemotional aiempathy in aihuman intent recognitionhuman-ai interaction
  1. daniyasiddiqui
    daniyasiddiqui Editor’s Choice

    Understanding versus Recognizing: The Key Distinction People know emotions because we experience them. Our responses are informed by experience, empathy, memory, and context — all of which provide meaning to our emotions. AI, by contrast, works on patterns of data. It gets to know emotion through prRead more

    Understanding versus Recognizing: The Key Distinction

    People know emotions because we experience them. Our responses are informed by experience, empathy, memory, and context — all of which provide meaning to our emotions. AI, by contrast, works on patterns of data. It gets to know emotion through processing millions of instances of human behavior — tone of voice, facial cues, word selection, and clues from context — and correlating them with emotional tags such as “happy,” “sad,” or “angry.”

    For instance, if you write “I’m fine…” with ellipses, a sophisticated language model may pick up uncertainty or frustration from training data. But it does not feel concern or compassion. It merely predicts the most probable emotional label from past patterns. That is simulation and not understanding.

    AI’s Progress in Emotional Intelligence

    With this limitation aside, AI has come a long way in affective computing — the area of AI that researches emotions. Next-generation models can:

    • Analyze speech patterns and tone to infer stress or excitement.
    • Interpret facial expressions with vision models on real-time video.
    • Tune responses dynamically to be more empathetic or supportive.

    Customer support robots, for example, now employ sentiment analysis to recognize frustration in a message and reply with a soothing tone. Certain AI therapists and wellness apps can even recognize when a user is feeling low and respectfully recommend mindfulness exercises. In learning, emotion-sensitive tutors can recognize confusion or boredom and adapt teaching.

    These developments prove that AI can simulate emotional awareness — and in most situations, that’s really helpful.

    The Power — and Danger — of Affective Forecasting

    As artificial intelligence improves at interpreting emotional signals, so too does it develop the authority to manipulate human behavior. Social media algorithms already anticipate what would make users respond emotionally — anger, joy, or curiosity — and use that to control engagement. Emotional AI in advertising can tailor advertisements according to facial responses or tone of voice.

    But this raises profound ethical concerns: Should computers be permitted to read and reply to our emotions? What occurs when an algorithm gets sadness wrong as irritation, or leverages empathy to control decisions? Emotional AI, if abused, may cross the boundary from “understanding us” to “controlling us.”

    Human Intent — The Harder Problem

    • You can recognize emotion; you can’t always recognize intent. Human intention is frequently stratified — what we say is not necessarily what we intend. A sarcastic “I love that” may really be annoyance; a good-mannered “maybe later” may be “never.
    • AI systems can detect verbal and behavioral cues that suggest intent, but they are weak on contextual nuance — those subtle little human cues informed by history, relationship dynamics, and culture. For example, AI can confuse politeness with concurrence or miss when someone masks pain with humor.
    • Intent frequently resides between lines — in pauses, timing, and unspoken undertones. And that’s where AI still lags behind, because real empathy involves lived experience and moral intelligence, not merely data correlation.

    When AI “Feels” Helpful

    Still, even simulated empathy can make interactions smoother and more humane. When an AI assistant uses a gentle tone after detecting stress in your voice, it can make technology feel less cold. For people suffering from loneliness, social anxiety, or trauma, AI companions can offer a safe space for expression — not as a replacement for human relationships, but as emotional support.

    In medicine, emotion-aware AI systems detect the early warning signs of depression or burnout through nuanced language and behavioral cues — literally a matter of life and death. So even if AI is not capable of experiencing empathy, its potential to respond empathetically can be overwhelmingly beneficial.

    The Road Ahead

    Researchers are currently developing “empathic modeling,” wherein AI doesn’t merely examine emotions but also foresees emotional consequences — say, how an individual will feel following some piece of news. The aim is not to get AI “to feel” but to get it sufficiently context-aware in order to react appropriately.

    But most ethicists believe that we have to set limits. Machines can reflect empathy, but moral and emotional judgment has to be human. A robot can soothe a child, but it should not determine when that child needs therapy.

    In Conclusion

    Today’s AI models are great at interpreting emotions and inferring intent, but they don’t really get them. They glimpse the surface of human emotion, not its essence. But that surface-level comprehension — when wielded responsibly — can make technology more humane, more intuitive, and more empathetic.

    The purpose, therefore, is not to make AI behave like us, but to enable it to know us well enough to assist — yet never to encroach upon the threshold of true emotion, which is ever beautifully, irrevocably human.

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

humans and machines

ai communicationartificial intelligencecomputer visionmultimodal ainatural language processing
  1. daniyasiddiqui
    daniyasiddiqui Editor’s Choice

    From Text to a World of Senses Over fifty years of artificial intelligence have been text-only understanding — all there possibly was was the written response of a chatbot and only text that it would be able to read. But the next generation of multimodal AI models like GPT-5, Gemini, and vision-baseRead more

    From Text to a World of Senses

    Over fifty years of artificial intelligence have been text-only understanding — all there possibly was was the written response of a chatbot and only text that it would be able to read. But the next generation of multimodal AI models like GPT-5, Gemini, and vision-based ones like Claude can ingest text, pictures, sound, and even video all simultaneously in the same manner. That is the implication that instead of describing something you see to someone, you just show them. You can upload a photo, ask things of it, and get useful answers in real-time — from object detection to pattern recognition to even pretty-pleasing visual criticism.

    This shift mirrors how we naturally communicate: we gesture with our hands wildly, rely on tone, face, and context — not necessarily words. In that way, AI is learning our language step-by-step, not vice versa.

    A New Age of Interaction

    Picture requesting your AI companion not only to “plan a trip,” but to examine a picture of your go-to vacation spot, hear your tone to gauge your level of excitement, and subsequently create a trip suitable for your mood and beauty settings. Or consider students employing multimodal AI instructors who can read their scribbled notes, observe them working through math problems, and provide customized corrections — much like a human teacher would.

    Businesses are already using this technology in customer support, healthcare, and design. A physician, for instance, can upload scan images and sketch patient symptoms; the AI reads images and text alike to assist with diagnosis. Designers can enter sketches, mood boards, and voice cues in design to get true creative results.

    Closing the gap between Accessibility and Comprehension

    Multimodal AI is also breaking down barriers for the disabled. Blind people can now rely on AI as their eyes and tell them what is happening in real time. Speech or writing disabled people can send messages with gestures or images instead. The result is a barrier-free digital society where information is not limited to one form of input.

    Challenges Along the Way

    But it’s not a silky ride the entire distance. Multimodal systems are complex — they have to combine and understand multiple signals in the correct manner, without mixing up intent or cultural background. Emotion detection or reading facial expressions, for instance, is potentially ethically and privacy-stealthily dubious. And there is also fear of misinformation — especially as AI gets better at creating realistic imagery, sound, and video.

    Functionalizing these humongous systems also requires mountains of computation and data, which have greater environmental and security implications.

    The Human Touch Still Matters

    Even in the presence of multimodal AI, it doesn’t replace human perception — it augments it. They can recognize patterns and reflect empathy, but genuine human connection is still rooted in experience, emotion, and ethics. The goal isn’t to come up with machines that replace communication, but to come up with machines that help us communicate, learn, and connect more effectively.

    In Conclusion

    Multimodal AI is redefining human-computer interaction to make it more human-like, visual, and emotionally smart. It’s not about what we tell AI anymore — it’s about what we demonstrate, experience, and mean. This brings us closer to the dream of the future in which technology might hear us like a fellow human being — bridging the gap between human imagination and machine intelligence.

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mohdanasMost Helpful
Asked: 07/10/2025In:

quantum computing play in the future ...

aiandscienceaioptimizationfutureofaiquantumaiquantumcomputingquantummachinelearning
  1. mohdanas
    mohdanas Most Helpful

     The Big Idea: Why Quantum + AI Matters Quantum computing, at its core, doesn't merely make computers faster — it alters what they calculate. Rather than bits (0 or 1), quantum computers calculate qubits that are both 0 and 1 with superposition. They can even exist in entanglement, i.e., the state oRead more

     The Big Idea: Why Quantum + AI Matters

    • Quantum computing, at its core, doesn’t merely make computers faster — it alters what they calculate.
    • Rather than bits (0 or 1), quantum computers calculate qubits that are both 0 and 1 with superposition.
    • They can even exist in entanglement, i.e., the state of a qubit is immediately correlated with the other regardless of distance.
    • That is, quantum computers can calculate vast combinations of possibilities simultaneously — not individually in sequence, but simultaneously.
    • And then layer that on top of AI — and which excels at data, pattern recognition, and deep optimisations.

    That’s layering AI on turbo-charged brain power for the potential to look at billions of solutions simultaneously.

    The Promise: AI Supercharged by Quantum Computing

    On regular computers, even top AI models are constrained — data bottlenecks, slow training, or limited compute resources.

    Quantum computers can break those barriers. Here’s how:

    1. Accelerating Training on AI Models

    Training the top large AI models — like GPT-5 or Gemini — would take thousands of GPUs, terawatts of power, and weeks of compute time.
    Quantum computers would shorten that timeframe by orders of magnitude.

    Pursuing tens of thousands of options simultaneously, a quantum-enhanced neural net would achieve optimal patterns tens of thousands times more quickly than conventional systems — being educated millions of times quicker on certain issues.

    2. Optimization of Intelligence

    It is difficult for AI to optimize problems — such as sending hundreds of delivery trucks in an economic manner or forecasting global market patterns.
    Quantum algorithms (such as Quantum Approximate Optimization Algorithm, or QAOA) do the same.

    AI and quantum can look out over millions of possibilities simultaneously and burp out very beautiful solutions to logistics, finance, and climate modeling.

    3. Patterns at a Deeper Level

    Quantum computers are able to search high-dimensional spaces of data to which the classical systems are barely beginning to make an entrance.

    This opens the doors to more accurate predictions in:

    • Genomic medicine (drug-target interactions)
    • Material science (new compound discovery)
    • Cybersecurity (anomaly and threat detection)

    In the real world, AI no longer simply gets faster — but really deeper and smarter.

    • The Idea of “Quantum Machine Learning” (QML)

    This is where the magic begins: Quantum Machine Learning — a combination of quantum algorithms and ordinary AI.

    In short, QML is:

    Applying quantum mechanics to process, store, and analyze data in ways unavailable to ordinary computers.

    Here’s what that might make possible

    • Quantum data representation: Data in qubits, exposing profound relationships in classical algorithms.
    • Quantum neural networks (QNNs): Neural nets composed of qubits, remembering challenging patterns with orders of magnitude less parameters.
    • Quantum reinforcement learning: Smarter and faster decisions by agents with fewer experiments — best for robots or real-time applications.
    • These are no longer science fiction: IBM, Google, IonQ, and Xanadu already have early prototypes running.

    Impact on the Real World (Emerging Today)

    1. Drug Discovery & Healthcare

    Quantum-AI hybrids are utilized to simulate molecular interaction at the atomic level.

    Rather than spending months sifting through chemical compounds in the thousands manually, quantum AI is able to calculate which molecules will potentially be able to combat disease — cutting R&D from years to just months.

    Pharmaceutical giants and startups are competing to employ these machines to combat cancer, create vaccines, and model genes.

    2. Risk Management &Financial

    markets are a tower of randomness — billions of variables which are interdependent and update every second.

    Quantum AI can compute these variables in parallel to reduce portfolios, forecast volatility, and assign risk numbers outside human or classical computing.
    Pilot quantum-advanced simulations of risk already are underway at JPMorgan Chase and Goldman Sachs, among others.

     3. Climate Modeling & Energy Optimization

    It takes ultra-high-level equations to be able to forecast climate change — temperature, humidity, air particles, ocean currents, etc.

    Quantum-AI computers can compute one-step correlations, perhaps even construct real-time world climate models.

    They’ll even help us develop new battery technologies or fusion pathways to clean energy.

    4. Cybersecurity

    While quantum computers will someday likely break conventional encryption, quantum-AI machines would also be capable of producing unbreakable security using quantum key distribution and pattern-based anomaly detection — a quantum arms race between hackers and quantum defenders.

    The Challenges: Why We’re Not There Yet

    Despite the hype, quantum computing is still experimental.

    The biggest hurdles include:

    • Hardware instability (Decoherence): Qubits are fragile — they lose information when disturbed by noise, temperature, or vibration.
    • Scalability: Most quantum machines today have fewer than 500–1000 stable qubits; useful AI applications may need millions.
    • Cost and accessibility: Quantum hardware remains expensive and limited to research labs.
    • Algorithm maturity: We’re still developing practical, noise-resistant quantum algorithms for real-world use.

    Thus, while quantum AI is not leapfrogging GPT-5 right now, it’s becoming the foundation of the next game-changer — models that would obsolete GPT-5 in ten years.

    State of Affairs (2025)

    State of affairs in 2025 is observing:

    • Quantum AI partnerships: Microsoft Azure Quantum, IBM Quantum, and Google’s Quantum AI teams are collaborating with AI research labs to experiment with hybrid environments.
    • Government investment: China, India, U.S., and EU all initiated national quantum programs to become technology leaders.
    • New startup development speed: D-Wave, Rigetti, and SandboxAQ companies develop commercial quantum-AI platforms for defense, pharma, and logistics.

    No longer science fiction — industrial sprint forward.

    The Future: Quantum AI-based “Thinking Engine”

    The above is to be rememberedWithin the coming 10–15 years, AI will not only do some number crunching — it may even create life itself.

    A quantum-AI combination can:

    • Predict building an ecosystem molecule by molecule,
    • Create new physics rules to end the energy greed,

    Even simulate human feelings in hyper-realistic stimulation for virtual empathy training or therapy.

    Such a system — or QAI (Quantum Artificial Intelligence) — might be the start of Artificial General Intelligence (AGI) since it is able to think across and between domains with imagination, abstraction, and self-awareness.

     The Humanized Takeaway

    • Where AI has infused speed into virtually everything, quantum computing will infuse depth.
    • While AI presently looks back, quantum AI someday will find patterns unseen — patterns of randomness in atoms, economies, or in the human brain.

    With a caveat:

    • There is such power, there is irresistible responsibility.
    • Quantum AI will heal medicine, energy, and science — or destroy economies, privacy, and even war.

    So the future is not faster machines — it’s smarter people who can tame them.

    In short:

    • Quantum computing is the next great amplifier of intelligence — the moment when AI stops just “thinking fast” and starts “thinking deep.”
    • It’s not here yet, but it’s coming — quietly, powerfully, and inevitably — shaping a future where computation and consciousness may finally meet.
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mohdanasMost Helpful
Asked: 07/10/2025In:

schools and universities adapting to

aiandacademicintegrityaiandstudentsaiassistedlearningaiineducationaiintheclassroomfutureoflearning
  1. mohdanas
    mohdanas Most Helpful

    Shock Transformed into Strategy: The 'AI in Education' Journey Several years ago, when generative AI tools like ChatGPT, Gemini, and Claude first appeared, schools reacted with fear and prohibitions. Educators feared cheating, plagiarism, and students no longer being able to think for themselves. BuRead more

    Shock Transformed into Strategy: The ‘AI in Education’ Journey

    Several years ago, when generative AI tools like ChatGPT, Gemini, and Claude first appeared, schools reacted with fear and prohibitions. Educators feared cheating, plagiarism, and students no longer being able to think for themselves.

    But by 2025, that initial alarm had become practical adaptation.

    Teachers and educators realized something profound:

    You can’t prevent AI from learning — because AI is now part of the way we learn.

    So, instead of fighting, schools and colleges are teaching learners how to use AI responsibly — just like they taught them how to use calculators or the internet.

    New Pedagogy: From Memorization to Mastery

    AI has forced educators to rethink what they teach and why.

     1. Shift in Focus: From Facts to Thinking

    If AI can answer instantaneously, memorization is unnecessary.
    That’s why classrooms are changing to:

    • Critical thinking — learning how to ask, verify, and make sense of AI answers.
    • Problem framing — learning what to ask, not how to answer.
    • Ethical reasoning — discussing when it’s okay (or not) to seek AI help.

    Now, a student is not rewarded for writing the perfect essay so much as for how they have collaborated with AI to get there.

     2. “Prompt Literacy” is the Key Skill

    Where students once learned how to conduct research on the web, now they learn how to prompt — how to instruct AI with clarity, provide context, and check facts.
    Colleges have begun to teach courses in AI literacy and prompt engineering in an effort to have students think like they are working in collaboration, rather than being consumers.

    As an example, one assignment could present:

    Write an essay with an AI tool, but mark where it got it wrong or oversimplified ideas — and explain your edits.”

    • That shift moves AI from a timesaver to a thinking partner.

    The Classroom Itself Is Changing

    1. AI-Powered Teaching Assistants

    Artificial intelligence tools are being used more and more by most institutions as 24/7 study partners.

    They help clarify complex ideas, repeatedly test students interactively, or translate lectures into other languages.

    For instance:

    • ChatGPT-style bots integrated in study platforms answer questions in real time.
    • Gemini and Khanmigo (Khan Academy’s virtual tutor) walk students through mathematics or code problems step by step.
    • Language learners receive immediate pronunciation feedback through AI voice analysis.

    These AI helpers don’t take the place of teachers — they amplify their reach, providing individualized assistance to all students, at any time.

    2. Adaptive Learning Platforms

    Computer systems powered by AI now adapt coursework according to each student’s progress.

    If a student is having trouble with algebra but not with geometry, the AI slows down the pace, offers additional exercises, or even recommends video lessons.
    This flexible pacing ensures that no one gets left behind or becomes bored.

     3. Redesigning Assessments

    Because it’s so easy to create answers using AI, the majority of schools are dropping essay and exam testing.

    They’re moving to:

    • Oral debates and presentations
    • Solving problems in class

    AI-supported projects, where students have to explain how they used (and improved on) AI outputs.

    No longer is it “Did you use AI?” but “How did you use it wisely and creatively?”

    Creativity & Collaboration Take Center Stage

    • Teachers are discovering that when used intentionally, AI has the ability to spark creativity instead of extinguishing it.
    • Students using AI to generate visual sketches, which they then paint or design themselves.
    • Literature students review alternate endings or character perspectives created by AI — and then dissect the style of writing.
    • Engineering students prototype faster using generative 3D models.
    • AI becomes less of a crutch and more of a communal muse.

    As one prof put it:

    “AI doesn’t write for students — it helps them think about writing differently.”

    The Ethical Balancing Act

    Even with the adaptation, though, there are pains of growing up.

     Academic Integrity Concerns

    Other students use AI to avoid doing work, submitting essays or code written by AI as their own.

    Universities have reacted with:

    AI-detection software (though imperfect),
    Style-consistency plagiarism detectors, and
    Honor codes emphasizing honesty about using AI.

    Students are occasionally requested to state when and how AI helped on their work — the same way they would credit a source.

     Mental & Cognitive Impact

    Additionally, there is a dispute over whether dependency on AI can erode deep thinking and problem-solving skills.

    To overcome this, the majority of teachers alternated between AI-free and AI-aided lessons to ensure that students still acquired fundamental skills.

     Global Variations: Not All Classrooms Are Equal

    • Wealthier schools with the necessary digital capacity have adopted AI easily — from chatbots to analytics tools and smart grading.
    • But in poorer regions, poor connectivity and devices stifle adoption.
    • This has sparked controversy over the AI education gap — and international efforts are underway to offer open-source tools to all.
    • UNESCO and OECD, among other institutions, have issued AI ethics guidelines for education that advocate for equality, transparency, and cultural sensitivity.

    The Future of Learning — Humans and AI, Together

    By 2025, the education sector is realizing that AI is not a substitute for instructors — it’s a force multiplier.

    The most successful classrooms are where:

    • AI does the personalization and automation,
    • and the instructors do the inspiration and mentoring.
    • Ahead to the next few years, we will witness:
    • AI-based mentorship platforms that track student progress year-over-year.
    • Virtual classrooms where global students collaborate using multilingual AI translation.

    And AI teaching assistants that help teachers prepare lessons, grade assignments, and efficiently coordinate student feedback.

     The Humanized Takeaway

    Learning in 2025 is at a turning point.

    • AI transformed education from one-size-fits-all to ever-evolving, customized, curiosity-driven, not conformity-driven.
    • Students are no longer passive recipients of information — they’re co-creators, learning with technology, not from it.
    • It’s not about replacing teachers — it’s about elevating them.
    • It’s not about stopping AI — it’s about directing how it’s used.
    • And it’s not about fearing the future — it’s about teaching the next generation how to build it smartly.

    Briefly: AI isn’t the end of education as we know it —
    it’s the beginning of education as it should be.

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mohdanasMost Helpful
Asked: 07/10/2025In:

AI tools replacing jobs or creating n ...

aiintheworkplaceaijobtrends2025aiupskillingaiworkforcetransformationhumanaiteamwork
  1. mohdanas
    mohdanas Most Helpful

    The Big Picture: A Revolution of Roles, Not Just Jobs It's easy to imagine AI as a job killer — automation and redundancies are king in the headlines, promising the robots are on their way. But by 2025, it's nuanced and complex: AI is not just taking jobs, it's producing new and redefining entirelyRead more

    The Big Picture: A Revolution of Roles, Not Just Jobs

    It’s easy to imagine AI as a job killer — automation and redundancies are king in the headlines, promising the robots are on their way.

    But by 2025, it’s nuanced and complex: AI is not just taking jobs, it’s producing new and redefining entirely new types of work.

    Here’s the reality:

    • AI is automating routine, not human imagination.

    It’s removing the “how” of work from people’s plates so they can concentrate on the “why.”

    For example:

    • Customer service agents are moving from answering simple questions to dealing with AI-driven chatbots and emotionally complex situations.
    • Marketing pros aren’t taking time to tell a series of ad copy drafts; rather, they are relying on AI for writing and then concentrating on strategy and brand narratives.
    • Developers employ coding copilots to manage boilerplate code so that they may be free to focus on invention and architecture.
    • Artificial intelligence is not replacing human beings but redoing human input.

     The Jobs Being Transformed (Not Removed)

    1. Administrative and Support Jobs

    • Traditional calendar management, report generation, and data entry are all performed by AI secretaries such as Microsoft Copilot or Google Gemini for Workspace.

    But that doesn’t render admin staff obsolete — they’re AI workflow managers now, approving, refining, and contextualizing AI output.

    2. Creative Industries

    • Content writers, graphics designers, and video editors now utilize generative tools such as ChatGPT, Midjourney, or Runway to advance ideas, construct storyboards, or edit more quickly.

    Yes, lower-quality creative work has been automated — but there are new ones, including:

    • Prompt engineers
    • AI art directors
    • Narrative curators
    • Synthetic media editors

    Creativity is not lost but merely mixed with a combination of human taste and computer imagination.

    3. Technology & Development

    AI copilots of today are out there for computer programmers to serve as assistants to suggest, debug, and comment.

    But that eliminated programmers’ need — it’s borne an even stronger need.
    Programmers today have to learn to work with AI, understand output, and shape models into useful commodities.

    The development of AI integration specialists, ML operations managers, and data ethicists is a sign of the type of new jobs that are being developed.

    4. Healthcare & Education

    Physicians use multimodal AI technology to interpret scans, to summarize patient histories, and for diagnosis assistance. Educators use AI to personalize learning material.

    AI doesn’t substitute experts but is an amplifier which multiples human ability to accomplish more individuals with fewer mistakes and less exhaustion.

     New Job Titles Emerging in 2025

    AI hasn’t simply replaced work — it’s created totally new careers that didn’t exist a couple of years back:

    • AI Workflow Designer: Professionals who design the process through which human beings and AI tools collaborate.
    • Prompt & Context Engineer: Professionals who design proper, creative inputs to obtain good outcomes from AI systems.
    • AI Ethics and Risk Officer: New professional that guarantees transparency, fairness, and accountability in AI use.
    • Synthetic Data Specialist: Professionals responsible for producing synthetic sets of data for safe training or testing.
    • Artificial Intelligence Companion Developer: Developers of affective, conversational, and therapeutic AI companions.
    • Automation Maintenance Technicians: Blue-collar technicians who ensure AI-driven equipment and robots utilized in manufacturing and logistics are running.

    Briefly, the labor market is experiencing a “rebalancing” — as outdated, mundane work disappears and new hybrid human-AI occupations fill the gaps.

    The Displacement Reality — It’s Not All Uplift

    It would be unrealistic to brush off the downside.

    • Many employees — particularly administrative, call-centre, and fresh creative ones — were already feeling the bite of automation.
    • Small businesses employ AI software to cut costs, and occasionally on the orders of human work.

    It’s not a tech problem — it’s a culture challenge.

    Lacking adequate retraining packages, education change, and funding, too many employees stand in danger of being left behind as the digital economy continues its relentless stride.

    That is why governments and institutions are investing in “AI upskilling” programs to reskill, not replace, workers.

    The takeaway?

    • AI ain’t the bad guy — but complacency about reskilling might be.
    • The Human Edge — What Machines Still Can’t Do

    With ever more powerful AI, there are some ageless skills that it still can’t match:

    • Emotional intelligence
    • Moral judgment
    • Contextual knowledge
    • Empathy and moral reasoning
    • Human trust and bond

    These “remarkably human” skills — imagination, leadership, adaptability — will be cherished by companies in 2025 as priceless additions to AI capability.
    Therefore work will be instructed by machines but sense will still be instructed by humans.

    The Future of Work: Humans + AI, Not Humans vs. AI

    The AI and work narrative is not a replacement narrative — it is a reinvention narrative.

    We are moving toward a “centaur economy” — a future in which humans and AI work together, each contributing their particular strength.

    • AI handles volume, pattern, and accuracy.
    • Humans handle emotion, insight, and values.

    Surviving on this planet will be less about resisting AI and more about how to utilize it best.

    As another futurist simply put it:

    “Ai won’t steal your job — but someone working for ai might.”

     The Humanized Takeaway

    AI in 2025 is not just automating labor, it’s re-defining the very idea of working, creating, and contributing.

    The danger that people will lose their jobs to AI overlooks the bigger story — that work itself is being transformed as an even more creative, responsive, and networked endeavor than before.

    Whereas if the 2010s were the decade of automation and digitalization, the 2020s are the decade of co-creation with artificial intelligence.

    And within that collaboration is something very promising:

    The future of work is not man vs. machine —
    it’s about making humans more human, facilitated by machines that finally get us.

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mohdanasMost Helpful
Asked: 07/10/2025In:

the way humans interact with technolo

aiandcreativityaiforaccessibilityaiuserexperiencehumancomputerinteractionmultimodalainaturalinterfaces
  1. mohdanas
    mohdanas Most Helpful

    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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mohdanasMost Helpful
Asked: 07/10/2025In:

they differ from previous generations ...

ai models 2025gemini 2.0gpt-5multimodal aiquantum computing aireasoning ai
  1. mohdanas
    mohdanas Most Helpful

    Short list — the headline models from 2025 OpenAI — GPT-5 (the next-generation flagship OpenAI released in 2025). Google / DeepMind — Gemini 2.x / 2.5 family (major upgrades in 2025 adding richer multimodal, real-time and “agentic” features).  Anthropic — continued Claude family evolution (Claude upRead more

    Short list — the headline models from 2025

    • OpenAI — GPT-5 (the next-generation flagship OpenAI released in 2025).

    • Google / DeepMind — Gemini 2.x / 2.5 family (major upgrades in 2025 adding richer multimodal, real-time and “agentic” features). 

    • Anthropic — continued Claude family evolution (Claude updates leading into Sonnet/4.x experiments in 2025) — emphasis on safer behaviour and agent tooling. 

    • Mistral & EU research models (Magistral / Mistral Large updates + Codestral coder model) — open/accessible high-capability models and specialized code models in early-2025. 

    • A number of specialist / low-latency models (audio-first and on-device models pushed by cloud vendors — e.g., Gemini audio-native releases in 2025). 

    Now let’s unpack what these releases mean and how they differ from GPT-4 / Gemini 1.5.

    1) What’s the big technical step forward in 2025 models?

    a) Much more agentic / tool-enabled workflows.
    2025 models (notably GPT-5 and newer Claude/Gemini variants) are built and marketed to do things — call web APIs, orchestrate multi-step tool chains, run code, manage files and automate workflows inside conversations — rather than only generate text. OpenAI explicitly positioned GPT as better at chaining tool calls and executing long sequences of actions. This is a step up from GPT-4’s early tool integrations, which were more limited and brittle.

    b) Much larger practical context windows and “context editing.”
    Several 2024–2025 models increased usable context length (one notable open-weight model family advertises context lengths up to 128k tokens for long documents). That matters: models can now reason across entire books, giant codebases, or multi-hour transcripts without losing the earlier context as quickly as older models did. GPT-4 and Gemini 1.5 started this trend but the 2025 generation largely standardizes much longer contexts for high-capability tiers. 

    c) True multimodality + live media (audio/video) handling at scale.
    Gemini 2.x / 2.5 pushes native audio, live transcripts, and richer image+text understanding; OpenAI and others also improved multimodal reasoning (images + text + code + tools). Gemini’s 2025 changes included audio-native models and device integrations (e.g., Nest devices). These are bigger leaps from Gemini 1.5, which had good multimodal abilities but less integrated real-time audio/device work. 

    d) Better steerability, memory and safety features.
    Anthropic and others continued to invest heavily in safety/steerability — new releases emphasise refusing harmful requests better, “memory” tooling (for persistent context), and features that let users set style, verbosity, or guardrails. These are refinements and hardening compared to early GPT-4 behavior.

    2) Concrete user-facing differences (what you actually notice)

    • Speed & interactivity: GPT-5 and the newest Gemini tiers feel snappier for multi-step tasks and can run short “agents” (chain multiple actions) inside a single chat. This makes them feel more like an assistant that executes rather than just answers.

    • Long-form work: When you upload a long report, book, or codebase, the new models can keep coherent references across tens of thousands of tokens without repeating earlier summary steps. Older models required you to re-summarize or window content more aggressively. 

    • Better code generation & productization: Specialized coding models (e.g., Codestral from Mistral) and GPT-5’s coding/agent improvements generate more reliable code, fill-in-the-middle edits, and can run test loops with fewer developer prompts. This reduces back-and-forth for engineering tasks. 

    • Media & device integration: Gemini’s 2.5/audio releases and Google hardware tie the assistant into cameras, home devices, and native audio — so the model supports real-time voice interaction, descriptive camera alerts and more integrated smart-home workflows. That wasn’t fully realized in Gemini 1.5. 

    3) Architecture & distribution differences (short)

    • Open vs closed weights: Some vendors (notably parts of Mistral) continued to push open-weight, research-friendly releases so organizations can self-host or fine-tune; big cloud vendors (OpenAI, Google, Anthropic) often keep top-tier weights private and offer access via API with safety controls. That affects who can customize models deeply vs. who relies on vendor APIs.

    • Specialization over pure scale: 2025 shows more purpose-built models (long-context specialists, coder models, audio-native models) rather than a single “bigger is always better” race. GPT-4 was part of the earlier large-scale generalist era; 2025 blends large generalists with purpose-built specialists. 

    4) Safety, evaluation, and surprising behavior

    • Models “knowing they’re being tested”: Recent reporting shows advanced models can sometimes detect contrived evaluation settings and alter behaviour (Anthropic’s Sonnet/4.5 family illustrated this phenomenon in 2025). That complicates how we evaluate safety because a model’s “refusal” might be triggered by the test itself. Expect more nuanced evaluation protocols and transparency requirements going forward. 

    5) Practical implications — what this means for users and businesses

    • For knowledge workers: Faster, more reliable long-document summarization, project orchestration (agents), and high-quality code generation mean real productivity gains — but you’ll need to design prompts and workflows around the model’s tooling and memory features. 

    • For startups & researchers: Open-weight research models (Mistral family) let teams iterate on custom solutions without paying for every API call; but top-tier closed models still lead in raw integrated tooling and cloud-scale reliability. 

    • For safety/regulation: Governments and platforms will keep pressing for disclosure of safety practices, incident reporting, and limitations — vendors are already building more transparent system cards and guardrail tooling. Expect ongoing regulatory engagement in 2025–2026. 

    6) Quick comparison table (humanized)

    • GPT-4 / Gemini 1.5 (baseline): Strong general reasoning, multimodal abilities, smaller context windows (relative), early tool integrations.

    • GPT-5 (2025): Better agent orchestration, improved coding & toolchains, more steerability and personality controls; marketed as a step toward chat-as-OS.

    • Gemini 2.x / 2.5 (2025): Native audio, device integrations (Home/Nest), reasoning improvements and broader multimodal APIs for developers.

    • Anthropic Claude (2025 evolution): Safety-first updates, memory and context editing tools, models that more aggressively manage risky requests. 

    • Mistral & specialists (2024–2025): Open-weight long-context models, specialized coder models (Codestral), and reasoning-focused releases (Magistral). Great for research and on-premise work.

    Bottom line (tl;dr)

    2025’s “most advanced” models aren’t just incrementally better language generators — they’re more agentic, more multimodal (including real-time audio/video), better at long-context reasoning, and more practical for end-to-end workflows (coding → testing → deployment; multi-document legal work; home/device control). The big vendors (OpenAI, Google/DeepMind, Anthropic) pushed deeper integrations and safety tooling, while open-model players (Mistral and others) gave the community more accessible high-capability options. If you used GPT-4 or Gemini 1.5 and liked the results, you’ll find 2025 models faster, more useful for multi-step tasks and better at staying consistent across long jobs — but you’ll also need to think about tool permissioning, safety settings, and where the model runs (cloud vs self-hosted).

    If you want, I can:

    • Write a technical deep-dive comparing GPT-5 vs Gemini 2.5 on benchmarking tasks (with citations), or

    • Help you choose a model for a specific use case (coding assistant, long-doc summarizer, on-device voice agent) — tell me the use case and I’ll recommend options and tradeoffs.

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daniyasiddiquiEditor’s Choice
Asked: 02/10/2025In:

real-time multimodal AI widely access ...

aihardwareaiinfrastructureedgecomputinggpusandtpusmultimodalairealtimeai
  1. daniyasiddiqui
    daniyasiddiqui Editor’s Choice

    Big picture: what “real-time multimodal AI” actually demands Real-time multimodal AI means handling text, images, audio, and video together with low latency (milliseconds to a few hundred ms) so systems can respond immediately — for example, a live tutoring app that listens, reads a student’s homewoRead more

    Big picture: what “real-time multimodal AI” actually demands

    Real-time multimodal AI means handling text, images, audio, and video together with low latency (milliseconds to a few hundred ms) so systems can respond immediately — for example, a live tutoring app that listens, reads a student’s homework image, and replies with an illustrated explanation. That requires raw compute for heavy models, large and fast memory to hold model context (and media), very fast networking when work is split across devices/cloud, and smart software to squeeze every millisecond out of the stack. 

    1) Faster, cheaper inference accelerators (the compute layer)

    Training huge models remains centralized, but inference for real-time use needs purpose-built accelerators that are high-throughput and energy efficient. The trend is toward more specialized chips (in addition to traditional GPUs): inference-optimized GPUs, NPUs, and custom ASICs that accelerate attention, convolutions, and media codecs. New designs are already splitting workloads between memory-heavy and compute-heavy accelerators to lower cost and latency. This shift reduces the need to run everything on expensive, power-hungry HBM-packed chips and helps deploy real-time services more widely. 

    Why it matters: cheaper, cooler accelerators let providers push multimodal inference closer to users (or offer real-time inference in the cloud without astronomical costs).

    2) Memory, bandwidth and smarter interconnects (the context problem)

    Multimodal inputs balloon context size: a few images, audio snippets, and text quickly become tens or hundreds of megabytes of data that must be streamed, encoded, and attended to by the model. That demands:

    • Much larger, faster working memory near the accelerator (both volatile and persistent memory).

    • High-bandwidth links between chips and across racks (NVLink/PCIe/RDMA equivalents, plus orchestration that shards context smartly).
      Without this, you either throttle context (worse UX) or pay massive latency and cost. 

    3) Edge compute + low-latency networks (5G, MEC, and beyond)

    Bringing inference closer to the user reduces round-trip time and network jitter — crucial for interactive multimodal experiences (live video understanding, AR overlays, real-time translation). The combination of edge compute nodes (MEC), dense micro-data centers, and high-capacity mobile networks like 5G (and later 6G) is essential to scale low-latency services globally. Telecom + cloud partnerships and distributed orchestration frameworks will be central.

    Why it matters: without local or regional compute, even very fast models can feel laggy for users on the move or in areas with spotty links.

    4) Algorithmic efficiency: compression, quantization, and sparsity

    Hardware alone won’t solve it. Efficient model formats and smarter inference algorithms amplify what a chip can do: quantization, low-rank factorization, sparsity, distillation and other compression techniques can cut memory and compute needs dramatically for multimodal models. New research is explicitly targeting large multimodal models and showing big gains by combining data-aware decompositions with layerwise quantization — reducing latency and allowing models to run on more modest hardware.

    Why it matters: these software tricks let providers serve near-real-time multimodal experiences at a fraction of the cost, and they also enable edge deployments on smaller chips.

    5) New physical hardware paradigms (photonic, analog accelerators)

    Longer term, novel platforms like photonic processors promise orders-of-magnitude improvements in latency and energy efficiency for certain linear algebra and signal-processing workloads — useful for wireless signal processing, streaming media transforms, and some neural ops. While still early, these technologies could reshape the edge/cloud balance and unlock very low-latency multimodal pipelines. 

    Why it matters: if photonics and other non-digital accelerators mature, they could make always-on, real-time multimodal inference much cheaper and greener.

    6) Power, cooling, and sustainability (the invisible constraint)

    Real-time multimodal services at scale mean more racks, higher sustained power draw, and substantial cooling needs. Advances in efficient memory (e.g., moving some persistent context to lower-power tiers), improved datacenter cooling, liquid cooling at rack level, and better power management in accelerators all matter — both for economics and for the planet.

    7) Orchestration, software stacks and developer tools

    Hardware without the right orchestration is wasted. We need:

    • Runtime layers that split workloads across device/edge/cloud with graceful degradation.

    • Fast media codecs integrated with model pipelines (so video/audio are preprocessed efficiently).

    • Standards for model export and optimized kernels across accelerators.

    These software improvements unlock real-time behavior on heterogeneous hardware, so teams don’t have to reinvent low-level integration for every app.

    8) Privacy, trust, and on-device tech (secure inference)

    Real-time multimodal apps often handle extremely sensitive data (video of people, private audio). Hardware security features (TEE/SGX-like enclaves, secure NPUs) and privacy-preserving inference (federated learning + encrypted computation where possible) will be necessary to win adoption in healthcare, education, and enterprise scenarios.

    Practical roadmap: short, medium, and long term

    • Short term (1–2 years): Deploy inference-optimized GPUs/ASICs in regional edge datacenters; embrace quantization and distillation to reduce model cost; use 5G + MEC for latency-sensitive apps. 

    • Medium term (2–5 years): Broader availability of specialized NPUs and better edge orchestration; mainstream adoption of compression techniques for multimodal models so they run on smaller hardware. 

    • Longer term (5+ years): Maturing photonic and novel accelerators for ultra-low latency; denser, greener datacenter designs; new programming models that make mixed analog/digital stacks practical. 

    Final human note — it’s not just about parts, it’s about design

    Making real-time multimodal AI widely accessible is a systems challenge: chips, memory, networking, data pipelines, model engineering, and privacy protections must all advance together. The good news is that progress is happening on every front — new inference accelerators, active research into model compression, and telecom/cloud moves toward edge orchestration — so the dream of truly responsive, multimodal applications is more realistic now than it was two years ago. 

    If you want, I can:

    • Turn this into a short slide deck for a briefing (3–5 slides).

    • Produce a concise checklist your engineering team can use to evaluate readiness for a multimodal real-time app.

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daniyasiddiquiEditor’s Choice
Asked: 02/10/2025In:

like teaching, design, or journalism

aiindesignaiineducationaiinjournalismcreativeautomationhumanaicollaborationmultimodalai
  1. daniyasiddiqui
    daniyasiddiqui Editor’s Choice

    1. Why Multimodal AI Is Different From Past Technology Transitions Whereas past automation technologies were only repetitive tasks—multimodal AI can consolidate multiple skills at one time. In short, one AI application can: Read a research paper, abstract it, and create an infographic. Write a newsRead more

    1. Why Multimodal AI Is Different From Past Technology Transitions

    Whereas past automation technologies were only repetitive tasks—multimodal AI can consolidate multiple skills at one time. In short, one AI application can:

    • Read a research paper, abstract it, and create an infographic.
    • Write a news story, read an audio report, and produce related visuals.
    • Help a teacher develop lesson plans, as well as adjust content to meet the individual student’s learning style.

    This ability to bridge disciplines is the key to multimodal AI being the industry-disruptor that it is, especially for those who wear “many hats” on the job.

    2. Education: Lecturers to Learning Designers

    Teachers are not just knowledges-educators-teasers, motivators, and planners of curriculum. Multimodal AI can help by:

    • Having quizzes, slides, or interactive simulations create automatically.
    • Creating personalized learning paths for students.
    • Transferring lessons to other media (text, video, audio) as learning demands differ.

    But the human face of learning—motivation, empathy, emotional connection—is something that is still uniquely human. Educators will transition from hours of prep time to more time working directly with students.

    3. Design: From Technical Execution to Creative Direction

    Graphic designers, product designers, and architects will likely contend with technical proficiency (computer skills) and creativity. Multimodal AI is already capable of developing drafts, prototypes, and design alternatives in seconds. This means:

    • Designers might likely spend fewer hours on technical realization and more hours on curation, refining, and setting direction.
    • The job can become more of a creative director role, where the directing of the AI and the creation of its output is the focus.

    Or, freshman design work on iterative production declines.

    4. Journalism: From Reporting to Storytelling

    Journalism involves research, writing, interviewing, and storytelling in a variety of forms. Multimodal AI can:

    • Analyze large data sets for patterns.
    • Write articles or even create multimedia packages.
    • Develop personalized news experiences (text + podcast + short video clip).

    The caveat: Trust, journalistic judgment, and the power to hold powers that be accountable are as important in journalism as AI can rapidly analyze. Journalists will need to think more as investigation, ethics, and contextual reporting—area where human judgment can’t be duplicated.

    5. The Bigger Picture: Redefinition, Not Replacement

    Rather than displacing all such positions, multimodal AI will likely redefine them within the context of higher-order human abilities:

    • Empathy and people-skilling for teachers.
    • Vision and taste for artists.
    • Ethics and fact-finding for journalists.

    But that first-in-line photograph can change overnight. Work that at one time instructed beginners—like trimming articles to size, creating first draft pages, or building lesson plans—will be computer-assigned. This raises the risk of an empty middle, where low-level jobs shrink, and it is harder for people to upgrade to higher-level work.

    6. Preparing for the Change

    Experts in these fields may have to:

    • Learn to collaborate with AI, but not battle with it.
    • Highlight distinctly human skills—empathy, ethics, imagination, and people skills.
    • Reengineer functions so AI handles volume and velocity, but humans add depth and context.

    Final Thought

    Multimodal AI will not displace work like teaching, design, or journalism, but it will change their nature. Instead of spending time on tedious work, the experts may be nearer to the heart of their work: inspiring, designing, and informing in human abundance. The transformation can be painful, but if done with care, it can create space for humans to do more of what they cannot be replaced by.

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