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

creative, logical, and empathetic

aimodelaireasoningconsistencyinaicreativeaiempatheticailogicalai
  1. daniyasiddiqui
    daniyasiddiqui Editor’s Choice

    1. The Nature of AI "Modes" Unlike human beings, who intuitively combine creativity, reason, and empathy in interaction, AI systems like to isolate these functions into distinct response modes. For instance: Logical mode: applying facts, numbers, or step-by-step calculation as reasons. Creative modeRead more

    1. The Nature of AI “Modes”

    Unlike human beings, who intuitively combine creativity, reason, and empathy in interaction, AI systems like to isolate these functions into distinct response modes. For instance:

    • Logical mode: applying facts, numbers, or step-by-step calculation as reasons.
    • Creative mode: generating ideas for fiction, creating images, or creating new ideas.
    • Empathetic mode: providing emotional comfort, reassurance, or comprehension of a person’s emotions.

    Consistency is difficult because these modes depend on various datasets, reasoning systems, and tone. One slipup—such as being overly analytical at a time when empathy is needed—can make the AI seem cold or mechanical.

    2. Why Consistency is Difficult to Attain

    AI never “knows” human values or emotions the way human beings do. It learns patterns of expressions. Mode-switching is a matter of rearranging tone, reason, and even morality in some cases. That creates the opportunity for:

    • Contradictions (sympathetic initially then providing emotionally unfeeling advice).
    • Over-simplifications (pre-digested empathy-talk that is out of context).
    • Loss of user trust if the user perceives the AI as “covering” too much.

    3. Where AI Already Shows Promise

    With rough edges set aside, contemporary AI is unexpectedly adept at combining modes in directed situations:

    • An AI instructor can instruct math (logical mode) while addressing a struggling student (empathetic mode).
    • A design program can generate innovative ideas but similarly scrutinize them with logical advantages and disadvantages.
    • Medical chatbots increasingly blend empathetic voice with plain, fact-based advice.

    This indicates that AI is capable of combining modes, but only with careful design and context sensitivity.

    4. The Human Factor: Why It Matters

    Consistency across modes isn’t a technical issue—it’s ethical. People are more confident in AI when it seems rational and geared toward their requirements. If a system seems to be switching between various “masks” with no unifying persona, it can be faulted on the basis of being manipulative. People not only appreciate correctness but also honesty and coherence in communication.

    5. The Road Ahead

    The possible future of AI would be to create meta-layers of consistency—where the system knows how it reasons and switches effortlessly without violating trust. For instance, AI would have a “core personality” and switch between logical, creative, and empathetic modes—much like a good teacher or leader would.

    Researchers are also looking into guardrails:

    • Ethical limits (to avoid being manipulated when using empathy).
    • Transparency features (so the user has an idea when the AI is changing modes).
    • Personalization options (so users can select how much empathetic or creative ability they require).

    Final Thought

    AI still can’t quite mimic the effortless way humans switch between reason, imagination, and sympathy, but it’s getting there fast. The problem is ensuring that when it does switch mode, it does so in a way that is consistent, reliable, and responsive to human needs. Bravo, this mode-switching might transform AI into an implement no longer, but an ever more natural collaborator in work, learning, and life.

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

text, image, video, voice

aiuxconversationalaihumancomputerinteractionimagerecognitionnaturaluserinterfacevoiceai
  1. daniyasiddiqui
    daniyasiddiqui Editor’s Choice

    Single-Channel to Multi-Sensory Communication Old school engagement: One channel, just once. You typed (text), spoke (voice), or sent a picture. Every interaction was siloed. Multimodal engagement: Multiple channels blended together in beautiful harmony. You might show the AI a picture of your kitchRead more

    Single-Channel to Multi-Sensory Communication

    • Old school engagement: One channel, just once. You typed (text), spoke (voice), or sent a picture. Every interaction was siloed.
    • Multimodal engagement: Multiple channels blended together in beautiful harmony. You might show the AI a picture of your kitchen, say “what can I cook from this?”, and get a voice reply with recipe text and step-by-step video.

    No longer “speaking to a machine” but about engaging with it in the same way that human beings instinctively make use of all their senses.

     Examples of Change in the Real World

    Healthcare

    • Former approach: Doctors once had to work with various systems for imaging scans, patient information, and test results.
    • New way: A multimodal AI can read the scan, interpret what the physician wrote, and even listen to a patient’s voice for signs of stress—then bring it all together into one unified insight.

    Education

    • Old way: Students read books or studied videos in isolation.
    • New way: A student can ask a math problem orally, share a photo of the assignment, and get a step-by-step description in text and pictures. The AI “educates” in multiple modes, differentiating by learning modality.

    Accessibility

    • Old way: Assistive technology was limited—text to speech via screen readers, audio captions.
    • New way: AI narrates what’s in an image, translates voice into text, and even generates visual aids for learning disabilities. It’s a sense-to-sense universal translator.

    Daily Life

    • Old way: You Googled recipes, watched a video, and then read the instructions.
    • New way: You snap a photo of ingredients, say “what’s for dinner?” and get a narrated, personalized recipe video—all done at once.

    The Human Touch: Less Mechanical, More Natural

    Multimodal AI is a case of working with a friend rather than a machine. Instead of making your needs fit into a tool (e.g., typing into a search bar), the tool shapes itself into your needs. It mimics the manner in which humans interact with the world—vision, hearing, language, and context—and makes it easier, especially for those who are not so techie.

    Take grandparents who are not good with smartphones. Instead of navigating menus, they might simply show the AI a medical bill and say: “Explain this to me.” That adjustment makes technology accessible.

    The Challenges We Must Monitor

    So, though, this promise does introduce new challenges:

    • Privacy issues: If AI can “see” and “hear” everything, what’s being recorded and who has control over it?
    • Bias amplification: If an AI is trained on faulty visual or audio inputs, it could misinterpret people’s tone, accent, or appearance.
    • Over-reliance: Will people forget to scrutinize information if the AI always provides an “all-in-one” answer?

    We need strong ethics and openness so that this more natural communication style doesn’t secretly turn into manipulation.

    Multimodal AI is revolutionizing human-machine interactions. It transposes us from tool users to co-creators, with technology holding conversations rather than simply responding to commands.

    Imagine a world where:

    • Travelers communicate using the same AI to interpret spoken language in real time and present cultural nuances in images.
    • Artists collaborate through talking about feelings, sharing drawings, and refining them with images generated by AI.
    • Families preserve memories by inserting aging photographs and voice messages into it, and having the AI create a living “storybook” that springs to life.
    • It’s a leap toward technology that doesn’t just answer questions, but understands experiences.

    Bottom Line: Multimodal AI changes technology from something we “operate” into something we can converse with naturally—using words, pictures, sounds, and gestures together. It’s making digital interaction more human, but it also demands that we handle privacy, ethics, and trust with care.

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

persuasive than humans—and what ethic ...

aiaccountabilityaiandethicsaimanipulationaitransparencymultimodalaipersuasiveai
  1. daniyasiddiqui
    daniyasiddiqui Editor’s Choice

     Why Artificial Intelligence Can Be More Convincing Than Human Beings Limitless Versatility One of the things that individuals like about one is a strong communication style—some analytical, some emotional, some motivational. AI can respond in real-time, however. It can give a dry recitation of factRead more

     Why Artificial Intelligence Can Be More Convincing Than Human Beings

    Limitless Versatility

    One of the things that individuals like about one is a strong communication style—some analytical, some emotional, some motivational. AI can respond in real-time, however. It can give a dry recitation of facts to an engineer, a rosy spin to a policymaker, and then switch to soothing tone for a nervous individual—all in the same conversation.

    Data-Driven Personalization

    Unlike humans, AI can draw upon vast reserves of information about what works on people. It can detect patterns of tone, body language (through video), or even usage of words, and adapt in real-time. Imagine a digital assistant that detects your rage building and adjusts its tone, and also rehashes its argument to appeal to your beliefs. That’s influence at scale.

    Tireless Precision

    Humans get tired, get distracted, or get emotional when arguing. AI does not. It can repeat itself ad infinitum without patience, wearing down adversaries in the long run—particularly with susceptible communities.

     The Ethical Conundrum

    This coercive ability is not inherently bad—it could be used for good, such as for promoting healthier lives, promoting further education, or driving climate action. But the same influence could be used for:

    • Stirring up political fervor.
    • Pricing dirty goods.
    • Unfairly influencing money decisions.
    • Make emotional dependency on users.

    The distinction between helpful advice and manipulative bullying is paper-thin.

    What Ethical Bounds Should There Be?

    To avoid exploitation, developers and societies should have robust ethical norms:

    Transparency Regarding Mode Switching

    AI needs to make explicit when it’s switching tone or reasoning style—so users are aware if it’s being sympathetic, convincing, or analytically ruthless. Concealed switches make dishonesty.

    Limits on Persuasion in Sensitive Areas

    AI should never be permitted to override humans in matters relating to politics, religion, or love. They are inextricably tied up with autonomy and identity.

    Informed Consent

    Persuasive modes need to be available for an “opt out” by the users. Think of a switch so that you can respond: “Give me facts, but not persuasion.”

    Safeguards for Vulnerable Groups

    The mentally disordered, elderly, or children need not be the target of adaptive persuasion. Guardrails should safeguard us from exploitation.

    Accountability & Oversight

    If an AI convinces someone to do something dangerous, then who is at fault—the developer, the company, or the AI? We require accountability features, because we have regulations governing advertising or drugs.

    The Human Angle

    Essentially, this is less about machines and more about trust. When the human convinces us, we can feel intent, bias, or honesty. We cannot feel those with AI behind the machines. Unrestrained AI would take away human free will by subtly pushing us down paths we ourselves do not know.

    But in its proper use, persuasive AI can be an empowerment force—reminding us to get back on track, helping us make healthier choices, or getting smarter. It’s about ensuring we’re driving, and not the computer.

    Bottom Line: AI may change modes and be even more convincing than human, but ethics-free persuasion is manipulation. The challenge of the future is creating systems that leverage this capability to augment human decision-making, not supplant it.

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

multimodal AI and traditional AI mode

aiexplainedaivstraditionalmodelsartificialintelligencedeeplearningmachinelearningmultimodalai
  1. daniyasiddiqui
    daniyasiddiqui Editor’s Choice

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

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

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

     Classic AI: One Track Mind

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

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

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

     Welcome Multimodal AI: The Human-Like Merge

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

    For instance:

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

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

     Key Differences at a Glance

    Input Diversity

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

    Contextual Comprehension

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

    Functional Applications

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

    Why This Matters for the Future

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

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

     The Human Angle

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

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

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

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daniyasiddiquiEditor’s Choice
Asked: 30/09/2025In: ,

health devices and health-tech tools

digitalhealthfitnesstrackershealthmonitoringhealthtechsmartwearableswearabletech
  1. daniyasiddiqui
    daniyasiddiqui Editor’s Choice

     The Seduction of Wearables: Why We Purchase Them Few purchase a wearable because they're data nerds—they buy it because they desire change. We want to be cajoled into more walking, improved sleep, or managing stress. A vibrating alarm to rise or a line graph of last night's deep sleep can be a softRead more

     The Seduction of Wearables: Why We Purchase Them

    Few purchase a wearable because they’re data nerds—they buy it because they desire change. We want to be cajoled into more walking, improved sleep, or managing stress. A vibrating alarm to rise or a line graph of last night’s deep sleep can be a soft nudge toward improvement.

    There’s also a psychological aspect: having something on your body is a promise to yourself each day—I’m going to take care of my health.

    The Benefits: When Wearables Really Deliver

    Most people, wearables definitely deliver benefits:

    • Accountability & Motivation: Watching your step count go up can get you on the stairs rather than the elevator.
    • Early Warnings: Certain trackers recognize abnormal heart rhythms, abnormally low oxygen, or even alert for infections when they’re not yet fully developed.
    • Personalized Insights: Rather than making an educated guess about how good you slept, you receive a crude drawing of your night’s sleep. Rather than making an educated guess that you’re “active enough,” you have hard numbers.
    • Behavior Change: Humans underestimate just how much little reminders—”you’ve walked only 3,000 steps today”—encourage long-term behavior change.

    For certain patients (such as those with diabetes, cardiovascular disease, or sleep apnea), wearables even enable physicians to track improvements more deeply and refine treatments.

    The Caveats: When They Don’t Deliver

    Wearables are not magic, however. People get bored after the honeymoon phase wears off. Here’s why:

    • Data Overload: There are too many graphs, charts, and numbers to overwhelm, not motivate.
    • Accuracy Problems: Wearables for consumers are excellent at tracking trends, but not ideal for measurements. A fitness band is not a medical-quality ECG.
    • Anxiety Due to Monitoring: Ironically, constant monitoring of heart rate or sleep duration can be more anxiety-causing. Some individuals even develop “sleep anxiety” if the watch informs them that they “did not sleep enough.”
    • Privacy Issues: The information you create—heart rate, sleep patterns, stress levels—is stored in company servers. Not everyone is okay with that.

    The Human Side: It’s Not About the Device, It’s About You

    A wearable is a tool, not a solution. It will remind you to move, but it won’t walk for you. It will tell you about poor sleeping habits, but it won’t tuck you into bed this evening. The benefit comes from how you act on the feedback.

    For instance:

    • When your watch tells you that you have sat for several hours and you get up to stretch, that’s a win.
    • If your sleep tracker tells you to reduce late-night coffee, and you do, you’ve won.
    • If your stress tracker recommends taking a deep breath and you take a moment to do so, the device is working.

    Without those tiny behavioral adjustments, the newest wearable is simply a fashion watch.

     Looking to the Future: Health-Tech Tomorrow

    Health-tech is coming rapidly. Devices tomorrow will be able to detect diseases sooner, customize doses of medicine, or even customize exercise regimens in real time. For those who find it hard to change their lifestyles, a tiny “coach” on the wrist might make healthier living more accessible.

    However, however intelligent they become, these devices will never substitute for human intuition, the doctor’s word of wisdom, or the plain old horse sense of paying attention to your own body.

    Last Thought

    • So are wearable health devices worth it?
    • Yes—if you use them as a helpful guide, not a tyrant.
    • Yes—if they guide you to habits you can realistically stick to.
    • Perhaps not—if you expect them to “heal” your health on their own.

    Think of them like a mirror: they reflect what’s happening, but you’re the one who decides what to do with that reflection. At the end of the day, the true “wearable” is your body itself—it’s always giving signals. Technology just makes those signals easier to see.

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daniyasiddiquiEditor’s Choice
Asked: 30/09/2025In: ,

a challenge to Google Chrome

artificialintelligencebrowserwarschromealternativecometbrowsergooglechromeindialaunchperplexityaitechnews
  1. daniyasiddiqui
    daniyasiddiqui Editor’s Choice

     Setting the Stage Google Chrome ruled the Indian browser space for years. On laptops, desktops, and even mobile phones, Chrome was the first choice for millions. It was speedy, seamless integration with Google products, and omnipresent globally. But with the introduction of Comet browser by PerplexRead more

     Setting the Stage

    Google Chrome ruled the Indian browser space for years. On laptops, desktops, and even mobile phones, Chrome was the first choice for millions. It was speedy, seamless integration with Google products, and omnipresent globally. But with the introduction of Comet browser by Perplexity AI in India, that grip is loosening, so the question now: Can it hold a candle to Chrome?

    What is Comet Browser?

    Comet isn’t a browser. It’s an AI-powered, productivity-focused tool that blends:

    • A web page summarizing, follow-up suggesting, and email autocomposing AI assistant integrated in.
    • Integration of Email Assistant to facilitate easier human writing, organizing, and cleaning inboxes.
    • Prioritizing privacy-first browsing over Chrome’s ad-dependent, user-data-based model.

    For a country like India, where the pace of digital adoption is soaring in the stratosphere, Comet presents a choice that is as simple as it is intelligent.

     Privacy vs. Personalization — The Core Debate

    Comet’s greatest feature is that it’s privacy-centric. Indian consumers are increasingly concerned about data security, especially after a string of cyber fraud and leakage cases. Chrome is wonderful, but its image is tarnished for being too intrusive in the information it accumulates in its efforts to provide the material for Google’s ad engine.

    Comet promises to flip that model on its side by:

    • Restricting data collection.
    • Offering users clear controls on what they’re tracking.
    • Offering AI-driven personalization without holding sensitive data for long periods.

    This may have the potential to appeal to an increasing number of individuals who hold digital performance and trust in equal regard.

    India’s Digital Landscape — A Tough Ground

    India is not a soft market to penetrate. While Chrome reigns supreme on the desktop, mobile phone browser leaders such as Samsung Internet, Safari (on iOS), and small browsers like UC Mini (previously when banned) have also had ginormous fan bases.

    Comet to be successful will need:

    • To seamlessly interoperate with popular apps Indians are already using (WhatsApp, Gmail, Paytm, UPI apps).
    • To function perfectly on low-cost phones with thin memory and processing.
    • Offer regional language assistance, as India’s net is not English-based.

    Could It Possibly Replace Chrome?

    Come on, be practical here: Chrome is not going to be replaced overnight. It’s had longer than a decade of well-ingrained dominance, pre-installs on Android, and extensive Google service integration.

    But Comet does have some tricks up its sleeve that could make it revolutionary:

    • AI integration: Chrome merely scratches the surface of generative AI; Comet knows it and makes it a brand-defining aspect.
    • Email Assistant: If it actually does save time for professionals and students, it can win over a loyal following overnight.
    • Trust factor: With some hype, the guarantee that it will not profiteer from user data can appeal to India’s growing middle class, which is increasingly privacy-conscious.

    Finally, browsers are not about lightening speed or bling—about making the user feel something when they use them. If Comet can make the user feel:

    • Smart (by accelerating long pages in a flash),
    • Safer (by allowing them to own their data),

    Simpler (by describing their online lives in plain English),then surely, it could quite possibly have a niche in Chrome. It may not immediately replace it, but it could plant seeds of competition in an already long ago won market.

     The Road Ahead

    Comet’s test of Chrome will be how fast it is able to:

    • Earn acceptance in urban and semi-urban India,
    • Build a trust and reliability community, and
    • Continuously innovate ahead of Chrome.

    If Perplexity ever manages to get its act together at last, then India might be the proving ground that forces Chrome to face for the first time its first serious challenger.

    Comet will not unseat Chrome overnight, but it can do the work of recharging Indians’ view of a browser—from simple surfing device to artificially intelligent personal digital assistant.

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daniyasiddiquiEditor’s Choice
Asked: 27/09/2025In: , ,

a sustainable driver for stock valuat ...

ai boommarket speculationspeculative bubblesustainable growthtechnology stocks
  1. daniyasiddiqui
    daniyasiddiqui Editor’s Choice

     First, What’s Driving the AI Boom? Since the launch of models like ChatGPT and the explosion of generative AI, we’ve seen: Skyrocketing demand for computing power (GPUs, data centers, cloud infrastructure). Surging interest in AI-native software across productivity, design, healthcare, coding, andRead more

     First, What’s Driving the AI Boom?

    Since the launch of models like ChatGPT and the explosion of generative AI, we’ve seen:

    • Skyrocketing demand for computing power (GPUs, data centers, cloud infrastructure).
    • Surging interest in AI-native software across productivity, design, healthcare, coding, and more.
    • Unprecedented capital allocation from tech giants (Microsoft, Google, Amazon) and venture capitalists alike.
    • Public excitement as people begin using AI in real life, every day.

    All this has culminated in huge stock market profits in AI-cored or even AI-peripherally related companies:

    • Nvidia (NVDA), perhaps the poster child of the AI rally, is up more than 200% in just the last year at times.
    • AI startups are overnight achieving billion-dollar valuations.
    • Even firms with nebulous AI strategies (such as dumping “AI” into investor presentations) are experiencing stock spikes—a telltale sign of a bubble.

    astructure (cloud, chips, data pipes) is being built today. The actual profit boom might still be years out, so high valuations today for the market leaders creating the infrastructure are understandable.

    Why Others Believe It’s a Bubble

    In spite of all the hope, there are some warning signs that cannot be overlooked:

    1. Valuations Are Very Extended

    A lot of AI stocks are priced at Price-to-Earnings ratios that are illogical, particularly if growth decelerates by even a fraction. Nvidia, for instance, is priced to perfection. Any miss in earnings could lead to violent falls.

    2. Herd Mentality & Speculation

    We’ve seen this before—in dot-com stocks in the late ‘90s, or crypto in 2021. When people invest because others are, not because of fundamentals, the setup becomes fragile. A single piece of bad news can unwind things quickly.

    3. Winner-Takes-Most Dynamics

    AI has huge scale economies, so a handful of companies can potentially grab everything (such as Nvidia, Microsoft, etc.), but there are hundreds of others—small caps in particular—that could be left in the dust. That is risk for individual investors pouring into “AI-themed” ETFs or microcaps.

    4. Too Much Emphasis on Frenzy, Not ROI

    Most firms are putting “AI” on earnings calls and press releases simply to get on the bandwagon. But not every AI is revenue-producing, and some won’t be. If firms can’t effectively monetize their AI strategies, the market could correct hard.

    So… Is It a Bubble?

    Perhaps it’s both.

    • A well-known Scott Galloway quote captures it well:
    • “Every bubble starts with something real.”

    AI exists. It’s revolutionary. But the rate of investor hopes might be outrunning the rate of real-world deployment.

    Over the near term, we could witness volatility, sector corrections, or even mini-bubbles burst (particularly for loss-making or overhyped companies). But in the long term, AI is set to become one of the greatest secular trends of the 21st century—comparable to electricity, the internet, and mobile computing.

    Last Thought

    Ask yourself this:

    • Will you expect to see AI applied to every business, every industry, and almost every job in the coming decade?
    • Will you expect that some firms will not change, while others will drive the next generation of innovation?

    If the answer is yes, then the AI boom has a solid fundamental argument. But as with all big technology changes, timing and picking are key. Not all stocks will be a winner—even if there is an AI boom.”.

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daniyasiddiquiEditor’s Choice
Asked: 25/09/2025In: ,

assessment and feedback

ai in educationai-feedbackedtechlanguage-assessmentlanguage-learningpersonalized-learning
  1. daniyasiddiqui
    daniyasiddiqui Editor’s Choice

     The Timeless Problem with Learning Language Language learning is intimate, but traditional testing just can't manage that. Students are typically assessed by rigid, mass-produced methods: standardized testing, fill-in-the-blank, checklist-graded essays, etc. Feedback can be delayed for days, frequeRead more

     The Timeless Problem with Learning Language

    Language learning is intimate, but traditional testing just can’t manage that. Students are typically assessed by rigid, mass-produced methods: standardized testing, fill-in-the-blank, checklist-graded essays, etc. Feedback can be delayed for days, frequently in the form of generic comments like “Good job!” or “Elaborate on your points.” There’s little nuance. Little context. Little you engaged.

    That’s where AI comes in—not to do the teachers’ job, but as a super-competent co-pilot.

     AI/LLMs Change the Game

    1. Measuring Adapted Skills

    • AI models can examine a learner’s language skills in real time, in listening, reading, writing, and even speech (if integrated with voice systems). For example:
    • As a learner writes a paragraph, my LLM can pass judgment on grammar, vocabulary richness, coherence, tone, and argument strength.
    • Instead of just giving a score, it can explain why a sentence may be unclear or how a certain word choice could be improved.
    • Over time, the model can track the learner’s progress, detect plateaus, and suggest focused exercises.

    It’s not just feedback—it’s insight.

    2. Personalized Feedback in Natural Language

    Instead of “Incorrect. Try again,” an AI can say:

    “‘You’re giving ‘advices’ as a plural, but ‘advice’ is an uncountable noun in English. You can say ‘some advice’ or ‘a piece of advice.’ Don’t worry—this is a super common error.'”

    This kind of friendly, particular, and human feedback promotes confidence, not nervousness. It’s immediate. It’s friendly. And it makes learners feel seen.

    3. Shifting to Level of Proficiency and Learning Style

    AI systems are able to adjust the level and tone of their feedback to meet the learner’s level:

    • For beginning learners: shorter, more direct explanations; focus on basic grammar and sentence structure.
    • For advanced learners: feedback might include stylistic remarks, rhetorical impact, tone modulations, and even cultural context.

    It also has the ability to understand how the individual learns best: visually, by example, by analogy, or by step-by-step instructions. Think of receiving feedback described in the mode of a story or in the way of colored correction, depending on your preference.

    4. Multilingual Feedback and Translation Support

    For multilingual students or ESL, AI can specify errors in the student’s home language, compare the structures of different languages, and even flag “false friends” (i.e., words that are the same but have different meanings in two languages).

    • “In Spanish, ’embarazada’ means pregnant—not embarrassed! Easy mix-up.”
    • That’s the type of contextual foundation that makes feedback sticky.

    5. Real-Time Conversational Practice

    With the likes of voice input and chat interfaces, LLMs can practice real-life conversations:

    • Job interview, travel scenario, or conversation practice course.
    • Giving feedback on your pronunciation, tone, or idiomatic usage.
    • Even role-reversal (e.g., “pretend that I were a traveler in Japan”) to get used to different contexts.

    And the best part? No judgment. You can make mistakes without blushing.

    6. Content Generation for Assessment

    Teachers or students may ask AI to create custom exercises based on a provided topic or difficulty level: teaching

    • Fill-in-blank exercises based on vocabulary from a recent lesson.
    • Comprehension questions based on a passage the learner wrote.
    • Essay prompts based on student interests (“Write about your favorite anime character in past tense.”)
    • This makes assessment more engaging—and more significant.

     Why This Matters: Personalized Learning Is Powerful Learning

    Language learning is not a straight line. Others struggle with verb conjugation, others with pronunciation or cultural uses of language. Others get speech-tongue-tied, others are grammar sticklers who can’t write a wonderful sentence.

    LLMs are able to identify such patterns, retain preferences (with permission), and customize not only feedback, but the entire learning process. Picture having a tutor who daily adjusts to your changing needs, is on call 24/7, never gets fatigued, and pumps you up each step of the way.

    That’s the magic of customized AI.

    Of Course, It’s Not Perfect

    • Come on, let’s be realistic—AI has its limits.
    • It will sometimes fail to pick up subtleties of meaning or tone.
    • Feedback at times was too pleasant, or not harsh.
    • It also lacks cultural awareness or emotional intelligence in edge cases.

    And let’s not forget the risk of students becoming too reliant on AI tools, instead of learning to think by themselves.

    That’s why human teachers matter more than ever before. The optimal model is AI-assisted learning: teachers + AI, not teachers vs. AI.

    What’s Next?

    The future may bring:

    • LLMs tracking a student’s work such as an electronic portfolio.
    • AI with voice recognition utilized in the assessment of speaking fluency.
    • AI grading lengthy essays with feedback that is written in a tone in which one would speak.

    Even writing partners who help you co-author tales and revise and explain along the way.

     Final Thought

    Personalized language assessment with LLMs isn’t a matter of time-saving or feedbackscaling—it’s a matter of giving the learner a sense of having been heard. Inspired. Empowered. When a student is informed, “I see what you’re attempting to say—here’s how to say it better,” that’s when real growth happens.

    And if AI can make that experience more available, more equitable, and more inspiring for millions of learners across the globe—well, that’s a very good application of intelligence.

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daniyasiddiquiEditor’s Choice
Asked: 25/09/2025In: ,

safe at scale and do we audit that sa ...

ai safetyai-auditingai-governanceresponsible-aiscalable-aitrustworthy-ai
  1. daniyasiddiqui
    daniyasiddiqui Editor’s Choice

    What Is "Safe AI at Scale" Even? AI "safety" isn't one thing — it's a moving target made up of many overlapping concerns. In general, we can break it down to three layers: 1. Technical Safety Making sure the AI: Doesn't generate harmful or false content Doesn't hallucinate, spread misinformation, orRead more

    What Is “Safe AI at Scale” Even?

    AI “safety” isn’t one thing — it’s a moving target made up of many overlapping concerns. In general, we can break it down to three layers:

    1. Technical Safety

    Making sure the AI:

    • Doesn’t generate harmful or false content
    • Doesn’t hallucinate, spread misinformation, or toxicity
    • Respects data and privacy limits
    • Sticks to its intended purpose

    2. Social / Ethical Safety

    Making sure the AI:

    • Doesn’t reinforce bias, discrimination, or exclusion
    • Respects cultural norms and values
    • Can’t be easily hijacked for evil (e.g. scams, propaganda)
    • Respects human rights and dignity

    3. Systemic / Governance-Level Safety

    Guaranteeing:

    • AI systems are audited, accountable, and transparent
    • Companies or governments won’t use AI to manipulate or control
    • There are global standards for risk, fairness, and access
    • People aren’t left behind while jobs, economies, and cultures transform

    So when we ask, “Is it safe?”, we’re really asking:

    Can something so versatile, strong, and enigmatic be controllable, just, and predictable — even when it’s everywhere?

    Why Safety Is So Hard at Scale

    • At a tiny scale — i.e., an AI in your phone that helps you schedule meetings — we can test it, limit it, and correct problems quite easily.
    • But at scale — when millions or billions are wielding the AI in unpredictable ways, in various languages, in countries, with access to everything from education to nuclear weapons — all of this becomes more difficult.

    Here’s why:

    1. The AI is a black box

    Current-day AI models (specifically large language models) are distinct from traditional software. You can’t see precisely how they “make a decision.” Their internal workings are of high dimensionality and largely incomprehensible. Therefore, even well-intentioned programmers can’t predict as much as they’d like about what is happening when the model is pushed to its extremes.

    2. The world is unpredictable

    No one can conceivably foresee every use (abuse) of an AI model. Criminals are creative. So are children, activists, advertisers, and pranksters. As usage expands, so does the array of edge cases — and many of them are not innocuous.

    3. Cultural values aren’t universal

    What’s “safe” in one culture can be offensive or even dangerous in another. A politically censoring AI based in the U.S., for example, might be deemed biased elsewhere in the world, or one trying to be inclusive in the West might be at odds with prevailing norms elsewhere. There is no single definition of “aligned values” globally.

    4. Incentives aren’t always aligned

    Many companies are racing to produce better-performance models earlier. Pressure to cut corners, beat the safety clock, or hide faults from scrutiny leads to mistakes. When secrecy and competition are present, safety suffers.

     How Do We Audit AI for Safety?

    This is the meat of your question — not just “is it safe,” but “how can we be certain?

    These are the main techniques being used or under development to audit AI models for safety:

    1. Red Teaming

    • Think about the prospect of hiring hackers to break into your system — but instead, for AI.
    • “Red teams” try to get models to respond with something unsafe, biased, false, or otherwise objectionable.
    • The goal is to identify edge cases before launch, and adjust training or responses accordingly.

    Disadvantages:

    • It’s backward-looking — you only learn what you’re testing for.
    • It’s typically biased by who’s on the team (e.g. Western, English-speaking, tech-aware people).

    Can’t test everything.

    2. Automated Evaluations

    • Some labs test tens of thousands or millions of examples against a model with formal tests to find bad behavior.
    • These can look for hate speech, misinformation, jailbreaking, or bias.

    Limitations:

    • AI models evolve (or get updated) all the time — what’s “safe” today may not be tomorrow.
    • Automated tests can miss subtle types of bias, manipulation, or misalignment.

    3. Human Preference Feedback

    • Humans rank outputs as to whether they’re useful, factual, or harmful.
    • These rankings are used to fine-tune the model (e.g. in Reinforcement Learning from Human Feedback, or RLHF).

    Constraints:

    • Human feedback is expensive, slow, and noisy.
    • Biases in who does the rating (i.e. political, cultural) could taint outcomes.
    • Humans typically don’t agree on what’s safe or ethical.

    4. Transparency Reports & Model Cards

    • Some of these AI creators publish “model cards” with details about the training data, testing, and safety testing of the model.
    • Similar to nutrition labels, they inform researchers and policymakers about what went into the model.

    Limitations:

    • Too frequently voluntary and incomplete.
    • Don’t necessarily capture the look of actual-world harms.

    5. Third-Party Audits

    • Independent researchers or regulatory agencies can audit models — preferably with weight, data, and testing access.
    • This is similar to how drug approvals or financial audits work.

    Limitations:

    • Few companies are happy to offer true access.
    • There isn’t a single standard yet on what “passes” an AI audit.

    6. “Constitutional” or Rule-Based AI

    • Some models use fixed rules (e.g., “don’t harm,” “be honest,” “respect privacy”) as a basis for output.
    • These “AI constitutions” are written with the intention of influencing behavior internally.

    Limitations:

    • Who writes the constitution?
    • Can there be inimical principles?
    • How do we ensure that they’re actually being followed?

    What Would “Safe AI at Scale” Actually Look Like?

    If we’re being a little optimistic — but also pragmatic — here’s what an actually safe, at-scale AI system might entail:

    •  Strong red teaming with different cultural, linguistic, and ethical
    • perspectives Regular independent audits with binding standards and consequences
    •  Override protections for users so people can report, mark, or block bad actors
    •  Open safety testing standards, such as car crash testing
    •  AI capability-adaptable governance organizations (e.g. international bodies, treaty-based systems)
    • Known failures, trade-offs, and deployment risks disclosed to the public
    •  Cultural localization so AI systems reflect local values, not Silicon Valley defaults
    • Monitoring and fail-safes in high-stakes domains (healthcare, law, elections, etc.)

    But. Will It Ever Be Fully Safe?

    No tech is ever 100% safe. Not cars, not pharmaceuticals, not the web. And neither is AI.

    But this is what’s different: AI isn’t a tool — it’s a general-purpose cognitive machine that works with humans, society, and knowledge at scale. That makes it exponentially more powerful — and exponentially more difficult to control.

    So no, we can’t make it “perfectly safe.

    But we can make it quantifiably safer, more transparent, and more accountable — if we tackle safety not as a one-time checkbox but as a continuous social contract among developers, users, governments, and communities.

     Final Thoughts (Human to Human)

    You’re not the only one if you feel uneasy about AI growing this fast. The scale, speed, and ambiguity of it all is head-spinning — especially because most of us never voted on its deployment.

    But asking, “Can it be safe?” is the first step to making it safer.
    Not perfect. Not harmless on all counts. But more regulated, more humane, and more responsive to true human needs.

    And that’s not a technical project. That is a human one.

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daniyasiddiquiEditor’s Choice
Asked: 25/09/2025In: ,

Job risk due to current-gen AI

ai-and-jobsai-impactautomation-riskcurrent-gen-aifuture-of-workjob-automationlabor-market
  1. daniyasiddiqui
    daniyasiddiqui Editor’s Choice

     First, the Big Picture Today's AI — especially large language models (LLMs) and generative tools — excels at one type of work: Processing information Recognizing patterns Generating text, images, audio, or code Automating formulaic or repetitive work Answering questions and producing structured outRead more

     First, the Big Picture

    Today’s AI — especially large language models (LLMs) and generative tools — excels at one type of work:

    • Processing information
    • Recognizing patterns
    • Generating text, images, audio, or code
    • Automating formulaic or repetitive work
    • Answering questions and producing structured output

    What AI is not fantastic at (yet):

    • Understanding deep context
    • Exercise judgment in morally or emotionally nuanced scenarios
    • Physical activities in dynamic environments
    • Actual creative insight (versus remixing existing material)
    • Interpersonal subtlety and trust-based relationships

    So, if we ask “Which jobs are at risk?” we’re actually asking:

    Which jobs heavily depend on repetitive, cognitive, text- or data-based activities that can now be done faster and cheaper by AI?

    ???? Jobs at Highest Risk from Current-Gen AI

    These are the types of work that are being impacted the most — not in theory, but in practice:

     1. Administrative and Clerical Jobs

    Examples:

    • Executive assistants
    • Data entry clerks
    • Customer service representatives (especially chat-based)
    • Scheduling coordinators
    • Transcriptionists

    Why they’re vulnerable:

    AI software can now manage calendars, draft emails, create documents, transcribe audio, and answer basic customer questions — more quickly and accurately than humans.

    Real-world consequences:

    Startups and tech-savvy businesses are substituting executive assistants with AI scheduling platforms such as x.ai or Reclaim.ai.

    • Voice-to-text applications lowered the need for manual transcription services.
    • AI-driven chatbots are sweeping up tier-1 customer support across sectors.

    Human touch:

    These individuals routinely offer unseen, behind-scenes assistance — and it feels demotivating to be supplanted by something inhuman. That being said, individuals who know how to work with AI as a co-pilot (instead of competing with it) are discovering new roles in AI operations management, automation monitoring, and “human-in-the-loop” quality assurance.

    2. Legal and Paralegal Work (Low-Level)

    Examples:

    • Contract reviewers
    • Legal researchers
    • Paralegal assistants
    • Why they’re at risk

    AI can now:

    • Summarize legal documents
    • Identify inconsistencies or omitted clauses
    • Create initial drafts of boilerplate contracts
    • Examine precedent for case law

    Real-world significance:

    Applications such as Harvey, Casetext CoCounsel, and Lexis+ AI are already employed by top law firms to perform these functions.

    Human touch:

    New lawyers can expect to have a more difficult time securing “foot in the door” positions. But there is another side: nonprofits and small firms now have the ability to purchase technology they previously could not afford — which may democratize access to the law, if ethically employed.

    3. Content Creation (High-Volume, Low-Creativity)

    Examples:

    • Copywriters (particularly for SEO/blog mills)
    • Product description writers
    • Social media content providers
    • Newsletter writers
    • Why they’re under threat

    AI applications such as ChatGPT, Jasper, Copy.ai, and Claude can create content quickly, affordably, and decently well — particularly for formulaic or keyword-based formats.

    Real-world impact:

    Those agencies that had been depending on human freelancers to churn out content have migrated to AI-first processes.

    • Clients are requesting “AI-enhanced” services at reduced costs.

    Human angle:

    There’s an immense emotional cost involved. A lot of creatives are having their work downvalued or undercut by AI-generating substitutions. But those who double down on editing, strategy, or voice differentiation are still needed. Pure generation is becoming commoditized — judgment and nuance are not.

    4. Basic Data Analysis and Reporting

    Examples:

    • Junior analysts
    • Business intelligence assistants
    • Financial statement preparers

    Why they’re at risk:

    AI and code-generating tools (such as GPT-4, Code Interpreter, or Excel Copilot) can already:

    • Clean and analyze data
    • Create charts and dashboards
    • Summarize trends and create reports
    • Explain what the data “says”

    Real-world impact:

    Several startups are utilizing AI in replacing tasks that were traditionally given to entry-level analysts. Mid-level positions are threatened as well, if these depend too heavily on templated reporting.

    Human angle:

    Data is becoming more accessible — but the human superpower to know why it matters is still essential. Insight-focused analysts, storytellers, and contextual decision-makers are still essential.

     5. Customer Support & Sales (Scripted or Repetitive)

    Examples:

    • Tier-1 support agents
    • Outbound sales callers
    • Survey takers

    Why they’re at risk:

    Chatbots, voice AI, and LLMs integrated into CRM can now take over an increasing percentage of simple questions and interactions.

    Real-world impact:

    • Call centers are cutting employees or moving to AI-first operations.
    • Outbound calling is being more and more automated with AI voice agents.

    Human perspective:

    Where “efficiency” is won, trust tends to be lost. Humans still crave empathy, improvisation, and genuine comprehension — so roles that value those qualities (e.g. relationship managers) are safer.

    Grey Zone: Roles That Are Being Transformed (But Not Replaced)

    Not everything risk-related is about being killed. A lot of work is being remade — where humans still get to do the work, but AI handles the repetitive or low-level stuff.

    These are:

    • Teachers → AI helps grade, generates quizzes, tutors. Teachers get to do more emotional, adaptive teaching.
    • Software engineers → AI generates boilerplate code, tests, or documentation. Devs get to do architecture, debugging, and tricky integration.
    • Physicians / Radiologists → AI assists in the interpretation of imaging or providing diagnoses. Humans deliver care, decision-making, and context.
    • Designers → AI provides ideas and layouts; designers craft and guide.
    • Marketers → AI produces content and A/B tests; marketers strategize and analyze.

    The secret here is adaptation. The more judgment, ethics, empathy, or strategy your job requires, the more difficult it becomes for AI to supplant — and the more it can be your co-pilot, rather than your competitor.

    Low-Risk Jobs (For Now)

    These are jobs that require:

    • Physical presence and dexterity (electricians, nurses, plumbers)
    • Deep emotional labor (social workers, therapists)
    • Complex interpersonal trust (high-end salespeople, mediators)
    • High degrees of unpredictability (emergency responders)
    • Roles with legal or ethical responsibility (judges, surgeons)
    • AI can augment these roles, but complete replacement is far in the future.

     Humanizing the Future: How to Remain Flexible

    Let’s face it: these changes are disturbing. But they’re not the full story.

    Here are three things to remember:

    1. Being human is still your edge

    • Empathy
    • Contextual judgment
    • Ethical decision-making
    • Relationship-building
    • Adaptability

    These are still unreplaceable.

    2. AI is a tool — not a judgment

    The individuals who succeed aren’t necessarily the most “tech-friendly” — they’re those who figure out how to utilize AI effectively within their own space. View AI as your intern. It’s quick, relentless, and helpful — but it still requires your head to guide it.

    3. Career stability results from adaptability, not titles

    The world is evolving. The job you have right now might be obsolete in 10 years — but the skills you’re acquiring can be transferred if you continue to learn.

    Last Thoughts

    The most vulnerable jobs to next-gen AI are the repetitive, language-intensive, and judgment-limited types. Even here, AI is not a total replacement for human concern, imagination, and morality.

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