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daniyasiddiquiEditor’s Choice
Asked: 24/08/2025In: , ,

brain development

aihealthtechnology
  1. daniyasiddiqui
    daniyasiddiqui Editor’s Choice

      Screens are ubiquitous — from the tablet that assists a toddler in watching cartoons, to the phone that keeps a teenager in touch with friends, to the laptop for online school. For parents, teachers, and even kids themselves, the genuine issue isn't whether screens are "good" or "bad." It's aRead more

     

    Screens are ubiquitous — from the tablet that assists a toddler in watching cartoons, to the phone that keeps a teenager in touch with friends, to the laptop for online school. For parents, teachers, and even kids themselves, the genuine issue isn’t whether screens are “good” or “bad.” It’s about how much, how often, and in what ways they influence the developing brain.

    Brain Plasticity in Childhood

    Kids’ brains are sponges. In early life, the brain structures that control concentration, memory, compassion, and critical thinking are in the process of development. Too much screen time can rewire them:

    • Repeated exposure to fast media can reduce attention spans.
    • Dopamine surges from video games or bottomless scrolling can instill a hunger for immediate gratification, where everyday tasks feel “too slow.
    • On the one hand, school apps and interactive media can solidify problem-solving and visual-spatial capabilities if used responsibly.

     Emotional & Social Development

    Screens become a substitute for in-person interactions. Although social media chatting is comfortable like connection, it doesn’t necessarily develop the emotional intelligence children learn from interpreting facial expressions or resolving everyday disputes.

    • Excessive screen time can postpone empathy development.
    • Bored or frustrated kids might have a harder time with self-regulation.
    • But moderate use can broaden social horizons — children interact with others worldwide, increasing cultural awareness.

     Sleep & Memory

    • Screen blue light inhibits melatonin, the sleep hormone. When kids scroll or game well into the night, it:
    • Slows sleep cycles, causing persistent tiredness.
    • Disrupts memory consolidation, which occurs during deep sleep — essential for learning.
    • Over time, poor sleep impacts mood, behavior, and performance.

     The Content Makes a Difference

    • Not every minute of screen time is created equal. Staring blankly at mindless videos for hours has a different impact than doing puzzles, coding, or taking a virtual class. Quality of use trumps quantity.
    • Passive use (aimless scrolling) → more associated with problems around attention.
    • Active use (problem-solving, creating, learning) → has the potential to enhance cognitive development.

     What Parents Need to Know & Balance

    • The priority isn’t keeping screens out, but regulating kids’ relationship with them.
    • Establish screen-free zones (such as during meals or at bedtime).
    • Promote outdoor play to counterbalance digital stimulation with actual discovery.
    • Co-view or co-play occasionally, so kids view technology as a collaborative activity instead of an individual escape.

     In Simple Words

    Screens are tools. Just as fire can heat food and prepare a meal or burn your hand — it’s up to you. Children’s long-term brain development isn’t sealed with screens, but it is guided by what we permit them to develop today. A child who learns to approach screens in balance, with purpose, and with awareness can succeed both online and offline.

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daniyasiddiquiEditor’s Choice
Asked: 23/08/2025In:

“emotional intelligence”

aitechnology
  1. daniyasiddiqui
    daniyasiddiqui Editor’s Choice

    The increased use of conversational AI modes makes it more capable of comprehending what is being said as well as how it is to be saying it. A virtual assistant might reassure an anxious person, or a customer service robot can shift its tone to placate annoyance when it hears something. Such AI machRead more

    The increased use of conversational AI modes makes it more capable of comprehending what is being said as well as how it is to be saying it. A virtual assistant might reassure an anxious person, or a customer service robot can shift its tone to placate annoyance when it hears something. Such AI machines are termed emotionally intelligent. Are they actually empathetic or is that just some form of sophisticated mimicry?

    The answer lies in how we define empathy—and the amount of “feeling” we expect from machines.

    1. What Emotional Intelligence Means for AI

    Emotional intelligence for humans is the ability to identify emotions in ourselves and others, manage our own response, and use empathy to create stronger relationships.

    With AI, “emotional intelligence” is no longer so much about actual feeling and more about pattern recognition. Through tone of voice analysis, words spoken, facial expression, or even biometrics, AI can predict states of emotion and then personalize its responses.

    Example:

    • If you type, “I’m actually really stressed out about making this deadline,” an emotionally aware AI might respond with, “I get it—it does sound overwhelming. Let’s tackle it step by step.
    • But behind the scenes, it’s not empathy. It’s executing algorithms that have been trained on millions of human exchanges.

    2. The Power of Mimicry

    Even if it’s “just mimicry,” it can seem real to us. Humans are programmed to react to tokens of empathy—like reassuring tones, reassuring words, or empathetic gestures. If AI successfully imitates those tokens, plenty of people will feel comforted or confirmed.

    In that sense, the effect of empathy is stronger than its origin. A child comforted by a talkative toy will not fret that the toy is not alive. In the same way, a desolate person chatting with an empathetic computer might well find actual consolation, even though they know it’s synthetic.

    3. Why Genuine Empathy Is Hard for Machines

    Real empathy demands awareness—actually feeling what another human experiences. AI isn’t aware, isn’t self-aware, and hasn’t existed; it doesn’t know the sensations of sadness, happiness, or fear; it merely senses patterns of data that seem to indicate those conditions.

    This is why most researchers contend that AI will never feel empathy in real terms, regardless of how sophisticated it may be. It can be at best an imitation, not the actual thing.

    4. Where This Imitation Still Counts

    • Though devoid of “actual” feelings, emotionally intelligent AI modes can nonetheless be of tremendous assistance:
    • Healthcare: AI-based chatbots offering mental health support can follow up with patients and assist them in coping.
    • Customer Service: Bots that remain calm and soothing in ireful exchanges can de-escalate.
    • Education: AI tutors can encourage frustrated students, staying motivated to learn.
    • These examples show that mimicry can still have positive human outcomes, even if the AI isn’t feeling anything.

    5. The Risks of Believing AI “Cares”

    • The danger is when people start to treat AI’s mimicry as real empathy. Over time, this could:
    • Deepen loneliness by replacing human connection with artificial comfort.
    • Manipulate emotions—companies might use AI’s “empathetic” voice to push people into purchases or decisions.
    • Blur lines—causing some to entrust AI with emotional weaknesses they’d otherwise keep for close humans.
    • Which brings key questions of ethics around transparency to the forefront: Should AI always let people know that it doesn’t actually “feel”?

    6. A Balanced Perspective

    It is perhaps useful to think of emotionally intelligent AI as a mirror—it reflects back our feelings again, but in a manner that is perceived as useful, but it doesn’t feel. That doesn’t mean it isn’t useful, but it is a reminder to be mindful of keeping things in context.

    Humanness adds empathy based on the experience of being human; AI adds empathy-like responses based on data-simulation. Both are desirable, but they are not equivalent.

     Short version: Emotional intelligence modes of conversational AI aren’t actually feeling empathy—though they’re emulating. But that emulating, if responsibly developed, can still improve human well-being, communication, and accessibility. The key is to make sure we have the illusion without losing the reality: AI doesn’t feel—we do.

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daniyasiddiquiEditor’s Choice
Asked: 23/08/2025In:

Integrated with AR/VR

aitechnology
  1. daniyasiddiqui
    daniyasiddiqui Editor’s Choice

    Man, AI's already turned the script on how we text, Google, buy random crap at 2am, and even punch the clock at work. But when you begin combining AI with all this AR and VR stuff? That's when things get crazy. All of a sudden, it's not just you tapping away at a screen or screaming at Siri—it's almRead more

    Man, AI’s already turned the script on how we text, Google, buy random crap at 2am, and even punch the clock at work. But when you begin combining AI with all this AR and VR stuff? That’s when things get crazy. All of a sudden, it’s not just you tapping away at a screen or screaming at Siri—it’s almost like you’re just hanging out with a digital friend who actually gets you. Seriously, the entire way we work, learn, and binge digital video might be revolutionized.

    1. Saying Goodbye to Screens for Real Spaces

    Currently, if you want to engage with AI, it’s largely tapping, typing, or perhaps barking voice orders at your phone. But immersive AI? You’re walking into 3D spaces. Imagine this: instead of a dull chatbot attempting to describe quantum physics, you’re in a virtual reality classroom and the AI is your instructor—giving you a tour of black holes as if you were on a school field trip. Or with augmented reality, you’re strolling by a historic building and BAM, your glasses give you the whole history of the building right in front of you. The border between “real” and “digital” becomes less distinct, and for real, it doesn’t feel so lonely anymore.

    2. Speaking Like a Real Human

    With immersive AI, you don’t have to type or speak. You get to use your hands, your face, your entire body—AI responds to all those subtle cues. Raise an eyebrow, wave your arm around, whatever—AI catches it. So if you’re in a VR painting studio and you just point at something with a look, your AI assistant gets it that you want to change it. It’s like having technology that speaks “human.

    3. Worlds Built Just For You

    AI’s go-to party trick? Getting everything to be about you. In immersive worlds, that translates to your space changing to fit what you require. Learning chemistry? Now molecules are hovering above your head. Preparing to be a surgeon? Your VR operating theater looks and feels just so for your skill level. Ditch those generic, one-size-fits-all apps. It’s all bespoke, all the time. Pretty cool, if you ask me.

    4. No More Borders

    Collaborating with folks from all around the globe? Once a nightmare. Now, you all just get into a VR conference room, and the AI handles the ugly stuff—translating everyone, keeping assignments organized, providing instant feedback. Collaborating is no longer this clunky Zoom hellhole. It’s silky, even enjoyable. The AI’s not some additional tool; it’s like the world’s greatest project manager who never has to take coffee breaks.

    5. Getting Emotional (But, Like, With Machines)

    AIs in AR/VR aren’t all cold, faceless automatons—they develop personalities, voices, even facial expressions. Picture your AI mentor goading you on with a wink or your virtual coach screaming, “Let’s go!” with actual enthusiasm (well, as real as computer code allows). It makes everything seem more. alive. But, yeah, it’s a bit strange too. You might start caring about your AI pal more than your real ones, which is kinda wild to think about.

    There’s a line somewhere, and we’ll have to figure out where to draw it.

    6. Not All Sunshine and Rainbows

    Look, this stuff isn’t perfect. Few things to worry about:
    – Privacy—AR glasses and VR headsets could be tracking your every blink and twitch. Creepy, right?
    – Getting too comfy—If the digital world feels too good, who even wants real life anymore?

    – Not for everyone—All this gear costs money, and not everyone can just drop cash on the latest headset.

    We gotta keep an eye on this, or we’ll end up in a Black Mirror episode real quick.

    7. Humans + Machines = Besties?

    Flash-forward a couple of years, and conversing with AI will be like texting your BFF, only they never leave you on read. Instead of swiping between a million apps, you’ll just walk into a virtual room and your AI is ready to assist or just chat. Less of that sterile, transactional feel—more like sharing stories, ideas, and experiences. Kinda crazy, but also kinda great. Bottom line? Immersive AI isn’t just making technology more flashy. It’s making it feel real—like it’s finally in your world, not just another device you need to learn to use. And that, sincerely, could change everything.

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daniyasiddiquiEditor’s Choice
Asked: 22/08/2025In: , ,

without privacy risks

aitechnology
  1. daniyasiddiqui
    daniyasiddiqui Editor’s Choice

    Artificial Intelligence has made huge leaps in recent years, but one issue continues to resurface—hallucinations. These are instances where an AI surely creates information that quite simply isn't there. From creating academic citations to quoting historical data incorrectly, hallucinations erode trRead more

    Artificial Intelligence has made huge leaps in recent years, but one issue continues to resurface—hallucinations. These are instances where an AI surely creates information that quite simply isn’t there. From creating academic citations to quoting historical data incorrectly, hallucinations erode trust. One promising answer researchers are now investigating is creating self-reflective AI modes.

     What do we mean by “Self-Reflection” in AI?

    Self-reflection does not imply that an AI is sitting quietly and meditating but instead is inspecting its own reasoning before it responds to you. Practically, it implies the AI stops, considers:

    • “Does my answer hold up against the data I was trained on?”
    • “Am I intermingling facts with suppositions?”
    • “Can I double-check this response for different paths of reasoning?”

    This is like how sometimes we humans pause in the middle of speaking and say, “Wait, let me double-check what I just said.”

    Why Do AI Hallucinations Occur in the First Place?

    Hallucinations are happening because:

    • Probability over Truth – AI is predicting the next probable word, not the absolute truth.
    • Gaps in Training Data – When information is missing, the AI improvises.
    • Pressure to Be Helpful – A model would rather provide “something” instead of saying “I don’t know.”
    • Lacking a way to question its own initial draft, the AI can safely offer misinformation.

     How Self-Reflection Could Help

    • Think of providing AI with the capability to “step back” prior to responding. Self-reflective modes could:
    • Perform several reasoning passes: Rather than one-shot answering, the AI could produce a draft, criticize it, and edit.
    • Catch contradictions: If part of the answer conflicts with known facts, the AI could highlight or adjust it.
    • Provide uncertainty levels: Just like a doctor saying, “I’m 70% sure of this diagnosis,” AI could share confidence ratings.
    • This makes the system more cautious, more transparent, and ultimately more trustworthy.

     Real-World Benefits for People

    • If done well, self-reflective AI could change everyday use cases:
    • Education: Students would receive more accurate answers rather than fictional references.
    • Healthcare: AI-aided physicians could prevent making up treatment regimens.
    • Business: Professionals conducting research with AI would not waste time fact-checking sources.
    • Everday Users: Individuals could rely on assistants to respond, “I don’t know, but here’s a safe guess,” rather than bluffing.

     But There Are Challenges Too

    • Self-reflection isn’t magic—it brings up new questions:
    • Speed vs. Accuracy: More reasoning takes more time, which might annoy users.
    • Resource Cost: Reflective modes are more computationally expensive and therefore costly.
    • Limitations of Training Data: Even reflection can’t compensate for knowledge gaps if the underlying model does not have sufficient data.
    • Risk of Over-Cautiousness: AI may begin to say “I don’t know” too frequently, diminishing usefulness.

    Looking Ahead

    We’re entering an era where AI doesn’t just generate—it critiques itself. This self-checking ability might be a turning point, not only reducing hallucinations but also building trust between humans and AI.

    In the long run, the best AI may not be the fastest or the most creative—it may be the one that knows when it might be wrong and has the humility to admit it.

    Human takeaway: Just as humans build up wisdom as they stop and think, AI programmed to question itself may become more trustworthy, safer, and a better friend in our lives.

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Anonymous
Asked: 20/08/2025In: , ,

the Future of Reasoning and Logic in ...

aiprogrammers
  1. Anonymous
    Anonymous

    When most people hear about AI these days, they imagine huge language models that can spit out copious text, create realistic pictures, or even talk like a human being. These are incredible things, but they still lag in one area: reasoning and logic. AI can ape patterns but tends to fail when facedRead more

    When most people hear about AI these days, they imagine huge language models that can spit out copious text, create realistic pictures, or even talk like a human being. These are incredible things, but they still lag in one area: reasoning and logic. AI can ape patterns but tends to fail when faced with consistency, abstract thinking, or solving problems involving multiple levels of logic.

    This is where neurosymbolic AI fills the gap—a hybrid strategy combining the pattern recognition capabilities of neural networks and the rule-based reasoning of symbolic AI.

    • Why Pure Neural AI Isn’t Enough

    Neural networks, such as those powering ChatGPT or image generators, are great at recognizing patterns within enormous datasets. They can produce human-sounding outputs but don’t actually “get” ideas the way we do. That’s how they make goofy errors now and then, such as confusing basic math problems or remembering rules halfway through an explanation.

    For instance: ask a neural model to compute a train schedule with multiple links, and it may falter. Not because it can’t handle words, but because it hasn’t got the logical skeleton to enforce coherence.

    • The Symbolic Side of Intelligence

    Prior to the age of deep learning, symbolic AI reigned supreme. They operated with definite rules and logic trees—imagine them as huge “if-this-then-that” machines. They excelled at reasoning but were inflexible, failing to adjust when reality deviated from the rules.

    Humans are not like that. We can integrate logical reasoning with instinct. Neurosymbolic AI attempts to get that balance right by combining the two.

    • What Neurosymbolic AI Looks Like in Action

    Suppose a medical AI is charged with diagnosing a patient:

    A neural network may examine X-ray pictures and identify patterns indicating pneumonia.

    A symbolic system may then invoke medical rules: “If the patient has pneumonia + high fever + low oxygen levels, hospitalize.”

    Hybridized, the system delivers a more accurate and explainable diagnosis than either component could independently provide.

    Another illustration: in robotics, neurosymbolic AI can enable a robot to not only identify objects (a neural process) but also reason about a sequence of actions to solve a puzzle or prepare a meal (a symbolic process).

    • Why This Matters for the Future

    Improved Reasoning – Neurosymbolic AI can potentially break the “hallucination” problem of existing AI by basing decisions on rules of logic.

    Explainability – Symbolic elements facilitate tracing why a decision was made, important for trust in areas such as law, medicine, and education.

    Efficiency – Rather than requiring enormous datasets to learn everything, models can integrate learned patterns with preprogrammed rules, reducing data requirements.

    Generalization – Neurosymbolic systems can get closer to genuine “common sense,” enabling AI to manage novel situations more elegantly.

    • Challenges on the Path Ahead

    Nor is it a silver bullet. Bringing together two so distinct AI traditions is technologically challenging. Neural networks are probabilistic and fuzzy, whereas symbolic logic is strict and rule-based. Harmonizing them to “speak the same language” is a challenge that researchers are still working through.

    Further, there’s the issue of scalability—can neurosymbolic AI accommodate the dirty, chaotic nature of the world outside as well as human beings do? That remains to be seen.

    • A Step Toward Human-Like Intelligence

    At its essence, neurosymbolic AI is about building machines that can not only guess what comes next, but genuinely reason through problems. If accomplished, it would be a significant step towards AI that is less like autocomplete and more like a genuine partner in solving difficult problems.

    Briefly: Neurosymbolic AI is defining the future of machine reasoning by bringing together intuition (neural networks) and logic (symbolic AI). It’s not perfect yet, but it’s among the most promising avenues toward developing AI that can reason with clarity, consistency, and trustworthiness—similar to ours.

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daniyasiddiquiEditor’s Choice
Asked: 15/08/2025In: , ,

 

aitechnology
  1. daniyasiddiqui
    daniyasiddiqui Editor’s Choice

    Global AI Rules & Open-Source: The Balancing Act Open-source AI has been the engine of creativity in the AI world—anyone with the skills and curiosity can take a model, improve it, and build something new. But as governments race to set rules for safety, privacy, and accountability, open-sourceRead more

    Global AI Rules & Open-Source: The Balancing Act

    Open-source AI has been the engine of creativity in the AI world—anyone with the skills and curiosity can take a model, improve it, and build something new. But as governments race to set rules for safety, privacy, and accountability, open-source developers are entering a trickier landscape.

    Stricter regulations could mean:

    More compliance hurdles – small developers might need to meet the same safety or transparency checks as tech giants.

    Limits on model release

    some high-risk models might only be shared with approved organizations.

    Slower experimentation

    extra red tape could dampen the rapid, trial-and-error pace that open-source thrives on.

    On the flip side, these rules could also boost trust in open-source AI by ensuring models are safer, better documented, and less prone to misuse.

    In short

    global AI regulation could be like adding speed limits to a racetrack—it might slow the fastest laps, but it could also make the race safer and more inclusive for everyone.

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Anonymous
Asked: 14/08/2025In: , ,

new tariff policies

aitechnology
  1. Anonymous
    Anonymous

    International supply chains are adapting to be more agile than ever to the latest tariff regimes — pretty much like an old traveler forced to shift flight paths halfway through the journey. This is what's going down on the ground: Rebasing trade routes – Businesses are redirecting sourcing from natiRead more

    International supply chains are adapting to be more agile than ever to the latest tariff regimes — pretty much like an old traveler forced to shift flight paths halfway through the journey.

    This is what’s going down on the ground:

    Rebasing trade routes – Businesses are redirecting sourcing from nations impacted with increased tariffs to nations with more amicable terms of trade. For instance, a company that previously depended on China would now diversify vendors in Vietnam, Mexico, or Eastern Europe.

    “Friendshoring” and regional hubs – Rather than a single massive manufacturing hub, supply chains are fragmenting into regional webs to manage risk. In this manner, if one trade lane becomes pricey or clogged, the others continue going.

    Tech-powered forecasting – AI and analytics are enabling firms to model “what if” tariff situations so they can reconfigure orders, shipping routes, and pricing before issues arise.

    Revival of local production – Increased tariffs make imports more expensive, so some businesses are taking some production steps in-house — creating local employment but also redefining cost profiles.

    Why it feels so human:

    Companies aren’t merely juggling figures; they’re being flexible and ingenious. Just as individuals learn to live with unexpected shifts in their own household budgets, companies are getting better at making shrewder trade-offs — safeguarding what’s most important while leveraging innovation to stay alive.

    Briefly put, tariffs are making supply chains more like nimble gymnasts than rigid production lines — agile, diversified, and able to roll with the punches.

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Anonymous
Asked: 14/08/2025In: ,

personalized experiences

aitechnolgy
  1. Anonymous
    Anonymous

    Yes "AI twins" are fast becoming one of the most thrilling frontiers in bespoke experiences, and here's why it already seems so futuristic but oddly natural. Picture a virtual you not a mere profile with your information, but a developing, learning AI that knows your tastes, recalls your idiosyncrasRead more

    Yes

    “AI twins” are fast becoming one of the most thrilling frontiers in bespoke experiences, and here’s why it already seems so futuristic but oddly natural.

    Picture a virtual you

    not a mere profile with your information, but a developing, learning AI that knows your tastes, recalls your idiosyncrasies, adjusts to your moods, and can execute on your behalf. It’s having an endless personal assistant, life guide, and social ambassador all in one, except that it dwells in your phone or in the cloud.

    Why everyone is abuzz about it:

    Ultra-personalized recommendations – Your AI twin is able to recommend what to watch, read, or eat, not according to broad trends but according to your actual history and present mood.

    Decision-making help

    It is able to simulate scenarios for you (“What if I relocate to another city?”) and provide data-driven, emotionally intelligent advice.

    Life administration

    It may organize your appointments, write your emails, or negotiate with other AI twins (yes, your AI could one day arrange a holiday with your friend’s AI without either of you lifting a finger on your phones).

    The people side of the thrill

    Individuals are fond of the concept since it guarantees less overload in an information-rich world. It’s sort of outsourcing your mental mess to a “you, but on autopilot” — without sacrificing the human touch.

    The flip side

    Of course, this also raises significant concerns about privacy, security, and who really “owns” your twin’s knowledge about you. I mean, a digital you might be more revealing than your actual you.

    in Short

    AI twins are looking to be the next big thing in personalization. If the 2010s were the decade of the recommendation engine and the 2020s are going to be the decade of AI assistants, then the next decade might be AI versions of us living alongside us in everyday life.

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daniyasiddiquiEditor’s Choice
Asked: 13/08/2025In: , ,

 

ainews
  1. daniyasiddiqui
    Best Answer
    daniyasiddiqui Editor’s Choice

    Governments today are teetering on a tightrope — and it's not a comfortable one. On one hand, there is AI innovation, which holds the promise of quicker healthcare diagnoses, more intelligent public services, and even economic expansion through industries powered by technology. On the other hand, thRead more

    Governments today are teetering on a tightrope — and it’s not a comfortable one.

    On one hand, there is AI innovation, which holds the promise of quicker healthcare diagnoses, more intelligent public services, and even economic expansion through industries powered by technology. On the other hand, there is data privacy, where the stakes are intensely personal: individuals’ medical records, financial information, and private discussions.

    The catch? AI loves data — the more, the merrier — but privacy legislation is meant to cap how much of it can be harvested, stored, or transmitted. Governments are thus attempting to find a middle ground by:

    Establishing clear limits using regulations such as GDPR in Europe or new AI-specific legislation that prescribes what is open season for data harvesting.

    Spurring “privacy-first” AI — algorithms that can be trained on encrypted or anonymized information, so personal information never gets shared.

    Experimenting sandbox spaces, where firms can try out AI in controlled, overseen environments before the public eye.

    It’s a little like having children play at a pool — the government wants the enjoyment and skill development to occur, but they’re having lifeguards (regulators) on hand at all times.

    If they move too far in the direction of innovation, individuals will lose faith and draw back from cooperating and sharing information; if they move too far in the direction of privacy, AI development could grind to a halt. The optimal position is somewhere in between, and each nation is still working on where that is.

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