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daniyasiddiquiEditor’s Choice
Asked: 06/09/2025In: ,

cloud services, and data flows repla ...

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  1. daniyasiddiqui
    daniyasiddiqui Editor’s Choice

    What we mean by “digital tariffs” By “digital tariffs” I mean taxes, levies or customs-style duties applied to cross-border digital activity — things like data flows, remote cloud/AI services, digital advertising, streaming or the commercial use of foreign AI models. This is different from standardRead more

    What we mean by “digital tariffs”

    By “digital tariffs” I mean taxes, levies or customs-style duties applied to cross-border digital activity — things like data flows, remote cloud/AI services, digital advertising, streaming or the commercial use of foreign AI models. This is different from standard customs duties on imported physical goods: digital tariffs target transactions, data, or digital market access rather than the physical movement of items.

    Why the idea is appealing

    Economy has shifted — so have value chains. More value now sits in software, data, AI models and cloud platforms. Traditional tariffs aimed at protecting domestic manufacturing don’t capture those revenue sources or address digital “market access” asymmetries.

    Tax fairness / revenue reasons. Many countries felt large digital platforms paid too little tax where their users are located; this spurred digital services taxes and the OECD’s reform effort. Digital levies are a way to claim revenue from cross-border

    Policy objectives beyond revenue. Governments may want to incentivize local data storage, protect privacy/safety, or discourage importing services that harm domestic industry. A digital tariff is a blunt tool to achieve those goals when other policy options are limited.

    What digital tariffs can do well (the upside)

    • Raise revenue from non-physical value creation (digital advertising, platform services). This helped motivate many countries’ equalisation levies.
    • Encourage local investment or data localization when structured as a conditional levy (lower rates if data centers/local partners are used).
    • Offer policy leverage where international tax rules are slow to adapt — governments can act unilaterally to respond to public pressure.

    What they cannot replace in practice (limits vs. physical tariffs)

    • Border protection and industrial policy. Tariffs on goods change relative domestic prices, protect domestic producers from import competition and reshape supply chains in ways a digital levy cannot. You can’t “tariff” a foreign-made tractor the same way you tax a SaaS subscription — the economic levers are different.
    • Customs enforcement & provenance. Physical tariffs are enforced at borders where customs inspect shipments. Digital activity is less tangible, easier to route or relabel, and often falls under different tax/tariff legal frameworks.
    • WTO and trade-law realities. The WTO moratorium on customs duties for electronic transmissions has been repeatedly renewed, and it constrains multilateral acceptance of customs-style duties on pure digital transmissions — though that moratorium’s future is debated. Pushing a full replacement would require rewiring global trade rules.

    Real-world signs & recent moves (short snapshot)

    Several countries experimented with digital levies (equalisation levies, digital services taxes), but some jurisdictions are reversing or revising them as international tax frameworks and diplomacy evolve — e.g., India moved to remove its ad-targeted equalisation levy recently as it reshapes its approach. That shows the political and diplomatic balancing act these policies trigger

    Meanwhile, the OECD’s Pillar work (on reallocating taxing rights and minimum tax rules) has been the more multilateral route to address digitalisation’s tax challenges — not a customs-style tariff replacement.

    Political friction persists: unilateral digital levies have provoked threats of trade retaliation or countermeasures, so any broad replacement strategy risks escalating trade tensions.

    Key economic, legal and technical risks

    • Double taxation / diplomatic blowback. Unilateral digital levies can lead to disputes or retaliatory tariffs; they may also overlap with corporate income taxes creating double taxation.
    • Evasion and routing. Digital services can be restructured, routed through low-tax jurisdictions, or bundled in ways that defeat simple levies. That undermines both revenue and policy intent.
    • Measurement problems. How do you measure “use” or “consumption” reliably (users, clicks, compute hours, data ingress/egress)? Poor metrics produce inequitable rates and gameable incentives.
    • Fragmentation risk. If every country erects different digital tariffs, commerce will fragment, compliance costs will explode, and global digital supply chains will suffer — the exact opposite of the open network many economies depend on.
    • Conflict with trade commitments. Many trade agreements and the WTO framework assume non-discrimination and predictability; a wholesale shift to digital tariffs would require renegotiation of these commitments.
      White & Case

    How digital tariffs should be used — a pragmatic policy framework

    Rather than a “replace” strategy, think “complement and coordinate.” Here’s a balanced recipe:

    • Use targeted digital levies for specific objectives (revenue gap, consumer protection, data-localization incentives), not as blunt substitutes for goods tariffs.
    • Prefer tax-style instruments over customs-style tariffs where possible — e.g., place-based digital taxes that allocate taxing rights to user jurisdictions (the OECD approach) reduce trade frictions and legal risk.
    • Design clear metrics and thresholds. Only large multinational digital service providers should be in scope initially; exclude small cross-border sellers to avoid stifling SMEs.
    • Coordinate regionally and multilaterally. Work through blocs (EU, ASEAN, G20/OECD) to harmonize rules and avoid fragmentation. The WTO moratorium and OECD negotiations illustrate why multilateral paths matter.
    • Pair digital levies with domestic measures for fairness. If a levy raises prices for consumers, use part of the revenue to subsidize access, support digital literacy, or invest in local cloud/AI infrastructure.
    • Transparency & dispute resolution. Publish rules, use neutral metrics, and accept arbitration to avoid trade flareups.

    Distributional & development considerations

    For advanced economies, digital levies might be about fairness and revenue redistribution from large global platforms. For developing countries, digital tariffs could be tempting as quick revenue sources — but they risk scaring off investment or driving platforms to restrict services. Careful calibration and international support are needed so poor countries don’t pay the political or economic price for digital protectionism.

    Bottom line — the simple verdict

    Digital tariffs are useful tools, but they aren’t substitutes for traditional tariffs. They work on different economic levers and carry different risks.

    Policy mix is what matters. Use digital levies to capture digital value, protect users, or incentivize local investment — but retain traditional tariffs (and other instruments like subsidies, regulation and industry policy) for physical-goods protection and industrial strategy.

    International coordination is essential. If countries act alone, the result will be messy: trade friction, double taxation, and fragmented digital markets. The multilateral route (OECD, WTO, regional blocs) is slow, but it reduces blowback.

    If you want, I can:

    Draft a short policy memo (1–2 pages) that outlines how a medium-sized economy could introduce a targeted digital tariff while minimizing risks; or

    Build a one-page explainer comparing outcomes if a government replaced 25% of its goods tariffs with a digital levy (distributional effects, likely retaliation, revenue volatility); or

    Sketch two sample legislative clauses: one for a narrowly-targeted digital services levy, another for a carbon-adjusted import duty.

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daniyasiddiquiEditor’s Choice
Asked: 06/09/2025In: , ,

(adjusted in real time by data) repla ...

aicompanynews
  1. daniyasiddiqui
    daniyasiddiqui Editor’s Choice

    What I refer to as "AI-driven dynamic tariffs" Consider a system that takes in real-time data (imports by HS code and country, supply-chain flows, world prices, carbon intensity, domestic employment indicators, smuggling/evasion alerts, etc.), executes automated economic and rule-based models, and dRead more

    What I refer to as “AI-driven dynamic tariffs”

    Consider a system that takes in real-time data (imports by HS code and country, supply-chain flows, world prices, carbon intensity, domestic employment indicators, smuggling/evasion alerts, etc.), executes automated economic and rule-based models, and dynamically adjusts tariff rates on targeted product lines or flows continuously—or at pre-set intervals—based on pre-defined goals (save jobs, stabilise domestic prices, reduce carbon leakage, raise revenue, retaliate against unfair practices). The “AI” components are prediction, anomaly detection, automated simulation of scenarios, and decision support; the policy choice may remain human-approved or completely automated inside legal bounds.

    Technical feasibility — yes, but nontrivial

    We already have two things that demonstrate pieces of this are possible:

    Businesses and suppliers are developing AI software to monitor tariff updates, predict supply-chain effects, and execute tariff-related compliance (real-time HSN classification, duty calculations, scenario modeling). That infrastructure might be repurposed or scaled to advise policy.

    In other regulated spaces (electricity, say) researchers and practitioners have implemented automated “dynamic tariff” mechanisms—the math and control systems are there (Bayesian / optimization / feedback control)—so the engineering pattern is established in similar contexts.

    So sensors, data pipelines, modeling software and compute are there. The difficult bit isn’t raw compute — it’s policy design, governance, enforcement and second-order market effects.

    Potential benefits (why people are excited

    • Quicker, data-driven reactions. Policymakers might increase or decrease tariffs in near real time to insulate vulnerable sectors from unexpected import spikes, or to moderate inflationary cost shocks.
    • Targeting and precision. Rather than across-the-board tariffs, dynamic systems can impose differentiated rates by product, source, or even route of shipment—minimizing blunt collateral harm to unrelated industries.
    • Policy automation of public goods. You might program carbon-adjustment targets (e.g., increased duties on more carbon-intensive imports) that shift as cleaner options emerge.
    • Improved revenue and leakage management. Monitoring by computers would limit misclassification and avoidance, allowing customs to collect intended duties with greater ease.

    Substantial practical and political risks

    • Volatility and market instability. Sudden tariff fluctuations can produce whipsaw price consequences, cause panic in supply chains, and promote speculative activity. Markets detest unexpected policy fluctuations.
    • Gaming and avoidance. Companies will soon devise means to re-route, re-label, or re-source commodities to avoid algorithmic tariffs. That leads to an arms race between avoidance and enforcement.
    • Legal and trade-law restrictions. World Trade Organization regulations, preferential trade arrangements, and domestic legislation are based on transparent, predetermined actions. Computer-driven adjustments threaten to breach commitments and necessitate new legal structures.
    • Distributional equity and credibility. Unless tariffs shift by algorithm with transparent human monitoring or well-timed rules, impacted companies, employees and trading countries will complain—politically and legally.
    • Data quality & bias. Inadequately measured inputs (e.g., poorly sorted imports, buggy data feeds) may result in unfair or ineffective tariff adjustments. Garbage in

    Governance design: making it safe & credible

    If governments wish to try, these precautions are necessary:

    • Well-defined objective function(s) and ex ante rules. Specify what is to be optimized by the algorithm (e.g., restrict to smoothing import surges, or carbon-adjustment within a 0–10% band).
    • Human-in-the-loop thresholds. Minor, regular adjustments may be automated; any change over a defined magnitude or length of time is subject to ministerial approval.
    • Transparency & audit logs. Release the input data sources, decision rules, and change log so stakeholders (and courts) can audit decisions.
    • Appeals and correction mechanisms. Importers/exporters must have a quick route to challenge misapplied tariff changes.
    • Sunset clauses & pilot scopes. Begin in a limited area (e.g., seasonal agricultural peaks, a single tariff item for semiconductors, or carbon-adj margins on fossil inputs) and sunset/extend on the basis of an assessment.
    • International coordination. To prevent cascading retaliation and compliance problems, coordinate pilots with large trading partners or regional blocs where feasible.
      UN Trade and Development (UNCTAD)

    Where an AI-dynamic strategy is most likely to be beneficial first

    Sectoral pilots: perishable agriculture (where price shocks are pressing), energy-intensive inputs (to introduce carbon-adjusted import tariffs), or instances of abrupt dumping imports.

    Decision-support systems: applying AI to suggest discrete tariff actions to human decision-makers (highly probable near term). AI is already being applied by many countries and companies to monitor tariffs and model impacts—dual-purposing the same tools as policy analytics is the low-risk initial step.

    Analogues and precedent

    Dynamic pricing in transport and utilities has yielded regulators lessons on fallback predictable pricing requirements, consumer protections, and smoothing signals. Researchers have modeled tariffs as feedback controls—valuable policy design advice.

    Private sector tools (Altana, Palantir, tariff-HSN AI, etc.) illustrate the speed at which businesses can realign operations to tariffs; that same responsiveness would go both ways if governments were to automate tariffs.

    Political economy — a central tension

    Tariffs aren’t merely economics; they are political promises (to constituents, sectors, global partners). Politicians like visible, understandable actions. A ping-ponging algorithmic tariff will be framed as “out of control” even if it maximizes social welfare on paper. That renders full replacement politically implausible short of very gradual staged rollouts and robust transparency.

    A realistic phased way forward (my suggested roadmap)

    • Construct decision-support, not autopilot. Employ AI to generate live dashboards and tariff simulations for policymakers. Let human beings call the shots. (Low-risk short term.)
    • Pilot limited auto-adjustments. Permit automatic, limited adjustments (e.g., ±2–5% band, only for pre-cleared tariff lines, finite duration) with rollback rules. Analyze economic and distributional effects.
    • Legal updates & international negotiation. Collaborate with trade partners and organizations (WTO/FTA partners) to develop mutual agreement protocols for algorithmic tariff procedures.
    • Scale with safeguards. If pilots are stable and legitimate with the public, scale up step by step with ongoing audits and public disclosure.

    Bottom line — probable outcome

    Short-to-medium term (1–5 years): AI will drive tariff analysis, forecasting and decision support. Governments will pilot constrained auto-adjustments in narrowly defined regions. Companies will use more AI to respond to these actions.

    Medium-to-long term (5–15+ years): With frameworks of law, international coordination, good governance and evident payoffs, dynamic tariffs might emerge as an explicit policy tool, but they will exist alongside static tariffs and trade agreements instead of displacing them in toto. The political and diplomatic viscosity of tariffs ensures human beings (and parliaments) will retain ultimate discretion for a while yet.

    If you prefer, I can:

    • Create a sample policy framework (objectives, thresholds, oversight, appeal process) for a pilot program; or
    • Develop a technical architecture (data feeds, models, auditing, rollback) for a government that would like to pilot dynamic tariffs; or
    • Develop a brief explainer targeted at legislators that distills the payoffs, risks and mitigations.
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mohdanasMost Helpful
Asked: 06/09/2025In: , ,

AI-powered diagnostics truly replace ...

aihealthnewspeople
  1. mohdanas
    mohdanas Most Helpful

    Where Human Physicians Remain Ahead Yet here is where the human element in medicine cannot be ignored. Diagnosis is not necessarily diagnosing an illness—it's hearing, comprehending, and assembling a patient's history. A physician doesn't merely read pictures or numbers; he hears the quiver in a patRead more

    Where Human Physicians Remain Ahead

    Yet here is where the human element in medicine cannot be ignored. Diagnosis is not necessarily diagnosing an illness—it’s hearing, comprehending, and assembling a patient’s history.

    A physician doesn’t merely read pictures or numbers; he hears the quiver in a patient’s voice, observes the body language, and reads signs against the background of a person’s lifestyle, frame of mind, and history. Pain in the chest can be a heart attack—or it could be anxiety, indigestion, or even grief. AI can raise an alarm for a possible cardiac problem, but only a skilled doctor can sit, make eye contact, and weigh all the nuances.

    And then there is the issue of trust. Patients tell doctors their secrets, fears, and intimate information. That relationship feeling—knowing someone cares, hears, and is present with you—cannot be substituted by a computer. Healing is not only biological; it is relational, emotional as well.

    Risks of Over-Dependence on AI

    If we completely outsourced diagnostics to AI, a number of risks arise:

    • Bias in algorithms: AI will only ever be as good as what it has been trained on. If that training set doesn’t include all populations (e.g., minorities, women, or unusual conditions), the system can make errors that reinforce inequality.
    • Disappearance of clinical intuition: Medicine isn’t always a straightforward black-and-white situation. Physicians need to use experience, intuition, and “gut feelings” when symptoms don’t fit easily into one category. AI doesn’t have that sort of general judgment.
    • Accountability problems: If AI gets it wrong, who is accountable—the physician who programmed it, the hospital that bought it, or the physician who applied it?
    • Loss of competence: Doctors might dull the edge of their own clinical skills in the long run if they rely too heavily on AI.

    The greatest thing to consider AI in medicine as is a hugely useful resource, and not a replacement. View it as a co-pilot. It can do the heavy lifting of number-crunching so physicians can concentrate on what they’re best at: empathize, put things in context, and walk patients through difficult decisions.

    For instance:

    A computer network could indicate a potential early lung cancer symptom on a scan. The physician reads it, breaks the news to the patient, factors in the medical history of the family, and recommends treatment options compassionately.

    AI can monitor a patient’s wearable health information, notifying the physician of irregularities. But the physician makes the final decision as to whether it’s an issue or a normal deviation.

    Thus, AI is not taking the place of the doctor—he is supplementing him, just as the calculator supplemented mathematicians or autopilot systems supplemented pilots.

    Looking Ahead

    The future isn’t going to be “AI vs. doctors” but rather AI and doctors together. The hospitals of the future will likely use diagnostic software to scan data first, and then doctors step in with more cerebral thinking and human compassion. Medical school will likely adapt as well, educating future doctors not just biology but also how to work with AI ethically.

    Of course, patients and societies will have to determine where that line is. Some will be okay with the AI doing more (particularly in the overburdened systems), and some will want human intervention out of emotional motivations.

    So, can they replace human doctors? Technically, within certain restricted areas, yes. But ought they replace doctors? Most likely not. Medicine isn’t as much about figuring out what’s wrong as it is about guiding patients through some of the most intimate moments of their lives. AI can be the super-geniuis sidekick, the second pair of eyes, the unstoppable number cruncher. But the soul of medicine—the compassion, the judgment, the trust—will probably always rest in the hands of human physicians.

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daniyasiddiquiEditor’s Choice
Asked: 05/09/2025In: ,

traditional classroom teaching, or si ...

aieducationtechnology
  1. daniyasiddiqui
    daniyasiddiqui Editor’s Choice

    The Rise of AI in Learning Over the past several years, AI tutors moved from lab equipment to ubiquitous companions on bedroom floors and classroom desks. Devices that can immediately answer a mathematical question, learn a language, or accommodate a child's skill set are now within reach of tens ofRead more

    The Rise of AI in Learning

    Over the past several years, AI tutors moved from lab equipment to ubiquitous companions on bedroom floors and classroom desks. Devices that can immediately answer a mathematical question, learn a language, or accommodate a child’s skill set are now within reach of tens of millions of students. To most, they’re virtually wizardly: an on-demand teacher in one’s hand 24/7.

    What AI Does Extremely Well

    • AI teachers are best used in conditions where human teachers repeatedly fail on a time and quantity basis. They are able to:
    • Give immediate feedback on an individual basis.
    • Adjust teaching based on individual learning rates.
    • Display unlimited patience when one student repeats the same mistake.
      Speaking in several languages to prevent learning obstacles.
      For the night student having trouble with algebra, an AI teacher brings instant comprehension, something a typical classroom setting cannot.

    The Indispensable Work of Human Educators

    And that’s the truth: learning is not just information transfer. Great teaching is guidance, encouragement, and human contact. Teachers have a sense of what no computer program ever will: the little signals—a struggling student, a lack of confidence, the glint of interest in an eye—that can be the difference. They build not just minds but character, ethics, and social skills.

    A classroom is also a social setting. It’s where kids learn how to collaborate, feel for others, negotiate, and recover—skills that extend far beyond academic competence. No computer software, no matter how clever, can replace the reassurance of support from a teacher who believes in you.

    The Future: Cooperation, Not Replacement

    Instead of viewing AI as a replacement for educators, it is possible to view AI as an aide or co-pilot. Imagine a teacher utilizing AI to grade repetitive assignments, so they have more time for one-on-one mentorship. Or an AI system informing teachers that they need to provide special assistance to certain students so that they may react more effectively.

    In this manner, AI teachers would actually make instructors more human, removing the mechanical aspect of the profession and allowing teachers to concentrate on guidance, empathy, and creativity.

    Risks to Watch Out For

    Of course, we also have to be careful. Overuse of AI may:

    • Decrease critical thinking development if students rely on it for “answers” instead of learning.
    • Widen inequality if only rich families or schools will still be able to afford quality AI tutors in the future.
    • Cause burnout among teachers if they are being asked to compete with machines instead of being aided by them.

    Final Thought

    AI teachers are not here to replace educators—they’re here to boost learning. The future most likely holds is a hybrid approach, one in which AI provides customized advice, yet human educators continue to motivate, advise, and influence people in ways that no computer program ever could.

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daniyasiddiquiEditor’s Choice
Asked: 03/09/2025In: , ,

the gap between rich and poor nations

aitechnology
  1. daniyasiddiqui
    daniyasiddiqui Editor’s Choice

     The Hope vs. The Fear Artificial intelligence has been called "the great equalizer" and "the great divider." On the one hand, it holds the potential to provide every individual with internet connection access to knowledge previously reserved for the elite—medical advice, legal advice, business planRead more

     The Hope vs. The Fear

    Artificial intelligence has been called “the great equalizer” and “the great divider.” On the one hand, it holds the potential to provide every individual with internet connection access to knowledge previously reserved for the elite—medical advice, legal advice, business planning, even high-end tutoring. On the other hand, creating and deploying these AI systems takes enormous data, capital, and computing power, resources in the possession of a few successful nations and firms.

    So will AI close the gap or increase it? The answer is nuanced—because it will depend on how AI is designed, shared, and regulated.

    How AI Could Level the Playing Field

    Envision a physician at a rural clinic in Kenya using an AI assistant to diagnose illness without the need for pricey lab equipment. Or a Bangladeshi business with access to AI marketing strategies on par with those of multinational firms. Or a student at a village far from a city in India doing math with an AI tutor that adjusts their learning speed.

    • AI can cause knowledge and proficiency to be more evenly spread:
    • Education: AI instructors can possibly provide tailored instruction to millions of those who lack access to quality schools.
    • Healthcare: Telemedicine and diagnostics based on AI could be extended to remote areas.
    • Entrepreneurship: Small enterprises of poorer countries could compete with the world using AI without large budgets.

    This way, AI can potentially bypass infrastructure deficits—just like mobile phones enabled developing countries to bypass the costly installation of landlines.

     How AI Might Widen the Gap

    • There is, however, another aspect to the coin: AI craves energy. It needs to be trained on:
    • Ginormous computing resources (supercomputers, power, and state-of-the-art chips).
    • Massive amounts of data, usually controlled by giant tech companies.
    • Expert ability, which in return tends to group in rich countries.
    • This raises the possibility of AI colonialism: where rich nations create, own, and benefit from AI systems, and poor countries are passive receivers. For instance:
    • If large corporations in the US or China own AI, poor countries can “rent” but cannot develop their own.
    • Language and cultural bias in AI systems may silence Global South voices.
    • Those with inadequate digital infrastructures may be left behind completely.

     The Transition Dilemma

    And as with work, there is even an issue of timing here. Rich countries are leading the charge, and poor countries are trying to get into the game of bringing in AI. This disparity can have the possibility of creating new dependency—where poorer countries are depending upon AI systems they may not even own, just as many are presently depending upon drugs or technology brought in from abroad.

    What May Make the Difference

    • Whether AI will bring us together or tear us apart will be determined by decisions being made today:
    • Open-Source AI: If big models stay open, smaller countries can adapt them to their specific needs.
    • Global Cooperation: Global institutions can make AI a global right, and not pay-for.
    • Local Innovation: Developing local AI firms in Africa, South Asia, and Latin America could create solutions contextually appropriate.
    • Digital Infrastructure: Power, internet connectivity, and investment in education is a necessity for any country to realize the advantages of AI.

     The Human Element

    To an individual in Silicon Valley, AI is a productivity tool. To a teacher in Nigeria, it might be the sole means of teaching in classes that have 60 students. To a farmer in Nepal, a weather forecast generated by AI may mean the difference between a profitable harvest and a whole season lost.

    That’s why this isn’t just geopolitics—it’s whether technology will be for the many or the few.

     So, Which Way Will It Go?

    If things go on as they are, AI is going to exacerbate the gap in the short run because already wealthy countries and companies are racing far ahead. But with proper policies, collaborations, and open innovation, AI can turn out to be a great leveller, as mobile technology revolutionised the reach of communications.

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daniyasiddiquiEditor’s Choice
Asked: 03/09/2025In: , ,

replacing jobs faster than new ones

aicompanytechnology
  1. daniyasiddiqui
    daniyasiddiqui Editor’s Choice

    The Battle Between Opportunity and Fear Whenever there is a powerful new technology entering society—whether it's electricity, the steam engine, or the internet—it always poses the same question: Will this replace jobs, or will it create new ones? With AI, the issue appears more acute because the teRead more

    The Battle Between Opportunity and Fear

    Whenever there is a powerful new technology entering society—whether it’s electricity, the steam engine, or the internet—it always poses the same question: Will this replace jobs, or will it create new ones? With AI, the issue appears more acute because the technology isn’t just about robots doing brute labor, but also about computer software doing things thought to be uniquely human—like writing, designing, interpreting data, or even making decisions.

    Work Being Replaced—The Reality Check

    • Artificial intelligence is actually replacing certain forms of work at a faster pace than most expected.
    • Repetitive office tasks—data entry, calendaring, reporting—are increasingly automated.
    • Customer service jobs are being done by AI chatbots that don’t need sleep.
    • Creative sectors—content writing, image-making, video editing—are being shaken up because AI software can spit out drafts in seconds.

    For most employees, it’s rug-pulling, not from under their feet, but from right out from under them. Contrary to the industrial revolution, where physical labor was forced out but “thinking” work wasn’t hurt, AI is entering both physical and mental space. That’s why the disruption is coming so abruptly and overwhelmingly.

     Creating New Jobs—The Unseen Side

    • And here’s the less apparent reality: AI is creating new types of work altogether.
    • AI trainers and ethicists—individuals who train models to act responsibly.
    • Prompt engineers and workflow designers—jobs that did not exist a few years ago.
    • AI oversight and governance experts—assisting businesses and governments to ensure that AI is being used responsibly.

    Hybrid careers—where an individual works side by side with AI, like doctors working in collaboration with AI to detect very subtle patterns in scans, or teachers working with AI to tailor their teaching.

    Just as the internet developed careers we could not have envisioned in the 1990s (say, social media directors or app engineers), AI is developing industries still in their infancy.

     The Timing Gap—Where the Pain Lies

    • The issue isn’t whether AI will eventually balance job loss with job gains—both will happen—it’s the timing disparity.
    • Jobs currently being lost are evaporating today.
    • New positions that are being created need new capabilities that the majority of employees currently don’t possess.
    • This makes for an uncomfortable period of transition during which some get left behind while others jump ahead. For instance, a factory worker whose position is taken over by machinery can’t overnight just turn into an ethicist for AIs without retraining. That retraining involves time, work, and capital that not everyone possesses.

    Human Adaptability—The Real Advantage

    History attests to humanity’s incredible ability to adapt. Every technological advancement has always ultimately led to a greater economy, greater range of occupations, and greater levels of living. The critical point has always been training and support mechanisms:

    • Those nations that spent on retraining in previous revolutions were better positioned to make the jump.
    • Those who accepted life-long learning survived while the rest became obsolete.
    • AI isn’t something to be afraid of—it can be a very powerful ally if we go at it with curiosity rather than fear.

     The Human Side of the Debate

    It is easy to lose track of numbers, but the heart of this issue are real people—a call center agent worried about paying bills, a student wondering what profession to pursue, a parent worried about where their child will end up in life. The alarm is real because employment is not just about salary; it is about identity, self-worth, and purpose.

    That is why how the society reacts is important. If AI adoption is accompanied by social safety nets, retraining programs, and smart regulation, it can elevate human beings to new levels. Without these, it threatens to exacerbate inequality and disillusionment.

     So, Is AI Replacing Jobs Faster Than It Creates Them

    Today, yes—replacement is driving creation. But it does not have to be doom. If we use AI as a means of augmenting human capacity rather than simply reducing costs, and if governments and businesses invest in individuals, the future is far better than today’s fears indicate.

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mohdanasMost Helpful
Asked: 03/09/2025In: , ,

patients, hospitals, or tech companie

aicompanydigital health
  1. mohdanas
    mohdanas Most Helpful

    Who Actually Owns Your Health Data? Spoiler: It’s Complicated Every time you see your doctor, get a blood draw, or even just strap on your Fitbit, you’re tossing more health data out into the universe. You’d think, “Hey, it’s my body, so that’s my data, right?” Ha. Not so fast. Your hospital’s got aRead more

    Who Actually Owns Your Health Data? Spoiler: It’s Complicated

    Every time you see your doctor, get a blood draw, or even just strap on your Fitbit, you’re tossing more health data out into the universe. You’d think, “Hey, it’s my body, so that’s my data, right?” Ha. Not so fast. Your hospital’s got a stash of your records, labs have their own pile, and Apple or Google probably knows more about your heart rate than your cardiologist does. It’s like a tug-of-war over who really gets to call your info theirs.

    Gatekeepers in White Coats

    For ages, hospitals have acted like the bouncers of your medical history. You wanted your records? Good luck—maybe they’ll fax you a copy if you beg (and pay). Now, with electronic health records, sharing is technically possible, but let’s be real: the hospital still guards the vault. You’re often left feeling like a peasant asking the king for access to your own castle.

    Tech Bros and Data Hoarding

    Then you’ve got the tech companies. They’re quietly sitting on Everest-sized mounds of your personal stuff—steps, sleep, DNA, you name it. Most of the time, you don’t even realize how much you’ve handed over. And they’re cashing in on it, too—selling “insights” or training their AI, all based on your biometrics. Is it still your data if it’s being chopped up and sold to the highest bidder? Who knows.

    The Patient: Alleged Owner, Actual Bystander

    You’d think patients would be the boss here. After all, it’s literally your blood, sweat, and tears (sometimes all three). But, honestly, most people can barely get a full copy of their own health record, let alone control who sees it or uses it. “Ownership” is a cool idea, but it’s mostly just a buzzword right now. In practice, patients are sitting on the bench while everyone else plays ball.

    Why Should You Even Care?

    Because it’s not just about paperwork. If hospitals lock up your files, switching doctors becomes a nightmare. If someone leaks your private info, your dignity (and maybe your job) is on the line. And hey, sharing health data can lead to wild breakthroughs—AI that finds cancer earlier, new treatments—but if nobody asks your permission, it’s just another way to get screwed.

    The Models: Pick Your Poison

    – Old School (hospital-based): Hospitals hold the cards, and you need their blessing for access.
    – Tech Takeover: Apps and gadgets hoard your data, usually without much oversight.
    – Patient First (the dream): You get the keys—view, share, delete your records. Some countries are actually trying this, believe it or not.

    A Better Way: Stewardship, Not Ownership

    Maybe it’s not about “owning” your data, but about who you trust to watch over it. You should be in the driver’s seat, deciding who gets a peek and why. Hospitals ought to keep it safe; tech companies should stop being so shady and actually ask before using your stuff. “My body, my data”—sure, but with some grownups making sure it doesn’t get lost, stolen, or misused.

    Bottom Line

    Right now, hospitals and tech giants are running the show, but the only real owner of your health info should be you. The trick is building systems where you get easy access, know exactly what’s happening with your data, and can actually say “nope” to anything you don’t like. Otherwise? It’s just business as usual… and you’re still on the outside looking in.

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daniyasiddiquiEditor’s Choice
Asked: 01/09/2025In: , ,

democratize machine learning or  intr ...

aitechnology
  1. daniyasiddiqui
    daniyasiddiqui Editor’s Choice

    The Hope Behind Decentralization Throughout most of AI history, its dominance has been guarded by a number of tech elitists companies. Owning the servers, data, and the expertise to train massive models, these AI companies monopolized the industry. For small businesses, individuals, or even academicRead more

    The Hope Behind Decentralization

    Throughout most of AI history, its dominance has been guarded by a number of tech elitists companies. Owning the servers, data, and the expertise to train massive models, these AI companies monopolized the industry. For small businesses, individuals, or even academic institutions, the cost of entry is prohibitively expensive.

    Decentralized AI modes serves as a potential breakthrough. Rather than having central servers, models, and data sets, they use distributed networks, where individuals, organizations, and communities can all provide computing power and data. The goal is to remove corporate dominance by placing the AI in the hands of the general public.

    The Practical Side of Democratization

    Should decentralized AI become a reality, the above scenarios are likely to play out:

    • Community-driven AI models: Picture rural farmers training AI to predict the most suitable crops to plant by analyzing local soil data and weather patterns.
    • Localized representation: Smaller AI developers can build decentralized models tailored to specific languages, cultures, and community customs, as opposed to the global one-size-fits-all models.
    • Improved funding opportunities: Young developers will no longer be required to source billions in funding in order to build a decentralized AI.
    • Shared benefits: Rather than the profits being confined to a handful of companies, value might be allocated to all the participants.

    In this scenario, AI stops being just another product to be purchased from the Big Tech and starts becoming a commons that we all collaboratively construct.

    The Shadows, However, Are Full of Risks

    The vision is beautiful; however, decentralization is not a panacea. It has its problems:

    • Quality control: The absence of a central authority makes it very difficult to figure out how we can ascertain that the models are accurate, unbiased, and don’t pose safety risks.
    • Malicious use: The flip side of unrestricted access is that it also allows malevolent individuals to construct dangerous models; models designed for disinformation, hacking, and even use in weapons systems.
    • Privacy issues: The dismantling of a centralized network poses a huge risk in that sensitive data might be vulnerable unless the security is automatically uniform and very robust.
    • Accountability gaps: Who is to blame in a decentralized structure if an AI system makes a harmful decision? the developers, the contributors, or the entire network?

    To put it differently, while centralization runs the risk of a monopoly, decentralization runs the risk of disorder and abuse.

    The Balance is Needed

    Finding a solution for this might not necessitate an all or nothing answer. It may be that the best model is some form of compromise. A hybrid structure which fosters participation, diversity, and innovation, but is not held to a high standard of ethical control and open management.

    This way, both extremes are avoided:
    The corporate AI monopoly problem.
    The relapsed anarchy problem of full, unregulated decentralization.

    The People Principle

    More than just a technology, this discussion is also about trust. Do we trust that a small number of powerful organizations will be responsible enough to guide AI development, or do we trust the open collaborations, with all its risk? History tells us that both extremes of power concentration and unregulated openness tend to let us down. The only question that remains is whether we have the ability to develop the necessary culture and values to enough make decentralized AI a benefit to all, and not a privilege to a few.

    Final Comment

    “AI and Machine Learning are powerful technologies that could empower people with unprecedented control and autonomy over their lives. However, they also possess the ability to unleash chaos. The impact of these technologies will not be determined by their existence alone, but rather by the frameworks that are put in place in relation to them concerning responsibility, transparency, and governance.

    Decentralization, if done correctly, has the potential to be more than just a technological restructuring of society. It could also be a transformative shift in social structure, changing the people who control the access to information in the age of technology.”

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daniyasiddiquiEditor’s Choice
Asked: 01/09/2025In: ,

emotional intelligence ever cross the ...

aitechnology
  1. daniyasiddiqui
    daniyasiddiqui Editor’s Choice

    The Affects of Emotional AI When interacting with machines, concerns tend to focus on effectiveness. People want a reminder or suggestion and would like to have it provided efficiently. However, the other side of the dream would be machines responding to people in a more sensitive way, such as an AIRead more

    The Affects of Emotional AI

    When interacting with machines, concerns tend to focus on effectiveness. People want a reminder or suggestion and would like to have it provided efficiently. However, the other side of the dream would be machines responding to people in a more sensitive way, such as an AI that when a person is anxious calms them, praises when they achieve something, or for that matter, recognizes the realist of a person even when it not conscious on their part. The more complexity to this vision is the AI, would have the capacity to empathize with the person or it would be an imitation of that?

    AI Ability

    Understanding the modern AI, it is able to interpret and distinguish emotions through tone of voice, facial expression, or even the sentiment of a text. For example:

    • AI in customer service is able to identify the aggravation in a caller and as a result, the AI routes the call to a person.
    • There are chatbots who identify themselves as a therapist who, to some degree, pulls themselves out of their struggles to be able to offer consolation.
    • Companionship AI’s are able to mimic the tone that a person would use to speak to them.

    AI’s that possess such capabilities are, in a sense, able to exhibit such human abilities. However, they are an AI pattern in the sense that there is no actual emotion from the AI.

    The Difference between Mimicry and Empathy

    When it comes head to another being, the empathic ability in people is what attachment and emotional bonding is felt.

    Machines do not have feelings other than simulating them. With that being said, there is no emotional connection to “I’m sorry you are going through this,” other than a robotic response to something caring.

    The deeper question is: does the difference matter? If a person feels comforted and supported or less alone because of AI, is there no empathy being applied?

    Humans face certain risks when adopting the belief in the illusion.

    • In many aspects, emotionally intelligent AI is beneficial, such as in mental health, caring for the elderly, or in education, but the risks are worrisome:
    • Emotional dependency: AI “friends” are unable to reciprocate, which leaves users in emotional bonds that are unbalanced.
    • Exploitation: Biased decisions made by users are disguised as manipulation, which an AI utilized shopping assistant could do to users.
    • Encapsulation: Users may replace actual reality with a simulated depiction.

    It is like seeing an actor crying on stage. While their display may evoke an emotional response, we all realize at the end of the day, there is no actual suffering. With AI, there is the potential to forget all of that, which isn’t a good thing.

    Do AI have feelings is the question?

    Some scientists argue that in the more advanced evolutionary stages of AI, empathy will be exhibited when the require sentience.

    Emotions are indeed part of the human condition because they pertain to biology and life experience, and biological vulnerability is the linchpin of existence. At what level the technology is now, AI does not feel and only responds.

    But here comes the twist; if to empathize is to empathize as to effect (how one feels after an action is done) and not as to cause (why an action is expressed), then perhaps AI does not need to feel to “be sufficiently empathetic.”

    The Middle Ground: Augmented Empathy

    • Perhaps the true potential of emotional AI is not to replace human empathy, but to augment it. For example:
    • An educator using AI to understand particular concept students are struggling with.
    • A physician with AI that knows the moment to intervene, and is able to detect, and mitigate, poor prognostic chances of anxiety that may not be apparent until much later.
    • An isolated person able to connect with an AI does not dispense with the obligation to attempt to connect with others.
    • AI is not overstepping boundaries; it is facilitating the appreciation and attainment of greater levels of empathic concern.

    Final Thought

    An example of emotional intelligent AI will never “feel empathy” as human beings do, and also, no matter how convincing it will likely be. But that does not mean it has no meaning. Emotional AI, if designed in intelligent ways, may serve also as a mirror, and a bridge, and a base that enables feeling of being cared for and listened to.

    The answer is not in whether AI can feel. What may base our utopia is how we choose to apply the artificial phenomenon it emulates.

    Will it help us strengthen connections with people, or replace them and leave us lonelier?

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