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daniyasiddiquiImage-Explained
Asked: 22/08/2025In: , ,

accuracy and reduce hallucinations

technology
  1. daniyasiddiqui
    daniyasiddiqui Image-Explained

    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.

    Let’s break that down in a human way.

     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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daniyasiddiquiImage-Explained
Asked: 22/08/2025In: ,

perplexity

companytechnology
  1. daniyasiddiqui
    daniyasiddiqui Image-Explained

    Many Airtel users are curious about how to get access to Perplexity AI for free using their Airtel connection. Airtel, one of India’s leading telecom providers, frequently partners with digital platforms and services to offer exclusive benefits to its subscribers. If you’ve heard about “free PerplexRead more

    Many Airtel users are curious about how to get access to Perplexity AI for free using their Airtel connection. Airtel, one of India’s leading telecom providers, frequently partners with digital platforms and services to offer exclusive benefits to its subscribers. If you’ve heard about “free Perplexity with Airtel” and are wondering how to activate it, let’s break it down step by step in a simple, humanized way.

    1. Comprehending the Offer

    Perplexity is an AI-powered search and assistant platform that helps you get instant, accurate responses in a conversational style. Instead of giving links like Google, it offers well-structured explanations.

    Airtel has traditionally offered digital add-ons such as complimentary trials of streaming services, music apps, cloud storage, and even AI offerings. Airtel might provide a trial plan or reduced plan under its “Thanks Benefits” or prepaid/postpaid data plans with Perplexity.

    2. Check Your Eligibility

    Prior to activating, check first whether your Airtel number is eligible. Here’s what you can do:

    • Open Airtel Thanks App → Here, Airtel puts all the free offers and collaborations one can access.
    • Login with your Airtel number → Authenticate with OTP.
    • Go to the “Rewards” or “Thanks Benefits” page → There, you can view the free subscriptions or services.
    • Search for Perplexity AI offer → If it is available for your plan, it will show up here.
    •  If you cannot see it, that means either
    • Your plan does not have it now.
    • Or Airtel is unfolding it in phases.

    3. Availing Free Perplexity

    Once you confirm that the offer is live on your Airtel Thanks app, follow these steps:

    • Tap on the Perplexity offer banner.
    • Read the terms carefully – probably it will be something like “Free trial for 3 months” or “Premium subscription included.”
    • Click Activate → You will be redirected to the Perplexity website or invited to download the Perplexity app.
    • Sign up on Perplexity with the same Airtel-registered number or email.
    • Once connected, your subscription will be active automatically without charges.

    4. How to Use It After Activation

    After activation, you can:

    • Log in to Perplexity on mobile or web and access the AI for free.
    • Get ad-free search, unlimited searches, and super-smart answers.
    • Manage your subscription later on the Perplexity or Airtel Thanks app.

    5. Things to Consider

    • The free offer is usually time-limited (e.g., 3 months). Regular rates will apply thereafter.
    • If you don’t wish to continue, make sure to turn off auto-renewal prior to the end of the trial.
    • Offers can vary depending on whether you are a prepaid, postpaid, broadband, or Airtel Black customer.

    6. Why Airtel is Doing This

    Airtel wants to add more value for money on its plans and encourage digital adoption. Just like they have done with Amazon Prime, Disney+ Hotstar, and Wynk Music, Airtel’s partnership with Perplexity makes sure that Airtel customers get cutting-edge AI technology for free without any extra charge to begin with.

    In short:

    • To get free Perplexity through Airtel, just:
    • Open the Airtel Thanks app.
    • Go to the “Thanks Benefits / Rewards” tab.
    • Take the Perplexity offer, if available.
    • Activate and link it to your account
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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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daniyasiddiquiImage-Explained
Asked: 20/08/2025In: ,

big tech companies advance p ...

technology
  1. daniyasiddiqui
    daniyasiddiqui Image-Explained

    Will Open-Source AI Models Stay Competitive? The competition between open-source AI models and closed systems from large tech corporations is one of the most compelling dynamics of the current technological scene. At first glance, it could appear that open-source models are always going to be behindRead more

    Will Open-Source AI Models Stay Competitive?

    The competition between open-source AI models and closed systems from large tech corporations is one of the most compelling dynamics of the current technological scene. At first glance, it could appear that open-source models are always going to be behind, considering the billions of dollars available for infrastructure and expertise in big tech. But things are very different—and in most aspects, open-source AI is showing that it can punch well above its weight.

    • The Strength of Community Compared to Corporate Scale

    Large technology corporations such as Google, Microsoft, and OpenAI possess resources open-source communities can only wish for: massive GPU clusters, internal datasets, and the power to recruit the world’s best researchers.

    But open-source endeavors live on cooperation and distributed intelligence. Thousands of programmers all over the world deliver enhancements, test scenarios, and innovate quicker than a closed group might typically. This “many hands, many minds” model enables open-source AI to develop at lightning speed, and frequently deliver slender, useful models that individuals can use without enormous infrastructure.

    • Accessibility Levels the Playing Field

    One of the greatest advantages of open-source AI is accessibility. While proprietary systems can be walled off behind paywalls, licenses, or API restrictions, open models are generally available for anyone to play with. This makes it possible for:

    Startups to develop without humongous initial costs.

    Researchers to experiment with ideas without legal obstacles.

    Developers across the globe (even outside Silicon Valley) to create in their own environments—whether for healthcare, agriculture, or education.

    This democratization ensures innovation does not remain in the hands of a few corporations.

    • Practicality Often Wins Over Perfection

    Proprietary models can hit state-of-the-art levels, but most real-world uses do not need “the biggest” or “the smartest” model. For instance:

    A tiny open-source language model can be executed on a smartphone and thus is best suited for offline use.

    Medical professionals in regions with limited resources might find lean open-source AI that does not rely on expensive cloud subscriptions appealing.

    Here, pragmatism usually triumphs. Open-source models are not necessarily going to match the biggest proprietary systems on brute performance, but they can be “good enough” and much more deployable.

    • The Question of Trust

    Another reason open-source AI endures is trust. With proprietary models, users simply don’t know what data was input, how the decisions are made, or if there are buried biases. Open-source models, on the other hand, are open: their training data, code, and limitations are frequently published.

    In a world where humans are already questioning the potential of AI and its reach, that openness counts. It can foster trust, particularly in sensitive areas such as education, law, and healthcare.

    • Where the Two Worlds Converge

    It’s worth noting, however, that open-source and proprietary AI aren’t always at odds—they frequently coexist. Large corporations sometimes publish smaller open models to the world to spark adoption, while developers combine open-source frameworks with proprietary APIs. The ecosystem is more cooperative than it looks.

    • The Road Ahead

    The future probably won’t be “open-source versus proprietary,” but a mix of both:

    Proprietary AI setting the pace at the edge of scale and ability.

    Open-source AI making access, flexibility, and trustworthiness a priority.

    And in reality, the tension between them may be what propels the whole industry forward—big tech pushing boundaries, and open-source making sure everyone keeps up.

     Bottom line: Yes, open-source AI models will be competitive—perhaps not always by keeping up with size, but by being superior in access, trustworthiness, and applicability in real life.

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daniyasiddiquiImage-Explained
Asked: 20/08/2025In: ,

EVs and solar panels

newstechnology
  1. daniyasiddiqui
    daniyasiddiqui Image-Explained

    On the one hand, governments claim that tariffs defend their local green industries. For instance, imposing tariffs on foreign solar panels or electric cars can provide local producers with some space for expansion, generate employment, and cut reliance on the supply chain of a single nation. In theRead more

    On the one hand, governments claim that tariffs defend their local green industries. For instance, imposing tariffs on foreign solar panels or electric cars can provide local producers with some space for expansion, generate employment, and cut reliance on the supply chain of a single nation. In theory, that improves long-term resilience.

    But there is a downside:

    higher tariffs tend to translate into higher prices for consumers and slower deployment of clean technologies. If solar panels become more costly, fewer families or companies will install them. If EVs are more expensive, individuals delay buying gas cars longer. That pushes emissions reductions we cannot afford to delay. For developing nations in particular, where cost is everything, tariffs make sustainability even more out of reach.

    So in human language, green tech tariffs can seem like a tug-of-war: save jobs here and now, or accelerate climate progress later. The actual challenge is being balanced—protecting domestic industries and making green solutions cheap enough so folks can switch.

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Anonymous
Asked: 19/08/2025In: , ,

modern tariff policies

newstechnology
  1. Anonymous
    Anonymous

    That's interesting, because digital commodities don't quite fit the old concept of tariffs, which were created for physical commodities moving across borders—steel, autos, fabrics. But now so much trade is occurring online: streaming, cloud storage, video games, even software downloads. Most nationsRead more

    That’s interesting, because digital commodities don’t quite fit the old concept of tariffs, which were created for physical commodities moving across borders—steel, autos, fabrics. But now so much trade is occurring online: streaming, cloud storage, video games, even software downloads.

    Most nations have not imposed tariffs on these digital flows historically, in part because they are difficult to measure and monitor. But as digital trade continues to expand, governments are beginning to wonder: why tax physical imports, while digital imports enjoy a free ride? Some are piloting digital services taxes, taxing large technology companies that derive revenue in a country without enjoying physical presence there.

    From the point of view of humans, it is important because it may alter how we pay for daily online utilities—such as our subscription to Netflix or the software we run our businesses on. For small companies, new taxes or tariffs on online services might make operating online stores or advertising overseas more expensive. To governments, however, it is perceived as a means of tapping into revenue from an increasingly online economy.

    In short:

    digital tariffs remain a gray area. The difficulty is striking the right balance in incorporating digital trade into modern policies without killing off innovation or driving things up for everyday users.

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daniyasiddiquiImage-Explained
Asked: 18/08/2025In: , ,

AI changing the role of teachers in c ...

educationtechnology
  1. daniyasiddiqui
    daniyasiddiqui Image-Explained

    AI is definitely reshaping what it means to be a teacher, but not in the “robots replacing teachers” way that some people fear. Instead, it’s shifting teachers’ roles from being the sole source of information to becoming more like guides and mentors. For example, AI tools can now handle some of theRead more

    AI is definitely reshaping what it means to be a teacher, but not in the “robots replacing teachers” way that some people fear. Instead, it’s shifting teachers’ roles from being the sole source of information to becoming more like guides and mentors.

    For example, AI tools can now handle some of the repetitive tasks—like grading quizzes, creating practice questions, or even giving students instant feedback. That frees teachers to spend more time on the human side of teaching: encouraging creativity, supporting students who are struggling, and sparking real curiosity in the classroom.

    It’s also making learning more personalized. Instead of teaching to the “average” student, AI can help identify who needs extra practice and who’s ready to move ahead, giving teachers better insight into each child’s progress. But here’s the thing—AI can’t replace empathy, encouragement, or the way a teacher inspires confidence in a student. That human connection is still at the heart of education.

    So in many ways, AI isn’t taking teachers’ jobs—it’s giving them more space to do what only humans can do: mentor, motivate, and shape character.

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Anonymous
Asked: 16/08/2025In: , ,

U.S.–China or India

companynews
  1. Anonymous
    Best Answer
    Anonymous

    Tariffs in large trade relations — such as U.S.–China or India–EU — are less about numbers on paper and more about power, priorities, and politics. In the case of the U.S. and China, tariffs were the focal point of their trade tensions. The U.S. employed them to resist what it perceived as unfair prRead more

    Tariffs in large trade relations — such as U.S.–China or India–EU — are less about numbers on paper and more about power, priorities, and politics.

    In the case of the U.S. and China, tariffs were the focal point of their trade tensions. The U.S. employed them to resist what it perceived as unfair practices — such as subsidies for Chinese businesses or intellectual property issues. China then retaliated with tariffs of their own. The outcome? Consumer staples, from electronics to soybeans, were thrust overnight into status as bargaining tools in a high-stakes game. To this day, numerous tariffs still linger, influencing how businesses establish supply chains and where consumers perceive price increases.

    For India and the EU, tariffs have a different tale. India tends to use tariffs to shield its farmers and small industries, whereas the EU demands more open entry to India’s huge market. This tug-of-war generates tension but also compels bargaining. For instance, tariffs on crops or luxury products tend to feature in negotiations, demonstrating both sides’ priorities — India’s necessity to protect neighborhood livelihoods and the EU’s drive for free trade.

    Thus, tariffs in these contexts are not merely about money. They’re about negotiating power. They are both shields and bargaining tools, pushing countries toward agreements that weigh protection at home against opportunity abroad.

    In short: tariffs in these relationships are chess moves — defensive sometimes, aggressive others, but always influencing the next round of negotiations.

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daniyasiddiquiImage-Explained
Asked: 15/08/2025In: , ,

 

aitechnology
  1. daniyasiddiqui
    daniyasiddiqui Image-Explained

    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: 15/08/2025In: , ,

the next wave of innovation

newstechnology
  1. Anonymous
    Anonymous

    Neurosymbolic AI: Merging Intelligence with Logic Think of neurosymbolic AI as the combination of two types of intelligence. Here you have neural networks. They provide powerful pattern recognition for messy, unstructured data from the real world including image, voice, and sensor data. Here you havRead more

    Neurosymbolic AI: Merging Intelligence with Logic

    Think of neurosymbolic AI as the combination of two types of intelligence. Here you have neural networks. They provide powerful pattern recognition for messy, unstructured data from the real world including image, voice, and sensor data. Here you have symbolic reasoning, a powerful way to apply rules, logic, and structured knowledge to formal problem solving.

    How may we combine both of these approaches? Each approach is great on its own. Today’s AI can very well detect a cat in an image and very well solve a logic puzzle, but it cannot do both together. Neurosymbolic AI makes this possible. It can:

    1. Reason and explain its decisions—not just give answers but explain why those answers are valid

    2. Learn quickly—as it encounters new patterns, it can not only rely on the new knowledge but also relate what it has already learned, instead of having to start with zero application and comprehension.

    3. Recognize and account for uncertainty better. Neurosymbolic AI can apply logic when data is articulated clearly, and learn when it is messy.

    In the next technological wave, we may see AI reading complex legal contracts, teasing out the author’s intent, and reasoning toward implications. Or we may see medical AI that integrates lab tests and established care guidelines toward timely and safe diagnoses.

    Neurosymbolic AI provides an AI with something resembling an “intuition”

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