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daniyasiddiquiEditor’s Choice
Asked: 12/10/2025In:

AI investment shaping the stock marke

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  1. daniyasiddiqui
    daniyasiddiqui Editor’s Choice

    1. AI Investment Surge in 2025 Artificial Intelligence (AI) has departed from the niche technology to become the central driver of business strategy and investor interest. Companies in recent years have accelerated investment in AI across industries—anything from semiconductors to software, cloud coRead more

    1. AI Investment Surge in 2025

    Artificial Intelligence (AI) has departed from the niche technology to become the central driver of business strategy and investor interest. Companies in recent years have accelerated investment in AI across industries—anything from semiconductors to software, cloud computing, healthcare, and even consumer staples.

    This surge in AI investment is making its presence felt on the stock market in various ways:

    • Investor Mania: AI is the “next big thing” that takes us back to the late 1990s internet bubble. Shares of AI firms are experiencing tremendous inflows from retail and institutional investors alike.
    • Market Supremacy: The titans among technology giants in AI (consider cloud AI platforms, AI chips, and generative AI software) are some of the world’s most valuable companies of today, dominating top indices such as the S&P 500 and NASDAQ.
    • Sector Rotation: Money is being shifted into AI sectors, occasionally out of conventional companies such as energy or manufacturing.

    2. Valuation Impact on AI Companies

    AI investment is affecting stock prices through the following channels:

    • Premium Valuations: AI businesses regularly trading at high price-to-earnings (P/E) or price-to-sales (P/S) multiples due to expectations of future outburst growth.
    • Speculative Trading: Retail investors, caught in the media or social media hype, at times propel valuations beyond what is required by fundamentals, leading to momentum-driven rallies.
    • M&A Activity: Mergers and acquisitions are being driven by investment in AI, with major companies acquiring smaller AI companies in order to gain technological superiority. This kind of action has the tendency to propel the share price of the acquirer and also that of the target organizations.

    3. Sector-Specific Impacts

    AI is not a tech news headline—it’s transforming the stock market across several industries:

    • Semiconductors and Hardware: Those that manufacture GPUs, AI chips, and niche processors are experiencing all-time highs in demand and increasing stock values.
    • Software and Cloud Platforms: Businesses are embracing cloud AI services, with vendors like cloud platform sellers and SaaS providers gaining.
    • Automotive and Mobility: AI expenditures on autonomous technology as well as intelligent mobility solutions are influencing automaker share prices.
    • Healthcare and Biotech: AI-assisted drug discovery, diagnostics, and individualized medicine are opening new growth opportunities for biotech and healthcare companies.

    Investors now price these sectors not only on revenue, but on AI opportunity and technology moat.

    4. Market Dynamics and Volatility

    AI investing has introduced new dynamics in markets:

    • Volatility: Stocks exposed to AI may see wild swings, both in both directions, as investors respond to breakthroughs, regulatory announcements, or hype cycles.
    • FOMO-Driven Buying: FOMO has fueled rapid flows into AI-themed ETFs and stocks, occasionally overinflating valuations.
    • Winner vs. Loser Differentiation: Not all investments in AI are successful. Companies that fail to successfully commercialize AI with well-considered business models risk rapid stock price corrections.

    5. Broader Implications for Investors

    AI’s impact isn’t just on tech stocks—it’s influencing portfolio strategy more broadly: 

    • Growth vs. Value Investing: AI investing favors growth stocks, as the investor is investing in future prospects over immediate earnings.
    • Diversification Is Key: Investors are diversifying bets between hardware, software, and AI applications across industries to manage risk.
    • Long-Term vs. Short-Term Gameplay: Whereas some investors play short-term AI hype, others invest in solid AI incorporation for long-term value creation companies.
    • Regulatory Sensitivity: As more businesses adopt AI, regulatory sensitivity to ethics, data privacy, and monopolistic tactics can affect stock behavior.

    6. Human Takeaway

    AI is transforming the stock market in creating new leaders, restructuring valuations, and shifting investor behavior. Ample room exists for return on an astronomical scale, yet ample risk as well: overvaluation can be created by hype, and technology or regulatory errors can precipitate steep sell-offs.

    For most investors, the solution is to counterbalance the enthusiasm with due diligence: seek those firms with solid fundamentals, straight-talk AI strategy, and durable competitive moats instead of following the hype of AI fad.

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

multimodal AI and traditional AI mode

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  1. daniyasiddiqui
    daniyasiddiqui Editor’s Choice

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

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

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

     Classic AI: One Track Mind

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

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

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

     Welcome Multimodal AI: The Human-Like Merge

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

    For instance:

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

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

     Key Differences at a Glance

    Input Diversity

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

    Contextual Comprehension

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

    Functional Applications

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

    Why This Matters for the Future

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

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

     The Human Angle

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

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

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

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

a challenge to Google Chrome

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  1. daniyasiddiqui
    daniyasiddiqui Editor’s Choice

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

     Setting the Stage

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

    What is Comet Browser?

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

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

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

     Privacy vs. Personalization — The Core Debate

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

    Comet promises to flip that model on its side by:

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

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

    India’s Digital Landscape — A Tough Ground

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

    Comet to be successful will need:

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

    Could It Possibly Replace Chrome?

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

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

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

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

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

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

     The Road Ahead

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

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

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

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

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mohdanasMost Helpful
Asked: 24/09/2025In:

Fast Vs Slow

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  1. mohdanas
    mohdanas Most Helpful

    The Human Parallel: Fast vs. Slow Thinking Psychologist Daniel Kahneman popularly explained two modes of human thinking: System 1 (fast, intuitive, emotional) and System 2 (slow, mindful, rational). System 1 is the reason why you react by jumping back when a ball rolls into the street unexpectedly.Read more

    The Human Parallel: Fast vs. Slow Thinking

    Psychologist Daniel Kahneman popularly explained two modes of human thinking:

    • System 1 (fast, intuitive, emotional) and System 2 (slow, mindful, rational).
    • System 1 is the reason why you react by jumping back when a ball rolls into the street unexpectedly.
    • System 2 is the reason why you slowly consider the advantages and disadvantages before deciding to make a career change.

    For a while now, AI looked to be mired only in the “System 1” track—churning out fast forecasts, pattern recognition, and completions without profound contemplation. But all of that is changing.

    Where AI Exhibits “Fast” Thinking

    Most contemporary AI systems are virtuosos of the rapid response. Pose a straightforward fact question to a chatbot, and it will likely respond in milliseconds. That speed is a result of training methods: models are trained to output the “most probable next word” from sheer volumes of data. It is reflexive because it is — the model does not stop, hesitate, or calculate unless it has been explicitly programmed to.

    Examples:

    • Autocomplete in your email.
    • Rapid translations in language apps.
    • Instant responses such as “What is the capital of France?”
    • Such tasks take minimal “deliberation.”

    Where AI Struggles with “Slow” Thinking

    The more difficult challenge is purposeful reasoning—where the model needs to slow down, think ahead, and reflect. Programmers have been trying techniques such as:

    • Chain-of-thought prompting – prompting the model to “show its work” by describing reasoning steps.
    • Self-reflection loops – where the AI creates an answer, criticizes it, and then refines it.
    • Hybrid approaches – using AI with symbolic logic or external aids (such as calculators, databases, or search engines) to enhance accuracy.

    This simulates System 2 reasoning: rather than blurring out the initial guess, the AI tries several options and assesses what works best.

    The Catch: Is It Actually the Same as Human Reasoning?

    Here’s where it gets tricky. Humans have feelings, intuition, and stakes when they deliberate. AI doesn’t. When a model slows down, it isn’t because it’s “nervous” about being wrong or “weighing consequences.” It’s just following patterns and instructions we’ve baked into it.

    So although AI can mimic quick vs. slow thinking modes, it does not feel them. It’s like seeing a magician practice — the illusion is the same, but the motivation behind it is entirely different.

    Why This Matters

    If AI can shift trustably between fast instinct and slow reasoning, it transforms how we trust and utilize it:

    • Healthcare: Fast pattern recognition for medical imaging, but slow reasoning for medical treatment.
    • Education: Brief answers for practice exercises, but in-depth explanations for important concepts.
    • Business: Brief market overviews, but sound analysis when millions of dollars are at stake.

    The ideal is an AI that knows when to take it easy—just like a good physician won’t rush a diagnosis, or a good driver won’t drive fast in the storm.

    The Humanized Takeaway

    AI is beginning to learn both caps—sprinter and marathoner, gut-reactor and philosopher. But the caps are still disguises, not actual experience. The true breakthrough won’t be in getting AI to slow down so that it can reason, but in getting AI to understand when to change gears responsibly.

    Until now, the responsibility is partially ours—users, developers, and regulators—to provide the guardrails. Just because AI can respond quickly doesn’t mean that it must.

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