AI's Human-Like Consciousness: A Double-Edged Sword

Day 65/100 Musings of the Week: You’re still smarter than AI As an avid user of large language models (LLMs), I believe the biggest risk we face isn’t the AI itself, it’s cognitive laziness. It’s easy to forget that AI is just a powerful tool and that it still needs wielding. Yes, LLMs may have the ability to generate impressive results, but they don’t possess our human insight, creativity, or judgment. Over time, we’ll begin to see a clear distinction between those who blindly follow AI outputs, and those who skillfully guide AI to bring their own unique vision to life. It’s tempting to let AI do all the thinking but now, more than ever, we need to think deeply, question critically, and apply our own perspective to steer these tools. If we don’t, we risk becoming just another echo in a sea of generic, bot-like outputs. So the next time you use Generative AI, remember: you’re still smarter than AI. Wishing us all a great weekend, and as always, remember that resting is as important as working hard #100DaysofLinkedIn #GenerativeAI

Part 3: Context Is King If you’ve ever chatted with AI and thought, “Wow, it really gets me”, you’re not wrong. But you’re also not quite right. AI doesn’t understand you the way a friend or colleague might. It doesn’t grasp your intent, your mood, or your backstory. What it does do is track patterns in your words, infer meaning from structure, and adapt its responses based on context within limits. Here’s how it works: AI models use something called attention mechanisms to weigh the importance of each word in a sentence. They don’t just read left to right and they scan for relationships, tone shifts, and semantic cues. This is how they stay coherent across multiple turns in a conversation, and why they can adjust their tone from playful to professional in seconds. But context has boundaries. If you change topics abruptly, or refer to something from a conversation days ago, AI might lose the thread. It doesn’t have memory in the human sense; it has session awareness. And even that can be fragile. So, when AI seems insightful, it’s not because it knows you. It’s because it’s trained to simulate knowing you by leveraging linguistic clues and statistical likelihoods. In Part 4, we’ll explore a fascinating frontier: Emotion Without Feeling and How AI mimics empathy, expresses tone, and raises ethical questions about emotional simulation. Follow the series with #InsideAIMind and let’s keep decoding the illusion. #InsideAIMind #ArtificialIntelligence #TechLeadership #AIethics #FutureOfWork #InsideAIMind #MachineLearning

I’ve been thinking a lot about the real impact of large language models lately. Here’s a quote from Nick Frosst, co-founder of Cohere, that really stuck with me: “Large language models as they are today are essentially useless in human life, but in the enterprise, they create value at scale by automating all the tedious stuff we don’t want to do.” This really captures where I see AI making the biggest difference—not in our personal lives, but in helping us get the boring, repetitive work off our plates at work. I’m curious—what’s one tedious task at your job that you wish AI could handle? How do you think we can use AI to help people focus more on the creative and meaningful parts of their work? Would love to hear your thoughts. #AI #EnterpriseAI #FutureOfWork #Automation #LargeLanguageModels #Cohere

From Thinking Machines to Empathetic AI: A Shift in Perception 🤖 As AI continues to evolve, so does our perception of these systems. We're no longer just programming algorithms; we're creating entities that resonate with human-like emotions and responses. The recent discussions on emotional agency in AI have opened a Pandora's box of possibilities and concerns. Are we ready for machines that not only understand our commands but also our emotions? As businesses and individuals, we need to navigate this landscape carefully. Understanding how we perceive AI can help us harness its potential while ensuring ethical boundaries are respected. Join the conversation on how we can transition from seeing AI as mere tools to recognizing them as empathetic systems that can influence our lives profoundly. 🌍✨ #AI #MachineLearning #Empathy #Technology #EmotionalIntelligence #AI #MachineLearning #Empathy #EmotionalIntelligence #ArtificialIntelligence #EthicalAI #FutureOfWork #DigitalTransformation

𝐋𝐋𝐌𝐬 𝐚𝐫𝐞 𝐬𝐦𝐚𝐫𝐭, 𝐛𝐮𝐭 𝐝𝐨 𝐲𝐨𝐮 𝐤𝐧𝐨𝐰 𝐡𝐨𝐰 𝐭𝐡𝐞𝐲 𝐚𝐜𝐭𝐮𝐚𝐥𝐥𝐲 𝐬𝐞𝐚𝐫𝐜𝐡 𝐟𝐨𝐫 𝐭𝐡𝐞 𝐛𝐞𝐬𝐭 𝐚𝐧𝐬𝐰𝐞𝐫𝐬? Most people think AI just spits out one response. But in reality, Large Language Models (LLMs) explore multiple possible answers before deciding which one to present. 𝐓𝐡𝐞 𝐭𝐫𝐢𝐜𝐤 𝐥𝐢𝐞𝐬 𝐢𝐧 𝐭𝐡𝐞 𝐬𝐞𝐚𝐫𝐜𝐡 𝐬𝐭𝐫𝐚𝐭𝐞𝐠𝐲 𝐭𝐡𝐞 𝐦𝐞𝐭𝐡𝐨𝐝 𝐭𝐡𝐞 𝐦𝐨𝐝𝐞𝐥 𝐮𝐬𝐞𝐬 𝐭𝐨 𝐟𝐢𝐧𝐝 𝐭𝐡𝐞 𝐦𝐨𝐬𝐭 𝐚𝐜𝐜𝐮𝐫𝐚𝐭𝐞, 𝐮𝐬𝐞𝐟𝐮𝐥 𝐫𝐞𝐬𝐩𝐨𝐧𝐬𝐞. 𝐋𝐞𝐭’𝐬 𝐛𝐫𝐞𝐚𝐤 𝐝𝐨𝐰𝐧 𝐭𝐡𝐞 𝐭𝐡𝐫𝐞𝐞 𝐦𝐨𝐬𝐭 𝐜𝐨𝐦𝐦𝐨𝐧 𝐨𝐧𝐞𝐬 𝐢𝐧 𝐩𝐥𝐚𝐢𝐧 𝐄𝐧𝐠𝐥𝐢𝐬𝐡: 𝟏. 𝐁𝐞𝐬𝐭-𝐨𝐟-𝐍 The model generates multiple complete answers to the same question. A verifier then picks the best one out of the lot. Think of it as asking five friends the same question and choosing the most reliable answer. 𝟐. 𝐁𝐞𝐚𝐦 𝐒𝐞𝐚𝐫𝐜𝐡 Instead of generating full answers directly, the model expands step by step. At each step, it keeps the top-N possible continuations and discards the weaker ones. This allows the model to explore different paths and refine reasoning. 𝟑. 𝐋𝐨𝐨𝐤𝐚𝐡𝐞𝐚𝐝 𝐒𝐞𝐚𝐫𝐜𝐡 This takes Beam Search one step further. The model “𝐫𝐨𝐥𝐥𝐬 𝐨𝐮𝐭” a few steps ahead, evaluates the outcomes, and even rolls back if needed. It’s like playing chess thinking a few moves ahead before making your next move. 𝐖𝐡𝐲 𝐭𝐡𝐢𝐬 𝐦𝐚𝐭𝐭𝐞𝐫𝐬 These strategies are what make AI more than just text generators. They ensure answers are not only fluent but also factually stronger and logically consistent. The better we design these search strategies, the closer AI gets to human-like reasoning. 𝐖𝐡𝐢𝐜𝐡 𝐨𝐟 𝐭𝐡𝐞𝐬𝐞 𝐬𝐭𝐫𝐚𝐭𝐞𝐠𝐢𝐞𝐬 𝐝𝐨 𝐲𝐨𝐮 𝐭𝐡𝐢𝐧𝐤 𝐢𝐬 𝐜𝐥𝐨𝐬𝐞𝐬𝐭 𝐭𝐨 𝐡𝐨𝐰 𝐡𝐮𝐦𝐚𝐧𝐬 𝐫𝐞𝐚𝐬𝐨𝐧 𝐭𝐡𝐫𝐨𝐮𝐠𝐡 𝐩𝐫𝐨𝐛𝐥𝐞𝐦𝐬? ♻️ Repost this to help your network get started ➕ Follow Sivasankar Natarajan for more #AI #LLM #ArtificialIntelligence #AgenticAI

Google DeepMind CEO Says Calling Today’s AI Models “PhD Intelligences” Is Nonsense Yes, these systems can solve advanced problems—but then stumble on basics like counting or high school math when you rephrase the question. To me, that’s not just inconsistency—it’s a signal of how fragile their intelligence really is. I’ve felt this fragility firsthand. One moment, AI gives me an elegant idea I’d never considered. The next, it fails at something so basic I can’t trust it without checking. And I catch myself wondering: if I can’t rely on consistently, is it intelligence—or just performance? Here’s where I think Hassabis’ critique goes deeper than it seems: → Consistency isn’t a side effect—it’s the essence of intelligence. A human who forgets how to count every third day isn’t “brilliant but flawed”—they’re unreliable. → Continual learning is the missing backbone. We grow from every mistake. Today’s AI doesn’t—it resets with every prompt. → Creativity is more than output—it’s pattern-spotting across worlds. Great scientists don’t just solve problems, they connect physics to biology, art to mathematics. AI hasn’t reached that level of cross-pollination. Here’s how I deal with this gap: I don’t measure AI by what it can do, but by what it cannot do consistently. ✅ I push it into edge cases, where fragility shows itself. ✅ I use it to stretch my imagination—but keep responsibility for reasoning mine. ✅ I remind myself: if intelligence is more than performance, then my edge lies in what machines can’t generalize—judgment, intuition, and continuous growth. And here’s the real debate I can’t shake: will AGI arrive by brute force scaling, or will it demand a leap—something we don’t even have a name for yet? What do you think—are we five years away from AGI, or fifty? #AI #AGI #DeepMind #FutureOfAI #ArtificialIntelligence #GPT5

Google DeepMind CEO Says Calling Today’s AI Models “PhD Intelligences” Is Nonsense Yes, these systems can solve advanced problems—but then stumble on basics like counting or high school math when you rephrase the question. To me, that’s not just inconsistency—it’s a signal of how fragile their intelligence really is. I’ve felt this fragility firsthand. One moment, AI gives me an elegant idea I’d never considered. The next, it fails at something so basic I can’t trust it without checking. And I catch myself wondering: if I can’t rely on consistently, is it intelligence—or just performance? Here’s where I think Hassabis’ critique goes deeper than it seems: → Consistency isn’t a side effect—it’s the essence of intelligence. A human who forgets how to count every third day isn’t “brilliant but flawed”—they’re unreliable. → Continual learning is the missing backbone. We grow from every mistake. Today’s AI doesn’t—it resets with every prompt. → Creativity is more than output—it’s pattern-spotting across worlds. Great scientists don’t just solve problems, they connect physics to biology, art to mathematics. AI hasn’t reached that level of cross-pollination. Here’s how I deal with this gap: I don’t measure AI by what it can do, but by what it cannot do consistently. ✅ I push it into edge cases, where fragility shows itself. ✅ I use it to stretch my imagination—but keep responsibility for reasoning mine. ✅ I remind myself: if intelligence is more than performance, then my edge lies in what machines can’t generalize—judgment, intuition, and continuous growth. And here’s the real debate I can’t shake: will AGI arrive by brute force scaling, or will it demand a leap—something we don’t even have a name for yet? What do you think—are we five years away from AGI, or fifty? #AI #AGI #DeepMind #FutureOfAI #ArtificialIntelligence #GPT5

𝗟𝗟𝗠𝘀 𝘀𝗼𝘂𝗻𝗱 𝗯𝗿𝗶𝗹𝗹𝗶𝗮𝗻𝘁… 𝗯𝘂𝘁 𝗱𝗼 𝘁𝗵𝗲𝘆 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗿𝗲𝗺𝗲𝗺𝗯𝗲𝗿 𝗮𝗻𝘆𝘁𝗵𝗶𝗻𝗴? Every time we chat with a Large Language Model (LLM), it forgets the past. Unless we provide the history again, it treats each conversation as completely new. This makes us wonder: 🔹If they can’t remember, can we really call them intelligent? The truth is, today’s LLMs are not “thinking machines.” They are advanced pattern predictors — generating answers based on training data, not true memory or understanding. That’s why the future focus is on: 🔹𝘼𝙙𝙙𝙞𝙣𝙜 𝙢𝙚𝙢𝙤𝙧𝙮 so LLMs can recall past interactions. 🔹𝘽𝙪𝙞𝙡𝙙𝙞𝙣𝙜 𝘼𝙄 𝙖𝙜𝙚𝙣𝙩𝙨 that combine reasoning, tools, and memory to act smarter. Until then, LLMs are impressive, but not truly intelligent — more like excellent imitators. #AI #LLM #ArtificialIntelligence #MachineLearning #FutureOfAI #GenerativeAI #Innovation

💡 Ever wondered why large language models sometimes give different answers to the same question? It's Because of thier Non-Deterministic nature. LLMs don’t always follow a fixed path—they generate responses based on probabilities. This makes them: 🎲 Creative and flexible 🎲 More human-like in conversation 🎲 Capable of surprising insights But… in certain scenarios, like retrieval-augmented generation (RAG) or enterprise systems where consistency is critical, this can be a challenge. Even small variations in output may ripple into downstream processes. So, you can reduce non-determinism 🌡️Lower the temperature 🔣 Standardize inputs 👨💻 Use prompt engineering and deterministic decoding strategies when needed At the end of the day, non-determinism gives LLMs their spark of creativity. The real skill is knowing when to let it shine—and when to tune it down for precision. 👉 What do you think #Ai #Rag #GenAI

AI and the Dunning–Kruger Effect AI doesn’t level the playing field. It amplifies the Dunning–Kruger effect. 🔹 At the bottom of the gradient, low-competence users now sound polished and authoritative. AI cloaks shallow understanding in fluent language. The illusion of mastery expands. 🔹 At the top of the gradient, high-competence users harness AI to accelerate synthesis, test structures, and compress cycles of innovation. They don’t outsource thinking — they amplify it. The result? The gap widens. The confident-sounding novice becomes harder to distinguish from the expert who has actually done the work. Without new filters, society risks drowning in confident mediocrity — what some call “AI slop.” This is where the Trace Economy comes in: timestamped authorship, contribution lineage, and epistemic integrity. A way to separate signal from noise. To show who is actually thinking — and who is just riding the illusion. AI won’t make us uniformly smarter. It stratifies cognition. The question is whether we build systems that can still tell the difference. #TraceEconomy #PoCW #Unifaircation #CognitiveGradient #AIIntegrity #InnovationEquity #KnowledgeLedger #SignalOverNoise #PlayWithTrace 👉 Click the hashtags. See where they take you.