The AI "Black Box" is officially open. I'm beyond excited to finally share what my team and I have been pouring our energy into for months. This isn't just another project for me; it's a personal mission to solve one of the biggest challenges in AI today: trust. For too long, we've interacted with large language models as magical, yet opaque, black boxes. We get an answer, but we have no real way of knowing how the AI arrived at it. What data did it use? What was its reasoning path? This lack of transparency is a major roadblock for deploying AI in critical sectors like healthcare, finance, and law where accountability is non-negotiable. Well, that's about to change. In this video, I'm giving a first look at our Inference Analyzer AI—a tool we built to peel back the layers and look directly into the "mind" of an AI model as it thinks. We can now: ✅ Visualize the Inference Path: See the specific neural nodes that activate to generate a response in real-time. ✅ Trace to the Source: Instantly identify the exact source documents and data points the model is referencing for its answers. ✅ Quantify Domain Contributions: Understand precisely which areas of knowledge (e.g., Medicine vs. Scientific Principles) are influencing the outcome. This is more than just a developer tool; it's the foundation for a new generation of accountable, verifiable, and truly transparent AI systems. It’s about moving from simply believing an AI's output to being able to prove it. This video is just the beginning. I believe this is a game-changer, and I'd love to hear your thoughts. What is the single biggest barrier to trusting AI in your industry today? Let's discuss in the comments. CodexCore #AI #ArtificialIntelligence #LLM #Transparency #ExplainableAI #XAI #MachineLearning #Innovation #FutureOfTech #EthicsInAI #DeepLearning

💡 Without data, AI is just an empty engine. We often get fascinated by algorithms, models, and buzzwords. But the reality is: data is the true fuel of AI. Here’s why: 1. Quantity matters but quality matters more. A small, clean dataset often outperforms a massive, noisy one. 2. Bias in, bias out. If data reflects bias, the model will replicate or even amplify that bias. 3. Representation is everything. A facial recognition model trained mostly on one demographic will perform poorly on others. Think of AI as a high-performance car: 1. The algorithm is the engine. 2. Data is the fuel. 3. Feed it low-grade fuel, and even the best engine will sputter. This is why some of the biggest breakthroughs in AI haven’t come from new algorithms, but from better access to large, diverse, high-quality datasets. 💭 My perspective: In the long run, organizations that win with AI will not necessarily be the ones with the most sophisticated models, but those with the best, cleanest, and most representative data pipelines. 👉 What do you think matters more: the sophistication of the algorithm, or the quality of the data it’s trained on? #MachineLearning #ArtificialIntelligence #AI #DeepLearning #ReinforcementLearning #DataScience #Innovation #AIForEveryone #LLM #FoundationModel #GenerativeAI #AgenticAI

The AI "Black Box" is officially open. I'm beyond excited to finally share what my team and I have been pouring our energy into for months. This isn't just another project for me; it's a personal mission to solve one of the biggest challenges in AI today: trust. For too long, we've interacted with large language models as magical, yet opaque, black boxes. We get an answer, but we have no real way of knowing how the AI arrived at it. What data did it use? What was its reasoning path? This lack of transparency is a major roadblock for deploying AI in critical sectors like healthcare, finance, and law where accountability is non-negotiable. Well, that's about to change. In this video, I'm giving a first look at our Inference Analyzer AI—a tool we built to peel back the layers and look directly into the "mind" of an AI model as it thinks. We can now: ✅ Visualize the Inference Path: See the specific neural nodes that activate to generate a response in real-time. ✅ Trace to the Source: Instantly identify the exact source documents and data points the model is referencing for its answers. ✅ Quantify Domain Contributions: Understand precisely which areas of knowledge (e.g., Medicine vs. Scientific Principles) are influencing the outcome. This is more than just a developer tool; it's the foundation for a new generation of accountable, verifiable, and truly transparent AI systems. It’s about moving from simply believing an AI's output to being able to prove it. This video is just the beginning. I believe this is a game-changer, and I'd love to hear your thoughts. What is the single biggest barrier to trusting AI in your industry today? Let's discuss in the comments. https://codexcore.io #AI #ArtificialIntelligence #LLM #Transparency #ExplainableAI #XAI #MachineLearning #Innovation #FutureOfTech #EthicsInAI #DeepLearning

By now, we’re sure you’ve heard the news: a new MIT study found that 95% of enterprise GenAI pilots are failing. The lesson isn’t whether to adopt AI, it’s how. - Internal AI builds were found to succeed 1 in 3 times - Partnering with specialized, domain-specific vendors succeeds about 2 in 3 times (67%) The issue isn’t the quality of the models. It’s the failure to integrate AI into real workflows, align with actual pain points, and deliver results professionals can trust. That’s exactly why NLPatent was built. We’re not a generic AI tool with “IP” bolted on. We’re a domain-specific solution solving domain-specific problems, engineered for patent certainty in a field where precision isn’t optional. As workflows evolve, the gap between generic pilots and purpose-built platforms will only grow wider, making domain expertise the real differentiator in the age of AI. Read the full article here: https://lnkd.in/eEiXCteC and learn more about how NLPatent can drive ROI for your team at nlpatent.com #AI #LegalTech #PatentTech

Why AI Hallucinates—and How to Fix It Based on the latest research from OpenAI, we can get a better understanding of why AI models hallucinate and how we can work to fix this issue. Key Takeaways & Solutions 1⃣️ Accuracy isn't everything. Simply aiming for higher accuracy won't eliminate hallucinations. Some questions are fundamentally unanswerable, and a model needs to be able to recognize that. 2⃣️ Smaller can be smarter. Surprisingly, smaller models can sometimes be better at avoiding hallucinations because they're more aware of their own limitations. 3⃣️ Reward humility. The key to reducing hallucinations is to change our evaluation methods. We must start rewarding uncertainty, or "abstention," in addition to correctness. Let models know that it's okay, and even preferable, to say "I don't know" when they're not confident. In short, the goal isn't to make models infinitely smarter; it's to make them more calibrated and humble. By teaching AI to recognize its own limits, we can build more trustworthy and reliable systems. 🌟 By leveraging all the state-of-the-art models you could join the waitlist of Adal Cli SylphAI https://adal.ml/waitlist This is the terminal agent that unites all intelligence in a single place; be it Claude, Gemini, GPT, or open-source models. https://lnkd.in/g9a8d4Ua Our goal isn't just to make AI smarter, but to make it more calibrated and humble by teaching it to recognize its own limits. This is how we build more trustworthy and reliable systems. Link in the comment! 😊 #opensource #Aiagent #LLM #Hallucinate #bayarea

Many AI experiments fail because they repeat the same mistakes from the early digital transformation era: lots of hype, many pilots, little that scales. Check out “Beware the AI Experimentation Trap” where the authors warn that 95% of generative AI investments produce no measurable returns. They argue that much of current experimentation is diffuse, disconnected from what customers really need, and overly focused on flashy or peripheral tests instead of core capabilities. Key lesson: anchor AI experiments in solving real customer problems.  “The takeaway is not that AI experimentation is broken, but that it must be disciplined — focused on solving core customer problems; chosen with frameworks like intensity, frequency, and density; run at low cost to enable iteration; and designed with scaling in mind through empowered ‘ninja’ teams.” For product designers & PMs, that means: before building or approving yet another pilot, ask: • What customer-pain does this address, and how often? • How intense is the need vs how visible is the opportunity? • Can we test cheaply, learn fast, and scale if successful? It’s tempting to chase novelty with AI. But without customer-centric discipline, we risk repeating the digital transformation cycles where many firms experimented a lot—and few really delivered. If you want strategies to escape this trap, this article is well worth your time. 🔗 https://lnkd.in/e4BFZNGG #productdesign #AI #PM #experimentation #customersuccess Thanks Dimitri Samutin for sharing this article initially.

🚀 AI reasoning just reached a turning point. MBZUAI and G42 have launched K2 Think, a leading open-source system for advanced AI reasoning. At only 32B parameters, it delivers performance on par with models more than 20 times larger. This matters because it shows that efficiency and smart design can beat brute force scale. So what is K2 Think? It is a model built on six innovations, including chain-of-thought training, reinforcement learning with verifiable rewards, and agentic planning. These breakthroughs mean the system is better at complex reasoning tasks where step-by-step logic matters. Why does this matter? Because reasoning is the missing piece for many AI applications. Generating fluent text is one thing, but reasoning allows AI to solve math problems, plan multi-step processes, and make structured decisions that humans can trust. What does this actually mean? K2 Think can tackle complex multi-step problems that require deep logical thinking. Imagine an AI that can work through advanced mathematical proofs, analyze intricate scientific hypotheses, debug complex code by reasoning through each logical step, or solve multi-layered business strategy problems. These aren't simple pattern-matching tasks but genuine reasoning challenges. K2 Think is fully open, from training data to deployment systems, giving the community transparency and accountability. It is a model built to be used, studied, and improved. 👉 Try it today at www.k2think.ai #AI #Reasoning #OpenSourceAI #Innovation #MBZUAI #Ad

🚀 AI reasoning just reached a turning point. MBZUAI and G42 have launched K2 Think, a leading open-source system for advanced AI reasoning. At only 32B parameters, it delivers performance on par with models more than 20 times larger. This matters because it shows that efficiency and smart design can beat brute force scale. So what is K2 Think? It is a model built on six innovations, including chain-of-thought training, reinforcement learning with verifiable rewards, and agentic planning. These breakthroughs mean the system is better at complex reasoning tasks where step-by-step logic matters. Why does this matter? Because reasoning is the missing piece for many AI applications. Generating fluent text is one thing, but reasoning allows AI to solve math problems, plan multi-step processes, and make structured decisions that humans can trust. What does this actually mean? K2 Think can tackle complex multi-step problems that require deep logical thinking. Imagine an AI that can work through advanced mathematical proofs, analyze intricate scientific hypotheses, debug complex code by reasoning through each logical step, or solve multi-layered business strategy problems. These aren't simple pattern-matching tasks but genuine reasoning challenges. K2 Think is fully open, from training data to deployment systems, giving the community transparency and accountability. It is a model built to be used, studied, and improved. 👉 Try it today at www.k2think.ai #AI #Reasoning #OpenSourceAI #Innovation #MBZUAI #Ad

AI is a powerful force, but its true nature is often misunderstood 🤷🏽♂️ Forget the sci-fi fantasies of sentient machines; today's AI, from advance chatbots to complex analytical systems, is all about optimising tasks. This perspective is crucial for understanding its limitations and harnessing its real potential. Every AI system, whether narrow or wide, relies on two fundamental engineering principles: representation and specification. Representation involves translating a problem into a format a computer can process, like converting language into tokens. Specification defines the computational steps to operate on that representation and produce outputs. If a problem cannot be represented and specified, it cannot be automated by AI. This inherent need for defined boundaries is why even the most advanced large language models (LLMs) operate within constraints. Their "breadth" comes from statistical scaling over massive datasets, not from an unconstrained understanding or true generalization. The "scaling hypothesis" and the relentless pursuit of bigger models and datasets, while initially fruitful, have exposed the limits of this approach. We haven't seen a path to true Artificial General Intelligence (AGI) emerge from simply making models larger. The illusion that AI is "getting smarter" stems from its increasing power within its defined domain. It succeeds because engineers are incredibly clever at representing narrow slices of the world as computational problems, then building algorithms to solve them. Think of how chess, checkers, and Go went from human pastimes to AI showcases – by narrowing the problem. This is precisely why LLMs, despite their ability to simulate conversational breadth, are still prone to "hallucinations" and brittle reasoning. These are symptoms of their underlying statistical nature and the fundamental constraint of representation and specification. They can mimic conversation across many topics because they are masters of statistical token prediction, not because they genuinely understand or possess open-ended thought. Therefore, seeing AI as what it truly is – sophisticated automation – is the first step toward using it effectively and mitigating potential risks. It’s an incredibly powerful tool for streamlining operations, boosting productivity, and reducing costs in specific, well-defined areas. Understanding its automation core allows us to focus on practical applications where AI can genuinely deliver value, rather than chasing elusive, overhyped promises of a sentient future. #AI #Automation #Technology #Innovation #Productivity

Not all AI Agents are built for business, some are just experiments in disguise! In the rush to adopt AI, it’s easy to confuse a prompt within a wrapper with a scalable solution. But the difference between a prototype and an enterprise-grade AI agent isn’t just about technology. It’s about purpose, reliability, and business impact. A prototype agent might give you insights but an enterprise-grade agent helps you take action. A prototype might answer a question but an enterprise agent understands context, nuances, and workflows and keeps learning over time. These days it’s so easy to build something that looks smart but much harder to build something that’s trustworthy, measurable, and enterprise-ready. In the carousel, I’ve broken down the key differences that separate AI Prototypes from enterprise-grade, real-world systems! If you’re building or evaluating AI agents, ask yourself: Are you solving a business problem or just testing a hypothesis? #AI #AIAgents #EnterpriseAI #DataScience #MachineLearning