MIT Report on Generative AI Deployments Criticized for Lack of Evidence

From Data to Decisions: The Full Spectrum of AI ,A beginners’ guide There’s so much being said about AI today, Machine Learning, Ge AI, LLMs, Agentic AI that it can feel overwhelming. I tried to create a simple, structured post for anyone who wants to understand how these layers fit together, from data-driven learning to autonomous intelligence.Here’s a quick overview: 1️⃣ Machine Learning (ML): Learning from Data The foundation of AI : systems trained to recognize patterns and make predictions. Helps answer: What’s likely to happen next? Used in: demand forecasting, fraud detection, risk scoring Examples: Scikit-learn, TensorFlow, PyTorch 2️⃣ Descriptive & Diagnostic AI: Explain the Past AI that turns raw data into insight and understanding. Descriptive: What happened? (dashboards, analytics) Diagnostic: Why did it happen? (root cause analysis) Examples: Power BI, Looker, Vertex AI 3️⃣ Predictive & Prescriptive AI: Guiding Decisions AI that forecasts outcomes and recommends next steps. Predictive: “What will happen?” Prescriptive: “What should we do?” Examples: DataRobot, Amazon Forecast, IBM Watson Decision Optimization 4️⃣ Generative AI: Creating What Never Existed AI that creates new content instead of just analyzing data. Text: ChatGPT, Claude, Gemini Audio: ElevenLabs, Suno Image: DALL·E, Midjourney, Leonardo Video: Runway, Pika Labs, Synthesia Code: GitHub Copilot, Replit Ghostwriter Each form of GenAI transforms creative work in its own domain. 5️⃣ Large Language Models (LLMs): Understanding Context LLMs make sense of language,they comprehend, reason, and converse. - Trained on massive text datasets - Enable summarization, Q&A, reasoning, and personalization Examples: GPT-5, Claude 3.5, Gemini 1.5, LLaMA 3 Engine behind most conversational and reasoning AI today. 6️⃣ Agentic AI: Acting with Autonomy The next evolution: AI that not only thinks but acts. Combines reasoning (LLMs) + action (APIs, tools, workflows) Example: An AI agent that schedules meetings, drafts reports, and executes tasks autonomously Examples: OpenAI’s GPT Agents, Microsoft Copilot+, Relevance AI Agents here AI shifts from copilot to collaborator. 7️⃣ Canva: Where ML, Generative & Agentic AI Converge “Design Made Intelligent” It is a live example of AI synergy: ML suggests layouts & color harmony. Generative AI creates text, visuals & designs. Agentic AI auto-adjusts formats, aligns visuals, and refines designs. A single click blends data-driven learning, creativity, and autonomous design. 💡 Leadership Lens: Architecting Intelligence True digital leadership means knowing: - When to analyze, when to predict, when to create and when to let AI act. - How to blend data reliability with creative flexibility and decision autonomy. AI isn’t replacing human intelligence it’s amplifying it into action.Which layer of AI do you find most valuable in your domain? #ArtificialIntelligence #MachineLearning #GenerativeAI #LLM #AgenticAI #Leadership #DigitalTransformation

Self-Supervised AI: Teaching Models Without Labeled Data The next revolution in AI isn’t just about bigger models — it’s about smarter learning. Self-supervised learning (SSL) is reshaping how machines understand the world, letting models train themselves on massive amounts of unlabeled data — text, images, audio, or video — and extract meaningful patterns without human annotation. In traditional supervised learning, humans label millions of data points, which is time-consuming and expensive. But self-supervised systems skip that step — they predict missing parts of input data (like words in a sentence or pixels in an image) and, in doing so, learn deep representations of context, semantics, and relationships. This is how some of today’s most powerful AI models — from GPT to Meta’s DINOv2 and Google’s Gemini — are trained. They learn by reconstructing what’s hidden, developing rich internal understanding without relying on external labels. For enterprises, SSL offers transformative potential: • Cost efficiency: Drastically reduces manual data labeling. • Domain scalability: Works across text, vision, speech, and sensor data. • Performance: Produces robust embeddings that generalize well across downstream tasks. • Data diversity: Learns effectively even with noisy, heterogeneous, or incomplete datasets. In industries like healthcare, finance, and autonomous systems, SSL allows organizations to build smarter predictive models using the data they already have — no costly annotation pipelines, no waiting for labeled sets. What makes this approach truly revolutionary is that it mimics human intuition. Just as humans learn by predicting what comes next or filling in missing pieces, self-supervised models use contextual gaps to build understanding. This “learn from yourself” paradigm is giving rise to generalist AI systems that adapt faster and perform better with minimal supervision. Self-supervised AI isn’t the future — it’s the foundation of next-gen intelligence. As models increasingly learn without human guidance, we’re witnessing a shift toward autonomy, creativity, and scalability in machine learning. For businesses, adopting SSL isn’t just a technical upgrade — it’s a strategic move toward faster insights, lower costs, and smarter automation. As AI governance frameworks mature, the coming wave of “meta-learning systems” will redefine how organizations view responsibility in machine learning. The future won’t just rely on creating smarter models — it will depend on building systems that can explain, adapt, and justify their actions transparently. Businesses that anticipate this shift will gain not only technical superiority but also the trust and credibility required to lead in the AI-driven era. #AI #MachineLearning #SelfSupervisedLearning #DeepLearning #ArtificialIntelligence #AITrends #FutureOfAI #GenerativeAI #TechInnovation #DataScience

📘 Overview of the 3D–10D AI DNN Engine – Cognitive Cube (Introduction) The 3D–10D AI DNN Engine – Cognitive Cube is a breakthrough architecture that transforms raw data and language into enterprise-ready intelligence. Beyond traditional machine learning or LLM systems, it layers deeper forms of intelligence — logical, emotional, contextual, governance, predictive, ethical, strategic, and sustainability — into one cohesive cognitive framework. The system’s visualization is structured into five stages, showing how data evolves into actionable outcomes: 🧠 1. Data → Engine: From Data Lake to Cognitive Intelligence Unstructured inputs — text, logs, sensor streams — flow from a Data Lake into the Cube, where they’re structured and enriched into governed, predictive, strategic insights. This is the first step from data chaos to clarity. 🧬 2. Inside the Brain: Neural Network Core At its core, the Cube is a deep neural network applying cognitive layers — logical, emotional, ethical, and more. Its multi-directional processing mimics the human brain, enabling it to adapt, validate, and reason beyond simple prediction. 🏗️ 3. The 3D–10D Stack: Layered Intelligence A pyramid shows the progressive layers: 3D – Logical: Structured reasoning 4D – Emotional: Context-aware decisions 5D – Contextual: Domain adaptation 6D – Governance: Compliance 7D – Predictive: Forecasting 8D – Ethical: Bias control 9D – Strategic: Long-term planning 10D – Sustainability: ESG impact Each layer builds on the last, delivering auditable, explainable, enterprise-grade intelligence. 📊 4. Analyzer Dashboard & Performance Lens Analyzer 1.0 monitors accuracy, compliance, monetization, and intelligence share. It drives ~70–80% of decision-making, showing its governance and reasoning strength. Real-time analytics and forecasting make the Cube a measurable, auditable system. 💡 5. LLM → Analyzer → Ideas: Language to Impact The final pipeline — LLM → Analyzer → Ideas — transforms language into validated, actionable decisions. Mission: “Transform AI language into enterprise-ready innovation.” — Dr. Pradeep K. Suri 🧭 Full-Stack Cognitive Framework The Cube captures the full journey: 🔄 Data Flow → structured intelligence 🧠 Computation → deep reasoning 📐 Architecture → multi-dimensional layers 📊 Oversight → governance & ethics 🚀 Outcome → actionable solutions ✅ Strategic Significance Enterprises: Deploy predictive, compliant AI. Researchers: Bridge LLMs and real-world outcomes. Mentorship: Foundation for cognitive AI education. Dr. Pradeep K. Suri (MPhil) Inventor & Architect — 3D–10D AI DNN Engine (Cognitive Cube)

AI isn’t replacing people, it’s replacing people who don’t understand AI. The next wave of leaders won’t just use AI. They’ll leverage it. When I first started exploring AI tools, I realized one truth: education is the real edge. The faster you learn, the faster you lead. So here are 14 powerful, 100% free AI courses to sharpen your edge in 2025 👇 1/ AI Essentials 🔗 lnkd.in/dyEt4DGt ↳ Build a rock-solid foundation on how AI works in real-world applications. 2/ ChatGPT Mastery 🔗 lnkd.in/eiRtk-6q ↳ Learn how to write prompts that multiply your productivity and creativity. 3/ Google AI Magic 🔗 lnkd.in/eBQXfBe9 ↳ Discover how Google’s tools use AI for smarter data and content. 4/ Harvard AI Introduction 🔗 lnkd.in/eu4mZaAG ↳ Understand the core principles of AI—direct from one of the world’s top institutions. 5/ Microsoft AI Basics 🔗 lnkd.in/eYNWzXUX ↳ Explore how Microsoft integrates AI into its ecosystem for business leaders. 6/ Prompt Engineering Pro 🔗 lnkd.in/eNi_YNSe ↳ Master the art of writing prompts that deliver better, faster AI outputs. 7/ Google's Ethical AI 🔗 lnkd.in/eTrwSU89 ↳ Learn the moral and practical frameworks shaping the future of AI. 8/ Machine Learning 🔗 lnkd.in/eX28syMJ ↳ Build hands-on understanding of how machines actually learn patterns. 9/ Language Models 🔗 lnkd.in/evZVJbNy ↳ Go deeper into how models like GPT think, reason, and respond. 10/ Bing Chat Applications 🔗 lnkd.in/ejN-qrVy ↳ Discover new ways to use AI chat for search, productivity, and marketing. 11/ Generative AI 🔗 lnkd.in/dqjnzcCD ↳ Learn to create images, videos, and stories using generative tools. 12/ Amazon's AI Strategy 🔗 lnkd.in/dFhmsvZC ↳ See how Amazon applies AI to logistics, recommendations, and innovation. 13/ AI for Everyone 🔗 lnkd.in/eFx7zCz7 ↳ Simplify complex AI concepts into actionable takeaways anyone can apply. 14/ AWS AI Foundations 🔗 lnkd.in/dEjN9PRm ↳ Learn to build scalable AI systems using AWS infrastructure. When you invest in learning AI: 📍 You future-proof your career. 📍 You multiply your output and decision-making power. 📍 You become the person others rely on for clarity in a fast-changing world. 💬 Which one will you start with? Or do you have another great AI course to recommend? Enjoy this? ♻️ Repost, follow Ravi Katta and check out the link in bio for more content and resources on building legacy wealth.

Ready for a fun AI Quiz? 15 Key Questions on AI Fundamentals and Applications - you find the answers and more in the dentro.de/ai Learning Resource: 1. What are the three phases of the AI “Black Box” (Before, Inside, After), and what key aspects does each phase include? 2. How does tokenization work in language models, and why is it a crucial first step for AI to process text? 3. What primary task are large language models (LLMs) trained to perform, and how do complex abilities like summarizing or translating derive from it? 4. How do modern AI models use “attention” to handle words with multiple meanings (for example, the word bank in different contexts)? 5. In an AI model with billions of parameters, what do these parameters represent, and what happens to them after the model’s training is complete? 6. Why does training a cutting-edge AI model require massive datasets and weeks of computing on specialized hardware? 7. What is the goal of alignment techniques such as Reinforcement Learning from Human Feedback (RLHF) in the context of AI model development? 8. What is the difference between a truly open-source AI model and one that only provides “open weights,” and why might a model be released as open weights instead of fully open-source? 9. In the car analogy for the AI model lifecycle, which stage of AI development is likened to a car’s mass production assembly line, and what happens during that AI stage? 10. What does “inference” refer to in AI, and why is using an AI model post-training compared to driving a finished car rather than redesigning it? 11. What are some tasks or creative outputs that generative AI tools can produce from just a user prompt, and why can this ease of use be misleading about the underlying complexity? 12. Name two risks or challenges associated with using AI models in real-world applications (for example, issues related to output accuracy or ethical concerns). 13. The AI industry features various types of key players. Can you give an example of a big tech company, an AI-focused lab/startup, and an “enabler” (infrastructure or tool provider) in the AI ecosystem, and briefly describe their roles? 14. What was significant about Meta’s release of the Llama 2 model for the AI community, particularly in terms of open-source AI development? 15. What similarities can be drawn between the current AI boom and earlier tech revolutions (like the PC or dot-com eras), and what lesson does this suggest for how we should approach adopting AI today?

The future of AI isn't just about bigger models—it's about smarter connections. After months of research and hands-on experience, I've written an in-depth exploration of GraphRAG, a game-changing approach that's reshaping how AI systems understand and utilize knowledge. Think of it this way: Traditional AI retrieval is like searching through a library one book at a time. GraphRAG? It's like having a librarian who knows exactly how every book relates to every other book—and can synthesize insights across them instantly. In my latest blog post, I explore: 🔹 Why knowledge graphs + generative AI = next-level intelligence 🔹 How companies are already using GraphRAG to transform operations 🔹 What developers need to know to get started The potential here is massive—from enterprise knowledge management to accelerating research breakthroughs. Check it out and let me know what you think: https://lnkd.in/eFHpuFsk Are you working with knowledge graphs or RAG systems? I'd love to hear about your experiences! 👇 #GraphRAG #AI #DataScience #KnowledgeManagement #TechInnovation #MachineLearning

RAG in AI: “Look it up first, then answer" - (Retrieval Augmented Generation) Imagine you have a super-smart AI friend who can answer almost any question. But, like anyone, this friend doesn't know everything. RAG is like giving your AI friend a really good set of notes to study before answering your question. This way, the AI can use its own smarts and the information in the notes to give you a much better, more accurate, and relevant answer. Let's break it down: - ✔️ Generation: This is the AI's ability to create new text, like answering your question in a complete sentence or paragraph. Think of it as the AI "writing" the answer. ✔️ Retrieval: This is the process of finding the right "notes" or information that will help the AI answer the question. It's like searching through a library to find the books that are most relevant to what you're asking. ✔️ Augmented: This means "enhanced" or "improved." In this case, the AI's answer is augmented or improved by the information it retrieved. So, RAG is a process where the AI retrieves relevant information and then uses that information to generate a better answer. For Example? Imagine you're asking a student to write an essay about the Global Tech Development? ✔️ Without RAG: The student relies solely on their memory and existing knowledge. The essay might be okay, but it could be missing important details or be slightly inaccurate. ✔️ With RAG: Before writing the essay, the student is given a set of relevant documents – historical texts, biographies, articles, etc. They read these documents and then use the information to write a much more comprehensive, accurate, and well-supported essay. ➡️ RAG is like giving the student those documents. It helps the AI provide better, more informed answers. Why you should care ⁉️ 1️⃣ Accuracy: Reduces made‑up answers by grounding responses in real docs. 2️⃣ Freshness: Pulls the latest info beyond the AI’s original training. 3️⃣ Trust: Can cite or link the sources it used. 4️⃣ Control: Limits answers to approved content, not the open internet. 📌 RAG is a technique that helps AI provide better, more accurate, and more relevant answers by giving it access to external information. It's like giving your AI friend a set of notes to study before answering your question. This leads to more reliable and trustworthy AI systems. It's a powerful tool for making AI more useful and helpful in a variety of applications. #RAG #AI #GenAI #AIForBusiness #LLM #MLOps #KnowledgeManagement #CustomerSupport #Productivity #DigitalTransformation #AITrends #DataStrategy #VectorSearch #EnterpriseAI #OpenAI #ChatGPT Abdulla S. Abdulrahman AlQutaim Dr. Jassim Al Awadhi (PhD, EMBA) Dr Hamad ALNUAIMI Aisha Thani Al Falasi

My Journey from Machine Learning to Generative AI: A New Building Block   A few years ago, back in 2021, I dove into the world of machine learning—neural networks, supervised and unsupervised learning—applying it all to build recommendation engines, particularly in the automotive industry. It felt like the future back then.   But technology never stands still. In 2022, along came generative AI, thanks to OpenAI and large language models. Suddenly, it wasn't just about predicting or categorizing—it was about creating. Now I’m exploring how these models generate new content from vast contexts, and it's blowing my mind.   With tools like LangChain and the MCP protocol, we’re stepping into a new world where giving AI the right tools changes how we consume technology.   My takeaway? Don’t just stop at large language models. Learn to automate workflows, dive into prompt engineering, and you'll be ready for what’s next. I’ll be sharing more as I go along!   What is Generative AI? Generative AI is a transformative technology with broad applications and immense economic potential, but it also brings new challenges related to ethics, accuracy, and risk management. Ongoing innovation and responsible implementation will be critical as the field develops.   Key points:   Distinction and Development: Generative AI is a branch of artificial intelligence (AI), which uses machine learning techniques to enable computers to mimic human intelligence. While classical machine learning classified data (e.g., recognizing cat photos), generative AI can now create new data (like generating an image or writing an essay).   How Models Work: Modern generative models are trained on vast datasets, using methods like self-supervised learning. This enables them to predict and generate plausible text, images, or other outputs, often mimicking human creativity.   Business Impact: Generative AI is influencing industries from IT and marketing to healthcare and media. It promises to significantly raise productivity and value, with McKinsey estimating a potential $4.4 trillion in annual global impact.   Risks and Limitations: While powerful, generative AI models can produce inaccurate or biased outputs, reflecting issues found in their training data. They can also present reputational and legal risks if used without oversight. Managing these risks involves careful data selection, model customization, and keeping humans in the decision loop—especially for high-stakes tasks.   Accessibility and Future Outlook: Developing large-scale generative AI models is resource-intensive, making it feasible mainly for well-funded tech leaders. As adoption accelerates, organizations must navigate evolving regulations and oversight while harnessing the benefits of these technologies.   #GenAI #NewJourney #AppliedAI 

AI and Data: A Deep Connection In common knowledge, we know that Artificial Intelligence (AI) is fundamentally based on data. Every intelligent system learns, reasons, and improves through the information it receives. In this analysis, we will take a deeper look at how AI and Data are connected exploring the ways they complement each other and how their interaction has transformed modern technology. Together, AI and Data form the backbone of today’s digital revolution: data provides the raw material, while AI extracts meaning and value from it. Let’s explore their relationship in a concise and structured way to clearly understand how they are connected, complementary, and transformative. 1. Data: The Foundation of Intelligence Data represents the raw facts and observations collected from different sources transactions, sensors, user activities, images, or texts. Without structured and high-quality data, AI systems cannot learn or make accurate predictions. 👉 In essence, data is the “fuel” that powers the intelligence of machines. 2. AI: The Engine of Interpretation AI refers to the set of methods and technologies that enable machines to analyze, learn, and make decisions from data. Through machine learning and deep learning, AI transforms raw data into insights, predictions, and automated actions. 👉 AI gives meaning and value to data turning information into knowledge and action. 3. The Connection Between AI and Data The relationship between AI and data is cyclical and interdependent: Data → Learning → AI Model → Prediction → New Data → Improved Learning Each stage feeds the next: 🪡 Data is used to train AI models 🪡 AI models use data to make predictions or decisions 🪡 New data from these outputs helps improve future performance 👉 The more diverse and accurate the data, the more powerful the AI becomes. 4. Types of data-AI relationships 🪡 Supervised Learning: Uses labeled data; for example, AI predicts house prices from historical data. 🪡 Unsupervised Learning: Uses unlabeled data; for example, AI clusters customers by behavior patterns. 🪡 Reinforcement Learning: Uses feedback-based data; for example, AI learns to play a game by trial and error. 🪡 Generative AI: Uses large datasets; for example, AI generates realistic images or text. 5. Transformative Impact The synergy between AI and Data has revolutionized nearly every field: 🪡 Healthcare → Predictive diagnosis and personalized treatment 🪡 Finance → Fraud detection, algorithmic trading, and credit scoring 🪡 Industry → Predictive maintenance and process automation 🪡 Marketing → Customer segmentation and targeted campaigns 🪡 Public Services → Smart cities and data-driven governance 👉 Data gives context, and AI turns that context into intelligent action. Below is a brief description of a diagram.

Differences between AI Agent, LLM, Gen AI, ML, DL, and AI — showing how each layer builds upon the other in the modern artificial intelligence ecosystem. 1. AI Agent An AI Agent autonomously perceives its environment, makes decisions, and executes tasks to fulfill goals. It involves: - Setting objectives - Detecting context - Triggering planners - Querying LLMs - Selecting tools - Acting to achieve results AI agents integrate reasoning, memory, and decision-making to operate independently. 2. Large Language Model (LLM) LLMs are advanced models trained on large text corpora to understand and generate human language. Their process includes: - Pretraining on vast datasets - Tokenizing text - Building transformer networks - Using attention mechanisms for context understanding They power APIs that enable querying, token optimization, and contextual task validation. 3. Generative AI (Gen AI) Generative AI focuses on creating new content — text, images, audio, or code — by learning from existing data. It follows a creative cycle: - Gathering and training on data - Understanding patterns - Generating new samples - Evaluating and tuning responses Gen AI tools such as ChatGPT, DALL·E, and Gemini illustrate this content-generation capability. 4. Machine Learning (ML) ML enables systems to learn from data without explicit programming. The workflow includes: - Collecting and preprocessing data - Splitting datasets - Selecting and training models - Evaluating results and retraining periodically ML helps detect patterns, make predictions, and improve decision-making accuracy. 5. Deep Learning (DL) DL is a subset of ML that employs neural networks with multiple layers. It works by: - Normalizing inputs - Designing network architecture - Training with GPUs - Preventing overfitting and tuning models Deep learning powers complex tasks such as speech recognition, computer vision, and autonomous systems. 6. Artificial Intelligence (AI) AI is the overarching field focused on creating intelligent systems capable of simulating human reasoning and decision-making. It involves: - Problem definition - Data collection and algorithm design - Model training and validation AI integrates ML, DL, and LLM technologies to improve performance and automate logic-based reasoning. 𝗕𝗼𝗻𝘂𝘀 𝗧𝗶𝗽: Free courses you’ll wish you started earlier in 2025 🪢 7000+ Course Free Access : https://lnkd.in/guy-gvK2   <>.Google Data Analytics: 🪢 https://lnkd.in/ggdMGT_i    1.Advanced Google Analytics https://lnkd.in/gtm2zhiX    2.Google Project Management https://lnkd.in/gV9TSe_Q   3.Agile Project Management https://lnkd.in/gk9t-h29   4. Project Initiation: Starting a Successful Project https://lnkd.in/gwzr6czZ   5.Agile Project Management  https://lnkd.in/gDgJk4Yt   6.Project Execution: Running the Project https://lnkd.in/gt47KyC5 7.Project Planning: Putting It All Together https://lnkd.in/gHMscB7G Follow Afrin Akter for more