Understanding AI Systems

I taught myself machine learning > 10 years ago. If I had to start again today, I wouldn’t touch models, LLMs, or agents first, as many AI experts suggest. I'd start with the math and the code. Ugly truth: 90% of people skip the foundations, then wonder why everything feels like magic or falls apart in production. If you want to be different, actually understand ML, not just copy-paste, this is the roadmap I'd follow: Start with fundamentals: Because no matter how fast LLMs or GenAI evolve, your math, code, and logic will keep you relevant. Here's what you should focus on: 📐 1. Linear Algebra Learn these core ideas: Vectors, matrices, tensors Matrix multiplication (dot products, broadcasting) Transpose, inverse, rank, determinants Eigenvalues & eigenvectors (especially for PCA & embeddings) Projections and orthogonality ✅ Use NumPy to implement everything yourself → Practice matrix ops, dot products, and visualizing transformations with Matplotlib 🔁 2. Calculus Focus on: Derivatives & partial derivatives Chain rule (for backpropagation in neural nets) Gradient descent Convex functions, minima/maxima ✅ Use SymPy or JAX to visualize and compute derivatives → Plot functions and their gradients to develop deep intuition 🎲 3. Probability You need a solid grip on: Random variables (discrete & continuous) Conditional probability & Bayes' rule Joint & marginal probability The Chain rule Expectation, variance, entropy Common distributions: Bernoulli, Binomial, Gaussian, Poisson Central limit theorem The law of large numbers ✅ Simulate simple probability experiments in Python with NumPy → E.g. simulate sampling from distributions 📊 4. Statistics These are must-know topics: Descriptive stats: mean, median, mode, standard deviation Hypothesis testing: p-values, confidence intervals, t-tests Correlation vs. causation Sampling, bias, and variance Overfitting/underfitting A/B testing basics ✅ Use Pandas & SciPy to explore real datasets → Calculate descriptive stats, create histograms/box plots, run t-tests 🔧 Essential Python libraries to learn early NumPy – for vectorized math and fast array ops Pandas – for loading, cleaning, and analyzing tabular data Matplotlib / Seaborn – for plotting and visualizing distributions, relationships, and trends SymPy – for symbolic math and calculus SciPy – for stats, optimization, and numerical methods Use Jupyter Notebooks(to combine math, code, & visuals in one place) 📚 Best resources to nail the fundamentals: ✅ Machine Learning Foundations Math series (ML Foundations: Linear Algebra, Calculus, Probability, and Statistics)-series of 4 courses that I've created together with LinkedIn learning ✅ Hands-On ML with TensorFlow & Keras book by Aurélien Géron ✅ The Hundred-page Machine Learning Book by Andriy Burkov If you want to become an actual ML engineer, not just someone who watches and copies demos, start here. ♻️ Repost to help others💚

AI is rapidly moving from passive text generators to active decision-makers. To understand where things are headed, it’s important to trace the stages of this evolution. 1. 𝗟𝗟𝗠𝘀: 𝗧𝗵𝗲 𝗘𝗿𝗮 𝗼𝗳 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗙𝗹𝘂𝗲𝗻𝗰𝘆 Large Language Models (LLMs) like GPT-3 and GPT-4 excel at generating human-like text by predicting the next word in a sequence. They can produce coherent and contextually appropriate responses—but their capabilities end there. They don’t retain memory, they don’t take actions, and they don’t understand goals. They are reactive, not proactive. 2. 𝗥𝗔𝗚: 𝗧𝗵𝗲 𝗔𝗴𝗲 𝗼𝗳 𝗖𝗼𝗻𝘁𝗲𝘅𝘁-𝗔𝘄𝗮𝗿𝗲 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 Retrieval-Augmented Generation (RAG) brought a major upgrade by integrating LLMs with external knowledge sources like vector databases or document stores. Now the model could retrieve relevant context and generate more accurate and personalized responses based on that information. This stage introduced the idea of 𝗱𝘆𝗻𝗮𝗺𝗶𝗰 𝗸𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗮𝗰𝗰𝗲𝘀𝘀, but still required orchestration. The system didn’t plan or act—it responded with more relevance. 3. 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜: 𝗧𝗼𝘄𝗮𝗿𝗱 𝗔𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 Agentic AI is a fundamentally different paradigm. Here, systems are built to perceive, reason, and act toward goals—often without constant human prompting. An Agentic system includes: • 𝗠𝗲𝗺𝗼𝗿𝘆: to retain and recall information over time. • 𝗣𝗹𝗮𝗻𝗻𝗶𝗻𝗴: to decide what actions to take and in what order. • 𝗧𝗼𝗼𝗹 𝗨𝘀𝗲: to interact with APIs, databases, code, or software systems. • 𝗔𝘂𝘁𝗼𝗻𝗼𝗺𝘆: to loop through perception, decision, and action—iteratively improving performance.    Instead of a single model generating content, we now orchestrate 𝗺𝘂𝗹𝘁𝗶𝗽𝗹𝗲 𝗮𝗴𝗲𝗻𝘁𝘀, each responsible for specific tasks, coordinated by a central controller or planner. This is the architecture behind emerging use cases like autonomous coding assistants, intelligent workflow bots, and AI co-pilots that can operate entire systems. 𝗧𝗵𝗲 𝗦𝗵𝗶𝗳𝘁 𝗶𝗻 𝗧𝗵𝗶𝗻𝗸𝗶𝗻𝗴 We’re no longer designing prompts. We’re designing 𝗺𝗼𝗱𝘂𝗹𝗮𝗿, 𝗴𝗼𝗮𝗹-𝗱𝗿𝗶𝘃𝗲𝗻 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 capable of interacting with the real world. This evolution—LLM → RAG → Agentic AI—marks the transition from 𝗹𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 to 𝗴𝗼𝗮𝗹-𝗱𝗿𝗶𝘃𝗲𝗻 𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲.

What if the most important AI device at the FIFA World Cup isn’t a camera—but the ball itself? ⚽🤖 Modern FIFA match balls contain embedded sensors that capture hundreds of data points every second. Combined with AI-powered computer vision, they help determine the exact moment the ball is played, improving offside decisions, enhancing VAR, and generating rich real-time match analytics. But this is bigger than football. It’s a powerful example of sensor fusion—where edge devices, AI models, and high-performance computing work together to deliver insights in milliseconds. The same principles are transforming industries far beyond sport: 🏭 Smart manufacturing with connected sensors. 🚗 Autonomous vehicles combining cameras, radar, and LiDAR. 🏥 Healthcare devices delivering real-time diagnostics. 🏙️ Smart cities optimizing traffic, energy, and public safety. As AI moves from the cloud to the edge, every connected device becomes a source of intelligence. Turning that data into instant, reliable decisions requires powerful compute infrastructure. This is where AMD is helping drive the next era of AI—from Ryzen AI PCs at the edge, to EPYC processors powering modern data centers, and Instinct accelerators enabling large-scale AI inference and training. The future of AI isn’t just about bigger models. It’s about connecting billions of intelligent devices with the compute needed to make every decision count—in real time. From the football pitch to the factory floor, AI is changing how the world works. #AI #AMD #EdgeAI via @untoldoddities #SportsTech #ComputerVision #SensorFusion #HighPerformanceComputing #DataCenter #DigitalTransformation #Innovation #FIFA

Ever wondered where the future of AI is being built? I just visited the data centre in Finland that's making it happen. Nebius’ data centre is the powerhouse where AI models are trained. Thousands of GPUs working in unison. It’s expanding to host up to 60,000 GPUs dedicated to intensive AI workloads. They’re building a full-stack AI cloud platform. Here’s what I learned: 1. There is a scarcity of GPUs in the US • Clusters are being sold in massive packages • People who need smaller requirements can’t find them 2. Nebius are building a self-serve platform • Cover infrastructure requirements from a single GPU to big GPU clusters • They’re not a GPU reseller—they’re designing the servers and the racks from the ground up 3. Applications • Helped Mistral train their multimodal models • Provide full-stack infrastructure for AI model development Something else that was unique about the visit. Nebius cools the servers in Finland using the outside air. The heat that’s generated from the servers is then shipped back into the grid. This means Nebius not only heats the onsite building, But it also heats homes nearby, benefitting the local community. They’re able to recover 70% of the heat generated. And it’s the first in the world to have this heat reuse application connected to the local municipal grid. They’re now investing over $1B in AI data centres in Europe. I feel the future of AI depends on infrastructure like this that balances performance with sustainability. Follow me Alex Banks for daily AI highlights & insights.

We’re used to thinking of Big Tech as a cage match: Apple vs Google, Meta vs OpenAI, Amazon vs Microsoft. But go deeper down the stack and these rivalries dissolve into something less cinematic - inter-dependent supply chains. Consider just this past week: ➰ Meta signed a $10B+ cloud deal with Google, its fiercest rival in digital advertising. ➰ OpenAI is feeding ChatGPT with Google Search results (via SerpAPI) and renting its GPUs, while trying to make Google Search obsolete. There’s an entangled web of interdependencies in AI: your most threatening competitor is often your most critical vendor. Everyone sells the shovel, even to the guy digging their grave. So, what gives? 1. Moats are now Rentable. And often leased to the very people trying to cross them. What used to be a moat - distribution (iOS/Android), data (Search), or compute (GPUs at hyperscale) - is increasingly sold as a SKU. If your “defensive asset” can be metered, it will be monetized… even to your rivals. That sounds contradictory until you realize the real moat isn’t the resource - it’s the flywheel that replenishes it. Google can lease GPUs and still deepen its Gemini feedback loops. 2. Infrastructure is too Expensive to own Alone. The modern AI stack is fractured and expensive: - Compute (GPUs, interconnects, custom silicon) - Indexing (web crawlers, real-time feeds, proprietary corpora) - Modeling (foundation models, adapters, RAG) - Orchestration (retrievers, agents, tool use) - Distribution (hardware, OS defaults, app interfaces) No single company can win all five. So they do what every industry does when vertical integration becomes unscalable: they trade. AI isn’t owned. It’s assembled - by companies renting from rivals they’d love to replace. 3. Market Power comes from Volume. Take Meta. It’s signed deals with every major cloud provider: AWS, Azure, Oracle, CoreWeave, and now Google Cloud. This isn’t loyalty; it’s pricing arbitrage and regional hedging. At that scale, cloud is a commodity and power comes from being the customer that can move someone else’s earnings call. 4. Time-to-Quality > Ideological Purity. If the fastest path to product quality is to buy accuracy while you build your own index, you do both. You can always replace a vendor. You can’t buy back time. Months matter. In AI, months are market share. Meanwhile, Google selling compute to OpenAI is not charity; it’s toll collection on a rival’s growth curve. 5. Optics matter Turning your enemies into customers is not just good business - it’s good politics. Each hyperscaler that lands a rival as a marquee customer bolsters its narrative: To Wall Street: “We grow no matter who wins.” To regulators: “We’re not a monopoly, we power our competitors.” The stack is too entangled, too capital-intensive, and too unevenly distributed for anyone to play lone wolf. In this economy, independence is expensive and rivalry is mostly theater. Monetize your enemy’s ambition. The best revenge is recurring revenue.

AI Engineering has levels to it: – Level 1: Using AI Start by mastering the fundamentals: -- Prompt engineering (zero-shot, few-shot, chain-of-thought) -- Calling APIs (OpenAI, Anthropic, Cohere, Hugging Face) -- Understanding tokens, context windows, and parameters (temperature, top-p) With just these basics, you can already solve real problems. – Level 2: Integrating AI Move from using AI to building with it: -- Retrieval Augmented Generation (RAG) with vector databases (Pinecone, FAISS, Weaviate, Milvus) -- Embeddings and similarity search (cosine, Euclidean, dot product) -- Caching and batching for cost and latency improvements -- Agents and tool use (safe function calling, API orchestration) This is the foundation of most modern AI products. – Level 3: Engineering AI Systems Level up from prototypes to production-ready systems: -- Fine-tuning vs instruction-tuning vs RLHF (know when each applies) -- Guardrails for safety and compliance (filters, validators, adversarial testing) -- Multi-model architectures (LLMs + smaller specialized models) -- Evaluation frameworks (BLEU, ROUGE, perplexity, win-rates, human evals) Here’s where you shift from “it works” to “it works reliably.” – Level 4: Optimizing AI at Scale Finally, learn how to run AI systems efficiently and responsibly: -- Distributed inference (vLLM, Ray Serve, Hugging Face TGI) -- Managing context length and memory (chunking, summarization, attention strategies) -- Balancing cost vs performance (open-source vs proprietary tradeoffs) -- Privacy, compliance, and governance (PII redaction, SOC2, HIPAA, GDPR) At this stage, you’re not just building AI—you’re designing systems that scale in the real world. What else would you add?

Most people think having a human approve an AI decision means the decision is safe. It does not. 👀 There is a term for what actually happens when humans rubber stamp AI outputs under time pressure. Automation bias. It is one of the most documented and underreported risks in enterprise AI right now. After 13 years and 200+ deployments, here is what I have learned about building genuine oversight into AI systems. The human reviewing an output needs three things to actually be in the loop. They need to understand what they are reviewing. They need the context to catch what the model gets wrong. And they need to be genuinely empowered to say no without institutional pressure to simply keep moving. Most organisations have none of those three in place. They have a signature process. That is not the same thing. Before any high-stakes AI output reaches a decision point in your organisation, ask these questions. ➡️ Does the person approving this understand the underlying data well enough to catch an error? ➡️ Is there time built in for genuine review or just enough time to click approve? ➡️ What happens if someone says no? Is that genuinely supported? If the answer to any of those is no… you do not have human oversight. You have automation bias with a human signature attached. What does genuine human oversight look like in your organisation right now? #ai #leadership #futureofwork #artificialintelligence #aistrategy #teamhuman #intellectualatrophy #criticalthinking

🚨 AI Privacy Risks & Mitigations Large Language Models (LLMs), by Isabel Barberá, is the 107-page report about AI & Privacy you were waiting for! [Bookmark & share below]. Topics covered: - Background "This section introduces Large Language Models, how they work, and their common applications. It also discusses performance evaluation measures, helping readers understand the foundational aspects of LLM systems." - Data Flow and Associated Privacy Risks in LLM Systems "Here, we explore how privacy risks emerge across different LLM service models, emphasizing the importance of understanding data flows throughout the AI lifecycle. This section also identifies risks and mitigations and examines roles and responsibilities under the AI Act and the GDPR." - Data Protection and Privacy Risk Assessment: Risk Identification "This section outlines criteria for identifying risks and provides examples of privacy risks specific to LLM systems. Developers and users can use this section as a starting point for identifying risks in their own systems." - Data Protection and Privacy Risk Assessment: Risk Estimation & Evaluation "Guidance on how to analyse, classify and assess privacy risks is provided here, with criteria for evaluating both the probability and severity of risks. This section explains how to derive a final risk evaluation to prioritize mitigation efforts effectively." - Data Protection and Privacy Risk Control "This section details risk treatment strategies, offering practical mitigation measures for common privacy risks in LLM systems. It also discusses residual risk acceptance and the iterative nature of risk management in AI systems." - Residual Risk Evaluation "Evaluating residual risks after mitigation is essential to ensure risks fall within acceptable thresholds and do not require further action. This section outlines how residual risks are evaluated to determine whether additional mitigation is needed or if the model or LLM system is ready for deployment." - Review & Monitor "This section covers the importance of reviewing risk management activities and maintaining a risk register. It also highlights the importance of continuous monitoring to detect emerging risks, assess real-world impact, and refine mitigation strategies." - Examples of LLM Systems’ Risk Assessments "Three detailed use cases are provided to demonstrate the application of the risk management framework in real-world scenarios. These examples illustrate how risks can be identified, assessed, and mitigated across various contexts." - Reference to Tools, Methodologies, Benchmarks, and Guidance "The final section compiles tools, evaluation metrics, benchmarks, methodologies, and standards to support developers and users in managing risks and evaluating the performance of LLM systems." 👉 Download it below. 👉 NEVER MISS my AI governance updates: join my newsletter's 58,500+ subscribers (below). #AI #AIGovernance #Privacy #DataProtection #AIRegulation #EDPB

📛 CVE 2025 32711 is a turning point Last week, we saw the first confirmed zero click prompt injection breach against a production AI assistant. No malware. No links to click. No user interaction. Just a cleverly crafted email quietly triggering Microsoft 365 Copilot to leak sensitive org data as part of its intended behavior. Here’s how it worked: • The attacker sent a benign-looking email or calendar invite • Copilot ingested it automatically as background context • Hidden inside was markdown-crafted prompt injection • Copilot responded by appending internal data into an external URL owned by the attacker • All of this happened without the user ever opening the email This is CVE 2025 32711 (EchoLeak). Severity 9.3 Let that sink in. The AI assistant did exactly what it was designed to do. It read context, summarized, assisted. But with no guardrails on trust boundaries, it blended attacker inputs with internal memory. This wasn’t a user mistake. It wasn’t a phishing scam. It was a design flaw in the AI data pipeline itself. 🧠 The Novelty What makes this different from prior prompt injection? 1. Zero click. No action by the user. Sitting in the inbox was enough 2. Silent execution. No visible output or alerts. Invisible to the user and the SOC 3. Trusted context abuse. The assistant couldn’t distinguish between hostile inputs and safe memory 4. No sandboxing. Context ingestion, generation, and network response occurred in the same flow This wasn’t just bad prompt filtering. It was the AI behaving correctly in a poorly defined system. 🔐 Implications For CISOs, architects, and Copilot owners - read this twice. → You must assume all inputs are hostile, including passive ones → Enforce strict context segmentation. Copilot shouldn’t ingest emails, chats, docs in the same pass → Treat prompt handling as a security boundary, not just UX → Monitor agent output channels like you would outbound APIs → Require your vendors to disclose what their AI sees and what triggers it 🧭 Final Thought The next wave of breaches won’t look like malware or phishing. They will look like AI tools doing exactly what they were trained to do but in systems that never imagined a threat could come from within a calendar invite. Patch if you must. But fix your AI architecture before the next CVE hits.

Every time I call an Uber I'm reminded of AI's biggest lie. That $100 ride used to cost $3 in 2015. We got hooked on convenience while VCs subsidized our addiction. Now I'm watching the exact same playbook unfold with AI tools and most people have no idea what's coming. I keep thinking about a conversation I had with a startup founder last week. She was bragging about how her team of 3 was now doing the work of 15 people thanks to AI tools. "We're saving $800K in salaries," she said. "It's incredible." But here's what she didn't realize: She's living in the calm before the storm. 𝗪𝗲'𝗿𝗲 𝗶𝗻 𝘁𝗵𝗲 𝗨𝗯𝗲𝗿 𝟮𝟬𝟭𝟱 𝗺𝗼𝗺𝗲𝗻𝘁 𝗼𝗳 𝗔𝗜. Remember when Uber rides cost $3 across town? When they threw promo codes at us like confetti? Venture capital was bleeding money to get us addicted to convenience. Then the subsidies stopped. That $3 ride became $25, then $100 . We were hooked, so we paid. AI is following the exact same playbook, and the signs are everywhere: → OpenAI just hired a "CEO of Applications" (hello, monetization strategy) → Claude, ChatGPT, and others are still pricing at consumer rates despite enterprise-level capability → VCs have pumped $50+ billion into AI companies that need to show returns 𝗧𝗵𝗲 𝗿𝗲𝗮𝗹𝗶𝘁𝘆 𝗰𝗵𝗲𝗰𝗸 𝗶𝘀 𝗰𝗼𝗺𝗶𝗻𝗴 𝗳𝗮𝘀𝘁. When an AI tool can genuinely replace 2 full-time employees, it won't cost $20/month forever. It'll cost what those employees cost potentially $100K+ annually. Think I'm being dramatic? Look at enterprise software pricing. Salesforce charges $300/user/month. Adobe Creative Suite went from $50/month to $600/year per license. These companies price based on value delivered, not development costs. 𝗛𝗲𝗿𝗲'𝘀 𝘄𝗵𝗮𝘁 𝘀𝗺𝗮𝗿𝘁 𝗽𝗿𝗼𝗳𝗲𝘀𝘀𝗶𝗼𝗻𝗮𝗹𝘀 𝗮𝗿𝗲 𝗱𝗼𝗶𝗻𝗴 𝗿𝗶𝗴𝗵𝘁 𝗻𝗼𝘄: 𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝘄𝗵𝗶𝗹𝗲 𝗶𝘁'𝘀 𝗰𝗵𝗲𝗮𝗽. That side project you've been planning? That startup idea collecting dust? This is your window. AI development costs will never be this low again. 𝗦𝗵𝗮𝗿𝗽𝗲𝗻𝗶𝗻𝗴 𝗰𝗼𝗿𝗲 𝘀𝗸𝗶𝗹𝗹𝘀. When AI becomes prohibitively expensive for daily tasks, professionals who can write, analyze, code, and strategize without $1,000/month in AI subscriptions will command premium rates. 𝗖𝗿𝗲𝗮𝘁𝗶𝗻𝗴 𝗔𝗜-𝗮𝘀𝘀𝗶𝘀𝘁𝗲𝗱 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀 𝘁𝗵𝗮𝘁 𝗱𝗼𝗻'𝘁 𝗱𝗲𝗽𝗲𝗻𝗱 𝗼𝗻 𝗔𝗜. Learn the processes. Understand the thinking. Use AI to accelerate, not replace, your capabilities. The gold rush pricing won't last. The question is: Are you building wealth during the gold rush, or just getting addicted to cheap gold?