Advanced AI Training

Many people often ask me how to learn Agentic AI and where to start. My answer keeps evolving — because the field itself is changing every few months. What I shared six months ago helped many people get started. But today, with newer frameworks, deeper integrations, and more real-world use cases, that learning path looks different. So I’ve put together this updated AI Agents Learning Map — a structured view of how I now see this space progressing. Level 1 – Foundations This is where every learner should begin. The goal is to understand how intelligent systems are built and connected. • Large Language Models – Core models that generate and understand natural language. • Embeddings and Vector Databases – Represent meaning and context for better search and reasoning. • Prompt Engineering – Techniques to guide model responses effectively. • APIs and External Data Access – Allow models to connect to external systems and data sources. At this level, focus on understanding how LLMs interact with structured and unstructured data. Level 2 – System Capabilities At this stage, models evolve into systems. You begin combining memory, context, and reasoning to build early agent behaviors. • Context Management – Managing dialogue and maintaining state across interactions. • Memory and Retrieval – Implementing persistent storage for short- and long-term information. • Function Calling and Tool Use – Letting AI take real actions beyond text generation. • Multi-step Reasoning – Enabling sequential decision-making and logical flow. • Agent Frameworks – Using orchestration tools like LangGraph, CrewAI, and Microsoft AutoGen. This level is where isolated models start becoming intelligent systems. Level 3 – Advanced Autonomy Here, agents collaborate, plan, and execute tasks independently. This is where agentic AI truly begins. • Multi-Agent Collaboration – Building systems where agents work together with defined roles. • Agentic Workflows – Structuring processes that allow autonomous execution. • Planning and Decision-Making – Defining goals, evaluating options, and acting without human prompts. • Reinforcement Learning and Fine-tuning – Improving outcomes based on feedback and experience. • Self-Learning AI – Systems that evolve continuously as they operate. At this level, AI transitions from reactive systems to proactive problem-solvers. Why this learning map matters This map is not about tools or frameworks. It’s about progression — how engineers and organizations move from using AI to building intelligence. Mastering each level leads to better design decisions, deeper understanding, and ultimately, the ability to create autonomous, adaptive systems. Where would you place your current AI understanding on this map?

This Stanford study examined how six major AI companies (Anthropic, OpenAI, Google, Meta, Microsoft, and Amazon) handle user data from chatbot conversations.  Here are the main privacy concerns. 👀 All six companies use chat data for training by default, though some allow opt-out 👀 Data retention is often indefinite, with personal information stored long-term 👀 Cross-platform data merging occurs at multi-product companies (Google, Meta, Microsoft, Amazon) 👀 Children's data is handled inconsistently, with most companies not adequately protecting minors 👀 Limited transparency in privacy policies, which are complex and hard to understand and often lack crucial details about actual practices Practical Takeaways for Acceptable Use Policy and Training for nonprofits in using generative AI: ✅ Assume anything you share will be used for training - sensitive information, uploaded files, health details, biometric data, etc. ✅ Opt out when possible - proactively disable data collection for training (Meta is the one where you cannot) ✅ Information cascades through ecosystems - your inputs can lead to inferences that affect ads, recommendations, and potentially insurance or other third parties ✅ Special concern for children's data - age verification and consent protections are inconsistent Some questions to consider in acceptable use policies and to incorporate in any training. ❓ What types of sensitive information might your nonprofit staff  share with generative AI?  ❓ Does your nonprofit currently specifically identify what is considered “sensitive information” (beyond PID) and should not be shared with GenerativeAI ? Is this incorporated into training? ❓ Are you working with children, people with health conditions, or others whose data could be particularly harmful if leaked or misused? ❓ What would be the consequences if sensitive information or strategic organizational data ended up being used to train AI models? How might this affect trust, compliance, or your mission? How is this communicated in training and policy? Across the board, the Stanford research points that developers’ privacy policies lack essential information about their practices. They recommend policymakers and developers address data privacy challenges posed by LLM-powered chatbots through comprehensive federal privacy regulation, affirmative opt-in for model training, and filtering personal information from chat inputs by default. “We need to promote innovation in privacy-preserving AI, so that user privacy isn’t an afterthought." How are you advocating for privacy-preserving AI? How are you educating your staff to navigate this challenge? https://lnkd.in/g3RmbEwD

I just finished reading three recent papers that every Agentic AI builder should read. As we push toward truly autonomous, reasoning-capable agents, these papers offer essential insights, not just new techniques, but new assumptions about how agents should think, remember, and improve. 1. MEM1: Learning to Synergize Memory and Reasoning Link: https://bit.ly/4lo35qJ Trains agents to consolidate memory and reasoning into a single learned internal state, updated step-by-step via reinforcement learning. The context doesn’t grow, the model learns to retain only what matters. Constant memory use, faster inference, and superior long-horizon reasoning. MEM1-7B outperforms models twice its size by learning what to forget. 2. ToT-Critic: Not All Thoughts Are Worth Sharing Link: https://bit.ly/3TEgMWC A value function over thoughts. Instead of assuming all intermediate reasoning steps are useful, ToT-Critic scores and filters them, enabling agents to self-prune low-quality or misleading reasoning in real time. Higher accuracy, fewer steps, and compatibility with existing agents (Tree-of-Thoughts, scratchpad, CoT). A direct upgrade path for LLM agent pipelines. 3. PAM: Prompt-Centric Augmented Memory Link: https://bit.ly/3TAOZq3 Stores and retrieves full reasoning traces from past successful tasks. Injects them into new prompts via embedding-based retrieval. No fine-tuning, no growing context, just useful memories reused. Enables reasoning, reuse, and generalization with minimal engineering. Lightweight and compatible with closed models like GPT-4 and Claude. Together, these papers offer a blueprint for the next phase of agent development: - Don’t just chain thoughts; score them. - Don’t just store everything; learn what to remember. - Don’t always reason from scratch; reuse success. If you're building agents today, the shift is clear: move from linear pipelines to adaptive, memory-efficient loops. Introduce a thought-level value filter (like ToT-Critic) into your reasoning agents. Replace naive context accumulation with learned memory state (a la MEM1). Storing and retrieving good trajectories, prompt-first memory (PAM) is easier than it sounds. Agents shouldn’t just think, they should think better over time.

The 2025 e-Conomy SEA report by Google, Bain & Company, and Temasek How will AI adoption reshape key digital sectors? The adoption of Artificial Intelligence (AI) is set to reshape key digital sectors by fundamentally changing consumer behaviour, driving operational efficiencies, and creating new competitive frontiers for platforms. Here is how AI is specifically reshaping key digital sectors: 🔹Consumer Experience and Discovery (Across all Digital Sectors) 1) Redefining the Journey AI is transforming the path to purchase, moving away from traditional linear searches towards a dynamic, AI-powered discovery process. 2) Intelligent Recommendations AI acts as an intelligent reductive filter, helping users narrow down choices. For example, 74% of consumers find smart recommendations and personalised feeds helpful, and 45% are motivated by AI saving time on research and comparisons. 3) Sophisticated Search Consumers are using tools like AI-powered search and multimodal inputs (e.g., visual search) to handle longer and more complex queries. 🔹E-commerce 1) Driving Conversion AI has a growing influence on purchase decisions. 62% of SEA consumers report that AI-powered features, such as hyper-personalised product recommendations, have influenced their shopping. 2) New Competitive Frontier Platforms are using AI to power these product recommendations, making AI capability a critical competitive advantage. 🔹Transport 1) Autonomous Disruption Ongoing autonomous vehicle (AV) pilots signal a major disruptive opportunity. 2) Economics of Robotaxis The economics of robotaxis have the potential to outperform human drivers within three to five years due to factors like reduced manufacturing costs and improved vehicle utilisation. 🔹Online Media (Advertising) 1) Improved Ad Performance AI is being used to improve ad performance and alter how users engage with advertisements. 🔹Digital Financial Services (DFS) 1) Agentic Transactions The future goal is agentic AI-driven transactions, where AI agents autonomously orchestrate purchases. This requires developing robust infrastructure for identity management, interoperability, and seamless payment verification. 2) Local Innovation Since Southeast Asia (SEA) is not a card-driven market, local innovation is necessary to tailor agentic payment infrastructure to leverage ewallets and interoperable QR codes. 🔹Enterprise Transformation (General Operational Impact) 1) Operational Efficiency AI is commonly used to improve efficiency across front office, middle office, and back office functions. For example, AI models have been implemented for customer service, operations, and financial reporting. 2) Measurable Value Early adopters are realising business value beyond productivity boosts, with large digital players already implementing hundreds of AI models for cost savings and value creation. 👇 Click the link in the comments to download the full report.

A clear path into AI engineering using 10 GitHub repos Step-by-step plan you can follow and show as proof of work Foundations 1. Learn the basics of machine learning and deep learning • ML for Beginners, AI for Beginners Output: 3 small projects with short READMEs that explain the goal, data, and result. Go deeper 2) Build neural nets from scratch • Neural Networks: Zero to Hero Output: a tiny GPT trained on a toy dataset, plus notes on what you changed and why. Read papers in code 3) Study real architectures by walking through annotated implementations • DL Paper Implementations Output: pick one model and re-implement a minimal version. Write what you simplified. Ship real software 4) Move from notebooks to apps and services • Made With ML Output: refactor one project with a simple API, tests, and a one-click run script. Work with LLMs 5) Learn the core pieces end to end • Hands-on LLMs Output: a basic RAG app (retrieval augmented generation) that answers questions on a small knowledge base. Make RAG better 6) Compare advanced techniques • Advanced RAG Techniques Output: run A/B tests on 3 settings and report latency, accuracy, and cost in a table. Learn agents 7) Build simple agents that take steps toward a goal • AI Agents for Beginners Output: an agent that checks a site, writes a summary, and files a ticket. Take agents toward production 8) Add memory, orchestration, and basic security • Agents Towards Production Output: logging, retry logic, and input checks. Note what fails and how you fixed it. Round out your portfolio 9) Adapt working examples • AI Engineering Hub Output: 2 more apps that solve real tasks, each with a clear demo and setup guide. How to pace this • One repo per week is a good rhythm. • Keep a single repo called “ai-engineering-journey” with subfolders per step. • After each step, post a short write-up with a 30-second screen recording. What hiring managers look for • Working code that runs on first try. • Clear README, data source, and limits. • Small tests and a simple eval, even if manual. • Changelog that shows steady progress. Save this and start with step 1 today. Repos and links 1. ML for Beginners — https://lnkd.in/dQ6nAJRC 2. AI for Beginners — https://lnkd.in/dXwJJjMm 3. Neural Networks: Zero to Hero — https://lnkd.in/dagQ3kmA 4. DL Paper Implementations — https://lnkd.in/dyw54m73 5. Made With ML — https://lnkd.in/duHjr2CY 6. Hands-On Large Language Models — https://lnkd.in/dxEGzsgc 7. Advanced RAG Techniques — https://lnkd.in/dd2TKA5P 8. AI Agents for Beginners — https://lnkd.in/deznrHdf 9. Agents Towards Production — https://lnkd.in/dz-WgU-3 10. AI Engineering Hub — https://lnkd.in/d9cNqy7c

AI mastery isn’t about learning everything. It’s about knowing what to learn next. Jumping into advanced models without foundations slows you down. Staying in basics too long keeps you stuck. The real progress comes from moving through the right layers at the right time. That’s what separates experimentation from mastery. Here’s a complete roadmap to mastering AI in 2026 - Foundations Start with Python, data structures, math, and statistics to build real understanding. - Machine learning loop Learn core ML concepts, evaluation techniques, and how to iterate on models. - Deep learning Understand neural networks, CNNs, RNNs, transformers, and modern architectures. - Generative AI Work with LLMs, prompt engineering, RAG, embeddings, and multimodal systems. - Applied AI Build real use cases across domains like NLP, vision, recommendation systems, and forecasting. - Tooling and deployment Move models to production with MLOps, APIs, cloud deployment, and monitoring. - Ethics and safety Design systems that are fair, explainable, secure, and aligned with regulations. - Career and ecosystem Turn skills into impact through projects, open source, portfolios, and real opportunities. AI isn’t one skill. It’s a stack. And each layer unlocks the next. Skip layers, and things don’t work. Build them right, and everything compounds. Where are you currently in this roadmap?

We did an experiment: 36 leaders. Same data. Different use of AI. Completely different outcomes. Last week, I was a guest lecturer in Dr. H /Hila Lifshitz (Hán, X也)’s Artificial Intelligence for Managers program, where we conducted a live experiment with 36 senior leaders, including CEOs, VPs, and CIOs from manufacturing, healthcare, public sector, and tech. The goal: to explore how different ways of working with AI change the quality of decisions. Each group received the same set of startup pitch decks, but with different AI access: Group A used AI from the start. Group B used AI only in the last 15 minutes. Group C used no AI until the end. The results were eye-opening. Here’s what we learned fast: → Prompts = process Give AI a role (“Act as a seed-stage angel with 100 investments”), set step-by-step criteria (team → market → moat → risk), and finish with a devil’s advocate challenge. → Stay in control Use AI as analyst and coach, but you make the final call. → Match the mode to the moment: ↳ Sentry (guardrails first) for high-risk or regulated work ↳ Cyborg (human + AI intertwined) for complex decision-making ↳ Autopilot (delegate and verify) only for low-risk, repeatable tasks In the second part of the lecture, I shared a practical framework I call “your new AI toolbox”: 1. NotebookLM for board prep Upload materials, ask for blind spots and key questions. 2. AI Studio for difficult conversations Draft, role-play, and refine your language. 3. Gemini for talent Recruit your best advisory board with personalized outreach. 4. AI Studio Live Share your screen, co-prompt, and capture real-time decisions. 5. Feedback loop Let AI critique your work, then decide what to keep or drop. Why it matters: same people, same data. But leaders who know how to use AI thoughtfully make faster, clearer, and more confident decisions. If your team is navigating how to integrate AI into daily decisions, feel free to DM me and consult with me. ♻️ Repost if you found this helpful. 🔔 Follow me, Yuval Passov, for weekly insights on startup growth, founder wellness, and leadership in the age of AI.

Today I got this mail from Meta saying they use my data to train AI under “legitimate interest.” 😱 Really? They surely have an interest — but is it really legitimate? Short answer: absolutely not. This feels like a stretch of the law at best, and most likely just doing what they want. I’m sure the majority of people just receive these emails and assume it’s all OK. But this isn’t a minor update to a privacy policy, this is as saying “we don’t even need your consent so process your data, we do it because it’s good for us”. Let’s be clear: “Legitimate interest” (Article 6(1)(f) GDPR) is a lawful basis for processing personal data, but only in very restrictive circumstances. It’s not a free pass just because a company wants to do something. To be considered legitimate, the processing must pass a strict 3-step test: 1. Purpose test – Is there a legitimate, specific interest behind the processing? That a company has a benefit from the activity is NOT enough ! 2. Necessity test – Is this processing necessary to achieve that interest? Aren’t there any other (even suboptimal) ways of achieving the objective ? 3. Balancing test – Does this interest override the fundamental rights and freedoms of the data subject? I am absolutely not against companies training AI models but we shall acknowledge that doing this, especially on user data, is a high-impact activity. Misusing a legal ground for processing is not just wrong — it’s fooling people. It undermines trust and turns GDPR into a checkbox exercise. #GDPR #Privacy #AI #DataProtection

Without doubt, AI innovation is changing the world — across government, business, education, and our everyday lives. Our global AI for Good Impact Report, created by Deloitte and the International Telecommunication Union, explores how AI is helping to advance the United Nations Office for Sustainable Development (UNOSD) Goals (SDG), details where progress is being made, and discusses the risks and challenges associated with AI.     94% of global business leaders view AI as critical to their organizations’ success in the next five years, and many organizations are seeing incredible success from their investments and innovations.  Intriguing applications of AI and GenAI are already hard at work. Here are a few of my favorite examples across industries:    🏥 In healthcare, AI is shifting the focus from treating diseases to early diagnosis and prevention, as smart algorithms identify patterns in digital data and images. Advancements are already making headway in stroke care, cardiology, oncology, and other fields.    📚In education, AI is enhancing the learning experience and improving educational outcomes for students. Intelligent Tutoring Systems use AI to gather data on students, assess their progress, and provide real-time feedback.    🌱 In agriculture, AI helps address food security challenges influenced by climate change. It aids in making real-time crop-placement decisions, monitoring crop health, and enhancing supply chain processes.    💡 In energy, AI models can predict energy consumption patterns, leading to better balancing of supply and demand, reducing waste, and optimizing energy procurement strategies.    🏦 In financial services, AI algorithms identify fraud cases and help prevent financial crimes. AI-driven tools like robo-advisors, digital wallets, and chatbots help to make financial services more accessible to underserved and previously unserved communities.    Explore the report for more innovative use cases that align with SDG goals, as well as recommendations for addressing AI challenges and building an effective governance framework: https://deloi.tt/4hhv6iA 

Imagine training for your first triathlon by just sitting in a classroom. That's essentially how we prepare managers to lead today. We gather people with wildly different experience, roles, and functions into the same room (or sometimes the same Zoom). We teach the same frameworks at the same pace. Then we expect them to perform under pressure with little to no practice or personalized coaching. For a long time, most of us accepted this messy learning process as the price of entry into leadership. Expecting personalized support, like leadership coaching, was unrealistic. Most organizations were insufficiently resourced to offer it to every manager, so those opportunities, if available at all, were reserved for the most senior leaders. What we haven't fully accounted for is the hidden cost of this approach. When early and mid-career managers lack practice and coaching, the consequences show up in subtle yet costly ways: → High-stakes conversations that land poorly with their colleagues → Talent that goes underdeveloped on their team → Conflicts that erode trust and team culture …and many other avoidable missteps that could be prevented. What’s exciting: AI is offering us an opportunity to reimagine how we develop leaders at scale. We're building this future at Rising Team with Supermanager, a new tech stack for leaders that combines: ✅ A leadership trainer (a personal trainer for your "leadership muscle") - https://lnkd.in/gMaAK2sS ✅ A roleplay coach - https://lnkd.in/g-FQ3Hf7 ✅ A team-building facilitator - https://lnkd.in/gDqWKNB8 This is the new era of leadership development. Managers at every level can get completely personalized, guided support and practical tools to lead their teams well, in far less time than it used to require. And companies can customize these tools to help every leader learn and practice their company-specific leadership frameworks and behaviors. No AI will make anyone a perfect leader. Leadership will always be a messy, human endeavor on some level. What's different now is that every manager can personalized support from the start. Just like elite athletes have their own personal trainers to help them reach key outcomes, every manager can now afford one too. Such a fun time to be building in this space!