Robotics In Science Projects

I’ve worked on AI my whole life because I’ve always believed it could unlock the ability to answer some of the biggest and most intractable problems in science. Our first big science breakthrough happened five years ago when we announced our solution to the protein structure prediction problem: AlphaFold 2. It has been incredible to see its impact since then. More than 3 million researchers across 190 countries have used this tool for disease understanding, drug discovery and more. And it was an honour of a lifetime for our work to be recognised last year with a Nobel Prize. One of our greatest ambitions is for AI to aid in accelerating drug design and help cure all diseases. This is what led me to found Isomorphic Labs, which is already making amazing progress. We’ve also expanded AlphaFold to predict the interactions of all of life’s molecules. But AlphaFold represents more than a solution to a biological puzzle. It demonstrated how AI can crack ‘root node’ problems - where a single breakthrough unlocks entire new avenues of research. It is a critical step towards a long-held dream of mine: building a virtual cell. Imagine running ‘in silico’ experiments orders of magnitude faster than in a wet lab. Scientists could rapidly test hypotheses, model complex pathways and see how a drug affects a cell. It would be an incredible boon not only for fundamental biology but also for medicine. Although for me, AlphaFold was never just about biology. It was the first major proof point for a much larger thesis: that AI could be the ultimate tool for advancing science. By processing data or helping us come up with new hypotheses, I think AI will help us tackle some of humanity’s greatest challenges and answer fundamental questions about the universe. From materials design to fusion energy to mathematics, I believe we’re on the cusp of a new golden age of discovery. We’re just getting started. Read more about AlphaFold’s impact: https://lnkd.in/eNeqxqQp

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?

Built for those serious about doing AI, not just talking about it. From strong foundations → applied intelligence → ethical deployment → career readiness. 🟥 Line 1: Foundations Station [ Your ability to build reliable systems starts here. ] → Python, NumPy, Pandas → Probability, linear algebra, stats, optimization → Data cleaning, Git, command line If you're shaky here, everything downstream becomes harder. -> Foundations aren’t exciting, they’re essential. 🟨 Line 2: Machine Learning Loop [ Where models meet iteration. ] → Regression, classification, clustering → Feature engineering, ensemble methods, cross-validation → Hyperparameter tuning, ROC/AUC, confusion matrix This is the core ML cycle, build, evaluate, repeat. -> builds your intuition and helps you avoid classic traps like data leakage and overfitting. 🟪 Line 3: Deep Learning Express Scale your models, and your responsibilities. → CNNs, RNNs, attention mechanisms, Transformers → GANs, Autoencoders, Dropout, BatchNorm → GPTs, in-context learning, LoRA, prompt engineering This is where compute meets complexity, debugging, reproducibility, and efficiency start to matter deeply. 🔵 Line 4: Generative AI Hub [ 2025’s busiest intersection. ] → LLMs (open source, API-based, fine-tuned) → RAG systems with vector databases → Multimodal models (text, image, audio), tool-use agents → LangChain, Mistral AI, Gemini, CLIP, Speech AI → Agentic frameworks like LangGraph, AutoGen, CrewAI This line moves fast, but the best builders ground it in systems thinking. 🤎 Line 5: Applied AI Sector [ Where “model” becomes “product.” ] → Docker, CI/CD, FastAPI, Streamlit → MLOps: PromptFlow, Vertex AI, Hugging Face Hub → Model monitoring: Evidently AI , Arize AI, WhyLabs → Inference optimization: caching, quantization, cost/latency tradeoffs Shipping AI is not just about accuracy, it’s about reliability, traceability, feedback, and scale. 🟫 Line 6: Tooling & Deployment Route [ Engineering-grade infrastructure for real-world AI. ] → Kubernetes, container scaling, TorchServe → ONNX, model conversion, portability → Automated testing, model versioning, rollout strategies → Observability and agent monitoring: Opik by Comet, LangSmith, OpenTelemetry The hardest part of AI is often everything after the model works. 🟧 Line 7: Ethics & Safety Line [ Alignment is not optional. ] → Explainability (XAI, SHAP, LIME), fairness, bias audits → Privacy-preserving ML: differential privacy, federated learning → Robustness, adversarial defense, red teaming → AI safety tooling: GPT Guard ,model constraints, output filters → Governance, policy, and risk-aware deployment This isn’t a side-track, it’s the spine of modern AI systems. 🟩 Line 8: Career Launchpad [ Turn skills into signal. ] → GitHub projects, Kaggle, blogging, community contributions → Open-source agents, portfolio demos, real app builds If you're a learner, this might orient you. If you're an expert, I'd love to hear what you'd add.

If you are building AI agents or learning about them, then you should keep these best practices in mind 👇 Building agentic systems isn’t just about chaining prompts anymore, it’s about designing robust, interpretable, and production-grade systems that interact with tools, humans, and other agents in complex environments. Here are 10 essential design principles you need to know: ➡️ Modular Architectures Separate planning, reasoning, perception, and actuation. This makes your agents more interpretable and easier to debug. Think planner-executor separation in LangGraph or CogAgent-style designs. ➡️ Tool-Use APIs via MCP or Open Function Calling Adopt the Model Context Protocol (MCP) or OpenAI’s Function Calling to interface safely with external tools. These standard interfaces provide strong typing, parameter validation, and consistent execution behavior. ➡️ Long-Term & Working Memory Memory is non-optional for non-trivial agents. Use hybrid memory stacks, vector search tools like MemGPT or Marqo for retrieval, combined with structured memory systems like LlamaIndex agents for factual consistency. ➡️ Reflection & Self-Critique Loops Implement agent self-evaluation using ReAct, Reflexion, or emerging techniques like Voyager-style curriculum refinement. Reflection improves reasoning and helps correct hallucinated chains of thought. ➡️ Planning with Hierarchies Use hierarchical planning: a high-level planner for task decomposition and a low-level executor to interact with tools. This improves reusability and modularity, especially in multi-step or multi-modal workflows. ➡️ Multi-Agent Collaboration Use protocols like AutoGen, A2A, or ChatDev to support agent-to-agent negotiation, subtask allocation, and cooperative planning. This is foundational for open-ended workflows and enterprise-scale orchestration. ➡️ Simulation + Eval Harnesses Always test in simulation. Use benchmarks like ToolBench, SWE-agent, or AgentBoard to validate agent performance before production. This minimizes surprises and surfaces regressions early. ➡️ Safety & Alignment Layers Don’t ship agents without guardrails. Use tools like Llama Guard v4, Prompt Shield, and role-based access controls. Add structured rate-limiting to prevent overuse or sensitive tool invocation. ➡️ Cost-Aware Agent Execution Implement token budgeting, step count tracking, and execution metrics. Especially in multi-agent settings, costs can grow exponentially if unbounded. ➡️ Human-in-the-Loop Orchestration Always have an escalation path. Add override triggers, fallback LLMs, or route to human-in-the-loop for edge cases and critical decision points. This protects quality and trust. PS: If you are interested to learn more about AI Agents and MCP, join the hands-on workshop, I am hosting on 31st May: https://lnkd.in/dWyiN89z If you found this insightful, share this with your network ♻️ Follow me (Aishwarya Srinivasan) for more AI insights and educational content.

These students were challenged to build a robot capable of scaling a vertical wall in record time, a task that mirrors real engineering problems faced by aerospace, manufacturing, and autonomous robotics teams worldwide. Will you be able to win? To succeed, each group had to master a full engineering cycle: 🔹 Mechanical design: calculating torque, motor ratios, surface grip, and center of gravity 🔹 Material selection: optimizing weight-to-strength ratios (aluminum, carbon fiber, 3D-printed composites) 🔹 Control algorithms: PID tuning, sensor feedback loops, and stability control 🔹 Energy efficiency: maximizing battery output and motor load under vertical stress 🔹 Failure analysis: testing, measuring, iterating, and rebuilding And this isn’t just academic. Challenges like this reflect real-world robotics breakthroughs: 📌 NASA’s Valkyrie robot uses similar balance and grip logic for climbing unstable surfaces in disaster response missions. 📌 Boston Dynamics spent over 10 years perfecting the control systems students experiment with on a smaller scale. 📌 Industrial robots used in warehouses face the same physics constraints — friction, payload, torque, and trajectory planning. 📌 Spacecraft design teams use identical modeling principles to ensure robots can maneuver on asteroids with extremely low gravity. And student innovation is accelerating fast: 🚀 University robotics teams report up to 40% faster prototype cycles thanks to rapid 3D printing. 🚀 High-school robotics programs now routinely use LIDAR, machine vision, and ROS, tools once limited to major research labs. 🚀 Over 90% of global robotics firms hire from hands-on competition pipelines like FIRST, VEX, and Eurobot. 🚀 The educational robotics market is growing 17% annually, driven by demand for engineers who can build, code, and troubleshoot under real conditions. Competitions like this create the mindset industry needs: not memorization, but building, breaking, fixing, optimizing — the same loop that drives innovation at the world’s leading tech companies. One student prototype at a time, the future of automation, AI, and robotics is already climbing upward. 🚀🤝 #Engineering #Robotics #STEM #Innovation #Education #AI #Automation #FutureOfWork #NextGenTech

Very promising! A new open-source platform for research on Human-AI teaming from Duke University uses real-time human physiological and behavioral data such as eye gaze, EEG, ECG, across a wide range of test situations to identify how to improve Human-AI collaboration. Selected insights from the CREW project paper (link in comments): 💡 Comprehensive Design for Collaborative Research. CREW is built to unify multidisciplinary research across machine learning, neuroscience, and cognitive science by offering extensible environments, multimodal feedback, and seamless human-agent interactions. Its modular design allows researchers to quickly modify tasks, integrate diverse AI algorithms, and analyze human behavior through physiological data. 🔄 Real-Time Interaction for Dynamic Decision-Making. CREW’s real-time feedback channels enables researchers to study dynamic decision-making and adaptive AI responses. Unlike traditional offline feedback systems, CREW supports continuous and instantaneous human guidance, crucial for simulating real-world scenarios, and making it easier to study how AI can best align with human intentions in rapidly changing environments. 📊 Benchmarking Across Tasks and Populations. CREW enables large-scale benchmarking of human-guided reinforcement learning (RL) algorithms. By conducting 50 parallel experiments across multiple tasks, researchers could test the scalability of state-of-the-art frameworks like Deep TAMER. This ability to scale the study of the interaction of human cognitive traits with AI training outcomes is a first. 🌟 Cognitive Traits Driving AI Success. The study highlighted key human cognitive traits—spatial reasoning, reflexes, and predictive abilities—as critical factors in enhancing AI performance. Overall, individuals with superior cognitive test scores consistently trained better-performing agents, underscoring the value of understanding and leveraging human strengths in collaborative AI development. Given that Humans + AI should be at the heart of progress, this platform promises to be a massive enabler of better Human-AI collaboration. In particular, it can help in designing human-AI interfaces that apply specific human cognitive capabilities to improve AI learning and adaptability. Love it!

🔍 Everyone’s discussing what AI agents are capable of—but few are addressing the potential pitfalls. IBM’s AI Ethics Board has just released a report that shifts the conversation. Instead of just highlighting what AI agents can achieve, it confronts the critical risks they pose. Unlike traditional AI models that generate content, AI agents act—they make decisions, take actions, and influence outcomes. This autonomy makes them powerful but also increases the risks they bring. ---------------------------- 📄 Key risks outlined in the report: 🚨 Opaque decision-making – AI agents often operate as black boxes, making it difficult to understand their reasoning. 👁️ Reduced human oversight – Their autonomy can limit real-time monitoring and intervention. 🎯 Misaligned goals – AI agents may confidently act in ways that deviate from human intentions or ethical values. ⚠️ Error propagation – Mistakes in one step can create a domino effect, leading to cascading failures. 🔍 Misinformation risks – Agents can generate and act upon incorrect or misleading data. 🔓 Security concerns – Vulnerabilities like prompt injection can be exploited for harmful purposes. ⚖️ Bias amplification – Without safeguards, AI can reinforce existing prejudices on a larger scale. 🧠 Lack of moral reasoning – Agents struggle with complex ethical decisions and context-based judgment. 🌍 Broader societal impact – Issues like job displacement, trust erosion, and misuse in sensitive fields must be addressed. ---------------------------- 🛠️ How do we mitigate these risks? ✔️ Keep humans in the loop – AI should support decision-making, not replace it. ✔️ Prioritize transparency – Systems should be built for observability, not just optimized for results. ✔️ Set clear guardrails – Constraints should go beyond prompt engineering to ensure responsible behavior. ✔️ Govern AI responsibly – Ethical considerations like fairness, accountability, and alignment with human intent must be embedded into the system. As AI agents continue evolving, one thing is clear: their challenges aren’t just technical—they're also ethical and regulatory. Responsible AI isn’t just about what AI can do but also about what it should be allowed to do. ---------------------------- Thoughts? Let’s discuss! 💡 Sarveshwaran Rajagopal

🔥 Ethics in AI-enabled medical devices is not an abstract debate. It is governance by design. In AI-enabled medical mobile health devices, ethics constitutes a governance-by-design framework that structures system behaviour and user interaction in domains where legal boundaries are evolving, indeterminate, or insufficiently expressive of the principles they intend to uphold. ⚖️ Even where permissible boundaries are formally defined, they may fail to capture proportionality, fairness, or human impact in adaptive systems. Ethics therefore performs both a pre-regulatory and interpretive function — ensuring that device architecture reflects the spirit as well as the letter of the law. Regulatory silence does not diminish responsibility. Formal compliance does not exhaust it. 🔖 With that lens in mind, I highly recommend "Teaching AI Ethics: A Guide for Educators" by Leon Furze. It is a remarkably practical resource for anyone teaching — or trying to structure thinking around — AI ethics. The book explores key domains including: 🔹Bias 🔹Environment 🔹Truth 🔹Copyright 🔹Privacy 💎 Despite not in the narrower regulatory ethics sense, I found particularly interesting the chapters on social chatbots, power concentration and the hidden workforce. A few reflections particularly resonated: 1️⃣ Copyright We are no longer debating hypotheticals. The Stability AI vs Getty Images case showed how far legal clarity still has to go. Courts may rule that models do not “store” copyrighted works, yet broader consensus questions whether algorithmic weights encode protected material. Copyright is becoming a volatile and imperfect proxy for ethical compliance, especially in multimodal GenAI and mixed authorship contexts. 2️⃣ Privacy Privacy now extends well beyond consent mechanisms. Retroactive use of training data, bystander privacy, national sovereignty, and the tension between GDPR data minimisation and large-scale model training all expose ethical boundaries that law alone does not resolve. 3️⃣ Conversational interfaces In healthcare, conversational components and adaptive interfaces further complicate emotional and relational boundaries — even in certified medical devices where boundaries must be clear and respected. 4️⃣ Power & the hidden workforce Behind AI systems lies invisible labour and increasing concentration of power. The question of alternative development models that distribute capability and accountability more broadly is not theoretical — it is structural. What this guide does exceptionally well is move ethics beyond slogans and into structured inquiry. For those working in adaptive AI, medical devices, digital health governance, or standards development — it is an excellent teaching companion and a useful provocation. Ethics, properly understood, is not about slowing innovation. It is about stabilising it. 📌 We are working to solve in this space. #AIethics #DigitalHealth #MedicalDevices #Governance #AI #Standards

We're living in a world where machines are increasingly making decisions on our behalf. But have we stopped to consider the moral implications of this new reality? 😧 A recent development has brought this question to the forefront: Trump's revocation of Biden's executive order addressing AI risks. This move highlights the need for a unified approach to regulating AI and ensuring its ethical use. 💯 A Deloitte survey found that 80% of executives believe AI will revolutionize their businesses, but only 30% have implemented AI ethics guidelines. This disparity underscores the urgency of prioritizing ethical AI. ✅ 🤔 Leaders should reflect on: 1️⃣ Biases in AI systems: What biases are we inadvertently coding into my AI systems? 2️⃣ Transparency: How transparent are we being about the data we are collecting and the decisions we are making? 3️⃣ Human impact: What are the long-term consequences of prioritizing efficiency over empathy? 💡 Tips for Embracing Ethical AI: 👉 Design for transparency: Build systems that explain their decision-making processes. 👉 Prioritize fairness: Regularly audit your algorithms for bias and take corrective action. 👉 Consider the human impact: Don't just optimize for efficiency; think about the people affected by your AI. The future of AI depends on our willingness to confront the ethics of code. Leaders need to choose to build a world where machines serve humanity, not the other way around. #EthicalAI #AIForGood #FutureOfWork #thoughtleadership #thethoughtleaderway

Five years ago, AlphaFold solved the protein structure prediction problem at CASP14, cracking a 50-year grand challenge in biology. It has been an absolute honour and privilege to have been part of this journey alongside Demis and John. Over 3 million researchers across 190 countries have since used AlphaFold to predict the structure of more than 200 million proteins. The impact spans from revealing apoB100's structure, advancing heart disease research, to supporting endangered honeybee conservation in Europe. Protein structure prediction was the root node problem in structural biology. By solving it, we opened up entirely new avenues for discovery. What AlphaFold demonstrated is that AI can accelerate scientific progress when applied to the right foundational challenges. We've since expanded this approach across biology. AlphaMissense and AlphaGenome are helping researchers understand genetic mutations and disease. AlphaProteo is designing new protein binders for targets in cancer and diabetes. We're applying similar thinking to challenges in fusion energy, materials discovery and climate science. Today, we're sharing The Thinking Game, following our team through the journey that made AlphaFold possible. To understand more about AlphaFold's impact, see the blog here: https://lnkd.in/eiPSAeKc #AlphaFold #AIforScience