Crow Intelligence

Crow Intelligence

AI is just a tool.  To use it effectively, you must understand how humans think and communicate. We know the strengths of both natural and artificial intelligence and how to combine them for optimal results. By bridging cognitive science and AI, we create solutions that enhance human capabilities and ensure seamless interaction.

Our Approach

Just as a well-designed tool feels like an extension of your hand, AI should feel like an extension of human intelligence. The best AI systems are built on two key principles:

Human Cognition

Understanding human thought and language ensures AI integrates seamlessly with natural cognitive processes.

Advanced AI Engineering

Cutting-edge AI technology, designed with cognitive awareness, creates powerful and intuitive systems.

Are you interested?

✉️ hello@crowintelligence.org

Blog

  • Managing Python Environments: pyenv and uv Tutorial (Data Science Engineering Gap Part 1)

    Managing Python Environments: pyenv and uv Tutorial (Data Science Engineering Gap Part 1)

    This is the second post in a series about bridging the gap from beginner programmer to advanced data science practitioner. These aren’t programming concepts – they’re software engineering practices that enable you to build robust, maintainable systems.

    How to Fix the “Works On My Machine” Problem in Python

    You’ve written some Python code that works perfectly on your laptop. You share it with a colleague, and suddenly nothing runs. Or worse – you come back to your own project from last year, and it’s completely broken. Python has been updated, some packages followed the new version, others didn’t, and your carefully crafted solution is now a pile of import errors.

    This isn’t a hypothetical scenario. It’s the daily reality of working with Python without proper environment management.

    I’ve seen this play out in painful ways. A colleague once spent hours trying to figure out why a package was running slowly, only to discover that the original implementation used PyPy (a super-fast Python implementation), but nobody had documented this crucial detail. Another project mysteriously failed because one developer used conda’s Python, another used the system Python, and a third had installed vanilla Python from python.org. Same code, three different Python installations, three different sets of problems.

    The fundamental issue: Python isn’t just Python. There are different versions (3.10, 3.11, 3.12), different implementations (CPython, PyPy, Jython), and countless package versions that may or may not work together. Without managing these variables explicitly, reproducibility becomes impossible.

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  • The Data Science Engineering Gap: Part 0 – Your Development Environment

    The Data Science Engineering Gap: Part 0 – Your Development Environment

    This is the first post in a series about bridging the gap from beginner programmer to advanced data science practitioner. This transition isn’t just about learning more Python – it’s about adopting the software engineering practices and tools that enable you to build robust, maintainable systems.

    The Hidden Complexity of Professional Practice

    Here’s what nobody tells you about becoming an advanced data science practitioner: the hardest part isn’t mastering algorithms or learning new libraries. It’s developing the software engineering discipline that separates beginners from professionals.

    You can solve problems with Python. You understand pandas, numpy, and scikit-learn. You might even know some deep learning frameworks. But there’s still a massive gap between “I can write code that works” and “I can build systems that others can use, maintain, and extend.”

    This gap isn’t about programming knowledge – it’s about engineering practices. And honestly? It’s complex and takes time to master. We’re talking about a completely different skillset from the algorithmic thinking you’ve been developing. These are the practices that make the difference between code that works once on your machine and code that works reliably for everyone.

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  • The Limits of Algorithmic Economic Planning: Why Data Can’t Replace Democracy

    The Limits of Algorithmic Economic Planning: Why Data Can’t Replace Democracy

    TL;DR

    Big Data and AI cannot solve the fundamental problems that have always plagued comprehensive social planning. Historical failures (Soviet shortages), philosophical limits (theory underdetermination), and reflexive feedback loops still apply to algorithmic systems. While machine learning excels in narrow, stable domains like meteorology and industrial optimization, it distorts complex social systems when given sweeping control over economic coordination. The dream of replacing democratic deliberation with algorithmic optimization ignores essential insights from economics, philosophy, and history about the nature of social knowledge and human choice.

    China’s government is building the world’s most ambitious experiment in data-driven governance. Under its new digital ID system, citizens submit facial scans and personal information to police databases, then use anonymized identities to access online services. The state maintains a comprehensive ledger of every person’s digital activity while companies see only streams of anonymous data. Chinese planners envision this as creating a unified national “data ocean” – treating data as a factor of production alongside labor, capital, and land. The goal is to harness unprecedented information flows to optimize economic coordination and social management through algorithmic systems.

    The dream of scientific management refuses to die. From Soviet planning bureaus to today’s tech evangelists promising “real-time economics,” each generation rediscovers the appeal of replacing messy human judgment with algorithmic precision.

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  • Navigating Industry Transitions: Books That Helped Me Lead Data Science Across Domains

    Navigating Industry Transitions: Books That Helped Me Lead Data Science Across Domains

    If you’re a data scientist stepping into leadership roles or moving between industries, this post is for you.

    Leading data science teams across different industries has taught me that technical expertise alone isn’t enough—each domain comes with its language, stakeholders, and business logic. Over the years, I’ve moved from enterprise search to fintech/regtech, and now to social media analytics for the FMCG sector. Each transition meant learning not just new technical challenges, but entirely different ways of thinking about business problems.

    As a head of data science, I’ve discovered that the most challenging part of these transitions isn’t adapting algorithms or learning new tools—it’s understanding how each industry operates and communicating effectively with stakeholders who have completely different backgrounds and priorities. Here are the books that became essential guides through these domain shifts.

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  • What Cold War Films Teach Us About AI Governance

    What Cold War Films Teach Us About AI Governance

    The parallels are unmistakable. America and China locked in technological rivalry, each racing to dominate the next transformative technology. Proxy conflicts erupting across the globe. Military budgets swelling with investments in revolutionary weapons systems. The specter of catastrophic miscalculation hanging over international relations.

    We are living through a new cold war, where artificial intelligence has replaced nuclear weapons as the ultimate strategic technology. Yet while the geopolitical dynamics mirror those of the 1950s and 1960s, our cultural understanding of the challenges lags dangerously behind.

    Contemporary science fiction obsesses over whether machines might become conscious, whether AI could fall in love, or whether robots will replace human workers. These philosophical questions miss the more pressing challenge: How do societies govern transformative technologies before those technologies reshape society beyond recognition?

    The original Cold War produced a remarkable body of cinema that grappled seriously with this governance challenge. Four films from that era asked precisely the questions we should be asking about AI today—questions about human coordination, institutional failure, and the moral weight of decisions involving powerful technologies.

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