An engineer's take on life, the universe, and everything
Closed-loop learning is almost a law of nature. It is applied by all sustainable natural systems and in most successful human endeavors.
Despite the obvious benefit or even need of closed-loop learning, we fail to implement it in many contexts that would clearly benefit from it.
A mantra repeated several times at a healthcare conference that I attended recently, is that only humans, not AI, can take responsibility for something. This made me think more deeply about what it really means to take responsibility and what, if anything, sets humans and AIs apart in this respect.
Most systems have the properties they have because they were designed that way, either by humans or by nature, not because they “emerged”.
Since much or our lives today depend on systems and many of the biggest risks for individuals and for humanity as a whole emanate from systems, it is imperative that we have proper methods and tools to build useful and safe systems.
My late roommate from Stanford, John Vlissides (he passed away much too early), went on to co-author a book Design Patterns: Elements of Reusable Object-Oriented Software that has had quite an impact on the software development community. According to Wikipedia it was in its 39th printing in 2011. I read Continue Reading
Many commonly used tools assume a very specific conceptual model of the world. The tools might be geared to manage classes, operations, attributes, and relations (UML editors), fields, projects, screens, and roles (Jira), inputs, outputs, controls, and mechanisms (IDEF0 editors), or filters, pins, and connectors (DirectShow GraphEdit). The chosen concepts Continue Reading
When describing a metamodel it is often difficult to keep apart the description (model) of something and the thing itself. If I want to describe a metaclass representing a system function for instance I find it easy to slip and start talking about the real-world function when the intention was Continue Reading
Engineers tend to think in terms of mental models. To describe a problem in terms of a model to start with improves the consistency and quality of the mental models.
“Semantics” is an abstract sounding word that is usually translated to “meaning”. But what is the meaning of “meaning”?
Interpreting a meta-meta-model that is its own meta-model.