INDEPENDENT RESEARCH / PRACTICE NOTES

Ideas in motion, not ideas behind glass.¶

This is my working notebook on AI engineering, strategy, knowledge infrastructure, organizational transformation, and the human systems that determine whether innovation becomes real capability.

The notes range from emerging research questions to practical operating models. Some will become papers, tools, talks, or products. Others are here because thinking improves when it is made visible.

Evidence before theater Systems over slogans Useful, accountable AI

Subscribe via RSS Follow Field Notes in any feed reader

Latest writing

November 6, 2023
in AI Governance, Knowledge Strategy, Product Management
3 min read

GPTs turn prompting into configuration management

At its first developer conference, OpenAI has introduced generative pre-trained transformers (GPTs): custom versions of ChatGPT that can combine instructions, uploaded knowledge, and selected capabilities for a particular purpose. People can build them without conventional programming and share them inside an organization or, eventually, through a public store.

This will make useful experimentation easier. It will also turn a large number of informal prompts into organizational configurations that can affect real work.

November 2, 2023
in Defense Technology, Innovation Management, AI Strategy
7 min read

Militarizing Innovation: The Path to Global Stability

In a recent dialogue with The Economist, Ukraine’s commander-in-chief, General Valery Zaluzhny, gave a stark assessment of their ongoing conflict with Russia – they lack the technological advantage to make strides and are at a stalemate with Russia. His reflections resonate with a long-standing consensus among military strategists and geopolitical analysts regarding the critical importance of technological advancement in warfare. The Russo-Ukrainian war, despite the employment of modern military technologies, demonstrates a broader imperative for the West: to win wars and mitigate risks, a nation must relentlessly pursue and attain technological dominance over its adversaries.

October 26, 2023
in AI Governance, Risk Management, Operating Models
3 min read

Frontier preparedness needs decision rights before the crisis

OpenAI has announced a Preparedness team and a challenge focused on risks from increasingly capable artificial intelligence models. The effort will examine areas such as cybersecurity, persuasion, autonomy, and other severe harms, with the aim of connecting evaluation to development and deployment decisions.

Preparedness is not only the ability to detect a dangerous capability. It is the ability to decide and act while the evidence is incomplete and the stakes are rising.

October 17, 2023
in Defense Technology, Digital Engineering, Organizational Learning
3 min read

Sim-to-real is an organizational-learning problem

The Defense Advanced Research Projects Agency (DARPA) is seeking proposals for Transfer from Imprecise and Abstract Models to Autonomous Technologies (TIAMAT). The program challenges a common assumption: instead of building ever more detailed simulations, diverse lower-fidelity environments may help autonomous systems learn concepts that transfer more readily to unfamiliar real-world settings.

The technical hypothesis is provocative. The management lesson is familiar: a model becomes dangerous when an organization forgets which parts of reality it left out.

October 15, 2023
in Human-Centered AI, Decision Support, Knowledge Strategy
3 min read

Expert judgment belongs in the system design

A new study from International Business Machines (IBM) researchers examines how machine-learning predictions might be adjusted when a domain expert's judgment conflicts with the model, particularly when a case is poorly represented in the training data. The work addresses a practical reality: experts and models often disagree for reasons that neither an accuracy score nor an appeal to experience can settle alone.

The disagreement should be treated as information.

October 5, 2023
in AI Strategy, Platform Engineering, Operating Models
3 min read

Inference is where AI strategy meets the budget

International Business Machines (IBM) Research has published a timely explanation of artificial intelligence inference—the moment when a trained model receives live input and produces a result. Training attracts attention because it creates the model. Inference is where the model becomes a recurring service, and where much of its lifetime cost and user experience accumulate.

For enterprise leaders, inference is not only an infrastructure concern. It is where an artificial intelligence (AI) portfolio meets a budget.

September 25, 2023
in Defense Technology, AI Assurance, Systems Engineering
3 min read

Trustworthy autonomy needs more than a better neural network

The Defense Advanced Research Projects Agency (DARPA) has selected teams for its Assured Neuro Symbolic Learning and Reasoning (ANSR) program. The program will explore architectures that combine data-driven neural learning with symbolic representations and reasoning, with the aim of improving robustness and assurance for autonomous systems.

The research matters because high performance and trustworthy behavior are not the same achievement.

September 19, 2023
in AI Governance, AI Security, Risk Management
3 min read

Scaling policies turn capability into a management trigger

Anthropic has published a Responsible Scaling Policy (RSP) that ties increasingly strong safety and security measures to evidence that a model has reached particular dangerous capabilities. The policy introduces Artificial Intelligence Safety Levels (ASLs), loosely inspired by the graduated containment used for biological hazards.

The specific thresholds will require continued research. The management pattern is already useful: decide in advance which evidence changes the organization's obligations.