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

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Latest writing

July 31, 2023
in Defense Technology, AI Assurance, Innovation Management
3 min read

Trustworthy AI needs a research agenda, not a slogan

Researchers from academia, industry, and government are gathering this week for the Defense Advanced Research Projects Agency's Artificial Intelligence (AI) Forward workshop. The agenda centers on a question that is easy to state and difficult to engineer: how can AI systems operate reliably, interact appropriately with people, and support national-security needs under demanding conditions?

Calling a system trustworthy does not make it so. Trustworthiness has to be decomposed into research questions, engineering evidence, and operational learning.

July 25, 2023
in AI Security, National Security Technology, Risk Management
3 min read

Frontier-model security is shared infrastructure

Anthropic has published an initiative focused on the security of advanced artificial intelligence models. The concern is straightforward: as models become more capable and expensive to produce, their weights, training systems, research, and deployment infrastructure become valuable targets for theft or misuse.

The security problem does not sit in one server room. It crosses the organization and its supply chain.

July 18, 2023
in AI Strategy, Platform Engineering, Enterprise Architecture
3 min read

Open models turn selection into engineering

Meta and Microsoft have announced commercial access to Llama 2, with support across Azure and Windows. The release expands the range of models organizations can host, adapt, and integrate under their own architectural choices rather than consume only through a closed service.

That optionality is valuable. It also moves more of the responsibility from procurement into engineering.

July 11, 2023
in Human-Centered AI, Knowledge Strategy, Product Management
3 min read

Long context changes the knowledge-work interface

Anthropic has released Claude 2 with a context window that can accept roughly 100,000 tokens—enough for hundreds of pages of material in one prompt. The immediate attraction is obvious: a user can bring a long report, technical documentation, or even a book into a conversation without dividing it into tiny fragments.

More context changes what a language model can see. It does not guarantee that the model will attend to the right thing.

July 6, 2023
in Platform Engineering, AI Governance, Product Management
3 min read

Stable model access is part of the control plane

OpenAI has made the Generative Pre-trained Transformer 4 (GPT-4) application programming interface generally available to existing paying developers and announced a retirement path for several older completion and embedding models. The two developments belong together. Production access is not only about opening capacity; it is about managing change.

When an external model becomes part of an application, version stability and migration become product concerns.

June 27, 2023
in Product Management, AI Engineering, Human-Centered AI
3 min read

The AI product is more than the model

Microsoft has spent the spring adding generative artificial intelligence capabilities across Azure, developer tools, and business applications. In this month's platform update, one sentence deserves more attention than another feature list: generative AI does not absolve builders from thinking about what good product-making looks like.

That should be the operating principle for the current wave of experimentation.

June 22, 2023
in AI Governance, Standards, Organizational Learning
3 min read

Generative AI guidance should be built in public

The National Institute of Standards and Technology (NIST) has formed a public working group to help develop guidance for generative artificial intelligence. The group will begin by considering how the Artificial Intelligence Risk Management Framework (AI RMF) applies to these systems and will draw input from industry, academia, government, and civil society.

That open process is not a detour on the way to a standard. It is part of how a credible standard is made.

June 13, 2023
in AI Security, Enterprise Architecture, Product Management
3 min read

Function calling moves risk beyond the chat window

OpenAI has added function-calling support to its chat models. Developers can describe functions using structured definitions, and the model can return arguments that an application may use to call external tools or retrieve information.

This is an important improvement for building reliable integrations. It also makes a boundary explicit: the model proposes; the application decides what happens next.