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

May 25, 2023
in AI Assurance, Risk Management, AI Governance
3 min read

Model evaluation needs an early-warning function

Researchers from Google DeepMind and several partner organizations have proposed a framework for evaluating general-purpose artificial intelligence models for dangerous capabilities and misalignment. Their central idea is to test for emerging risks early enough that developers can change training, security, or deployment decisions before a capability becomes difficult to contain.

That makes evaluation more than a scorekeeping function. It becomes an early-warning system.

May 18, 2023
in AI Engineering, Human-Centered AI, Platform Engineering
3 min read

AI performance is a workflow requirement

International Business Machines (IBM) and Intel have reported a substantial throughput improvement for natural-language processing tasks after integrating software optimizations with newer processors. The engineering result is a reminder that artificial intelligence performance comes from a stack: model, library, compiler, hardware, deployment environment, and workload.

For product teams, one more layer belongs in that stack—the human workflow.

May 12, 2023
in AI Strategy, Platform Engineering, Operating Models
3 min read

The enterprise AI platform is really a coordination platform

International Business Machines (IBM) has introduced watsonx, an enterprise platform that brings together a studio for foundation models, a data layer, and governance capabilities. The announcement reflects the direction many large organizations are moving: away from isolated model experiments and toward a common environment for building, adapting, and operating artificial intelligence.

The technology matters. The larger challenge is coordination.

May 1, 2023
in AI Governance, Operating Models, Organizational Learning
3 min read

Responsible AI needs distributed ownership

Microsoft's chief responsible artificial intelligence officer has published a reflection on the company's program, emphasizing leadership commitment, inclusive governance, and actionable standards. The timing is useful. Generative systems are moving into products rapidly, and many organizations are discovering that an ethics statement does not tell a product team what to do on Tuesday afternoon.

Responsible artificial intelligence (AI) needs a central function. It cannot remain the central function's job alone.

April 25, 2023
in AI Security, Enterprise Architecture, AI Assurance
3 min read

Guardrails are architecture, not decoration

NVIDIA has released NeMo Guardrails, an open-source toolkit for adding programmable controls to applications built with large language models. The toolkit is intended to help developers keep conversations on topic, reduce harmful or inaccurate responses, and restrict unsafe connections to external applications.

The word guardrail can sound like a thin layer around an otherwise complete product. In a consequential artificial intelligence system, it should be treated as architecture.

April 20, 2023
in AI Strategy, Innovation Management, Organizational Learning
3 min read

Research integration is an organizational-design problem

Google has combined DeepMind and the Google Brain team into a new unit called Google DeepMind. The stated ambition is to bring together talent, computing resources, infrastructure, and research advances to accelerate progress in artificial intelligence.

Mergers of technical groups are often described as exercises in scale. Their success depends just as much on whether distinct communities can combine knowledge without losing the differences that made each one valuable.

April 13, 2023
in AI Strategy, Enterprise Architecture, AI Governance
3 min read

Model choice creates a portfolio to govern

Amazon Web Services (AWS) has announced Amazon Bedrock, a managed service intended to give customers access to foundation models from several providers through a common cloud environment. The appeal is obvious: teams can experiment with different models and choose the capability that fits the application without building the underlying infrastructure themselves.

Choice reduces dependence on a single model. It also creates a portfolio that somebody has to govern.

April 5, 2023
in AI Assurance, Product Management, Operating Models
3 min read

AI safety must travel with the product

OpenAI has described its approach to artificial intelligence safety, including pre-release testing, external input, human feedback, monitoring, and phased deployment. The account reflects an important reality: safety is not a property added once to a model and inherited automatically by everything built with it.

Safety has to travel with the product.