INDEPENDENT RESEARCH / PRACTICE NOTES
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.
Amazon Web Services (AWS) has made Knowledge Bases for Amazon Bedrock generally available. The service can ingest organizational documents, create a searchable vector index, retrieve relevant passages, and use them to ground a foundation model's response—with source attribution included.
Managed retrieval removes a meaningful amount of engineering work. It does not decide which organizational knowledge should be trusted.
Microsoft has announced the public preview of an artificial intelligence (AI) development environment, Azure AI Studio, at Ignite. It brings model selection, data grounding, evaluation, content safety, and deployment tooling into a common workspace. The platform reflects how quickly generative AI development is moving from isolated notebooks toward managed application delivery.
A studio can make the path to a prototype remarkably short. The path to a dependable product still needs discipline.
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.
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.
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.
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.
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.
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.