Nine works tracing AI from machine intuition to systems that increasingly act on our behalf.
Over little more than a decade, machine learning has moved from recognizing patterns to generating language, reasoning with tools, and increasingly acting on our behalf.
These works trace that progression—and the questions of responsibility, control, and human agency that follow from it.
Google DeepMind
A machine plays a move no human expected. The board remains still; our idea of intelligence does not.
AlphaGo made machine discovery legible: a system trained on human play found a move that expert intuition had not prepared to see.
Open the note →Andrej Karpathy
Andrej Karpathy explains how language models learn, fail, and become something like a new operating system.
The operating-system metaphor connects language prediction to a general interface for computing, tools, and human intent.
Open the note →OpenAI
An early look at the chatbot becoming a programmable tool for knowledge work.
The demonstration catches the model leaving the chat box and entering workflows—the step from conversational novelty toward computing platform.
Open the note →Madeleine Clare Elish
Madeleine Clare Elish shows how humans with little control can absorb the blame when automated systems fail.
Responsibility can remain attached to a person after meaningful control has moved elsewhere. Calling someone 'in the loop' does not put power back in their hands.
Open the note →Stuart Russell
Stuart Russell asks what capable systems do with objectives that only approximate what humans meant.
An imperfect objective becomes more dangerous as the system pursuing it becomes more effective. Capability and control cannot be separate conversations.
Open the note →Tristan Harris and Aza Raskin
Tristan Harris and Aza Raskin connect generative AI to the incentives and failures of the attention economy.
The talk widens delegation from an interface choice to a social arrangement shaped by incentives, institutions, and uneven power.
Open the note →Andrej Karpathy
Andrej Karpathy maps Software 3.0, partial autonomy, and the long middle ground between a tool and an agent.
The autonomy slider makes delegation a design decision rather than a binary choice between passive software and unsupervised action.
Open the note →Demis Hassabis
Demis Hassabis discusses what today's models still lack: robust reasoning, planning, world models, and scientific discovery.
A frontier claim becomes more useful when its missing capabilities are named: reasoning, planning, world models, and discovery can then be examined rather than merely awaited.
Open the note →CSharpCorner
Fifty-seven talks from the awkward present: production agents, multi-agent systems, trust, failure, and autonomous workflows.
The uneven move from models that answer to systems that act is easier to see in production reports than in one immaculate keynote.
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