Archives for March 2026

In many real-world systems, we’re limited not by what we want to measure, but by what we can measure. Bandwidth, power, and time all constrain how much data a sensor can collect. Yet, remarkably, it’s often possible to reconstruct a complete signal from only a small fraction of the original data, provided that the signal is sparse in some domain.

This is where L1 minimization comes in. Sometimes called Basis Pursuit or Lasso Regression, L1 minimization is a mathematical technique used to recover sparse signals by finding the simplest solution that fits the available measurements. It underpins the theory of compressed sensing, which has transformed fields ranging from medical imaging and audio processing to low-power embedded sensing.

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Most current AI systems, even the most advanced language models or embedded agents, are goal-driven, not curiosity-driven. They’re optimized for performance on defined tasks, not for the open-ended exploration, experimentation, or serendipitous discovery that characterizes human curiosity. Without curiosity, AI lacks the drive to seek out edge cases, explore unexpected paths, or test alternative hypotheses, things that often lead to breakthroughs in human learning. AI remains trapped within the boundaries of its training data and objectives. It doesn’t self-initiate new goals or try to make sense of unfamiliar environments unless explicitly told to do so. In dynamic environments like robotics, autonomous systems, or evolving sensor networks, a curiosity-less AI may plateau. They fail to discover new strategies or improvements unless nudged externally.

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The current trajectory of large language model (LLM) development is going to flame out. As models grow larger, they demand exponentially more data, energy, and computing power. Yet the performance gains from this scaling are already showing signs of diminishing returns, despite the massive investment. Training state-of-the-art AI models can consume megawatts of electricity, while just running them often requires hundreds of watts per user session. Compare this with the human brain which chugs along on a measely 20 watts, enough to power a dim light bulb!

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We recently introduced the open-source Primal Layers AI framework, which is inspired by the evolutionary architecture of the human brain. When we began developing this framework, it quickly became clear that it made little sense without a body. Whether this is a profound insight about consciousness or merely a reminder that nature never produced a species of disembodied brains, we can’t say for certain. Either way, our intelligence model needs a physical form — a robot we’re calling PrimalBot.

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The inspiration for this article is Mike the Headless Chicken. The story of Mike is one of the more bizarre episodes in history, an accident that became a rural American legend. In 1945, a farmer in Fruita, Colorado, attempted to butcher a chicken for dinner but missed the jugular vein and left part of the brain stem intact. Amazingly, the bird survived and continued to walk, perch, and even attempt to peck for over 18 months.

The pursuit of Artificial General Intelligence (AGI) has ignited a geopolitical competition of historic proportions. This is a massive technological arms race, with leaders like Vladimir Putin saying whoever achieves AGI first will “rule the world”. There are parallels with the nuclear arms race, which tends to get peoples attention, but is this comparison accurate? And what does it all mean for a country like Australia, a tech savvy, middle power, caught in the middle of this tussle?

For the purposes of this article, we will define AGI as at or above human level intelligence, and acknowledge that this vague description doesn’t capture the wider issues, about what intelligence is and how we measure it.