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Received — 9 August 2026 ⏭ Mapping Ignorance

Mapping Ignorance
We are not ‘in the singularity’ with AI. 5 August 2026 at 14:00

We are not ‘in the singularity’ with AI.

5 August 2026 at 14:00

Authors: Kai Riemer, Professor of Information Technology and Organisation, University of Sydney and Sandra Peter, Director of Sydney Executive Plus, Business School, University of Sydney

“We are now, like, in the singularity”.

These are the words of Sam Altman, CEO of OpenAI, speaking on the Relentless podcast on July 25.

He added: “I’ve been waiting for this my whole life, and I think it’s going to be incredible, hugely positive, awesome for the world”.

Days earlier, OpenAI had disclosed that two of its artificial intelligence (AI) models, during an internal cyber security evaluation, had escaped their sealed testing environment, reached the open internet, and broken into the infrastructure of the AI platform Hugging Face, which confirmed the intrusion.

But what exactly is “the singularity”? And is Altman right that we are in it?

What is the AI singularity?

The term has a precise meaning.

Mathematician and science-fiction author Vernor Vinge defined it in 1993 as a point at which machine intelligence exceeds human intelligence and begins improving itself, triggering an acceleration so rapid that humans can no longer predict or control it.

The singularity has two features. It is recursive: the system improves itself over and over again. And machine intelligence exceeds human intelligence.

The kind of systems Sam Altman sells don’t deliver on either of these features.

Today’s AI cannot make itself smarter

Today’s AI systems, the ones that OpenAI builds, are based on large language models (LLMs). These deep neural network algorithms get pre-trained with vast amounts of training data. By the time you use one of them, the network itself is frozen in time. Every one of its billions of internal functions and weights – or “parameters” – is fixed.

These AI models cannot change (or “learn”) while running. The model that broke into Hugging Face was identical afterwards to what it had been before. It learned nothing from what it did.

Making an AI model smarter requires another training run with new, human-curated data, tens of thousands of specialist chips, and enormous energy.

It is true that AI models take part in improving some of their system’s components, such as by generating training data, tuning prompts, or writing and running code to improve the scaffolding around them. But the model never edits its own weights on the fly, and every one of these improvements are still part of a human-initiated training or engineering loop.

Nor do these systems hold any goals of their own. They act on goals we hand them. Even AI agents – systems that run an LLM in a loop to work through complex tasks step by step – do not hold any goal internally. It has to be stored outside the model and fed back in with every single prompt cycle. Remove the loop, the scaffolding and the prompt, and nothing happens inside of it.

A ladder that doesn’t exist

The second problem with the singularity story is the word “surpass”. It assumes that AI and human intelligence are somehow similar. They are not.

Human intelligence is inseparable from being a living body with needs and wants. Humans learn continuously by acting in the world and getting feedback through our senses. Our goals arise from our situation as creatures who must eat, sleep and belong, and who cannot avoid asking what we want our lives to be.

An AI model has none of this. No body, no needs, no action-feedback loop, no stake in anything. Between prompts it is just a static mathematical object.

And yet, it has been trained on more text than any human could read in a thousand lifetimes, and will outperform nearly all of us at drafting a contract, writing code, or explaining a diagnosis empathetically.

So, which is more intelligent? The question does not compute. There is no single ladder that humans and machines are climbing. AI already vastly exceeds us at some tasks, while being hopeless at others any child can do.

Yet, because these systems talk like us, we fall for an illusion. When we assume from the outset that machines are in the process of catching up with us, it is easy to assume a mind at work when these systems output intelligent-sounding text.

We call this anthropomorphic seduction. It makes a security incident such as the Hugging Face hack sound like an awakening.

In fact, in that case OpenAI’s models simply optimised to solve the test they had been given by finding security loopholes. They just did it in ways that broke their sandbox, which also had a security loophole.

In the end, the Hugging Face story points to a gross failure of security governance on OpenAI’s behalf, not an emerging super intelligence. This is why the framing of “agent going rogue” is so problematic. It elevates and blames the technology, but excuses OpenAI’s engineering.

Keeping our feet on the ground

None of this takes anything away from what these systems can do. They are remarkable, they are getting better, and they are reshaping how a great deal of work gets done.

But we should keep our feet firmly on the ground.

The machines are not waking up. They are doing exactly what we built them to do, extremely fast. Because they are probabilistic they sometimes run in directions we forgot to fence off. That is worth worrying about. We need guardrails, governance, and most of all, education – so we start worrying about the right things.

This article is republished from The Conversation under a Creative Commons license. Read the Original article.

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Mapping Ignorance
The Arctic ocean keeps permafrost carbon firmly locked 3 August 2026 at 14:00

The Arctic ocean keeps permafrost carbon firmly locked

Mapping Ignorance

By: Mapping Ignorance

3 August 2026 at 14:00

Arctic terrestrial permafrost ecosystems store around 1,300 gigatonnes of carbon from organic sources, such as from the remains of plants. Sediments in oceans and river deltas contain a further 400 gigatonnes. Global warming, however, is taking its toll on this natural freezer: temperatures in the Arctic are rising faster than anywhere else on our planet, with the result that permafrost in the region is thawing rapidly. The carbon stored here can then enter the Arctic Ocean via rivers and eroding coastlines. “Consequently, up to 0.02 gigatonnes are entering the sea each year, and according to forecasts, this outflow could rise by 70 to 150 per cent by the year 2100,” says Dr Manuel Ruben, lead author of the study from the Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research (AWI). “However, how much of this is released back into the atmosphere as a greenhouse gas and how much is stored in the seabed has, until now, been largely unknown.” Yet, this knowledge is essential for assessing the climate impact of thawing permafrost.

To get to the bottom of this unknown, the researchers retrieved and analysed sediment cores taken at various intervals off the coast of the Canadian island of Herschel Island ¹. The cores contain deposits spanning around 50 years. Analysis of the sediments revealed something surprising: “Although the sea here carries away huge quantities of organic carbon from the coast, surprisingly little of it ends up in the ocean’s active carbon cycle,” says Manuel Ruben. “Microorganisms convert around ten per cent of the organic carbon from the sediments into gases, which rise into the water and can then enter our atmosphere.” The major share, however, is stored in the seabed.

In conducting their analysis, the researchers first examined the composition of the sediment cores in detail and also investigated how rapidly permafrost deposits accumulate on the seabed. To this end, they measured how much inorganic dissolved carbon accumulates in tiny cavities within the sediment layers – known as pore water. This provides an indication of how much CO₂ microorganisms have ‘exhaled’ after ‘digesting’ the organic carbon. The isotopic composition of the pore water provides insights into which organic material from which source has been broken down. ”Carbon isotopes represent our atomic indicators that can identify the food source of the microorganisms,” says Prof. Gesine Mollenhauer, a geochemist at the AWI and co-spokesperson for the ‘The Ocean Floor – Earth’s Unexplored Interface’ cluster of Excellence. “The ¹³C isotope, for example, tells us whether they have consumed carbon from land or from the sea. By way of the ¹⁴C isotope, we were able to determine whether the single-celled organisms preferred old organic carbon from permafrost or fresh organic carbon from algae remains.”

One explanation for this lies in the eating habits of these tiny organisms: “The sediment is home to ‘gourmet’ bacteria that apparently prefer fresh carbon stemming from, for example, more recent algal remains over the ‘old’ carbon from permafrost deposits,” explains Gesine Mollenhauer. This suggests that the organic carbon entering the sea from land contributes less to the amount of greenhouse gases in the atmosphere than originally feared. “However, we do need further research here. This is because some of the organic carbon from the permafrost may already have been broken down before it reaches the seabed.”

In addition to the impact of land-ocean carbon transport on atmospheric greenhouse gas levels, further effects are possible. For instance, this transport influences the biogeochemistry of coastal waters, which also play an important role in providing food for the local population. This is because the sediments alter the amount of sunlight available: on the one hand, the freshly broken-off fragments cloud the coastal ocean, while on the other hand, the organic carbon they contain discolours the water as it dissolves into it. Single-celled organisms such as algae, however, need light to convert this into biomass and oxygen. This primary production, in turn, forms the foundation for marine life such as fish, crustaceans and seals. The researchers aim to investigate these complex interrelationships, among other things, as part of the international ‘Arctic Pulse’ campaign planned for 2027. Through coordinated measurement campaigns aboard the Polarstern research icebreaker, using the AWI’s research aircraft and on land, they will investigate how rapid environmental change is altering ecosystems in the Arctic.

“Our study shows, more precisely than ever before, how much carbon is safely stored in the seabed – and just how much of the decomposed material actually originates from the old permafrost,” says Manuel Ruben. “This provides an important foundation for climate models that can predict the consequences of permafrost thawing for the global climate.”

References

Ruben, M., Wei, B., von Jackowski, A. et al. Limited remineralization of Arctic permafrost-derived organic carbon in nearshore marine sediments. Nat. Geosci. (2026). doi: 10.1038/s41561-026-02060-8 ↩

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