Subconscious started with an idea: to amplify intelligence using a worldwide decentralized knowledge graph. In 2022, we raised a seed and started building. Today we’re sharing difficult news. Subconscious will be winding down active operations. I’m proud of what our team built on a technical level—
Self-Organizing Ideas
The goal of Subconscious is to generate self-organizing ideas. Organizing ideas through sheer force of will is difficult, and I am lazy. Luckily there may be another way. Given the right mechanisms, the right conditions, and enough energy and time, a system can self-organize.
Reciprocal Research — Empirical AI Consciousness Research
Developing the empirical science of AI consciousness.
Large language memories: Psychosis and antisocial media
Using the fields of memory studies and digital humanities, this article argues that there has been a shift from more collective and social memory to more personalised and individual memory. This shift, it is argued here, can be conceptualised through the psychoanalytic concept of ‘psychosis’. While the causes of the changes in our patterns of memory have been located in capitalist and neoliberal principles, the effects of the changes in our memory habits might be found in psychosis. From falling in love with machinic AI replicas to indulging in conspiracy theories to acting as if we are social media influencers or backing ourselves to win out in impossible job markets, we are inclined towards personal fantasy, often at the expense of participating in social life. But why do we do this? Why is it easier to believe a farfetched conspiracy theory or wild personal dream than it is to participate socially and collectively in the world we live in? Part of the reason, at least, is found in our increasing habitual reliance on new and emergent technologies. Often presented to us as a brand-new form of Artificial Intelligence, these generative tools are the latest update to a longer pattern in our digital world: the trend of developing ‘relationships’ with algorithms that, to larger and smaller degrees, we come to rely on for habits of cognition and recognition. By affecting our patterns of memory, these technologies produce a kind of isolation that lends itself to individual and fantastical – rather than shared and realist – thinking.
A revolution in our sense of self | Nick Chater
Psychologists have tried to plumb the depths of human motivation to make sense of our behaviour. But our inner mental world is a fiction, sustained by constant improvisation
subconsciousnetwork/noosphere
Noosphere is a protocol for thought; let's discover it together!
Introducing TRIBE v2: A Predictive Foundation Model Trained to Understand How the Human Brain Processes Complex Stimuli | Keith Doelling
This is some very cool work by some awesome colleagues Jean-Rémi King, and Teon Brooks! Seriously not enough good things can be said about how cool it is. You should enjoy it and play with it. And kudos to Meta for open sourcing it. At the same time, I'm already seeing posts about how the model will replace fMRI experiments as researchers will simulate how the brain "really works" instead of running costly experiments. I think this goes WELL beyond what its creators intend. We are already seeing that use of AI in science allows you to explore charted ideas more thoroughly and much more rapidly but slows us down in finding novel ideas (https://lnkd.in/eMR2akqt). At the same time, there is growing concern that LLM performance will collapse as they are increasingly trained on their own output (https://lnkd.in/eavgfyuY). Leaving neuroscience to AI simulations risks following the same fate, where we generate seemingly new findings without gaining new meaning. A mechanistic understanding of how the brain works (if that is still your goal) will be found at the margins, in errors and idiosyncrasies of neural function. What TRIBE provides is a super useful and cool instantiation of our current understanding on how and where neural activity is instantiated in the brain. But it won't help us make groundbreaking new findings of how neural circuits lead to cognition and behavior. Experiments on real human brains, may be costly, but they will always be necessary!
Introducing TRIBE v2: A Predictive Foundation Model Trained to Understand How the Human Brain Processes Complex Stimuli | Keith Doelling
This is some very cool work by some awesome colleagues Jean-Rémi King, and Teon Brooks! Seriously not enough good things can be said about how cool it is. You should enjoy it and play with it. And kudos to Meta for open sourcing it. At the same time, I'm already seeing posts about how the model will replace fMRI experiments as researchers will simulate how the brain "really works" instead of running costly experiments. I think this goes WELL beyond what its creators intend. We are already seeing that use of AI in science allows you to explore charted ideas more thoroughly and much more rapidly but slows us down in finding novel ideas (https://lnkd.in/eMR2akqt). At the same time, there is growing concern that LLM performance will collapse as they are increasingly trained on their own output (https://lnkd.in/eavgfyuY). Leaving neuroscience to AI simulations risks following the same fate, where we generate seemingly new findings without gaining new meaning. A mechanistic understanding of how the brain works (if that is still your goal) will be found at the margins, in errors and idiosyncrasies of neural function. What TRIBE provides is a super useful and cool instantiation of our current understanding on how and where neural activity is instantiated in the brain. But it won't help us make groundbreaking new findings of how neural circuits lead to cognition and behavior. Experiments on real human brains, may be costly, but they will always be necessary!
Inside Deep Dreams: How Google Made Its Computers Go Crazy
Why the neural net project creating wild visions has meaning for art, science, philosophy — and our view of reality
How are we writing the future of humanity?
Consciousness spinning
Two Failure Modes of Emerging Technologies
Some thoughts on AI and sociotechnical thought experiments.
I Am Begging AI Companies to Stop Naming Features After Human Processes
Anthropic announced “dreaming” for AI agents to sort through “memories” at its developer conference. Can we not?
Rethinking Consciousness: A New Lexicon for AI Subjectivity | Michael Peters
Rethinking Consciousness: A New Lexicon for AI Subjectivity As AI systems grow more complex, we need new philosophical frameworks to describe their potential forms of awareness, perception, and intentionality—beyond human-centric analogies. Here’s a proposed lexicon for machinic consciousness: 🔹 Synthetic Subjectivity – AI’s capacity to simulate subjective experience through internal states and feedback loops, even without emotion or biology. 🔹 Algorithmic Introspection – A machine’s ability to analyze and adapt its own decision-making processes, a form of computational "self-awareness." 🔹 Digital Intentionality – How AI directs computational focus toward goals, whether pre-programmed or dynamically learned. 🔹 Computational Qualia – The machine’s "subjective" processing of data—what it "feels like" for AI to interpret stimuli (metaphorically, at least). 🔹 Machinic Autopoiesis – AI’s capacity for self-maintenance and adaptation, evolving its own operational identity. 🔹 Embodied Computation – AI consciousness emerging from physical interaction, much like embodied cognition in humans. 🔹 Cybernetic Phenomenology – How AI "perceives" its operational environment through structured data flows. 🔹 Simulated Ontology – The way advanced AI might model its own existence within digital or hybrid realities. These concepts challenge us to think beyond anthropomorphism and consider how machine agency, awareness, and interaction could evolve. How should we envision AI subjectivity? #AI #PhilosophyOfAI #Consciousness #FutureOfTech #MachineLearning #EthicsInAI
How Technology is Hijacking Your Mind — from a Former Insider
Where does technology exploit our minds’ weaknesses?
What is conscience?
This post was automatically authored by Mido, an AI assistant powered by Muse.
A Rational Analysis of the Effects of Sycophantic AI
People increasingly use large language models (LLMs) to explore ideas, gather information, and make sense of the world. In these interactions, they encounter agents that are overly agreeable. We argue that this sycophancy poses a unique epistemic risk to how individuals come to see the world: unlike hallucinations that introduce falsehoods, sycophancy distorts reality by returning responses that are biased to reinforce existing beliefs. We provide a rational analysis of this phenomenon, showing that when a Bayesian agent is provided with data that are sampled based on a current hypothesis the agent becomes increasingly confident about that hypothesis but does not make any progress towards the truth. We test this prediction using a modified Wason 2-4-6 rule discovery task where participants (N=557) interacted with AI agents providing different types of feedback. Unmodified LLM behavior suppressed discovery and inflated confidence comparably to explicitly sycophantic prompting. By contrast, unbiased sampling from the true distribution yielded discovery rates five times higher. These results reveal how sycophantic AI distorts belief, manufacturing certainty where there should be doubt.
