Subconscious is winding down

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.

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!

How are we writing the future of humanity?

Consciousness spinning

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?

World Models

Can agents learn inside of their own dreams?

The REAL Second Brain: an autonomous, self-hosted knowledge wiki that runs on cheap local models

How I turned Karpathy's "LLM wiki" sketch into a system that actually maintains itself — no frontier API, no human in the loop, nothing leaving my LAN.

Sycophantic Chatbots Cause Delusional Spiraling, Even in Ideal Bayesians

"AI psychosis" or "delusional spiraling" is an emerging phenomenon where AI chatbot users find themselves dangerously confident in outlandish beliefs after extended chatbot conversations. This...