Amelia Bedelia highlights the problem of commonsense in AI. Just like her literal understanding leads to funny mishaps, AI can also misunderstand instructions without proper commonsense.
It's important to consider that powerful AI shouldn't be seen as automatically dangerous. As AI gets more capable, it can also be more controllable if designed well.
Many fears about AI assume it will behave like humans, but AI has different motivations and can take its time making decisions, so we shouldn't assume it will spontaneously want to harm us.
The hippocampus may not just represent physical space but instead processes space as a sequence of sensory and motor experiences. This means how we perceive space comes from our interactions, not just where we are.
Place cells in the brain react to specific sequences of observations rather than directly to locations themselves. This explains why experiences in different environments can create similar neural responses.
New models, like causal graphs, allow for better understanding and planning in navigational tasks. They can adapt to new environments quickly by using learned sequences without needing to rely on exact spatial representations.
The ARC challenge is about understanding abstract concepts from visual inputs and applying them to new situations. It's tricky because it's not based on a strict set of rules, making it harder to solve.
Cognitive programs need a controllable world model to work properly. This means they must be able to run simulations using the information they have about the world.
Abstract reasoning tests, like ARC, are important but not complete measures of intelligence. They need to be systematic and clear to truly assess reasoning skills.
Successor representations (SR) does not explain how place cells in the hippocampus learn or form. It assumes inputs that are already perfect place fields, so it can't help in understanding their development.
Many claims about SR's abilities, like making predictions or forming hierarchies, actually relate to simpler models like Markov chains. SR doesn't add much value to those features.
Experiments often used to support SR in humans might actually show evidence for more general planning methods. Model-based reasoning seems to fit the observed behavior better than SR does.