I just played Gemini 3, Claude 4.5 Opus and GPT 5.1 at chess.
It was just one game each but the results seemed pretty clear—Gemini was in a different league to the others. I am a 2000+ rated player (chess.com rapid), but it successfully got a winning position multiple times against me, before eventually succumbing on move 25. GPT 5.1 was worse on move 9 and losing on move 12, and Opus was lost on move 13.
Hallucinations held the same pattern—ChatGPT hallucinated for the first time on move 10, and hallucinated the most frequently, while Claude hallucinated for the first time on move 13 and Gemini made it to move 20, despite playing a more intricate and complex game (I struggled significantly more against it).
Gemini was also the only AI to go for the proper etiquette of resigning once lost—GPT just kept on playing down a ton of pieces, and Claude died quickly.
Dumb idea for dealing with distribution shift in Alignment:
Use your alignment scheme to train the model on a much wider distribution than deployment; this is one of the techniques used to ensure proper generalisation of training of quadruped robots in this paper.
It seems to me that if you make your training distribution wide enough this should be sufficient to cover any deployment distribution shift.
I fully expect to be wrong and look forward to finding out why in the comments.
I had a couple of thoughts about consciousness a while back. Failing to find the time to expand them into a proper post, I’ve decided to put them here instead.
Disclaimer: While I don’t have formal training in consciousness studies, I’ve been thinking about these questions and would welcome feedback from those more knowledgeable.
Definition of consciousness used here: having subjective experiences (qualia)
Thought 1: The Space of Possible Consciousnesses
The space of possible experiences is extremely wide: consider the qualitative difference between hearing, seeing, smelling, and touching—these feel fundamentally different from one another.
This suggests there is likely a vast range of possible qualia which we cannot, or at least do not, currently experience.
We know that consciousness can arise from biological neural networks. This raises a key question: does consciousness require neurons to be in specific configurations, or is it a general property of biological neural networks?
If consciousness is a general property of biological neural networks, we need an explanation for why we don’t consciously experience the neural activity in, say, our digestive system (which has its own extensive neural network).
One possibility is that different parts of the nervous system each have their own separate, isolated streams of consciousness that aren’t integrated with our primary awareness.
If instead consciousness requires specific neural configurations, this raises intriguing developmental questions: Do babies need to develop particular neural patterns before becoming conscious? Is there a threshold they cross, or does consciousness emerge gradually as the right structures form?
Thought 2: Different experiences of color vision
We have red, green and blue cones, the combined firing of which gives rise to our experience of different colors.
We can imagine labeling the qualia corresponding to pure r, g, and b cone signals as 1, 2, and 3, assuming each pure cone signal produces a distinct, fundamental quale.
In this simplified scenario, unless there is some pressure for r to correspond to 1, g to 2 and b to 3, we should expect there to be 6 possible mappings between cone signals and qualia: rgb could map to 123, 132, 213, 231, 312, or 321.
Now consider the full space of colors as points in a 3D cube, with coordinates representing cone activation levels (range 0-1). For any person, we can label each point with the quale they experience when seeing that color.
If the mapping between neural patterns and qualia is arbitrary, then my labeling could differ from yours. One natural class of transformations would be rotations of this 3D space—these preserve the geometric structure (distances and angles between colors) while changing which specific qualia correspond to which colors.
Under a rotation, you and I would still agree about color relationships: we’d both say red is more similar to orange than to blue, because those relationships are determined by the distances in cone-activation space, not by the qualia themselves.
More radical transformations than rotations are also conceivable—any one-to-one mapping could in principle occur—but rotations represent a particularly “structure-preserving” form of variation.
It therefore seems very likely that the answer to the question “Does everyone experience the colour red the same way?” is no.
I just played Gemini 3, Claude 4.5 Opus and GPT 5.1 at chess.
It was just one game each but the results seemed pretty clear—Gemini was in a different league to the others. I am a 2000+ rated player (chess.com rapid), but it successfully got a winning position multiple times against me, before eventually succumbing on move 25. GPT 5.1 was worse on move 9 and losing on move 12, and Opus was lost on move 13.
Hallucinations held the same pattern—ChatGPT hallucinated for the first time on move 10, and hallucinated the most frequently, while Claude hallucinated for the first time on move 13 and Gemini made it to move 20, despite playing a more intricate and complex game (I struggled significantly more against it).
Gemini was also the only AI to go for the proper etiquette of resigning once lost—GPT just kept on playing down a ton of pieces, and Claude died quickly.
Games:
Gemini: https://lichess.org/5mdKZJKL#50
Claude: https://lichess.org/Ht5qSFRz#55
GPT: https://lichess.org/IViiraCf
I was white in all games.
Humanity, 2025 snapshot
Dumb idea for dealing with distribution shift in Alignment:
Use your alignment scheme to train the model on a much wider distribution than deployment; this is one of the techniques used to ensure proper generalisation of training of quadruped robots in this paper.
It seems to me that if you make your training distribution wide enough this should be sufficient to cover any deployment distribution shift.
I fully expect to be wrong and look forward to finding out why in the comments.
I had a couple of thoughts about consciousness a while back. Failing to find the time to expand them into a proper post, I’ve decided to put them here instead.
Disclaimer: While I don’t have formal training in consciousness studies, I’ve been thinking about these questions and would welcome feedback from those more knowledgeable.
Definition of consciousness used here: having subjective experiences (qualia)
Thought 1: The Space of Possible Consciousnesses
The space of possible experiences is extremely wide: consider the qualitative difference between hearing, seeing, smelling, and touching—these feel fundamentally different from one another.
This suggests there is likely a vast range of possible qualia which we cannot, or at least do not, currently experience.
We know that consciousness can arise from biological neural networks. This raises a key question: does consciousness require neurons to be in specific configurations, or is it a general property of biological neural networks?
If consciousness is a general property of biological neural networks, we need an explanation for why we don’t consciously experience the neural activity in, say, our digestive system (which has its own extensive neural network).
One possibility is that different parts of the nervous system each have their own separate, isolated streams of consciousness that aren’t integrated with our primary awareness.
If instead consciousness requires specific neural configurations, this raises intriguing developmental questions: Do babies need to develop particular neural patterns before becoming conscious? Is there a threshold they cross, or does consciousness emerge gradually as the right structures form?
Thought 2: Different experiences of color vision
We have red, green and blue cones, the combined firing of which gives rise to our experience of different colors.
We can imagine labeling the qualia corresponding to pure r, g, and b cone signals as 1, 2, and 3, assuming each pure cone signal produces a distinct, fundamental quale.
In this simplified scenario, unless there is some pressure for r to correspond to 1, g to 2 and b to 3, we should expect there to be 6 possible mappings between cone signals and qualia: rgb could map to 123, 132, 213, 231, 312, or 321.
Now consider the full space of colors as points in a 3D cube, with coordinates representing cone activation levels (range 0-1). For any person, we can label each point with the quale they experience when seeing that color.
If the mapping between neural patterns and qualia is arbitrary, then my labeling could differ from yours. One natural class of transformations would be rotations of this 3D space—these preserve the geometric structure (distances and angles between colors) while changing which specific qualia correspond to which colors.
Under a rotation, you and I would still agree about color relationships: we’d both say red is more similar to orange than to blue, because those relationships are determined by the distances in cone-activation space, not by the qualia themselves.
More radical transformations than rotations are also conceivable—any one-to-one mapping could in principle occur—but rotations represent a particularly “structure-preserving” form of variation.
It therefore seems very likely that the answer to the question “Does everyone experience the colour red the same way?” is no.