Do you have a strategy for aligning future-you to current-you’s values (not sharing them exactly, obviously, but making the sort of changes that meta-you would improve of)? I think any implementable hopes in that area should be investigated carefully, mostly because they’ll plausibly transfer to aligning artificial systems.
The main advantage of Intelligence Augmentation is that we know that our current minds are both generally or near-generally intelligent and more-or-less aligned with our values, and we also have some level of familiarity with how we think (edit: and likely must link our progress in IA to our understanding of our own minds, due to the neurological requirements).
So we can find smaller interventions that are certainly, or at least almost certainly, going to have no effect on our values, and then test them over long periods of time, using prior knowledge of human psychology and the small incremental differences each individual change would make to identify value drift without worrying about the intelligence differences allowing concealment.
The first viable and likely-safe approach that comes to mind is to take the individual weaknesses in our thinking relative to how we use our minds in the modern day, and make it easy enough to use external technology to overcome them that they no longer count as cognitive weaknesses. For most of the process we wouldn’t be accessing or changing our mind’s core structure, but instead taking skills that we learn imperfectly through experience and adding them as fundamental mental modules (something impossible through mere meditation and practice), allowing our own minds to then adapt to those modules and integrate them into the rest of our thinking.
This would likely be on the lines of allowing us to transfer our thoughts to computational ‘sandboxes’ for domains like “visual data” or “numbers”, where we could then design and apply algorithms to them, allowing for domain-specific metacognition beyond what we are currently capable of. For the computer-to-brain direction we would likely start with something like a visual output system (on a screen or smart-glasses), but could eventually progress to implants or direct neural stimulation.
Eventually this would progress to transferring the contents of any arbitrary cognitive process to and from computational sandboxes, allowing us to enhance the fundamental systems of our minds and/or upload ourselves completely (piece by piece, hopefully neuron-by-neuron to maintain continuity of consciousness) to a digital substrate. However, like Narrow AI this would be a case of progressive object-level improvements until recursive optimization falls within the field’s domain, rather than reaching AGI-levels of self-improvement immediately.
The main bottlenecks to rate of growth would be research speed and speed + extent of integration.
Regarding research speed, the ability to access tools like algebraic solvers or Machine Learning algorithms without any interface costs (time, energy, consciously noting an idea and remembering to explore it, data transformation to and from easily-human-interpretable formats, etc.) would still allow for increases in our individual productivity, which could be leveraged to increase research speeds and also reduce resource constraints on society (which brings short-term benefits unrelated to alignment, potential benefits for solving other X-risks, and reduced urgency for intelligent & benevolent people working to develop AGI to ‘save humanity’). These augmentations would also make it easier to filter out good ideas from our idle thoughts, since now there is essentially no cost to taking such a thought and actually checking whether our augmented systems say it’s consistent with itself and online information. Similarly, problems like forgetfulness could be somewhat mitigated by using reminders and indices linked directly to our heads and updated automatically based on e.g. word-associations with specific experiences or visualizations. If used properly, this gives us a mild boost to overall creativity simply because of the increased throughput, feedback, and retention, which is also useful for research.
Regarding speed/extent of integration, this is entirely dependent on the brain’s own functioning. I don’t see many ways to improve this until the end state of full self-modification, although knowledge of neurology would increase the interface efficiency and recommended-best-practices (possibly integrating an offshoot of traditional mental practices like meditation to increase the ability to interact with the augments).
On the other hand, this process requires a lot of study in neurology and hardware, and so will likely be much slower than AGI timelines all-else-being-equal. To be a viable alternative/solution, there would have to be a sufficient push that the economic pressures towards AGI are instead diverted towards IA. This is somewhat helped along by the fact that narrow AI systems could be integrated into this approach, so if we assume that Narrow AI isn’t a solution to AGI (and that the above push succeeds in at least creating commercially-viable augments and brain-to-computer data transferal), the marginal incentives for productivity-rates should lean towards gearing AI research towards IA, rather than experimenting to create autonomous intelligent systems.
Do you have a strategy for aligning future-you to current-you’s values (not sharing them exactly, obviously, but making the sort of changes that meta-you would improve of)? I think any implementable hopes in that area should be investigated carefully, mostly because they’ll plausibly transfer to aligning artificial systems.
The main advantage of Intelligence Augmentation is that we know that our current minds are both generally or near-generally intelligent and more-or-less aligned with our values, and we also have some level of familiarity with how we think (edit: and likely must link our progress in IA to our understanding of our own minds, due to the neurological requirements).
So we can find smaller interventions that are certainly, or at least almost certainly, going to have no effect on our values, and then test them over long periods of time, using prior knowledge of human psychology and the small incremental differences each individual change would make to identify value drift without worrying about the intelligence differences allowing concealment.
The first viable and likely-safe approach that comes to mind is to take the individual weaknesses in our thinking relative to how we use our minds in the modern day, and make it easy enough to use external technology to overcome them that they no longer count as cognitive weaknesses. For most of the process we wouldn’t be accessing or changing our mind’s core structure, but instead taking skills that we learn imperfectly through experience and adding them as fundamental mental modules (something impossible through mere meditation and practice), allowing our own minds to then adapt to those modules and integrate them into the rest of our thinking.
This would likely be on the lines of allowing us to transfer our thoughts to computational ‘sandboxes’ for domains like “visual data” or “numbers”, where we could then design and apply algorithms to them, allowing for domain-specific metacognition beyond what we are currently capable of. For the computer-to-brain direction we would likely start with something like a visual output system (on a screen or smart-glasses), but could eventually progress to implants or direct neural stimulation.
Eventually this would progress to transferring the contents of any arbitrary cognitive process to and from computational sandboxes, allowing us to enhance the fundamental systems of our minds and/or upload ourselves completely (piece by piece, hopefully neuron-by-neuron to maintain continuity of consciousness) to a digital substrate. However, like Narrow AI this would be a case of progressive object-level improvements until recursive optimization falls within the field’s domain, rather than reaching AGI-levels of self-improvement immediately.
The main bottlenecks to rate of growth would be research speed and speed + extent of integration.
Regarding research speed, the ability to access tools like algebraic solvers or Machine Learning algorithms without any interface costs (time, energy, consciously noting an idea and remembering to explore it, data transformation to and from easily-human-interpretable formats, etc.) would still allow for increases in our individual productivity, which could be leveraged to increase research speeds and also reduce resource constraints on society (which brings short-term benefits unrelated to alignment, potential benefits for solving other X-risks, and reduced urgency for intelligent & benevolent people working to develop AGI to ‘save humanity’).
These augmentations would also make it easier to filter out good ideas from our idle thoughts, since now there is essentially no cost to taking such a thought and actually checking whether our augmented systems say it’s consistent with itself and online information. Similarly, problems like forgetfulness could be somewhat mitigated by using reminders and indices linked directly to our heads and updated automatically based on e.g. word-associations with specific experiences or visualizations. If used properly, this gives us a mild boost to overall creativity simply because of the increased throughput, feedback, and retention, which is also useful for research.
Regarding speed/extent of integration, this is entirely dependent on the brain’s own functioning. I don’t see many ways to improve this until the end state of full self-modification, although knowledge of neurology would increase the interface efficiency and recommended-best-practices (possibly integrating an offshoot of traditional mental practices like meditation to increase the ability to interact with the augments).
On the other hand, this process requires a lot of study in neurology and hardware, and so will likely be much slower than AGI timelines all-else-being-equal. To be a viable alternative/solution, there would have to be a sufficient push that the economic pressures towards AGI are instead diverted towards IA. This is somewhat helped along by the fact that narrow AI systems could be integrated into this approach, so if we assume that Narrow AI isn’t a solution to AGI (and that the above push succeeds in at least creating commercially-viable augments and brain-to-computer data transferal), the marginal incentives for productivity-rates should lean towards gearing AI research towards IA, rather than experimenting to create autonomous intelligent systems.