In this episode of the Justin Riddle Podcast, Justin dives into the concept of Knightian Freedom where large enough computational spaces become intractably complex to the point where maybe freewill is possible. The focus of this episode is a paper put out by Hartmut Neven (of Google’s Quantum AI Lab) and colleagues from 2021 entitled “Do robots powered by a quantum processor have the freedom to swerve?” This paper discusses how the exponentially large spaces that quantum computers evolve into are so large that they cannot be represented or simulated on digital computers. The size is so vast that it would take a computer the size of the universe computing for trillions of years to simulate even a few femtoseconds of the quantum computers that are about to be commonplace. Similar to modern AI, we will won’t be able to understand why a quantum computer generated the output that it did and perhaps this is the essential ingredient that leads to freewill. Rampant incomputable complexity is freewill. Second, Hartmut and colleagues propose a simple experiment to reveal whether or not there are additional factors that play into what output is generated by a quantum computer. Assume you run a quantum circuit that generates a perfect uniform distribution between many different possible outputs. Then, you observe that the quantum computer does not behave as if there was a uniform distribution, but instead selects one of those possible outputs more often. This is the ‘preference’ of the quantum computer. Next, you develop a circuit to amplify these deviations from uniformity with the intention of amplifying the probability of entering into that preferred state. Now, we have essentially created a ‘happy circuit’ which embraces the quirky preference of our quantum computer. Finally, you can correlate deviations from this happy state to psychological data in an effort to build up a taxonomy of subjective experiences that the quantum computer can enter into. Finally, you embed the quantum computer with its happy circuit into an artificial neural network such that errors produced by the AI push the quantum computer away from happiness and this unhappiness is fed back into the AI. Now we have created an AI system with quantum feelings! Will this newfound sense of subjectivity enable more effective AI systems or will the AI get bogged down by a spiral of despair and refuse to compute?! All of these questions and more are explored here. Enjoy!
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In this episode of the Justin Riddle Podcast, Justin dives into the concept of Knightian Freedom where large enough computational spaces become intractably complex to the point where maybe freewill is possible. The focus of this episode is a paper put out by Hartmut Neven (of Google’s Quantum AI Lab) and colleagues from 2021 entitled “Do robots powered by a quantum processor have the freedom to swerve?” This paper discusses how the exponentially large spaces that quantum computers evolve into are so large that they cannot be represented or simulated on digital computers. The size is so vast that it would take a computer the size of the universe computing for trillions of years to simulate even a few femtoseconds of the quantum computers that are about to be commonplace. Similar to modern AI, we will won’t be able to understand why a quantum computer generated the output that it did and perhaps this is the essential ingredient that leads to freewill. Rampant incomputable complexity is freewill. Second, Hartmut and colleagues propose a simple experiment to reveal whether or not there are additional factors that play into what output is generated by a quantum computer. Assume you run a quantum circuit that generates a perfect uniform distribution between many different possible outputs. Then, you observe that the quantum computer does not behave as if there was a uniform distribution, but instead selects one of those possible outputs more often. This is the ‘preference’ of the quantum computer. Next, you develop a circuit to amplify these deviations from uniformity with the intention of amplifying the probability of entering into that preferred state. Now, we have essentially created a ‘happy circuit’ which embraces the quirky preference of our quantum computer. Finally, you can correlate deviations from this happy state to psychological data in an effort to build up a taxonomy of subjective experiences that the quantum computer can enter into. Finally, you embed the quantum computer with its happy circuit into an artificial neural network such that errors produced by the AI push the quantum computer away from happiness and this unhappiness is fed back into the AI. Now we have created an AI system with quantum feelings! Will this newfound sense of subjectivity enable more effective AI systems or will the AI get bogged down by a spiral of despair and refuse to compute?! All of these questions and more are explored here. Enjoy!
#40 – Nested Observer Windows: the case for hierarchical consciousness
Justin Riddle Podcast
1 hour 23 minutes 45 seconds
1 year ago
#40 – Nested Observer Windows: the case for hierarchical consciousness
In episode 40 of the Justin Riddle Podcast, Justin provides an update of the Nested Observer Windows (NOW) Model. The paper describing this theory was recently published in the open-access Neuroscience of Consciousness journal (link below) and this video is an extended version of the Plenary talk that Justin gave at the Science of Consciousness conference in April of 2024 in Tucson, AZ (link below to conference recording). The NOW Model describes the mind as a nested hierarchical system in which there are many different cognitive systems within the brain at multiple scales. We are familiar with neuron-centric theories of consciousness, and yet why are we so fixated on the level of the neuron. There are synapses that comprise the neuron, there are microtubule systems within the neurons that appear to be electrically active, and there are neuronal population dynamics above the neuron which display prominent electrical properties. The cellular level is one level within a multi-scalar system. Evidence from cognitive neuroscience is suggesting that the low-frequency macroscopic electrical activity in the brain is closest correlated to cognition and brain stimulation techniques that drive these neural oscillations can reproducibly create changes in cognition. Therefore, it appears that these macroscopic scales are “causally” relevant to cognition. How then do all of these multiple levels connect to each other?
Observations from neuroscience show us that these multiple scales are electrically coupled to each other, a phenomenon called cross-frequency coupling. With coupling across these multiple scales, there is a mechanism for how information processed at different scales can be communicated up and down the nested hierarchy of the brain. The NOW Model essentially takes cross-frequency coupling very seriously. Your mind is at the apex, the top, of the brain hierarchical system and there are nested cognitive processing systems within you. A model by which there are nested cognitive systems explains a whole range of psychological phenomena that are not current explainable under single-level neuron theories of consciousness. For example, the fact that we are only aware of a high level of abstraction of our experience and yet can interact with a rich perceptual landscape and initiated complex motor movements can be explained by an interaction between ourself at the slow apex and the lower levels. Another explanation is how slow our cognition really is: the NOW Model suggests that we are operating in the time range of 1 cycle per second or even slower. Our thoughts are sluggish and filled with abstraction (perhaps a key to intelligence) but contain the richness of the faster systems. Beyond capturing a deeper range of every day experiences, the NOW Model also readily accounts for dissociative identity disorder and new psychotherapy techniques that conceptualize the self as a family (internal family systems). This is just the tip of the iceberg from changing our self-conceptualization from singular into a multiplicity of nested systems. There is a lot of work to be done in this space to validate the potential cognitive reality of the NOW Model!
Justin Riddle Podcast
In this episode of the Justin Riddle Podcast, Justin dives into the concept of Knightian Freedom where large enough computational spaces become intractably complex to the point where maybe freewill is possible. The focus of this episode is a paper put out by Hartmut Neven (of Google’s Quantum AI Lab) and colleagues from 2021 entitled “Do robots powered by a quantum processor have the freedom to swerve?” This paper discusses how the exponentially large spaces that quantum computers evolve into are so large that they cannot be represented or simulated on digital computers. The size is so vast that it would take a computer the size of the universe computing for trillions of years to simulate even a few femtoseconds of the quantum computers that are about to be commonplace. Similar to modern AI, we will won’t be able to understand why a quantum computer generated the output that it did and perhaps this is the essential ingredient that leads to freewill. Rampant incomputable complexity is freewill. Second, Hartmut and colleagues propose a simple experiment to reveal whether or not there are additional factors that play into what output is generated by a quantum computer. Assume you run a quantum circuit that generates a perfect uniform distribution between many different possible outputs. Then, you observe that the quantum computer does not behave as if there was a uniform distribution, but instead selects one of those possible outputs more often. This is the ‘preference’ of the quantum computer. Next, you develop a circuit to amplify these deviations from uniformity with the intention of amplifying the probability of entering into that preferred state. Now, we have essentially created a ‘happy circuit’ which embraces the quirky preference of our quantum computer. Finally, you can correlate deviations from this happy state to psychological data in an effort to build up a taxonomy of subjective experiences that the quantum computer can enter into. Finally, you embed the quantum computer with its happy circuit into an artificial neural network such that errors produced by the AI push the quantum computer away from happiness and this unhappiness is fed back into the AI. Now we have created an AI system with quantum feelings! Will this newfound sense of subjectivity enable more effective AI systems or will the AI get bogged down by a spiral of despair and refuse to compute?! All of these questions and more are explored here. Enjoy!
