How to build a General Intelligence: Circuits and Pathways

Figure 1: Our headline image is from the Cognitive Consilience: An atlas of key pathways cross-referenced to supporting literature articles. The complexity and variety of routing within the brain can be appreciated with this beautiful illustration. Note in particular the specialisation of cortical cells and the way this affects their interactions with other cells in the cortex and elsewhere in the brain. Explore this fantastic resource yourself.

By David Rawlinson and Gideon Kowadlo

This is part 3 of our series “how to build an artificial general intelligence” (AGI). Part 1 was a theoretical look at General Intelligence (follow the link if you don’t know what General Intelligence is).

We believe that the Thalamo-Cortical system is the origin of General Intelligence in people. In Part 2 we presented very broadly how the Thalamo-Cortical system is structured and organised. We applied some core concepts, such as hierarchy, to help us describe the system.

We also looked at the cellular structure of the Cortex and in particular introduced Pyramidal cells.

This article is again about what we can learn from reverse-engineering the Thalamo-Cortical system, but this time from its connectivity, which we present in terms of circuits and pathways.

Pathways and Circuits

A pathway is a gross pattern of sequential connectivity between brain regions - for example, if part A is highly connected to part B, and activity in A is followed by activity in B, we say there exists a pathway between A and B. Cells in the Thalamo-Cortical system are connected to each other in quite restricted and specific ways, so these pathways are quite informative.

Circuits are more specific and precise details of both connectivity and functional interaction between neurons. In computational neuroscience there exists a concept called the Canonical Cortical Micro-Circuit. The specifics of this circuit are not widely agreed, because (a) the Cortex is complex and (b) many of the evidence-gathering exercises are statistical observations (e.g. “X% of outputs from A and Y% of output from B projects to region C”) which may obscure fundamental functional or topological features. For example, outputs from A and B may project to cells with exclusive roles, but physically co-located in C. Statistical, regional approaches will not capture such distinctions.

In the neuroscience literature there’s a frustrating habit of selectively reporting supporting details while ignoring others. Perhaps this is simply because it's impossible to describe any part exhaustively. In particular, there is a lot of contradictory information about Cortical circuits. But the research can still shed some light on what is happening. Just don’t expect all sources to be consistent or complete!

Key Cortical Pathways

There are several widely-cited and well established cortical pathways (i.e. routes with at least one end in the Cortex). To understand these, it is important to remember both the physical and logical structure of the Cortex as described in the previous article. Physically, the cortex is made of layers, and logically, Columns within the Cortex form a hierarchy.

The hierarchy defines a structure made of Columns, and determines which Columns interact. Pathways describe the patterns of interaction between cells within a Column, and between Columns. We assume that all Columns are functionally identical prior to training.

Cells within Columns are usually identified by both the physical location of cell bodies within particular Layers in the Column, and by the morphology (shape) of the cell. Data flow to and from Cortical cells is largely restricted to a handful of core pathways that begin and terminate in particular cell types in specific cortical layers.

There are many descriptions of cortical pathways and circuits in the literature. We will first introduce just 4 well-established cortical-cortical pathways, and then some thalamo-cortical pathways. Note that although the existence of these pathways is unambiguous, their purpose and function is poorly understood. They appear to be consistent across various somatosensory regions of the cortex, especially in comparison to variations in other brain tissues.

Hawkins’ Hierarchical Temporal Memory (HTM) introduces 3 of the 4 pathways in a single, coherent scheme and relates them to a general intelligence algorithm. We will borrow this terminology and describe them in detail below. Their names describe the direction of data flow and the routing used:

Feed-Forward Direct Pathway: C2/3 → C4 → C2/3
Feed-Forward Indirect Pathway: C5 → Thalamus → C4 → C2/3
Feed-Back Direct Pathway #1: from C6 → C1

We are also interested in a second Feed-Back Direct “pathway” implemented by cortically projecting C6 pyramidal cells with axons that terminate in both C6 and C1 in hierarchically lower regions (see here for a diagram).

Feed-Back Direct Pathway #2: from C6 → C6

Note that cells in all cortical layers (except, perhaps, C4) receive input via their dendrites in C1. In other words, feedback from C6 to C1 is then used as input to many layers. Feedback from C6 to C6 is generally not input for other layers.

In neuroscience, Feed-Forward usually means the flow of data away from external sources such as sensors (towards greater abstraction, if you believe in a cortical hierarchy). Feed-Back means the opposite - data flow towards regions that have direct interaction with external sensors and motors.

Direct pathways are so-called because data is routed directly from one cortical column or region to another, without a stop along the way. Indirect pathways are routed via other structures. The  “Feed-Forward-Indirect” pathway described by Hawkins is routed via the Thalamus.

Figure 2, derived from a Hawkins/Numenta publication, shows graphically how information flows between columns and between layers within columns, as part of these 3 pathways according to the HTM theory. As mentioned before, the community is welcome to contribute by updating and adding to the figure.

Hawkins assigns specific roles to these pathways, but we will be re-interpreting them in the next article.

Figure 2: Routing of 3 core pathways, based on a diagram from the HTM/CLA White Paper. Note the involvement of specific cortical layers with each pathway, and the central role of the Thalamus. The names of the pathways indicate direct (cortex-to-cortex) and indirect (cortex-thalamus-cortex) variants, with direction being either forward (away from external sensors and motors, towards increasing abstraction) or backward (towards more concrete regions dealing with specific sensor/motor input). 

The role of the Thalamus

Let’s recap: The Cortex is composed of Columns, organised into a hierarchy. Cells pass messages directly to other Columns that are higher or lower in the hierarchy. Messages may also be transmitted indirectly between Columns, via the Thalamus.

The Thalamus is often viewed as having a gating or relaying function. The Thalamus is particularly associated with control of attention.

This section will describe indirect pathways involving the Thalamus. Figure 3 is a reproduction of a figure from Sherman and Guillery (2006) that has two new features of interest. These authors use the terminology “first order” to denote cortical regions receiving direct sensor input and “higher order” to denote cortical regions receiving input from “first order” cortical regions. This corresponds with the notion of hierarchy levels 1 and 2.

The Thalamus is a significant part of the “Feed-Forward Indirect” pathway. This pathway originates at Cortex layer 5 and propagates to a nucleus in the Thalamus. There, the nucleus may react by transmitting a (presumably corresponding) signal to one or more other Cortical Columns, in a different region. In some theories of cortical function, the target Column is conceptually “higher” in the hierarchy. The Thalamic input enters the Cortex via Thalamic axons terminating in Cortex layer 4 and is then propagated to Cortex Layer 5 where the pathway begins again.

Figure 3 also shows Cells in Columns in Cortex layer 6 fairly accurately form reciprocal modulatory connections to Thalamic nuclei that provide input to the Column via C4 and C5! Therefore, a Column within the Cortex has influence on data that it receives from the Thalamus. In effect, the Cortex is not a passive recipient but works with the Thalamus to control its input. The figure also depicts C6 cells projecting to C6 in lower regions (our second feedback pathway).

Figure 3: Pathways between cortical columns in different regions, showing layer involvement in each pathway and the role of the Thalamus. Sherman and Guillery use the terminology “first order” to denote cortical regions receiving direct sensor input and “higher order” to denote cortical regions receiving input from lower (e.g. “first order”) cortical regions. This corresponds with the notion of hierarchy levels 1 and 2. Note that in addition to the 3 pathways shown in the previous figure, we see additional direct feedback pathways and reciprocal feedback from Cortex layer 6 to the Thalamic nuclei that stimulate the cortical region. Image source.

Motor output

At this point it is interesting to look at how the Cortex can influence or control behaviour, particularly the generation of motor output. There are two pathways that allow the cortex to influence or control behaviour:

Cortical Control: Basal Ganglia → Thalamus → Cortex → Motors
Cortical Influence: Cortex → Basal Ganglia → Motors

Note that in both cases, the origin of action selection is the Basal Ganglia. In the first case, the Basal Ganglia control signals emitted by the Thalamus, with these signals in turn affecting activity within Cortex layer 5 (C5). C5, particularly in motor areas, has been studied in detail. 10-15% of the cells in these areas are very large pyramidal neurons known as Betz cells, that can be observed to drive muscles very directly with few synapses in between. These cells are more prevalent in primates and are especially important for control of the hands. This makes sense given that manual tasks are typically more complex and require greater dexterity than movements by other parts of the body. The human Cortex is believed to be crucial for innovative and sophisticated manual tasks such as tool-making.

Within the Cortical layers, C5 seems to be uniquely involved in motor output. Figure 4 shows some of the ways output from Pyramidal cells in C5 project output to areas of the brain associated with motor output and control. In contrast, pyramidal cells in C2/3 predominantly project to other areas of the cortex and are not directly involved in control.

Figure 4: Pyramidal cells in C5 project output to areas of the brain associated with motor output and control. In contrast, pyramidal cells in C2/3 predominantly project to other areas of the cortex and are not directly involved in control. Image source.

The second way that the Cortex can influence motor output is via the Basal Ganglia. In this case, we propose that the Cortex might provide contextual information to assist the Basal Ganglia in its direct control outputs, but we found no evidence that the Cortex is able to exert control over the Basal Ganglia.

We suggest Cortical influence over the Basal Ganglia is less interesting from a General Intelligence perspective, because the hierarchical representations formed within the Cortex are not exploited, and execution is performed by more ancient brain systems not associated with General Intelligence qualities.

For the rest of this article series, we will ignore control pathways that do not involve the Cortex,  and will focus on direct control output from Cortex layer 5.

Action Selection

It is widely believed that action selection occurs within the flow of information from Cortex through the Basal Ganglia, a group of deep, centralised brain structures adjacent to the Thalamus. There are a number of theories about how this occurs, but it is generally believed to involve a form of Reinforcement Learning used to select ideas from the options presented by the Cortex, with competitive mechanisms for clean switching and conflict resolution.

A major output of the Basal Ganglia is to the Thalamus; one prevailing theory of this relationship is that the Basal Ganglia controls the gating or filtering function performed by the Thalamus, effectively manipulating the state of the Cortex in consequence. The full loop then becomes Cortex → Basal Ganglia → Thalamus → Cortex (see Wikipedia for a good illustration, or figure 5).

As discussed above, this article will focus on motor output generated directly by the Cortex.

Figure 5: Pathways forming a circuit from Cortex to Basal Ganglia to Thalamus and back to Cortex. Image Source.

Canonical Cortical Circuit

We now have the all the background information needed to define a “Canonical Cortical micro-Circuit” at a cellular level. All the information presented so far has been relatively uncontroversial, but this circuit is definitely our interpretation, not an established fact. However, we will present some evidence to (inconclusively) support our interpretation.

Figure 6: Our interpretation of the canonical cortical micro-circuit. Only a single cortical region or Column is shown. Arrow endings indicate the type of connection - driver, modulator or inhibitor. The numbers 2/3, 4, 5, and 6 refer to specific cortical layers. Each shape represents a set of cells of a particular type, not an individual cell. Self-connections and connections within each set are not shown, but often exist. Shapes T and B refer to Thalamus and Basal Ganglia, not broken down into specific cell layers or types. Data enters the diagram at 4 points, labelled A-D, but does not exit; in general the system forms a circuit not a linear path. Note that shape T occurs twice, because the circuit receives data from only one part of the Thalamus but projects to two areas in forward and backward directions.

Diagram Explanation

We will use variants of the diagram shown in figure 6 to explain our interpretation of cortical function. In this diagram, only a single Cortical region or Column (used interchangeably here) is shown. In later diagrams, we will show 3 hierarchy levels together so the flow of information between hierarchy levels is apparent.

In these diagrams, shapes represent a class of Neurons within a specific Cortical Layer. The numbers 2/3, 4, 5 and 6 refer to the Cortical layers in which these cell classes occur. The shapes labelled T and B refer to the Thalamus and Basal Ganglia (internal cell types and layers are not shown). Arrows on the diagram show the effect of each connection, either driving (providing information or input that causes another cell to become active), modulation (stimulating or inhibiting the activity of a target cell) or inhibition (exclusively inhibiting the activity of a target cell).

If you want more detail on the thalamic end of the thalamocortical circuitry, an excellent source is this paper by Sherman.

There are many interneurons (described in the previous article) that are not shown in this diagram. We chose to omit these because we believe they are integral to the function of a layer of pyramidal cells within a Column, rather than an independent system. Specifically, we suggest that inhibitory interneurons implement local self-organising and local competitive functions (e.g. winner-take-all), ensuring sparse activation of the cell types represented by shapes in our diagram (C2/3, C4, C5, and C6). The self-organising behaviour also ensures that cells within each column optimise coverage of observed input patterns given a finite cell population. Inclusion of the interneurons would clutter the diagram without adding much explanatory value.

We also omit self-connections within a class of cells represented by a shape. These self-connections likely provide context and contribute to learning and exclusive activity within the class, but don’t make it easier to understand circuits in terms of cortical layers and hierarchy levels.

Excitatory Circuit

Figure 7 shows a multilevel version of the cortical circuit, similar to the multi-level figure from Sherman and Guillery (figure 3). We can now understand where the inputs to the circuit come from, in terms of other layers and external Sensors (S) and Motors (M). Note that Motors are driven directly from C5.

Figure 7: The cortical micro-circuit across several levels of Cortex with involvement of Thalamus and Basal Ganglia. The red highlight shows a single excitatory ‘circuit’. See text for details.

The red path in figure 7 shows our excitatory “canonical circuit”: Data flows from the Thalamus to spiny stellate (star-shape in figures) cells in C4 (see source and source), from where it propagates to pyramidal cells in C2/3, and then to pyramidal cells in C5. C6 is known as the multiform layer, but also contains many pyramidal cells of unusual proportions and orientations. C6 cells are driven by C5, and in turn modulate the Thalamus. Note that C6 cells within a region modulate the same Thalamic nuclei that provide input to that region of Cortex.

Inhibitory Circuit

A second, inhibitory circuit exists alongside our excitatory circuit. In addition to providing input to the Cortex via C4, axons from the Thalamus also drive inhibitory Parvalbumin-expressing (PV) neurons in C4 (shown as circles in the diagram). These inhibitory neurons make up a large fraction of all the cells in C4, and inhibit pyramidal cells in C5 (see source or source ).

This means that the input from the Thalamus can be both informative and executive. It is executive in that it actually manipulates the activity of layer 5 within the Cortex, and informative by providing a copy of the signal driving the manipulation to C4. Figure 8 shows our inhibitory circuit. We believe this circuit is of critical importance because it provides a mechanism for the Thalamus to centrally manipulate the state of the Cortex, specifically layer 5 and 6 pyramidal cells. This hypothesis will be expanded in the next article.

Figure 9 catalogues inhibitory cells, notably showing the cells used in our inhibitory circuit.

Figure 8: The inhibitory micro-circuit. The red highlight shows how the Thalamus controls activity in C5 within a Column by activating inhibitory cells in C4. The circuit is completed by C5 pyramidal cells driving C6 cells, which in turn modulate the activity of the same Thalamic nuclei that selectively activates C5. Each shape denotes a population of cells of a specific type within a single Column, excluding ‘T’ and ‘B’ that refer to the Thalamus and Basal Ganglia respectively.

Figure 9: Inhibitory interneurons in the Cortex. Of particular interest are the “PV” cells that are driven by axons from the Thalamus terminating in layer 4 and in turn inhibit pyramidal cells in layer 5. Image source. 

Pathways and the Canonical Circuits

Now let’s look at how pathways emerge from our cortical micro-circuit. Figures 10, 11, 12 show the Feed-Forward Direct, Feed-Forward Indirect and first Feed-Back pathways respectively. We also include another direct, Feed-Back pathway terminating at C6 (figure 13). Feed-back direct pathways terminating at C1, where many fibres are intermingled, are harder to interpret than feedback terminating directly at C6. Pyramidal neurons from many layers have dendrites in C1.

Figure 10: Feed-Forward Direct pathway within our canonical cortical micro-circuit.

Figure 10 highlights the Feed-Forward direct pathway. Signals propagate from C4 to C2/3 and then to C4 in a higher Column. This pattern is repeated up the hierarchy. This pathway is not filtered by the Thalamus or any other central structure. Although activity from C2/3 propagates to C5, it does not ascend the hierarchy via this route: C5 in one Column does not directly connect to C5 in a higher Column, only via an indirect pathway (see below).

Figure 11: Feed-Forward Indirect pathway.

Figure 11 highlights the Feed-Forward Indirect pathway. The Thalamus is involved in this pathway, and may have a gating or filtering effect. Data flows from the Thalamus to C4, to C2/3, to C5 and then to a different Thalamic nucleus that serves as the input gateway to another cortical Column in a different region of the Cortex.

Figure 12: The first of two Feed-Back Direct pathways.

Figure 12 highlights the first type of Feed-Back Direct pathway. This pathway may be more concerned with provision of broader and more abstract (i.e. hierarchically higher) contextual information to be used in the Feed-Forward pathways for better prediction. This suggestion is supported by evidence that axons from C6 via C1 synapse with apical dendrites of pyramidal cells in C2/3, C5 and C6, in hierarchically lower regions.

Figure 13 highlights the second of two Feed-Back Direct pathways. This pathway might be involved in cascading control activity down the hierarchy towards sensors and motors - the next article will expand on this idea. Activity propagates from C6 to C6 directly. C6 modulates the activity of local C5 cells and relevant Thalamic nuclei that drive local C5 cells. Note that connections from a Column to the Thalamus are reciprocal; feedback from C6 to the Thalamus targets the same nuclei that project axons to C4.

Figure 13: The second of two Feed-Back Direct pathways.

Summary

We’ve presented some additional, detailed perspectives on the organisation and function of circuits and pathways within the Thalamo-Cortical system and presented our interpretation of the canonical cortical micro-circuit.

So what’s the point of all this information? What do these circuits and pathways do, and why are they connected this way? How do they work?

It might seem that we’ve stopped short of really trying to interpret all this information and that’s because we are, indeed, holding back. After having spent so much time presenting background information, the next article finally attempts to understand why the thalamocortical system is connected in the ways described here, and how this system might give rise to general intelligence. 

Consciousness & "Free Will": The Elephants in the Room

By David Rawlinson and Gideon Kowadlo

The aim of this post is to take a short departure from more technical issues and deal with some of the philosophical questions about Artificial General Intelligence (AGI).

The Elephant in the room

This metaphor implies willful ignorance of something blindingly obvious. Something that's difficult to ignore. It requires effort to avoid talking about it.

While consciousness & "free will" are hot topics for philosophers, most "serious" Artificial Intelligence (AI) literature avoids them (exceptions include 1,2,3). However, I'm yet to meet someone with an interest in AI who hasn't thought about these deep questions (consciousness being the other big one). We just don't talk about them in scholarly company.

The history of AI is a series of speculative bubbles: False hopes, dashed promises and unrealized dreams have created the mainstream perception that AI has gone nowhere for decades. We seem to re-brand AI every 10 years to shed past disappointment. For example, Machine Learning currently enjoys huge popularity, yet half the community do not consider ML to be a subset of AI, viewing the latter as a narrower discipline concerned with symbolic reasoning. A decade ago everything was about explicit treatment of uncertainty.

Today, it is rare to write about “AI” in scholarly journals; we talk about specific approaches instead. AI is tainted terminology.

Having been repeatedly burnt, AI & ML researchers now focus on short-term, achievable goals while (publicly) ignoring questions about fundamental, qualitative distinctions between artificial intelligence methods that already work, and the natural intelligence of people and animals.

Many others make the same implicit distinction, assuming that no form of software has "true" intelligence. When challenged, few are able to describe what is missing; for others the goalposts keep moving. Despite computers being able to drive cars on busy roads, fly on another planet, excel in games, reasoning & insight, perception, fine, adaptive motor control and many other intellectual pursuits, there remains a popular suspicion that this is merely sophisticated trickery and not true intelligence at all!

We have a “God of the Gaps” problem: The qualitative differences between man and machine keep receding into ineffable gaps in machine capability. Humans keep raising the bar.

Physicists have repeatedly proposed (e.g. Penrose) that consciousness requires some special kind of physics. This rationalizes machine inferiority, but seems like an answer in search of a problem without strong prior evidence of such unusual phenomena.

The idea that consciousness requires some form of exceptional phenomena may be a peculiarly Western philosophical trait, resulting from exposure to Descartes' Dualism.

An opposing view is that when properly defined these problems might simply be emergent characteristics of a particular type of algorithm, making the type of physical embodiment unimportant.

The "mere algorithm" claim makes many people uncomfortable, as if it removes part of their humanity or individuality. We would argue it does not. Besides, as the philosopher Daniel Dennett said, "Only a theory that explained conscious events in terms of unconscious events, could explain consciousness at all".

A Problem of Definition

Our position is that the ongoing difficulty with consciousness & free will is simply lack of a satisfactory definition for these features. For those of us who regard Artificial Intelligence as a practical toolkit akin to spanners or wrenches, it's time to hold the less applied thinkers to account. Exactly what performance, qualities or abilities do people accept as demonstrating consciousness?

To improve the specificity of these problems let's replace “Free Will” with Self-Determination and swap Consciousness with Self-Awareness.

Self-Determination

The most problematic aspect of free will seems to be determinism. In a deterministic system the relationship between inputs and outputs is fixed; there are no choices. Instead the inputs determine the outputs. Clearly there is no room for free will if our sensory inputs exactly determine our actions in response.

"Free" will implies that normal causation doesn't apply to thinking - as if it were "free" from any and all constraints. This smacks of a preoccupation with Dualism and the theoretical constraints of hard determinism derived from Newtonian models of the universe. Both Descartes and Newton lived and wrote 300 years ago. We have moved on since then.

A much better description might be "self determination": deciding what to do via some reasoning process, making use of knowledge, experience and personal biases. The big issue is then whether this definition is lacking some essential “freedom” quality.

What if our sensor inputs don't determine our actions, but an unconscious neurological process does instead? Brain imaging suggests that conscious awareness of decisions often occurs long after decisions are actually made and even after action begins. In this model, due to "our" lack of access and control over decisions, free will is merely an illusion. Although this looks hopelessly conclusive, the word "our" is key to unlocking a greater range of more satisfying answers. It is ownership and access that is lacking. We will argue that self-determination restores both.

Self-Awareness

If we are to make our own choices, we need a framework for modelling the world and the values we attach to concepts, events and entities within it. The framework enables us to become aware of ourselves and the world we inhabit.

Consciousness is often described as awareness - of the self, of the past and future, and of the world around. There's also awareness of sensation - Qualia - and an ongoing "stream" of consciousness, such as an internal monologue. Attention - selective awareness - is also entwined with consciousness.

This article isn’t going to be able to describe how to artificially reproduce human awareness. For one thing, evidence from animals and inter-personal variation suggest that the qualities and properties of human awareness are a matter of degree, not a binary feature. Most of the other articles on this blog discuss ways to generate artificial representations of the world from sensory data, but there’s more about current progress in artificial awareness in a postscript to this article.

In contrast, we can ask some pretty definitive questions about the feasibility of self-determination, which will be the focus of the rest of this article.

Models of Self-Determination

Let's explore some thought experiments to see if we can find a set of physically-plausible qualities that fulfill a satisfying definition of self-determination.

Free Will is typically described as the ability to make decisions, or choose actions, in a way that is not determined by external events. In some sense, we are "free" to express personal preference and values over strong external persuasion or evidence.

But Free Will is not simply a random or unintelligent reaction; we would like it to be a choice of some kind. We would like to make informed, deliberate choices that balance our interpretation of objective and subjective criteria: Let our history, knowledge and experiences shape our choices.

A choice without knowledge and understanding is not really a choice at all. Only via an understanding of consequences can we assign value to the choices available. Comprehension of available choices relies on existing knowledge and experience. Execution of decisions requires ongoing, grounded understanding of the world and our action capabilities, to ensure we execute choices as intended. And you're not going to "choose" an action that has no personal meaning - for example, if you live in a desert where it never rains, you won’t invent an umbrella*.

We believe the answer to the whole Free Will problem is also the source of limits to our freedom: Our knowledge, experience and personal values. This is no bad thing: Individual characteristics develop from our personal history, and experience of consequences affects the values we express in future decisions. We don't have to express these limits negatively; we can say that experience, knowledge and beliefs guide us to choices we find reasonable. Personal identity is defined by experience and knowledge and expressed in choices: It’s what makes you, you. And it makes the choices yours.

This is not a new idea. In philosophy, those who believe that Free Will is compatible with some level of determinism are known as compatibilists. Compatibilitists (such as us) believe that the free-will vs determinism debate is a false dilemma that can be sidestepped by clearly defining the objective.

Self-determination is a positive way to summarise the influence of internal constraints. So, if we were to accept these constraints, what freedom do we have, to make choices?

* The etymology of the word “Umbrella” is relevant. It derives from the Latin Umbra, meaning shade. The novel application of an existing sunshade tool to rain protection may be an example of ideas being guided, inspired and yet constrained by previous experience. The adaptation of an existing idea to a new problem is a common innovation technique.

Uncertainty and Stability

The universe we inhabit appears to be mostly deterministic, at human scales, but also quite random, at smaller scales. Some systems, such as weather, are unpredictable at large scales because they are very sensitive to small-scale changes. This phenomenon is known as “sensitive-dependence”: Chaos theory is the study of such systems. A famous example is the “Butterfly Effect”, in which the beating of a butterfly’s wings alters the path of a tropical storm thousands of miles away.

Other large-scale systems are stable against small-scale changes. For example, the behaviour of a brick house or a steel girder does not change significantly in response to butterflies’ flapping. The structure of these systems absorbs these changes, and the behaviour we care about is not affected.

We can easily design information-processing systems to have specific sensitivity to random events. Even simple functions can exhibit sensitive-dependence.

Conversely, we can design robust information processing systems. For example, your computer’s processor can execute millions of instructions per second without error. Nature can do this too: Error-checking mechanisms ensure remarkably accurate copying of DNA into every cell in your body; but mutations do occur at a low but necessarily nonzero rate. Natural selection of some mutations amplifies the frequency of some variants, resulting in evolution.

In Estimation theory, events are represented by a combination of explicit models and terms that capture the effects of random events. We call these random events “noise”. In a sensitive-dependent system, these random events can make the future state of the system unpredictable.

Noise can be selectively amplified or suppressed by carefully designed systems. Complex, large-scale systems can be sensitive to small-scale random events, or they can be designed to be almost entirely unaffected by noise.

Exploiting Uncertainty

For our self-determination model, the ideal solution is a mix of the two extremes. We need deterministic reasoning to accurately evaluate potential actions and outcomes. And we can exploit uncertainty and stability in a system to amplify the consequences of random events when exploring options, and stabilize decisions once made.

Consider perception. You want your senses to reliably tell you what's going on outside your head. This is essential: If all you saw were crazy swirls and flashing imaginary lights, you wouldn't be able to find your way around. But we also want some uncertainty in perception to allow us to test different hypotheses for what we're seeing and decide which fits best. For example, the perception of ambiguous images depends on expectations.

Imagine a scenario with 2 alternative choices: A, and B. Our internally "preferred" choice is A. But perceptual cues and third party advice strongly suggests action B. Let’s assume this "external bias" means that the probability of us even conceiving plan A is small (e.g. 0.1). So in this case it seems that external causes are dominating internal preferences. We're simply responding to external cues, like a puppet. Not ideal.

Now let's change the system a bit. We include a method of repeatedly, randomly generating combinations of ideas, and strongly reinforcing the ideas with the greatest internal value. Imagine rolling a 10-sided die. If we get a '1', we are lucky enough to think of plan A. If we get any other number, we only think of plan B, due to the external bias.

The dice will only roll a '1' rarely. But if we allow ourselves 100 dice-rolls we will get quite a few '1's. As long as we produce a strong response to rolling the occasional '1' (due to the anticipated outcome of plan A), we can design a system that will be largely determined by internal preferences, not dictated by chance properties of the outside world.

This setup is relatively easy to create in software. For example, Arunava Banerjee has designed an artificial neural network that behaves just like this. What’s more, models of human decision-making in the Basal Ganglia appear very similar to the dynamics described here. A recursive, competitive process between competing potential actions are eventually reduced to a set of selected actions that are then reinforced, and executed. A similar process occurs in the Superior Colliculus, the brain component that decides what your eyes will look at.

Figure: An ambiguous image; the same sensory input can be perceived in two ways (in this case either an old or young lady), depending on the prior or internal bias of the viewer.

The Cortex is widely accepted as the origin of high-level, abstract thought, including strategy and planning. In essence, the Cortex provides understanding of contexts and potential choices. The design of the brain routes inter-cortical connections via central hubs - the Basal Ganglia, and the Thalamus. During routing, messages are filtered. This design closely matches the architecture needed for a competitive evaluation of competing plans influenced by an understanding of their consequences.

If the part of your brain that generates ideas is sensitive-dependent, then it can overcome the influence of external factors and suggest all sorts of things until an internally resonating plan is found. Of course, your evaluation of the ideas should be quite deterministic, based on previous experience and knowledge.

Figure: Flow of information from the cortex through deeper structures such as the Basal Ganglia and Thalamus, before routing back to Cortex. This architecture allows deeper structures that filter and select the activity in the cortex. Image from Scholarpedia.

We can predict the weather a couple of days into the future, but beyond that we're no better than random chance at guessing whether it will be sunny. The impact of noise increases over time. Noise in the brain could also lead to vastly different outcomes even given similar initial states.

In just a few seconds of thought, our brains are sensitive and complex enough to generate and evaluate thousands of different ideas. With a little time for consideration, the probability of generating your "preferred" plan may be very high. It’s not the end of the world if this doesn't always happen; sometimes we need expert or friendly advice!

Attributes of Self-Determinism

Let's review the attributes of self-determinism, as defined above:

• Choices cannot be exactly predicted
• Demonstrates internal motivations and preference
• Stable despite irrelevant and/or gross variations in external stimuli
• Sensitive to external changes that do impact internal value system
• Consistent perception and decision-making in value-equivalent scenarios
• Can't decide to do things completely outside our previous experience, although innovation possible via novel composition and extrapolation
• Values based on previous decisions, lived consequences, experience and acquired knowledge
• Continuity of experience (i.e. an online learning system)

As discussed above, this form of self-determination requires only a highly tuned, sensitive, but perfectly ordinary physical mechanism.

Retrospective Future Self-Determination!

Sometimes we have to pause and think carefully about things. Sometimes we will mull over a big decision for a few days. Other “decisions” are trivial and are made instantly – perhaps most. But in these cases retrospective awareness of instinctive choices makes us more than helpless bystanders: We can reflect consciously on decisions already made, and re-assess for next time.

Our imagined experience of potential outcomes might modify the values we associate with various actions. We can imagine better outcomes from other choices, that will then be more readily selected in future. In this model, you can improve yourself by conscious reflection on your actions.

Retrospective re-evaluation of actions and consequences allows self-determination even in the face of evidence that decisions can be made before we're aware of them. As long as we later take the time to review the consequences, we can modify the values that will determine future choices.

Conclusion

The issue of free will has profound moral implications: Are we responsible for our actions? The answer is often said to lie in the existence (or otherwise) of a magic property of conscious awareness that allows understanding and decision-making to occur outside conventional physical processes. This process is often called “free-will”: The ability to choose a course that is not pre-determined, but instead a novel and unpredictable response: Chosen, not inevitable. The problem is, we know of no physical mechanism for this type of choice; we would argue that it can't even be defined coherently.

Instead, this article offers a limited but positive alternative to free will as intelligent self-determinism, exploiting both sensitive-dependence and stability via feedback within ordinary physics.

It's interesting to consider our concept of identity in relation to this type of self-determinism. Preferences and emotional expectations resulting from past experiences and personal consequences define our personalities and character via the values and emotional responses we learn to attach to events. This means that self-determined decisions define our identities. We are literally the product of our choices: Your personality is formed by a continuity of experience, choice and consequence.

Having described self-determinism without resorting to special physics or magic beans, there’s nothing to prevent us creating artificial copies of it. We have every reason to believe we can create machines that self-determine their own destiny just as we do. I can't wait to have this debate with one of them.

Postscript

Capabilities of Artificial Self-Awareness

Machines can already construct sophisticated internal representations of the world. Machines can interpret data in ways that have similar qualities and performance to human vision (e.g. how-old.net). So is there anything that fundamentally divides machines' internal representations from the experience of awareness we humans enjoy? Some philosophers call this the "hard problem" of consciousness; other philosophers say the problem doesn't exist, because the missing qualities can't be properly defined or do not really exist! This paradox is best illustrated with the Zombie thought experiment.

The Zombie Conundrum

A philosophical "zombie" is an entity that has the external appearance of consciousness, but internally is merely a simulation of the real thing. In fact, we might all be zombies, depending on the quality of consciousness required for the "real" thing. If you set impossible requirements, then we are all zombies.

Actually, it is easier to prove the veracity of conscious experience in software than in humans. This is because we can “pause” and explore software brains in great detail, with access to all internal state. Given an algorithm that is expected to produce the qualities of consciousness, we can inspect and measure these qualities directly. It might even be easier to build genuine consciousness, than a convincing simulation.

Consciousness may be a continuum rather than a binary feature: Awareness with varying degrees of quality and depth. Chimpanzees deliberately construct tools for later use. Dogs are capable of sophisticated social interactions. So there may not be a yes/no answer to the Hard Problem.

Progress in Artificial Awareness

The purpose of this blog is to look at practical techniques for automatically creating hierarchical, increasingly abstract representations of an embodied, adaptive agent in its environment. There's also some discussion of "symbol grounding" - making the jump between sensory and symbolic representations (we believe the problem goes away when defined as "accumulating invariances" instead).

Persistent belief in the hard problem of consciousness may stem from our inability to imagine both how our awareness would scale down to current computer levels of complexity, and how computer representations would feel when scaled to human proportions.

I find it fascinating to visualize internal representations created by AI, such as the image below. They do seem to capture the essential qualities and variations of broad classes, such as “Cat”. Often, when machines are wrong, their mistakes are reminiscent of the errors made by young children (e.g. “sheeps” or “shoeses” overgeneralization).

Figure: Google trained an artificial neural network (ANN) on stills from YouTube videos. Reverse engineering of one of the resulting neurons reveals this input; it is effectively a self-taught class-label for a common set of inputs. We call this class "Cat". Although the ANN didn't give it a name, it was able to experience this concept. If we had also taught it to speak like SIRI, it is likely the ANN would correctly associate this visual perception with the "cat" word. How is this different to human qualia of Cat?

Consciousness and AI

I really enjoyed reading this article Why can’t the world’s greatest minds solve the mystery of consciousness? published recently in the Guardian. It's an engaging re-exploration of the contemporary discourse on the classic mind-body dilemma.

One view is that the 'hard problem' of consciousness is an illusion. There is no separation between mind and body, and that consciousness is simply a result of the computational machinery of the brain - an algorithm. Whether you are willing to make this leap completely or not, there is no denying that the physical reality of the brain influences the experience and existence of consciousness.

This leads us to questions of the relationship between AI and consciousness. The topic is not often discussed. It is somewhat taboo, much like the topic of consciousness was itself, until recently. As the author points out with regard to an influential conference on consciousness at the University of Arizona in 1994, "in many quarters, consciousness was still taboo, too weird and new agey to take seriously, and some of the scientists in the audience were risking their reputations by attending."

However, the relationship between consciousness and intelligence is one of the most interesting aspects of cognition. Consciousness may even prove to be an important factor in higher intelligence, and conversely, understanding human level intelligence may shed light on the meaning and fabric of consciousness. We can only be certain of our own consciousness, yet we have no way to understand what it is.

There are a few salient attempts to describe cognition and consciousness in computational terms, two of which are listed below. Please leave a comment if you know of other examples. It will be great to see more active discussion on the topic.

The Computational Theory of Mind, Stanford Encyclopedia of Philosophy

Edited by Edward N. Zalta, published in The Stanford Encyclopedia of Philosophy, 2011.

and

Accounting for the computational basis of consciousness: a connectionist approach

By Ron Sun, published in Consciousness and Cognition, 1999.