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I'm currently reading through some relevant literature for preparing my FLI grant proposal on the topic of concept learning and AI safety. I figured that I might as well write down the research ideas I get while doing so, so as to get some feedback and clarify my thoughts. I will posting these in a series of "Concept Safety"-titled articles.
In the previous post in this series, I talked about how one might get an AI to have similar concepts as humans. However, one would intuitively assume that a superintelligent AI might eventually develop the capability to entertain far more sophisticated concepts than humans would ever be capable of having. Is that a problem?
Just what are concepts, anyway?
To answer the question, we first need to define what exactly it is that we mean by a "concept", and why exactly more sophisticated concepts would be a problem.
Unfortunately, there isn't really any standard definition of this in the literature, with different theorists having different definitions. Machery even argues that the term "concept" doesn't refer to a natural kind, and that we should just get rid of the whole term. If nothing else, this definition from Kruschke (2008) is at least amusing:
Models of categorization are usually designed to address data from laboratory experiments, so “categorization” might be best defined as the class of behavioral data generated by experiments that ostensibly study categorization.
Because I don't really have the time to survey the whole literature and try to come up with one grand theory of the subject, I will for now limit my scope and only consider two compatible definitions of the term.
Definition 1: Concepts as multimodal neural representations. I touched upon this definition in the last post, where I mentioned studies indicating that the brain seems to have shared neural representations for e.g. the touch and sight of a banana. Current neuroscience seems to indicate the existence of brain areas where representations from several different senses are combined together into higher-level representations, and where the activation of any such higher-level representation will also end up activating the lower sense modalities in turn. As summarized by Man et al. (2013):
Briefly, the Damasio framework proposes an architecture of convergence-divergence zones (CDZ) and a mechanism of time-locked retroactivation. Convergence-divergence zones are arranged in a multi-level hierarchy, with higher-level CDZs being both sensitive to, and capable of reinstating, specific patterns of activity in lower-level CDZs. Successive levels of CDZs are tuned to detect increasingly complex features. Each more-complex feature is defined by the conjunction and configuration of multiple less-complex features detected by the preceding level. CDZs at the highest levels of the hierarchy achieve the highest level of semantic and contextual integration, across all sensory modalities. At the foundations of the hierarchy lie the early sensory cortices, each containing a mapped (i.e., retinotopic, tonotopic, or somatotopic) representation of sensory space. When a CDZ is activated by an input pattern that resembles the template for which it has been tuned, it retro-activates the template pattern of lower-level CDZs. This continues down the hierarchy of CDZs, resulting in an ensemble of well-specified and time-locked activity extending to the early sensory cortices.
On this account, my mental concept for "dog" consists of a neural activation pattern making up the sight, sound, etc. of some dog - either a generic prototypical dog or some more specific dog. Likely the pattern is not just limited to sensory information, either, but may be associated with e.g. motor programs related to dogs. For example, the program for throwing a ball for the dog to fetch. One version of this hypothesis, the Perceptual Symbol Systems account, calls such multimodal representations simulators, and describes them as follows (Niedenthal et al. 2005):
A simulator integrates the modality-specific content of a category across instances and provides the ability to identify items encountered subsequently as instances of the same category. Consider a simulator for the social category, politician. Following exposure to different politicians, visual information about how typical politicians look (i.e., based on their typical age, sex, and role constraints on their dress and their facial expressions) becomes integrated in the simulator, along with auditory information for how they typically sound when they talk (or scream or grovel), motor programs for interacting with them, typical emotional responses induced in interactions or exposures to them, and so forth. The consequence is a system distributed throughout the brain’s feature and association areas that essentially represents knowledge of the social category, politician.
The inclusion of such "extra-sensory" features helps understand how even abstract concepts could fit this framework: for example, one's understanding of the concept of a derivative might be partially linked to the procedural programs one has developed while solving derivatives. For a more detailed hypothesis of how abstract mathematics may emerge from basic sensory and motor programs and concepts, I recommend Lakoff & Nuñez (2001).
Definition 2: Concepts as areas in a psychological space. This definition, while being compatible with the previous one, looks at concepts more "from the inside". Gärdenfors (2000) defines the basic building blocks of a psychological conceptual space to be various quality dimensions, such as temperature, weight, brightness, pitch, and the spatial dimensions of height, width, and depth. These are psychological in the sense of being derived from our phenomenal experience of certain kinds of properties, rather than the way in which they might exist in some objective reality.
For example, one way of modeling the psychological sense of color is via a color space defined by the quality dimensions of hue (represented by the familiar color circle), chromaticness (saturation), and brightness.
The second phenomenal dimension of color is chromaticness (saturation), which ranges from grey (zero color intensity) to increasingly greater intensities. This dimension is isomorphic to an interval of the real line. The third dimension is brightness which varies from white to black and is thus a linear dimension with two end points. The two latter dimensions are not totally independent, since the possible variation of the chromaticness dimension decreases as the values of the brightness dimension approaches the extreme points of black and white, respectively. In other words, for an almost white or almost black color, there can be very little variation in its chromaticness. This is modeled by letting that chromaticness and brightness dimension together generate a triangular representation ... Together these three dimensions, one with circular structure and two with linear, make up the color space. This space is often illustrated by the so called color spindle
This kind of a representation is different from the physical wavelength representation of color, where e.g. the hue is mostly related to the wavelength of the color. The wavelength representation of hue would be linear, but due to the properties of the human visual system, the psychological representation of hue is circular.
Gärdenfors defines two quality dimensions to be integral if a value cannot be given for an object on one dimension without also giving it a value for the other dimension: for example, an object cannot be given a hue value without also giving it a brightness value. Dimensions that are not integral with each other are separable. A conceptual domain is a set of integral dimensions that are separable from all other dimensions: for example, the three color-dimensions form the domain of color.
From these definitions, Gärdenfors develops a theory of concepts where more complicated conceptual spaces can be formed by combining lower-level domains. Concepts, then, are particular regions in these conceptual spaces: for example, the concept of "blue" can be defined as a particular region in the domain of color. Notice that the notion of various combinations of basic perceptual domains making more complicated conceptual spaces possible fits well together with the models discussed in our previous definition. There more complicated concepts were made possible by combining basic neural representations for e.g. different sensory modalities.
The origin of the different quality dimensions could also emerge from the specific properties of the different simulators, as in PSS theory.
Thus definition #1 allows us to talk about what a concept might "look like from the outside", with definition #2 talking about what the same concept might "look like from the inside".
Interestingly, Gärdenfors hypothesizes that much of the work involved with learning new concepts has to do with learning new quality dimensions to fit into one's conceptual space, and that once this is done, all that remains is the comparatively much simpler task of just dividing up the new domain to match seen examples.
For example, consider the (phenomenal) dimension of volume. The experiments on "conservation" performed by Piaget and his followers indicate that small children have no separate representation of volume; they confuse the volume of a liquid with the height of the liquid in its container. It is only at about the age of five years that they learn to represent the two dimensions separately. Similarly, three- and four-year-olds confuse high with tall, big with bright, and so forth (Carey 1978).
The problem of alien concepts
With these definitions for concepts, we can now consider what problems would follow if we started off with a very human-like AI that had the same concepts as we did, but then expanded its conceptual space to allow for entirely new kinds of concepts. This could happen if it self-modified to have new kinds of sensory or thought modalities that it could associate its existing concepts with, thus developing new kinds of quality dimensions.
An analogy helps demonstrate this problem: suppose that you're operating in a two-dimensional space, where a rectangle has been drawn to mark a certain area as "forbidden" or "allowed". Say that you're an inhabitant of Flatland. But then you suddenly become aware that actually, the world is three-dimensional, and has a height dimension as well! That raises the question of, how should the "forbidden" or "allowed" area be understood in this new three-dimensional world? Do the walls of the rectangle extend infinitely in the height dimension, or perhaps just some certain distance in it? If just a certain distance, does the rectangle have a "roof" or "floor", or can you just enter (or leave) the rectangle from the top or the bottom? There doesn't seem to be any clear way to tell.
As a historical curiosity, this dilemma actually kind of really happened when airplanes were invented: could landowners forbid airplanes from flying over their land, or was the ownership of the land limited to some specific height, above which the landowners had no control? Courts and legislation eventually settled on the latter answer. A more AI-relevant example might be if one was trying to limit the AI with rules such as "stay within this box here", and the AI then gained an intuitive understanding of quantum mechanics, which might allow it to escape from the box without violating the rule in terms of its new concept space.
More generally, if previously your concepts had N dimensions and now they have N+1, you might find something that fulfilled all the previous criteria while still being different from what we'd prefer if we knew about the N+1th dimension.
In the next post, I will present some (very preliminary and probably wrong) ideas for solving this problem.
I'm currently reading through some relevant literature for preparing my FLI grant proposal on the topic of concept learning and AI safety. I figured that I might as well write down the research ideas I get while doing so, so as to get some feedback and clarify my thoughts. I will posting these in a series of "Concept Safety"-titled articles.
A frequently-raised worry about AI is that it may reason in ways which are very different from us, and understand the world in a very alien manner. For example, Armstrong, Sandberg & Bostrom (2012) consider the possibility of restricting an AI via "rule-based motivational control" and programming it to follow restrictions like "stay within this lead box here", but they raise worries about the difficulty of rigorously defining "this lead box here". To address this, they go on to consider the possibility of making an AI internalize human concepts via feedback, with the AI being told whether or not some behavior is good or bad and then constructing a corresponding world-model based on that. The authors are however worried that this may fail, because
Humans seem quite adept at constructing the correct generalisations – most of us have correctly deduced what we should/should not be doing in general situations (whether or not we follow those rules). But humans share a common of genetic design, which the OAI would likely not have. Sharing, for instance, derives partially from genetic predisposition to reciprocal altruism: the OAI may not integrate the same concept as a human child would. Though reinforcement learning has a good track record, it is neither a panacea nor a guarantee that the OAIs generalisations agree with ours.
Addressing this, a possibility that I raised in Sotala (2015) was that possibly the concept-learning mechanisms in the human brain are actually relatively simple, and that we could replicate the human concept learning process by replicating those rules. I'll start this post by discussing a closely related hypothesis: that given a specific learning or reasoning task and a certain kind of data, there is an optimal way to organize the data that will naturally emerge. If this were the case, then AI and human reasoning might naturally tend to learn the same kinds of concepts, even if they were using very different mechanisms. Later on the post, I will discuss how one might try to verify that similar representations had in fact been learned, and how to set up a system to make them even more similar.
A particularly fascinating branch of recent research relates to the learning of word embeddings, which are mappings of words to very high-dimensional vectors. It turns out that if you train a system on one of several kinds of tasks, such as being able to classify sentences as valid or invalid, this builds up a space of word vectors that reflects the relationships between the words. For example, there seems to be a male/female dimension to words, so that there's a "female vector" that we can add to the word "man" to get "woman" - or, equivalently, which we can subtract from "woman" to get "man". And it so happens (Mikolov, Yih & Zweig 2013) that we can also get from the word "king" to the word "queen" by adding the same vector to "king". In general, we can (roughly) get to the male/female version of any word vector by adding or subtracting this one difference vector!
Why would this happen? Well, a learner that needs to classify sentences as valid or invalid needs to classify the sentence "the king sat on his throne" as valid while classifying the sentence "the king sat on her throne" as invalid. So including a gender dimension on the built-up representation makes sense.
But gender isn't the only kind of relationship that gets reflected in the geometry of the word space. Here are a few more:
It turns out (Mikolov et al. 2013) that with the right kind of training mechanism, a lot of relationships that we're intuitively aware of become automatically learned and represented in the concept geometry. And like Olah (2014) comments:
It’s important to appreciate that all of these properties of W are side effects. We didn’t try to have similar words be close together. We didn’t try to have analogies encoded with difference vectors. All we tried to do was perform a simple task, like predicting whether a sentence was valid. These properties more or less popped out of the optimization process.
This seems to be a great strength of neural networks: they learn better ways to represent data, automatically. Representing data well, in turn, seems to be essential to success at many machine learning problems. Word embeddings are just a particularly striking example of learning a representation.
It gets even more interesting, for we can use these for translation. Since Olah has already written an excellent exposition of this, I'll just quote him:
We can learn to embed words from two different languages in a single, shared space. In this case, we learn to embed English and Mandarin Chinese words in the same space.
We train two word embeddings, Wen and Wzh in a manner similar to how we did above. However, we know that certain English words and Chinese words have similar meanings. So, we optimize for an additional property: words that we know are close translations should be close together.
Of course, we observe that the words we knew had similar meanings end up close together. Since we optimized for that, it’s not surprising. More interesting is that words we didn’t know were translations end up close together.
In light of our previous experiences with word embeddings, this may not seem too surprising. Word embeddings pull similar words together, so if an English and Chinese word we know to mean similar things are near each other, their synonyms will also end up near each other. We also know that things like gender differences tend to end up being represented with a constant difference vector. It seems like forcing enough points to line up should force these difference vectors to be the same in both the English and Chinese embeddings. A result of this would be that if we know that two male versions of words translate to each other, we should also get the female words to translate to each other.
Intuitively, it feels a bit like the two languages have a similar ‘shape’ and that by forcing them to line up at different points, they overlap and other points get pulled into the right positions.
After this, it gets even more interesting. Suppose you had this space of word vectors, and then you also had a system which translated images into vectors in the same space. If you have images of dogs, you put them near the word vector for dog. If you have images of Clippy you put them near word vector for "paperclip". And so on.
You do that, and then you take some class of images the image-classifier was never trained on, like images of cats. You ask it to place the cat-image somewhere in the vector space. Where does it end up?
You guessed it: in the rough region of the "cat" words. Olah once more:
This was done by members of the Stanford group with only 8 known classes (and 2 unknown classes). The results are already quite impressive. But with so few known classes, there are very few points to interpolate the relationship between images and semantic space off of.
The Google group did a much larger version – instead of 8 categories, they used 1,000 – around the same time (Frome et al. (2013)) and has followed up with a new variation (Norouzi et al. (2014)). Both are based on a very powerful image classification model (from Krizehvsky et al. (2012)), but embed images into the word embedding space in different ways.
The results are impressive. While they may not get images of unknown classes to the precise vector representing that class, they are able to get to the right neighborhood. So, if you ask it to classify images of unknown classes and the classes are fairly different, it can distinguish between the different classes.
Even though I’ve never seen a Aesculapian snake or an Armadillo before, if you show me a picture of one and a picture of the other, I can tell you which is which because I have a general idea of what sort of animal is associated with each word. These networks can accomplish the same thing.
These algorithms made no attempt of being biologically realistic in any way. They didn't try classifying data the way the brain does it: they just tried classifying data using whatever worked. And it turned out that this was enough to start constructing a multimodal representation space where a lot of the relationships between entities were similar to the way humans understand the world.
How useful is this?
"Well, that's cool", you might now say. "But those word spaces were constructed from human linguistic data, for the purpose of predicting human sentences. Of course they're going to classify the world in the same way as humans do: they're basically learning the human representation of the world. That doesn't mean that an autonomously learning AI, with its own learning faculties and systems, is necessarily going to learn a similar internal representation, or to have similar concepts."
This is a fair criticism. But it is mildly suggestive of the possibility that an AI that was trained to understand the world via feedback from human operators would end up building a similar conceptual space. At least assuming that we chose the right learning algorithms.
When we train a language model to classify sentences by labeling some of them as valid and others as invalid, there's a hidden structure implicit in our answers: the structure of how we understand the world, and of how we think of the meaning of words. The language model extracts that hidden structure and begins to classify previously unseen things in terms of those implicit reasoning patterns. Similarly, if we gave an AI feedback about what kinds of actions counted as "leaving the box" and which ones didn't, there would be a certain way of viewing and conceptualizing the world implied by that feedback, one which the AI could learn.
"Hmm, maaaaaaaaybe", is your skeptical answer. "But how would you ever know? Like, you can test the AI in your training situation, but how do you know that it's actually acquired a similar-enough representation and not something wildly off? And it's one thing to look at those vector spaces and claim that there are human-like relationships among the different items, but that's still a little hand-wavy. We don't actually know that the human brain does anything remotely similar to represent concepts."
Here we turn, for a moment, to neuroscience.
Multivariate Cross-Classification (MVCC) is a clever neuroscience methodology used for figuring out whether different neural representations of the same thing have something in common. For example, we may be interested in whether the visual and tactile representation of a banana have something in common.
We can test this by having several test subjects look at pictures of objects such as apples and bananas while sitting in a brain scanner. We then feed the scans of their brains into a machine learning classifier and teach it to distinguish between the neural activity of looking at an apple, versus the neural activity of looking at a banana. Next we have our test subjects (still sitting in the brain scanners) touch some bananas and apples, and ask our machine learning classifier to guess whether the resulting neural activity is the result of touching a banana or an apple. If the classifier - which has not been trained on the "touch" representations, only on the "sight" representations - manages to achieve a better-than-chance performance on this latter task, then we can conclude that the neural representation for e.g. "the sight of a banana" has something in common with the neural representation for "the touch of a banana".
A particularly fascinating experiment of this type is that of Shinkareva et al. (2011), who showed their test subjects both the written words for different tools and dwellings, and, separately, line-drawing images of the same tools and dwellings. A machine-learning classifier was both trained on image-evoked activity and made to predict word-evoked activity and vice versa, and achieved a high accuracy on category classification for both tasks. Even more interestingly, the representations seemed to be similar between subjects. Training the classifier on the word representations of all but one participant, and then having it classify the image representation of the left-out participant, also achieved a reliable (p<0.05) category classification for 8 out of 12 participants. This suggests a relatively similar concept space between humans of a similar background.
We can now hypothesize some ways of testing the similarity of the AI's concept space with that of humans. Possibly the most interesting one might be to develop a translation between a human's and an AI's internal representations of concepts. Take a human's neural activation when they're thinking of some concept, and then take the AI's internal activation when it is thinking of the same concept, and plot them in a shared space similar to the English-Mandarin translation. To what extent do the two concept geometries have similar shapes, allowing one to take a human's neural activation of the word "cat" to find the AI's internal representation of the word "cat"? To the extent that this is possible, one could probably establish that the two share highly similar concept systems.
One could also try to more explicitly optimize for such a similarity. For instance, one could train the AI to make predictions of different concepts, with the additional constraint that its internal representation must be such that a machine-learning classifier trained on a human's neural representations will correctly identify concept-clusters within the AI. This might force internal similarities on the representation beyond the ones that would already be formed from similarities in the data.
Next post in series: The problem of alien concepts.
Abstract: Discussion of how we might want to define human preferences, particularly in the context of building an AI intended to learn and implement those preferences. Starts with actual arguments about the applicability of the VNM utility theorem, then towards the end gets into hypotheses that are less well defended but possibly more important. At the very end, suggests that current hypothesizing about AI safety might be overemphasizing “discovering our preferences” over “creating our preferences”.
I was re-reading the chapter on status in Impro (excerpt), and I noticed that Johnstone seemed to be implying that different people are comfortable at different levels of status: some prefer being high status and others prefer being low status. I found this peculiar, because the prevailing notion in the rationalistsphere seems to be that everyone's constantly engaged in status games aiming to achieve higher status. I've even seen arguments to the effect that a true post-scarcity society is impossible, because status is zero-sum and there will always be people at the bottom of the status hierarchy.
But if some people preferred to have low status, this whole dilemma might be avoided, if a mix of statuses could be find that left everyone happy.
First question - is Johnstone's "status" talking about the same thing as our "status"? He famously claimed that "status is something you do, not something that you are", and that
I should really talk about dominance and submission, but I'd create a resistance. Students who will agree readily to raising or lowering their status may object if asked to 'dominate' or 'submit'.
Viewed via this lens, it makes sense that some people would prefer being in a low status role: if you try to take control of the group, you become subject to various status challenges, and may be held responsible for the decisions you make. It's often easier to remain low status and let others make the decisions.
But there's still something odd about saying that one would "prefer to be low status", at least in the sense in which we usually use the term. Intuitively, a person may be happy being low status in the sense of not being dominant, but most people are still likely to desire something that feels kind of like status in order to be happy. Something like respect, and the feeling that others like them. And a lot of the classical "status-seeking behaviors" seem to be about securing the respect of others. In that sense, there seems to be something intuitive true in the "everyone is engaged in status games and wants to be higher-status" claim.
So I think that there are two different things that we call "status" which are related, but worth distinguishing.
1) General respect and liking. This is "something you have", and is not inherently zero-sum. You can achieve it by doing things that are zero-sum, like being the best fan fiction writer in the country, but you can also do it by things like being considered generally friendly and pleasant to be around. One of the lessons that I picked up from The Charisma Myth was that you can be likable by just being interested in the other person and displaying body language that signals your interest in the other person.
Basically, this is "do other people get warm fuzzies from being around you / hearing about you / consuming your work", and is not zero-sum because e.g. two people who both have great social skills and show interest in you can both produce the same amount of warm fuzzies, independent of each other's existence.
But again, specific sources of this can be zero-sum: if you respect someone a lot for their art, but then run across into even better art and realize that the person you previously admired is pretty poor in comparison, that can reduce the respect you feel for them. It's just that there are also other sources of liking which aren't necessarily zero-sum.
2) Dominance and control of the group. It's inherently zero-sum because at most one person can have absolute say on the decisions of the group. This is "something you do": having the respect and liking of the people in the group (see above) makes it easier for you to assert dominance and makes the others more willing to let you do so, but you can also voluntarily abstain from using that power and leave the decisions to others. (Interestingly, in some cases this can even increase the extent to which you are liked, which translates to a further boost in the ability to control the group, if you so desired.)
Morendil and I previously suggested a definition of status as "the general purpose ability to influence a group", but I think that definition was somewhat off in conflating the two senses above.
I've always had the vague feeling that the "everyone can't always be happy because status is zero-sum" claim felt off in some sense that I was unable to properly articulate, but this seems to resolve the issue. If this model were true, it would also make me happy, because it would imply that we can avoid zero-sum status fights while still making everybody content.
See here for the previous update if you missed / forgot it.
In this update, no new game content, but new graphics.
I wasn’t terribly happy about the graphical representation of the various nodes in the last update. Especially in the first two networks, if you didn’t read the descriptions of the nodes carefully, it was very easy to just click your way through them without really having a clue of what the network was actually doing. Needless to say, for a game that’s supposed to teach how the networks function, this is highly non-optimal.
Here’s the representation that I’m now experimenting with: the truth table of the nodes is represented graphically inside the node. The prior variable at the top doesn’t really have a truth table, it’s just true or false. The “is” variable at the bottom is true if its parent is true, and false if its parent is false.
You may remember that in the previous update, unobservable nodes were represented in grayscale. I ended up dropping that, because that would have been confusing in this representation: if the parent is unobservable, should the blobs representing its truth values in the child node be in grayscale as well? Both “yes” and “no” answers felt confusing.
Instead the observational state of a node is now represented by its border color. Black for unobservable, gray for observable, no border for observed. The metaphor is supposed to be something like, a border is a veil of ignorance blocking us from seeing the node directly, but if the veil is gray it’s weak enough to be broken, whereas a black veil is strong enough to resist a direct assault. Or something.
When you observe a node, not only does its border disappear, but the truth table entries that get reduced to a zero probability disappear, to be replaced by white boxes. I experimented with having the eliminated entries still show up in grayscale, so you could e.g. see that the “is” node used to contain the entry for (false -> false), but felt that this looked clearer.
The “or” node at the bottom is getting a little crowded, but hopefully not too crowded. Since we know that its value is “true”, the truth table entry showing (false, false -> false) shows up in all whites. It’s also already been observed, so it starts without a border.
After we observe that there’s no monster behind us, the “or” node loses its entries for (monster, !waiting -> looks) and (monster, waiting -> looks), leaving only (!monster, waiting -> looks): meaning that the boy must be waiting for us to answer.
This could still be made clearer: currently the network updates instantly. I’m thinking about adding a brief animation where the “monster” variable would first be revealed as false, which would then propagate an update to the values of “looks at you” (with e.g. the red tile in “monster” blinking at the same time as the now-invalid truth table entries, and when the tiles stopped blinking, those now-invalid entries would have disappeared), and that would in turn propagate the update to the “waiting” node, deleting the red color from it. But I haven’t yet implemented this.
The third network is where things get a little tricky. The “attacking” node is of type “majority vote” - i.e. it’s true if at least two of its parents are true, and false otherwise. That would make for a truth table with eight entries, each holding four blobs each, and we could already see the “or” node in the previous screen being crowded. I’m not quite sure of what to do here. At this moment I’m thinking of just leaving the node as is, and displaying more detailed information in the sidebar.
Here’s another possible problem. Just having the truth table entries works fine to make it obvious where the overall probability of the node comes from… for as long as the valid values of the entries are restricted to “possible” and “impossible”. Then you can see at a glance that, say, of the three possible entries, two would make this node true and one would make this false, so there’s a ⅔ chance of it being true.
But in this screen, that has ceased to be the case. The “attacking” node has a 75% chance of being true, meaning that, for instance, the “is / block” node’s “true -> true” entry also has a 75% chance of being the right one. This isn’t reflected in the truth table visualization. I thought of adding small probability bars under each truth table entry, or having the size of the truth table blobs reflect their probability, but then I’d have to make the nodes even bigger, and it feels like it would easily start looking cluttered again. But maybe it’d be the right choice anyway? Or maybe just put the more detailed information in the sidebar? I’m not sure of the best thing to do here.
If anyone has good suggestions, I would be grateful to get advice from people who have more of a visual designer gene than I do!
The philosopher John Danaher has posted a list of all the posts that he's written on the topic of robotics and AI. Below is the current version of the list: he says that he will keep updating the page as he writes more.
- The Singularity: Overview and Framework: This was my first attempt to provide a general overview and framework for understanding the debate about the technological singularity. I suggested that the debate could be organised around three main theses: (i) the explosion thesis -- which claims that there will be an intelligence explosion; (ii) the unfriendliness thesis -- which claims that an advanced artificial intelligence is likely to be "unfriendly"; and (iii) the inevitability thesis -- which claims that the creation of an unfriendly AI will be difficult to avoid, if not inevitable.
- The Singularity: Overview and Framework Redux: This was my second attempt to provide a general overview and framework for understanding the debate about the technological singularity. I tried to reduce the framework down to two main theses: (i) the explosion thesis and (ii) the unfriendliness thesis.
- The Golem Genie and Unfriendly AI (Part One, Part Two): This two-parter summarises what I think is the best argument for the unfriendliness thesis. The argument was originally presented by Muehlhauser and Helm, but I try to simplify its main components.
- AIs and the Decisive Advantage Thesis: Many people claim that an advanced artificial intelligence would have decisive advantages over human intelligences. Is this right? In this post, I look at Kaj Sotala's argument to that effect.
- Is there a case for robot slaves? - If robots can be persons -- in the morally thick sense of "person" -- then surely it would be wrong to make them cater to our every whim? Or would it? Steve Petersen argues that the creation of robot slaves might be morally permissible. In this post, I look at what he has to say.
- The Ethics of Robot Sex: A reasonably self-explanatory title. This post looks at the ethical issues that might arise from the creation of sex robots.
- Will sex workers be replaced by robots? A Precis: A short summary of a longer article examining the possibility of sex workers being replaced by robots. Contrary to the work of others, I suggest that sex work might be resilient to the phenomenon of technological unemployment.
- Bostrom on Superintelligence (1) The Orthogonality Thesis: The first part in my series on Nick Bostrom's book Superintelligence. This one covers Bostrom's orthogonality thesis, according to which there is no necessary relationship between intelligence and benevolence.
- Bostrom on Superintelligence (2) The Instrumental Convergence Thesis: The second part in my series on Bostrom's book. This one examines the instrumental convergence thesis, according to which an intelligent agent, no matter what its final goals may be, is likely to converge upon certain instrumental goals that are unfriendly to human beings.
- Bostrom on Superintelligence (3) Doom and the Treacherous Turn: The third part in my series on Bostrom's book. This time I finally get to look at Bostrom's argument for the AI doomsday scenario, and for why it may be difficult to avoid.
- Bostrom on Superintelligence (4) Malignant Failure Modes: The fourth part in my series on Bostrom's book. This one explains why Bostrom thinks it would be difficult to simply program the AI with the right set of values.
- Bostrom on Superintelligence (5) Limiting an AIs Capabilities: The fifth part in my series on Bostrom's book. This one looks at the possibility of hampering or restricting an AI's capabilities, and whether that could help to avoid the doomsday scenario.
- Bostrom on Superintelligence (6) Motivation Selection Methods: The sixth (and for now final) part in my series on Bostrom's book. This one considers the advantages and disadvantages of different methods for selecting the motivations of an advanced AI.
I just got home from a four-day rationality workshop in England that was organized by the Center For Applied Rationality (CFAR). It covered a lot of content, but if I had to choose a single theme that united most of it, it was listening to your emotions.
That might sound like a weird focus for a rationality workshop, but cognitive science has shown that the intuitive and emotional part of the mind (”System 1”) is both in charge of most of our behavior, and also carries out a great deal of valuable information-processing of its own (it’s great at pattern-matching, for example). Much of the workshop material was aimed at helping people reach a greater harmony between their System 1 and their verbal, logical System 2. Many of people’s motivational troubles come from the goals of their two systems being somehow at odds with each other, and we were taught to have our two systems have a better dialogue with each other, harmonizing their desires and making it easier for information to cross from one system to the other and back.
To give a more concrete example, there was the technique of goal factoring. You take a behavior that you often do but aren’t sure why, or which you feel might be wasted time. Suppose that you spend a lot of time answering e-mails that aren’t actually very important. You start by asking yourself: what’s good about this activity, that makes me do it? Then you try to listen to your feelings in response to that question, and write down what you perceive. Maybe you conclude that it makes you feel productive, and it gives you a break from tasks that require more energy to do.
Next you look at the things that you came up with, and consider whether there’s a better way to accomplish them. There are two possible outcomes here. Either you conclude that the behavior is an important and valuable one after all, meaning that you can now be more motivated to do it. Alternatively, you find that there would be better ways of accomplishing all the goals that the behavior was aiming for. Maybe taking a walk would make for a better break, and answering more urgent e-mails would provide more value. If you were previously using two hours per day on the unimportant e-mails, possibly you could now achieve more in terms of both relaxation and actual productivity by spending an hour on a walk and an hour on the important e-mails.
At this point, you consider your new plan, and again ask yourself: does this feel right? Is this motivating? Are there any slight pangs of regret about giving up my old behavior? If you still don’t want to shift your behavior, chances are that you still have some motive for doing this thing that you have missed, and the feelings of productivity and relaxation aren’t quite enough to cover it. In that case, go back to the step of listing motives.
Or, if you feel happy and content about the new direction that you’ve chosen, victory!
Notice how this technique is all about moving information from one system to another. System 2 notices that you’re doing something but it isn’t sure why that is, so it asks System 1 for the reasons. System 1 answers, ”here’s what I’m trying to do for us, what do you think?” Then System 2 does what it’s best at, taking an analytic approach and possibly coming up with better ways of achieving the different motives. Then it gives that alternative approach back to System 1 and asks, would this work? Would this give us everything that we want? If System 1 says no, System 2 gets back to work, and the dialogue continues until both are happy.
Again, I emphasize the collaborative aspect between the two systems. They’re allies working for common goals, not enemies. Too many people tend towards one of two extremes: either thinking that their emotions are stupid and something to suppress, or completely disdaining the use of logical analysis. Both extremes miss out on the strengths of the system that is neglected, and make it unlikely for the person to get everything that they want.
As I was heading back from the workshop, I considered doing something that I noticed feeling uncomfortable about. Previous meditation experience had already made me more likely to just attend to the discomfort rather than trying to push it away, but inspired by the workshop, I went a bit further. I took the discomfort, considered what my System 1 might be trying to warn me about, and concluded that it might be better to err on the side of caution this time around. Finally – and this wasn’t a thing from the workshop, it was something I invited on the spot – I summoned a feeling of gratitude and thanked my System 1 for having been alert and giving me the information. That might have been a little overblown, since neither system should actually be sentient by itself, but it still felt like a good mindset to cultivate.
Although it was never mentioned in the workshop, what comes to mind is the concept of wu-wei from Chinese philosophy, a state of ”effortless doing” where all of your desires are perfectly aligned and everything comes naturally. In the ideal form, you never need to force yourself to do something you don’t want to do, or to expend willpower on an unpleasant task. Either you want to do something and do, or don’t want to do it, and don’t.
A large number of the workshop’s classes – goal factoring, aversion factoring and calibration, urge propagation, comfort zone expansion, inner simulation, making hard decisions, Hamming questions, againstness – were aimed at more or less this. Find out what System 1 wants, find out what System 2 wants, dialogue, aim for a harmonious state between the two. Then there were a smaller number of other classes that might be summarized as being about problem-solving in general.
The classes about the different techniques were interspersed with ”debugging sessions” of various kinds. In the beginning of the workshop, we listed different bugs in our lives – anything about our lives that we weren’t happy with, with the suggested example bugs being things like ”every time I talk to so-and-so I end up in an argument”, ”I think that I ‘should’ do something but don’t really want to”, and ”I’m working on my dissertation and everything is going fine – but when people ask me why I’m doing a PhD, I have a hard time remembering why I wanted to”. After we’d had a class or a few, we’d apply the techniques we’d learned to solving those bugs, either individually, in pairs, or small groups with a staff member or volunteer TA assisting us. Then a few more classes on techniques and more debugging, classes and debugging, and so on.
The debugging sessions were interesting. Often when you ask someone for help on something, they will answer with direct object-level suggestions – if your problem is that you’re underweight and you would like to gain some weight, try this or that. Here, the staff and TAs would eventually get to the object-level advice as well, but first they would ask – why don’t you want to be underweight? Okay, you say that you’re not completely sure but based on the other things that you said, here’s a stupid and quite certainly wrong theory of what your underlying reasons for it might be, how does that theory feel like? Okay, you said that it’s mostly on the right track, so now tell me what’s wrong with it? If you feel that gaining weight would make you more attractive, do you feel that this is the most effective way of achieving that?
Only after you and the facilitator had reached some kind of consensus of why you thought that something was a bug, and made sure that the problem you were discussing was actually the best way to address to reasons, would it be time for the more direct advice.
At first, I had felt that I didn’t have very many bugs to address, and that I had mostly gotten reasonable advice for them that I might try. But then the workshop continued, and there were more debugging sessions, and I had to keep coming up with bugs. And then, under the gentle poking of others, I started finding the underlying, deep-seated problems, and some things that had been motivating my actions for the last several months without me always fully realizing it. At the end, when I looked at my initial list of bugs that I’d come up with in the beginning, most of the first items on the list looked hopelessly shallow compared to the later ones.
Often in life you feel that your problems are silly, and that you are affected by small stupid things that ”shouldn’t” be a problem. There was none of that at the workshop: it was tacitly acknowledged that being unreasonably hindered by ”stupid” problems is just something that brains tend to do. Valentine, one of the staff members, gave a powerful speech about ”alienated birthrights” – things that all human beings should be capable of engaging in and enjoying, but which have been taken from people because they have internalized beliefs and identities that say things like ”I cannot do that” or ”I am bad at that”. Things like singing, dancing, athletics, mathematics, romantic relationships, actually understanding the world, heroism, tackling challenging problems. To use his analogy, we might not be good at these things at first, and may have to grow into them and master them the way that a toddler grows to master her body. And like a toddler who’s taking her early steps, we may flail around and look silly when we first start doing them, but these are capacities that – barring any actual disabilities – are a part of our birthright as human beings, which anyone can ultimately learn to master.
Then there were the people, and the general atmosphere of the workshop. People were intelligent, open, and motivated to work on their problems, help each other, and grow as human beings. After a long, cognitively and emotionally exhausting day at the workshop, people would then shift to entertainment ranging from wrestling to telling funny stories of their lives to Magic: the Gathering. (The game of ”bunny” was an actual scheduled event on the official agenda.) And just plain talk with each other, in a supportive, non-judgemental atmosphere. It was the people and the atmosphere that made me the most reluctant to leave, and I miss them already.
Would I recommend CFAR’s workshops to others? Although my above description may sound rather gushingly positive, my answer still needs to be a qualified ”mmmaybe”. The full price tag is quite hefty, though financial aid is available and I personally got a very substantial scholarship, with the agreement that I would pay it at a later time when I could actually afford it.
Still, the biggest question is, will the changes from the workshop stick? I feel like I have gained a valuable new perspective on emotions, a number of useful techniques, made new friends, strengthened my belief that I can do the things that I really set my mind on, and refined the ways by which I think of the world and any problems that I might have – but aside for the new friends, all of that will be worthless if it fades away in a week. If it does, I would have to judge even my steeply discounted price as ”not worth it”. That said, the workshops do have a money-back guarantee if you’re unhappy with the results, so if it really feels like it wasn’t worth it, I can simply choose to not pay. And if all the new things do end up sticking, it might still turn out that it would have been worth paying even the full, non-discounted price.
CFAR does have a few ways by which they try to make the things stick. There will be Skype follow-ups with their staff, for talking about how things have been going since the workshop. There is a mailing list for workshop alumni, and the occasional events, though the physical events are very US-centric (and in particular, San Francisco Bay Area-centric).
The techniques that we were taught are still all more or less experimental, and are being constantly refined and revised according to people’s experiences. I have already been thinking of a new skill that I had been playing with for a while before the workshop, and which has a bit of that ”CFAR feel” – I will aim to have it written up soon and sent to the others, and maybe it will eventually make its way to the curriculum of a future workshop. That should help keep me engaged as well.
We shall see. Until then, as they say in CFAR – to victory!
Some of you may remember me proposing a game idea that went by the name of The Fundamental Question. Some of you may also remember me talking a lot about developing an educational game about Bayesian Networks for my MSc thesis, but not actually showing you much in the way of results.
Insert the usual excuses here. But thanks to SSRIs and mytomatoes.com and all kinds of other stuff, I'm now finally on track towards actually accomplishing something. Here's a report on a very early prototype.
This game has basically two goals: to teach its players something about Bayesian networks and probabilistic reasoning, and to be fun. (And third, to let me graduate by giving me material for my Master's thesis.)
We start with the main character stating that she is nervous. Hitting any key, the player proceeds through a number of lines of internal monologue:
I am nervous.
I’m standing at the gates of the Academy, the school where my brother Opin was studying when he disappeared. When we asked the school to investigate, they were oddly reluctant, and told us to drop the issue.
The police were more helpful at first, until they got in contact with the school. Then they actually started threatening us, and told us that we would get thrown in prison if we didn’t forget about Opin.
That was three years ago. Ever since it happened, I’ve been studying hard to make sure that I could join the Academy once I was old enough, to find out what exactly happened to Opin. The answer lies somewhere inside the Academy gates, I’m sure of it.
Now I’m finally 16, and facing the Academy entrance exams. I have to do everything I can to pass them, and I have to keep my relation to Opin a secret, too.
???: “Hey there.”
Eep! Someone is talking to me! Is he another applicant, or a staff member? Wait, let me think… I’m guessing that applicant would look a lot younger than staff members! So, to find that out… I should look at him!
[You are trying to figure out whether the voice you heard is a staff member or another applicant. While you can't directly observe his staff-nature, you believe that he'll look young if he's an applicant, and like an adult if he's a staff member. You can look at him, and therefore reveal his staff-nature, by right-clicking on the node representing his apperance.]
Here is our very first Bayesian Network! Well, it's not really much of a network: I'm starting with the simplest possible case in order to provide an easy start for the player. We have one node that cannot be observed ("Student", its hidden nature represented by showing it in greyscale), and an observable node ("Young-looking") whose truth value is equal to that of the Student node. All nodes are binary random variables, either true or false.
According to our current model of the world, "Student" has a 50% chance of being true, so it's half-colored in white (representing the probability of it being true) and half-colored in black (representing the probability of it being false). "Young-looking" inherits its probability directly. The player can get a bit of information about the two nodes by left-clicking on them.
The game also offers alternate color schemes for colorblind people who may have difficulties distinguishing red and green.
Now we want to examine the person who spoke to us. Let's look at him, by right-clicking on the "Young-looking" node.
Not too many options here, because we're just getting started. Let's click on "Look at him", and find out that he is indeed young, and thus a student.
This was the simplest type of minigame offered within the game. You are given a set of hidden nodes whose values you're tasked with discovering by choosing which observable nodes to observe. Here the player had no way to fail, but later on, the minigames will involve a time limit and too many observable nodes to inspect within that time limit. It then becomes crucial to understand how probability flows within a Bayesian network, and which nodes will actually let you know the values of the hidden nodes.
The story continues!
Short for an adult, face has boyish look, teenagerish clothes... yeah, he looks young!
He's a student!
...I feel like I’m overthinking things now.
...he’s looking at me.
I’m guessing he’s either waiting for me to respond, or there’s something to see behind me, and he’s actually looking past me. If there isn’t anything behind me, then I know that he must be waiting for me to respond.
Maybe there's a monster behind me, and he's paralyzed with fear! I should check that possibility before it eats me!
[You want to find out whether the boy is waiting for your reply or staring at a monster behind you. You know that he's looking at you, and your model of the world suggests that he will only look in your direction if he's waiting for you to reply, or if there's a monster behind you. So if there's no monster behind you, you know that he's waiting for you to reply!]
Slightly more complicated network, but still, there's only one option here. Oops, apparently the "Looks at you" node says it's an observable variable that you can right-click to observe, despite the fact that it's already been observed. I need to fix that.
Anyway, right-clicking on "Attacking monster" brings up a "Look behind you" option, which we'll choose.
You see nothing there. Besides trees, that is.
Boy: “Um, are you okay?”
“Yeah, sorry. I just… you were looking in my direction, and I wasn’t sure of whether you were expecting me to reply, or whether there was a monster behind me.”
Boy: “You thought that there was a reasonable chance for a monster to be behind you?”
I’m embarrassed to admit it, but I’m not really sure of what the probability of a monster having snuck up behind me really should have been.
My studies have entirely focused on getting into this school, and Monsterology isn’t one of the subjects on the entrance exam!
I just went with a 50-50 chance since I didn’t know any better.
'Okay, look. Monsterology is my favorite subject. Monsters avoid the Academy, since it’s surrounded by a mystical protective field. There’s no chance of them getting even near! 0 percent chance.'
[Your model of the world has been updated! The prior of the variable 'Monster Near The Academy' is now 0%.]
Then stuff happens and they go stand in line for the entrance exam or something. I haven't written this part. Anyway, then things get more exciting, for a wild monster appears!
AAAAAAH! A MONSTER BEHIND ME!
Huh, the monster is carrying a sword.
Well, I may not have studied Monsterology, but I sure did study fencing!
[You draw your sword. Seeing this, the monster rushes at you.]
He looks like he's going to strike. But is it really a strike, or is it a feint?
If it's a strike, I want to block and counter-attack. But if it's a feint, that leaves him vulnerable to my attack.
I have to choose wisely. If I make the wrong choice, I may be dead.
What did my master say? If the opponent has at least two of dancing legs, an accelerating midbody, and ferocious eyes, then it's an attack!
Otherwise it's a feint! Quick, I need to read his body language before it's too late!
Now get to the second type of minigame! Here, you again need to discover the values of some number of hidden variables within a time limit, but here it is in order to find out the consequences of your decision. In this one, the consequence is simple - either you live or you die. I'll let the screenshot and tutorial text speak for themselves:
[Now for some actual decision-making! The node in the middle represents the monster's intention to attack (or to feint, if it's false). Again, you cannot directly observe his intention, but on the top row, there are things about his body language that signal his intention. If at least two of them are true, then he intends to attack.]
[Your possible actions are on the bottom row. If he intends to attack, then you want to block, and if he intends to feint, you want to attack. You need to inspect his body language and then choose an action based on his intentions. But hurry up! Your third decision must be an action, or he'll slice you in two!]
In reality, the top three variables are not really independent of each other. We want to make sure that the player can always win this battle despite only having three actions. That's two actions for inspecting variables, and one action for actually making a decision. So this battle is rigged: either the top three variables are all true, or they're all false.
...actually, now that I think of it, the order of the variables is wrong. Logically, the body language should be caused by the intention to attack, and not vice versa, so the arrows should point from the intention to body language. I'll need to change that. I got these mixed up because the prototypical exemplar of a decision minigame is one where you need to predict someone's reaction from their personality traits, and there the personality traits do cause the reaction. Anyway, I want to get this post written before I go to bed, so I won't change that now.
Right-clicking "Dancing legs", we now see two options besides "Never mind"!
We can find out the dancingness of the enemy's legs by thinking about our own legs - we are well-trained, so our legs are instinctively mirroring our opponent's actions to prevent them from getting an advantage over us - or by just instinctively feeling where they are, without the need to think about them! Feeling them would allow us to observe this node without spending an action.
Unfortunately, feeling them has "Fencing 2" as a prerequisite skill, and we don't have that. Neither could we have them, in this point of the game. The option is just there to let the player know that there are skills to be gained in this game, and make them look forward to the moment when they can actually gain that skill. As well as giving them an idea of how the skill can be used.
Anyway, we take a moment to think of our legs, and even though our opponent gets closer to us in that time, we realize that our legs our dancing! So his legs must be dancing as well!
With our insider knowledge, we now know that he's attacking, and we could pick "Block" right away. But let's play this through. The network has automatically recalculated the probabilities to reflect our increased knowledge, and is now predicting a 75% chance for our enemy to be attacking, and for "Blocking" to thus be the right decision to make.
Next we decide to find out what his eyes say, by matching our gaze with his. Again, there would be a special option that cost us no time - this time around, one enabled by Empathy 1 - but we again don't have that option.
Except that his gaze is so ferocious that we are forced to look away! While we are momentarily distracted, he closes the distance, ready to make his move. But now we know what to do... block!
Now the only thing that remains to do is to ask our new-found friend for an explanation.
"You told me there was a 0% chance of a monster near the academy!"
Boy: “Ehh… yeah. I guess I misremembered. I only read like half of our course book anyway, it was really boring.”
“Didn’t you say that Monsterology was your favorite subject?”
Boy: “Hey, that only means that all the other subjects were even more boring!”
“. . .”
I guess I shouldn’t put too much faith on what he says.
[Your model of the world has been updated! The prior of the variable 'Monster Near The Academy' is now 50%.]
[Your model of the world has been updated! You have a new conditional probability variable: 'True Given That The Boy Says It's True', 25%]
And that's all for now. Now that the basic building blocks are in place, future progress ought to be much faster.
As you might have noticed, my "graphics" suck. A few of my friends have promised to draw art, but besides that, the whole generic Java look could go. This is where I was originally planning to put in the sentence "and if you're a Java graphics whiz and want to help fix that, the current source code is conveniently available at GitHub", but then getting things to his point took longer than I expected and I didn't have the time to actually figure out how the whole Eclipse-GitHub integration works. I'll get to that soon. Github link here!
I also want to make the nodes more informative - right now they only show their marginal probability. Ideally, clicking on them would expand them to a representation where you could visually see what components their probability composed of. I've got some scribbled sketches of what this should look like for various node types, but none of that is implemented yet.
I expect some of you to also note that the actual Bayes theorem hasn't shown up yet, at least in no form resembling the classic mammography problem. (It is used implicitly in the network belief updates, though.) That's intentional - there will be a third minigame involving that form of the theorem, but somehow it felt more natural to start this way, to give the player a rough feeling of how probability flows through Bayesian networks. Admittedly I'm not sure of how well that's happening so far, but hopefully more minigames should help the player figure it out better.
What's next? Once the main character (who needs a name) manages to get into the Academy, there will be a lot of social scheming, and many mysteries to solve in order for her to find out just what did happen to her brother... also, I don't mind people suggesting things, such as what could happen next, and what kinds of network configurations the character might face in different minigames.
(Also, everything that you've seen might get thrown out and rewritten if I decide it's no good. Let me know what you think of the stuff so far!)
Abstract: Sophisticated autonomous AI may need to base its behavior on fuzzy concepts that cannot be rigorously defined, such as well-being or rights. Obtaining desired AI behavior requires a way to accurately specify these concepts. We review some evidence suggesting that the human brain generates its concepts using a relatively limited set of rules and mechanisms. This suggests that it might be feasible to build AI systems that use similar criteria and mechanisms for generating their own concepts, and could thus learn similar concepts as humans do. We discuss this possibility, and also consider possible complications arising from the embodied nature of human thought, possible evolutionary vestiges in cognition, the social nature of concepts, and the need to compare conceptual representations between humans and AI systems.
I just got word that this paper was accepted for the AAAI-15 Workshop on AI and Ethics: I've uploaded a preprint here. I'm hoping that this could help seed a possibly valuable new subfield of FAI research. Thanks to Steve Rayhawk for invaluable assistance while I was writing this paper: it probably wouldn't have gotten done without his feedback motivating me to work on this.
Some time back, I wrote that I was unwilling to continue with investigations into mass downvoting, and asked people for suggestions on how to deal with them from now on. The top-voted proposal in that thread suggested making Viliam_Bur into a moderator, and Viliam gracefully accepted the nomination. So I have given him moderator privileges and also put him in contact with jackk, who provided me with the information necessary to deal with the previous cases. Future requests about mass downvote investigations should be directed to Viliam.
Thanks a lot for agreeing to take up this responsibility, Viliam! It's not an easy one, but I'm very grateful that you're willing to do it. Please post a comment here so that we can reward you with some extra upvotes. :)
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