Key points are not available for this paper at this time.
Can machines ever be sentient? Could they perceive and feel things, be conscious of their surroundings? What are the prospects of achieving sentience in a machine? What are the dangers associated with such an endeavor, and is it even ethical to embark on such a path to begin with? In the series of articles of this column, I discuss one possible path toward “general intelligence” in machines: to use the process of Darwinian evolution to produce artificial brains that can be grafted onto mobile robotic platforms, with the goal of achieving fully embodied sentient machines.After reviewing the history of Artificial Intelligence research (Adami, 2021) and discussing the components, topology, and optimization methods used in artificial neural network research (Adami, 2022), we now take a step back to ask ourselves, What is intelligence? In our quest to evolve an intelligent system, this is not an idle question. In fact, asking this question will help us focus on essential features of what we call intelligence, rather than being distracted by incidental attributes. Our answer will be guided by the principle that intelligence is an evolutionary response to uncertain environments: that the primary purpose of intelligence is to increase the organism’s fitness.Just as it is unlikely that there will ever be a unique and universal definition of intelligence, it is also unlikely that there will be widespread agreement about what the processes are that contribute to intelligence: the elements of intelligence. The five elements that I will discuss here are rooted in the idea that intelligence is a (biological or computational) trait that enables its bearer to reduce the uncertainty about the world in which it lives (both in time and space) and harness the information it has gained to succeed against its competitors, cooperate with its supporters, and extract the resources it needs from its environment without coming to harm. Recognizing who is friend and who is foe (and using information to defeat the foe and support the friend) ultimately leads to a greater number of offspring.To leverage information in support of organismal fitness, the organism needs to perceive the environment; extract the salient features (those that matter to the organism and can be perceived by its sensory system); make predictions and plans based on the sensed world as well as on what was learned from experience; and, finally, act according to those predictions.1 Such a view of intelligence is very much aligned with the “knowledge-level systems” view of the late 20th century (Anderson, 1983; Newell, 1990), except that those attempts to formulate a “unified theory of cognition” made no attempt to quantify said knowledge in terms of information. An information-theoretic view of intelligence and cognition has the advantage that it can quantify the relation between the “symbols” manipulated by the knowledge system and the things in the physical world that they represent. This is important because, historically, one of the most common criticisms of attempts to formalize (and ultimately engineer) thinking systems conjured up an apparent dichotomy between the “zeros and ones” of computer systems, which are devoid of intrinsic meaning (“strings by themselves can’t have any meaning”; Searle, 1984, p. 31), and the fact that “thoughts are about things.” Information theory quantifies precisely that link, both in computers and in people.Whereas most theories of cognition posit that sensing and acting are integral elements of intelligence because they are clearly part of the “sensory–action loop” (Bongard Clark, 2016; Newell, 1990), here I take the point of view that the sensors and motors themselves are “given” (even though cognition does affect sensing and acting), and I discuss only the elements of intelligence that take place within the neurons of the brain, excluding sensors and motors (often called “peripheral neurons”; McCulloch see, e.g., Feynman, 1974), is without a doubt a complex one, involving (as I will describe) a shift from perceptual characters that are described by continuous values to conceptual ones described by discrete values. For discrete categories, Shannon’s (1948) uncertainty function is given byH(X)=−∑i=1np(xi)logp(xi),(1)where p(xi) is the likelihood of encountering an object within category xi in world X. In general, the number of categories (as well as the “distance” between different categories) depends on how useful the differentiation is. For example, in some situations, it might be relevant to make a distinction between two categories (say, “chair“ and “stool”) that is not necessary in others. In other words, the brain tends to operate with just the categories that are necessary to best understand (and predict) the world, given the particular circumstances.How do categories emerge? This is a difficult question to answer, because although they clearly emerge over time via a process of use, feedback, and learning, those processes themselves are somewhat vague. Furthermore, some categories are clearly innate: The fear of the color red in certain birds (Pryke, 2009) is one such example. In a very real sense, categories evolve. Here we will think of the process of categorization as creating a certain number of image schemas that represent the different categories.4 Generally speaking, we can say that categories emerge so that there is a balance between a large enough number of different images to be able to describe the range of salient differences and a small enough number that manipulating these images in one’s head (or wherever they are is not This of categories is described by a shift from perceptual that very much the object in to conceptual that have most of the to the so conceptual that there is no to the perceptual such are called We that even perceptual a certain of because the sensory system can perceive only a range of values. for example, the of do not our perceptual of they without a doubt do so in (and for all we know, even in see & have that predictions on increase an organism’s for it is also important to have a to with in which a to be In general, the that allows us to our based on is called & is not an intrinsic of an and This needs to evolve. The on is and different (see, e.g., is an important concept in computer cognitive and Here I will focus on only one form of to neural I will discuss a very that is to is also and to the see also This is not a has been used in such as learning, theory, and evolutionary very well be an of what is on in our brains as we what I describe the how to one from a set of n and that We can imagine that this is on so we are in the that will our the particular environment that the each a particular based on information or the This could be one and the is given a and the all of the are An which of the will be to the which the are based on the to a or a This is the part of the We would to the to an based on the and how to best these given the Here we only the in which the likelihood that any particular will be for time is to the is the the that has the of it is make it from the how this to artificial we can think of each of the as a particular of a of neurons in response to a to a particular and the is to the of the given the In a sense, we are a particular sensed (and the it how we the likelihood of each of the possible response given the we for the the we are for the to the given the of the or a that the is that time point we i with a to its we a If the is then we increase the of this a by an to the If is we the is a small We see that the is then the time step this will have a likelihood of all other will be with a to we can say that those that to a will be that the likelihood of this to given the sensory is other are This is of the that the between neurons they (and a was only in the the not on the between two neurons but rather on the likelihood of a particular of a set of neurons which the is given depends on the a large tends to (and it also the than We that the = given the that on that it is possible to that the given by the over the is just a than the n is the number of (or the number of possible in a particular of and is the is by the who would have the a large might make the system but the on the the is a is to on as the are given by the in the for the that we For example, that the in as we the for the of has instead that to 1 in each the with = does not the all, but we can now this using the the of this using = and a = 1 any time the was it was to be (or the and a of = the was We also the to that how the can be to the the was the in the were very to the in well (and is there is an about the of the and the the relevant in a is to the brain example, by a brains have to what variable a that because is such a which sensory (or of useful In one such & with brains with a different of their neurons to their motors that the now is as what was now be the of a with the a system in which a by the with sensors the to the of without from a might ask this was and we only a particular and not discuss The answer is that an has learned to associate a particular with a given sensed we can view this as the the from a series of which are the behaviors used to the for each In the a set of is to which is precisely what in the step of the in the sense of the word by cognitive is a of the world that for the real world 1997). is an of the world within the brain that can be used to in the without to to the some of the world are clearly in our I will focus on neural here because they our behaviors for the most part does not take a of to how important it is to carry these of the The system, for example, on of what an object might so that we have to only a salient of a scene to in the so to to up with an will to discussing in in the can how much a brain about the world in terms of information If we a variable for the world states and variable for brain then what the brain about the environment is just the entropy this is not a because the brain states might be by what the in the sensors in are of the In other words, might be not because the brain is able to the world but because the brain the To a measure of we need to the sensory states so that they are to a brain needs to be able to represent the world there is no in the sensory Such a representation can be by the entropy that the information that the brain has about the world given the This entropy is in terms of an entropy in The representation = can be even than the information the brain has about the world by the of and because the can be This can the sensed information (see, e.g., are because they are relationships between and allows us to our so that our predictions example, what we to sense are how much a brain about the world it to be that representation with in the representation = could be a function for that we can imagine that what we about the world what we will increase our because we can make for intelligent are not has described an based on the entropy between sensors and what we would call are of this function is which is a measure of how much one’s in have what one will perceive some time in the is also possible to the entropy between the states one time point and a time to evolve This to some but does not to of these into of the and principle does take a variable into the context here is given by of we can say that they the of the variable the with is that the information can be (as see the in and is (as we see discussing it is to imagine that a has to a the representation = is the best function that into and using an evolutionary (see, e.g., in which representation is as a to than any other so according to the point of view I be into a it is from elements that build on each other, and have different on the we I have described five elements of intelligence but it is that the number is and there is between these Furthermore, other important elements as have been could that is part of prediction, but although there is we can also that a does not tell us how we use to that because for in an of the elements of intelligence would be both and for a column, I have to these five categorization, memory, prediction, learning, and In the I have described all these elements are rooted in the concept of so we can say that intelligent is possible (and allows us to how much we do not information information to make in allows us to our the was and information about the world in our brains so that we can make based on an understanding of the we use these also to a focus on the information-theoretic of the elements of intelligence is from a point of it does not on particular that our brains use for be that the to and time described is used in very well be the that in brains the In we do not have for or against such a and much is is about how brains perceive sensory such as or In the I will discuss some of and to a to how perceive in to a large number of and who have on the evolution of intelligence, in particular, and I in particular to who brains with and the (and a of as early as for an to We can only about we would be that had been The in this was by I also the in of the Artificial who have their time to has these articles and all are
Christoph Adami (Sun,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: