Computer Scientists

"What you need to do is to make sure your threads become immutable. So you can change a data within a thread, but if you got two threads communicating with each other, you need to mimic this whole message passing approach. You need to copy the data from one threads to another, avoiding having shared state."

"You need to become unimportant as a subject, that is, if you are a philosopher, believe is not a verb. ...You have to submit to the things that are possibly true and... follow wherever your inquiry leads, but it's not about you, it has nothing to do with you."

"You cannot define objective truth without understanding the nature of truth... So what does the brain mean by saying that it's discovered something as truth... A model can be predictive or not... [T]here can be a sense in which a mathematical statement is true because it's defined as true under certain conditions. So it's... a particular state that a variable can have in the assembled game and then you can have a correspondence between systems and talk about truth, which is again a type of model correspondence."

"At some point you have to understand the comedy of your own situation. If you take yourself seriously, and you are not functional, it ends in tragedy, as it did for Nietzsche. ...[Y]ou find the same thing in Hesse... The Steppenwolf syndrome is classic in all its sense, where you... feel misunderstood by the world and you don't understand that all the misunderstandings are the result of your own lack of self-awareness, because you think that you are [the] prototypical human and the others around you should behave the same way as you expect... based on your innate instincts; and it doesn't work out, and you become a transcendentalist to deal with that. ...It's very... understandable and I have great sympathies for this, to the degree that I can have sympathy for my own intellectual history. But you have to grow out of it."

"[T]here also seems to be a particular kind of ground truth, [e.g.,] you are confronted with the enormity of something existing at all. ...It's stunning when you realize something exists, rather than nothing. ...[T]his seems to be true. There is an absolute truth in the fact that something seems to be happening."

"The easiest answer is existence is the default. ...So this is the lowest number of bits that you need to encode this. ...Nonexistence might not be a meaningful notion. ...If everything that can exist, exists... it probably needs to be implementable. The only thing that can be implemented is finite automata so maybe the whole of existence is... a superposition of all finite automata, and we are in some region of the fractal that has the properties that it can contain us. ...Imagine that every automaton is... an operator that acts on some substrate [something that can store information], and as a result you get emergent patterns."

"Computational systems are machines that can be described apriori and systematically, and implemented on every substrate that elicits the causal properties that are necessary to capture the respective states and transition functions."

"[T]o encode a brain genetically, based on the hardware that we are using, we need something like at least 500 kilobytes of code... actually... it's going to be a little more, I guess. It sounds like surprisingly little... but in terms of scientific theories this is a lot. ...The universe, according to the core theory of quantum mechanics... it's like half a page of code... to generate the universe. ...[I]f you want to understand evolution, it's like a paragraph... a couple lines, really, to understand an evolutionary process. ...[T]here's lots ...of details that you get afterwards, because this process itself doesn't define what all the animals are going to look like. In a similar way, the code of the universe doesn't tell you what this planet is going to look like and you... are going to look like. It's just defining the rule book."

"The absence of an understanding of substrate independent machines lead Leibniz to the rejection of mechanist philosophy..."

"[T]he genome defines the rule book by which our brain is built. The brain boots itself, in a development process, and this booting takes some time... formation learning in which some connections are formed, basic models are built of the world so we can operate in it. How long does this booting take... about 80 megaseconds. That's the time a child is awake until it's 3 1/2 years old. By this age you understand Star Wars, and I think everything after Star Wars is cosmetics."

"How difficult is it to define a brain? We know that the brain must be somewhere hidden in the genome [which] fits in a CD-ROM. It's not that complicated. It's easier than Microsoft Windows. ...[A]bout 2% of the genome is coding for proteins, and maybe about 10%... tells you when to express which protein, and the remainder is mostly garbage. It's old viruses that are left over and it's never been properly deleted [etc.] because there are no real code revisions in the genome. ...How much of this 10%, [i.e.,] about 75 megabytes code for the brain, we don't really know. What we do know is that we share almost all of this with mice. Genetically speaking, a human is a pretty big mouse, with a few bits changed to fix some of the genetic expressions. ...Most of the stuff there is going to code for cells and metabolism and what your body looks like, [etc.]..."

"[W]hile dynamical systems often cannot be effectively computed on a finite state machine (such as a von Neumann computer), they can often be efficiently approximated."

"With this vector space you can do amazing things, [e.g.,] if you take the vector from king to queen, it's pretty much the same vector as between man and woman. ...[B]ecause [these concept spaces are] really a high dimension manifold, we can do interesting things like machine translation without understanding what it means, that is, without doing any proper mental representation that predicts the world. ...[T]his is [a] type of mental representation that is somewhat incomplete, but it captures the landscape that we share in a culture."

"[T]here is another type of mental representation that is linguistic protocols, which is... a form of grammar and a vocabulary. ...[W]e need these ...protocols to transfer mental representations between people ...by scannning our ...representations, disassembling them ...and ...we use a discrete set of symbols to get this to somebody else... [who] trains an assembler that reverses this process and builds something that is... similar to what we intended to convey."

"[I]f you look at the progression of AI models, it... went the opposite direction. ...AI started with linguistic protocols, which were expressed in formal grammars, and then it got to concept spaces, and now it's about to address percepts. ...At some point in the near future it's going to get better at mental simulations and at some point after that we'll get to attention directed and motivationally connected systems that make sense of the world, that are in some sense able to address meaning. This is the hardware that we have..."

"Mathematics is the domain of all formal languages, and allows the expression of arbitrary statements (most of which are uncomputable). Computation may be understood in terms of computational systems, for instance via defining states (which are sets of discernible differences, i.e. bits), and transition functions that let us derive new states."

"[W]e saw that mental representation is about percepts, mental simulations, conceptual representations... [C]onceptual representations give us concept spaces, and... these concept spaces... give us an interface for our mental representations we can use to address and manipulate them, and we can share them in cultures. [T]hese concepts are compositional. We can put them together to create new concepts. ...[T]hey can be described using higher dimensional vector spaces. They [vectors] don't do mental simulation and prediction, and so on, but we can capture regularity in our concepts with them."

"Artificial Intelligence was the attempt of thinkers like Marvin Minsky, John McCarthy and others to treat the mind as a computational system, and thereby open its study to experimental exploration by building computational machines that would attempt to replicate the functionality of minds."

"Computation is different. Computation can exist. It starts with an initial state, and then you have a transition function. You do the work. You apply the transition function [and] you get into the next state. Computation is always finite."

"For a long time people have thought that the universe is written in mathematics... In fact nothing is mathematical. Mathematics is just the domain of formal languages. It doesn't exist. Mathematics starts with a void. Just throw in a few axioms and if those are nice axioms, then you get infinite complexity. Most of it is not computable. In mathematics you can express arbitrary statements, because it's all about formal languages. Many of these statements will not make sense. Many of these statements will make sense in some way, but you cannot test whether they make sense because they're not computable."

"Mathematics is the kingdom of specification and computation is the kingdom of implementation. It's very important to understand this difference."

"If we want to understand music we have to go beyond understanding sound. We have to understand the transformations that sound can have if you play a different pitch. We have to arrange the sound in a sequencer that gives you rhythms, and so on, and then we want to identify some kind of musical grammar that we can use to... control the sequencer. So we have stacked structures that simulate the world. ...If you want to model a world of music you need to have the lowest level of the precepts, then the higher levels of mental simulations, which give the sequences... and the grammars of music... [B]eyond this you have the conceptual landscape that you can use to describe the different styles of music. ...[I]f you go up in the hierarchy, you get to more and more abstract models, more and more conceptual models, and more and more analytic models. ...[T]hese are causal models..."

"[C]ausal models can be weakly deterministic, basically associative models, which tell you if this state [S1] happens, it is quite probable that this one [S2] comes afterwards. Or you can get to a strongly determined model... one which tells you, if you are in this state [S1], and this condition [c1] is met, you're going to go exactly in this state [S2]. If this state is not met, or a different condition [c2] is met, you go into this state [S3]. And this is what we call an algorithm. Now you're in the domain of computation."

"All our access to mathematics... is because we do computation. We can understand mathematics because our brain can compute some part of mathematics, very very little of it and to a very constrained complexity, but enough so we can map some of the infinite complexity and noncomputability of mathematics into computational patterns which we can explore."

"It may not have a why. This might be the wrong direction to ask... [T]here could be no relation in the "why" direction... It doesn't mean that everything has to have a purpose or a cause..."

"The failure to deliver on some of the early, optimistic promises of machine intelligence, as well as cultural opposition, lead to cuts in funding for cognitive AI, and eventually the start of the new discipline of Cognitive Science. ...Cognitive Science did not develop a cohesive methodology and theoretical outlook, and became an umbrella term for neuroscience, AI, cognitive psychology, linguistics and philosophy of mind."

"[C]omputation is about doing the work... executing a transition function."

"Whereas mathematics is the realm of specification, computation is the realm of implementation; it captures all those systems that can actually be realized."

"What's the best algorithm that you should be using to fix your world model? ...This question ...has been answered for the first time by in the 1960s. He discovered an algorithm that you can apply when you've discovered that you're a robot and all you've got is data. What is the world like? ...[H]is algorithm is... a combination of Bayesian Reasoning, Induction and Occam's Razor. ...[W]e can mathematically prove that we cannot do better than Solomonoff Induction. Unfortunately, Solomonoff Induction is not quite computable."

"Just as the extensive theoretical bodies of physics, chemistry, [etc.]... unified theories of cognition are not isolated statements discarded when... predictions [are] refuted. Rather they are paradigms... that direct a research program..."

"[E]verything that we're going to do is some approximation of Solomonoff Induction. ...[O]ur concepts cannot really refer to facts in the world out there. We do not get the truth by referring to stuff out there in the world. We get meaning by suitably encoding the patterns in our systemic interface."

"Everything we know about ourselves is... ordering... over features available at the interface; we know of mental phenomena only insofar as they are patterns or constructed over patterns."

"Functionalist psychology is... compatible with... scientific positivism, because it makes emperically falsifiable predictions... The... model is capable of producing [or predicting] specific behavior [and] [t]he model is the sparsest, simplest one..."

"AI has recently made huge progress in encoding data at perceptual interfaces. is about using a stacked hierarchy of feature detectors. ...[W]e use pattern detectors and we build them into networks that are arranged in hundreds of layers and then we adjust the links between these layers, usually using some kind of . ...[Y]ou can use this to classify [e.g.,] images and parts of speech. ...[W]e get to features that are more and more complex. They start with these very... simple patterns, and then get more and more complex until we get to object categories. ...[N]ow the systems are able, in image recognition tasks, to approach performance that's very similar to human performance. ...[I]t seems to be somewhat similar to what the brain seems to be doing in visual processing."

"[I]nfluences that lead to the study of... mental entities and structures... came from... s and cybernetics, and from formal linguistics. They fostered an understanding that mental activity amounts to ... that can be modeled as... an algorithm—working over states that encode representations."

"Functional constructivism is based on... philosophical constructivism... that all knowledge about the world is based on... our systematic interface. ...We do not ...recognize ...objects of our environment; we construct them over the regularities ...at the system interface of our cognitive system."

"What the universe makes visible... to any observer... is... functionality."

"The goal of building cognitive architectures is to achieve an understanding of mental processes by constructing testable information processing models."

"To perceive means... to find order over patterns; these orderings are what we call objects. ...[I]t amounts to ...identification of these objects by their related patterns ...intuitively ...its features ..."

"If you take the activation at different levels of these networks and you... enhance this activation a little... you get stuff that looks very psychadelic, which might be similar to what happens if you put certain illegal substances into people and enhance the activity on certain layers of their visual processing."

"Because there is no narrow, concise understanding of what constitutes mental activity and what is part of mental processes... cognition, the cognitive sciences and the related notions span a wide and convoluted terrain... most of [which] lies outside psychology... This methodological discrepancy can only be understood in the context of the recent ."

"[T]he relationship between cognition and neurobiological processes might be similar to the one between a car engine and locomotion. ...[A] car's locomotion is facilitated mainly by its engine, but the understanding of the engine does not aid much in finding out where the car goes. ...[T]he integration of... parts, the intentions of the driver and even the terrain might be more crucial ..."

"Behaviorism... in the form of ... not only neglected the nature of mental entities as an object of inquiry, but denied their existence..."

"Cognitive architectures are... Leibnizian machines... designed to bring forth the feats of cognition, and built to allow us to enter... examine them, and to watch their individual parts... pushing and pulling... thereby explaining how a mind works."

"Our... cognitive architecture is based on a formal [psychological] theory... the PSI theory... [which] has been turned into a computational model... MicroPSI... partially implemented as a computer program."

"[N]egligence of internal states of the mind makes it difficult to form conclusive theories of cognition, especially with regard to language... and consciousness, so radical behaviorism... lost its foothold. Yet, methodological behaviorism is still prevalent..."

"This book is an attempt to explain cognition—thought, perception, emotion, experience—in terms of a machine, that is, using a cognitive architecture."

"[T]aking [the design of a cognitive architecture] to the AI laboratory... requires the theory not merely to be plausible, but... requires it to be fit for implementation, and delivers it to the... merciless battle of testing."

"Unlike physics, where previously unknown entities and mechanisms... are routinely postulated... and... evidence is sought in favor or against these... psychology shuns [this methodology]... Thus... cognitive psychology shows reluctance... to building unified theories of mental processes. ...Piaget's work ...might be one of the notable exceptions ..."

"According to [the hypothesis]... an implemented ', has the necessary and sufficient means for general intelligent action. ...[A]ny system that exhibits general intelligence will ...be a physical symbol system. ...[A]ny physical symbol system of sufficient size can be organized further to exhibit general intelligence.""