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On the Differences Between Human and Machine Intelligence

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On the Differences Between Human and Machine Intelligence On the Differences between Human and Machine Intelligence Roman V. Yampolskiy Computer Science and Engineering, University of Louisville [email protected] Abstract [Legg and Hutter, 2007a]. However, widespread implicit as- Terms Artificial General Intelligence (AGI) and Hu- sumption of equivalence between capabilities of AGI and man-Level Artificial Intelligence (HLAI) have been HLAI appears to be unjustified, as humans are not general used interchangeably to refer to the Holy Grail of Ar- intelligences. In this paper, we will prove this distinction. tificial Intelligence (AI) research, creation of a ma- Others use slightly different nomenclature with respect to chine capable of achieving goals in a wide range of general intelligence, but arrive at similar conclusions. “Lo- environments. However, widespread implicit assump- cal generalization, or “robustness”: … “adaptation to tion of equivalence between capabilities of AGI and known unknowns within a single task or well-defined set of HLAI appears to be unjustified, as humans are not gen- tasks”. … Broad generalization, or “flexibility”: “adapta- eral intelligences. In this paper, we will prove this dis- tion to unknown unknowns across a broad category of re- tinction. lated tasks”. …Extreme generalization: human-centric ex- treme generalization, which is the specific case where the 1 Introduction1 scope considered is the space of tasks and domains that fit within the human experience. We … refer to “human-cen- Imagine that tomorrow a prominent technology company tric extreme generalization” as “generality”. Importantly, as announces that they have successfully created an Artificial we deliberately define generality here by using human cog- Intelligence (AI) and offers for you to test it out. You decide nition as a reference frame …, it is only “general” in a lim- to start by testing developed AI for some very basic abilities ited sense. … To this list, we could, theoretically, add one such as multiplying 317 by 913, and memorizing your phone more entry: “universality”, which would extend “general- number. To your surprise, the system fails on both tasks. ity” beyond the scope of task domains relevant to humans, When you question the system’s creators, you are told that to any task that could be practically tackled within our uni- their AI is human-level artificial intelligence (HLAI) and as verse (note that this is different from “any task at all” as un- most people cannot perform those tasks neither can their AI. derstood in the assumptions of the No Free Lunch theorem In fact, you are told, many people can’t even compute 13 x [Wolpert and Macready, 1997; Wolpert, 2012]).” [Chollet, 17, or remember name of a person they just met, or recog- 2019]. nize their coworker outside of the office, or name what they had for breakfast last Tuesday2. The list of such limitations 2 Prior work is quite significant and is the subject of study in the field of Artificial Stupidity [Trazzi and Yampolskiy, 2018; Trazzi We call some problems ‘easy’, because they come naturally and Yampolskiy, 2020]. to us like understanding speech or walking and we call other Terms Artificial General Intelligence (AGI) [Goertzel et problems ‘hard’ like playing Go or violin, because those are al., 2015] and Human-Level Artificial Intelligence (HLAI) not human universals and require a lot of talent and effort [Baum et al., 2011] have been used interchangeably (see [Yampolskiy, 2012]. We ignore ‘impossible’ for humans to [Barrat, 2013], or “(AGI) is the hypothetical intelligence of master domains, since we mostly don’t even know about a machine that has the capacity to understand or learn any them or see them as important. As LeCun puts it: “[W]e can't intellectual task that a human being can.” [Anonymous, imagine tasks that are outside of our comprehension, right, Retrieved July 3, 2020]) to refer to the Holy Grail of Artifi- so we think, we think we are general, because we're general cial Intelligence (AI) research, creation of a machine capa- of all the things that we can apprehend, but there is a huge ble of: achieving goals in a wide range of environments world out there of things that we have no idea” [LeCun, August 31, 2019]. Others, agree: “we might not even be 1 Copyright © 2021 for this paper by its authors. Use permitted 2Some people could do that and more, for example 100,000 digits under Creative Commons License Attribution 4.0 International of π have been memorized using special mnemonics. (CC BY 4.0). aware of the type of cognitive abilities we score poorly on.” such a thing as universal intelligence, and if human intelli- [Barnett, December 23, 2019]. gence is an implementation of it, then this algorithm of uni- This is most obvious in how we test for intelligence. For versal intelligence should be the end goal of our field, and example, Turing Test [Turing, 1950], by definition, doesn’t reverse-engineering the human brain could be the shortest test for universal general intelligence, only for human-level path to reach it. It would make our field close-ended: a riddle intelligence in human domains of expertise. Like a drunkard to be solved. If, on the other hand, human intelligence is a searching for his keys under the light because there it is eas- broad but ad-hoc cognitive ability that generalizes to hu- ier to find them, we fall for the Streetlight effect observation man-relevant tasks but not much else, this implies that AI is bias only searching for intelligence in domains we can easily an open-ended, fundamentally anthropocentric pursuit, tied comprehend [Yampolskiy, 2019]. “The g factor, by defini- to a specific scope of applicability.” [Chollet, 2019]. tion, represents the single cognitive ability common to suc- Humans have general capability only in those human ac- cess across all intelligence tests, emerging from applying cessible domains and likewise artificial neural networks in- factor analysis to test results across a diversity of tests and spired by human brain architecture do unreasonably well in individuals. But intelligence tests, by construction, only en- the same domains. Recent work by Tegmark et al. shows compass tasks that humans can perform – tasks that are im- that deep neural networks would not perform as well in ran- mediately recognizable and understandable by humans (an- domly generated domains as they do in those domains hu- thropocentric bias), since including tasks that humans mans consider important, as they map well to physical prop- couldn’t perform would be pointless. Further, psychomet- erties of our universe. “We have shown that the success of rics establishes measurement validity by demonstrating pre- deep and cheap (low-parameter-count) learning depends not dictiveness with regard to activities that humans value (e.g. only on mathematics but also on physics, which favors cer- scholastic success): the very idea of a “valid” measure of tain classes of exceptionally simple probability distributions intelligence only makes sense within the frame of reference that deep learning is uniquely suited to model. We argued of human values.” [Chollet, 2019]. that the success of shallow neural networks hinges on sym- Moravec further elaborates the difference between future metry, locality, and polynomial log-probability in data from machines and humans: “Computers are universal machines, or inspired by the natural world, which favors sparse low- their potential extends uniformly over a boundless expanse order polynomial Hamiltonians that can be efficiently ap- of tasks. Human potentials, on the other hand, are strong in proximated.” [Lin et al., 2017]. areas long important for survival, but weak in things far re- moved. Imagine a “landscape of human competence,” hav- 3 Humans are not AGI ing lowlands with labels like “arithmetic” and “rote memo- An agent is general (universal [Hutter, 2004]) if it can learn rization,” foothills like “theorem proving” and “chess play- anything another agent can learn. We can think of a true AGI ing,” and high mountain peaks labeled “locomotion,” agent as a superset of all possible NAIs (including capacity “hand-eye coordination” and “social interaction.” Advanc- to solve AI-Complete problems [Yampolskiy, 2013]). Some ing computer performance is like water slowly flooding the agents have limited domain generality, meaning they are landscape. A half century ago it began to drown the low- general, but not in all possible domains. The number of do- lands, driving out human calculators and record clerks, but mains in which they are general may still be Dedekind-infi- leaving most of us dry. Now the flood has reached the foot- nite, but it is a strict subset of domains in which AGI is ca- hills, and our outposts there are contemplating retreat. We pable of learning. For an AGI it’s domain of performance is feel safe on our peaks, but, at the present rate, those too will any efficiently learnable capability, while humans have a be submerged within another half century.” [Moravec, smaller subset of competence. Non-human animals in turn 1998]. may have an even smaller repertoire of capabilities, but are Chollet writes: “How general is human intelligence? The nonetheless general in that subset. This means that humans No Free Lunch theorem [Wolpert and Macready, 1997; can do things animals cannot and AGI will be able to do Wolpert, 2012] teaches us that any two optimization algo- something no human can. If an AGI is restricted only to do- rithms (including human intelligence) are equivalent when mains and capacity of human expertise, it is the same as their performance is averaged across every possible prob- HLAI. lem, i.e. algorithms should be tailored to their target problem Humans are also not all in the same set, as some are ca- in order to achieve better-than-random performance. How- pable of greater generality (G factor [Jensen, 1998]) and can ever, what is meant in this context by “every possible prob- succeed in domains, in which others cannot.
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