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AI is one of the most debated subjects of today and there seems little common understanding concerning the differences and similarities of human intelligence and artificial intelligence. Discussions on many relevant topics, simon nickerson phd thesis, such as trustworthiness, explainability, and ethics are characterized by implicit anthropocentric and anthropomorphistic conceptions and, for instance, the pursuit of human-like intelligence as the golden standard for Artificial Intelligence, simon nickerson phd thesis.
In simon nickerson phd thesis to provide more agreement and to substantiate possible future research objectives, this paper presents three notions on the similarities and differences between human- and artificial intelligence: 1 the fundamental constraints of human and artificial intelligence, 2 human simon nickerson phd thesis as one of many possible forms of general intelligence, and 3 the high potential impact of multiple integrated forms of narrow-hybrid AI applications.
For the time being, AI systems will have fundamentally different cognitive qualities and abilities than biological systems. For what tasks and under what conditions, decisions are safe to leave to AI and when is human judgment required? How can we capitalize on the specific strengths of human- and artificial intelligence?
How to deploy AI systems effectively to complement and compensate for the inherent constraints of human cognition and vice versa? So, in order to obtain well-functioning human-AI systems, Intelligence Awareness in humans should be addressed more vigorously. For this purpose a first framework for educational content is proposed. Recent advances in information technology and in AI may allow for more coordination and integration between of humans and technology.
Krämer et al. However, no matter how intelligent and autonomous AI agents become in certain respects, at least for the foreseeable future, they probably will remain unconscious machines or special-purpose devices that support humans in specific, complex tasks.
As digital machines they are equipped with a completely different operating system digital vs biological and with correspondingly different cognitive qualities and abilities than biological creatures, like humans and other animals Moravec, ; Klein et al.
In general, digital reasoning- and problem-solving agents only compare very superficially to their biological counterparts, e. Boden, ; Shneiderman, b. Keeping that in mind, it becomes more and more important that human professionals working with advanced AI systems, e. This issue will become increasingly relevant when AI systems become more advanced and are deployed with higher degrees of autonomy, simon nickerson phd thesis. Goertzel et al. Many different definitions of A G I have already been proposed, e.
Russell and Norvig, for an overview. Many of them boil down to: technology containing or entailing human-like intelligencee. Kurzweil, This is problematic. Second, the idea that A G I should be human-like seems unwarranted. At least in natural environments there are many other forms and manifestations of highly complex and intelligent behaviors that are very different from specific human cognitive abilities see Grind, simon nickerson phd thesis an overview. Finally, like what is also frequently seen in the field of biology, these A G I definitions use human intelligence as a central basis or analogy for reasoning about the—less familiar—phenomenon of A G I Coley and Tanner, Because of the many differences between the underlying substrate and architecture of biological and artificial intelligence this anthropocentric way of reasoning is probably unwarranted.
These goals may pertain to narrow, restricted tasks narrow AI or to broad task domains AGI. Building on this definition, and on a definition of AGI proposed by Bieger et al. AGI systems should be able to identify and extract the most important features for their operation and learning process automatically and efficiently over a broad range of tasks and contexts. Relevant AGI research differs from the ordinary AI research by addressing the versatility and wholeness of intelligence, and by carrying out the engineering practice according to a system comparable to the human mind in a certain sense Bieger et al.
It will be fascinating to create copies of ourselves which can learn iteratively by interaction with partners and thus become able to collaborate on the basis of common goals and mutual understanding and adaptation, e.
Bradshaw et al. This would be very useful, for example when a high degree of social intelligence of AI will contribute to more adequate interactions with humans, for example in health care or for entertainment purposes Wyrobek et al.
True collaboration on the basis of common goals and mutual understanding necessarily implies some form of humanoid general intelligence. For the time being, this remains a goal on a far-off horizon. The fact that humans possess general intelligence does not imply that new inorganic forms of general intelligence should comply to the criteria of human intelligence. In this connection, the present paper addresses the way we think about natural and artificial intelligence in relation to the most probable potentials and real upcoming issues of AI in the short- and mid-term future.
This will provide food simon nickerson phd thesis thought in anticipation of a future that is difficult to predict for a field as dynamic as AI. Indeed, as humans we know ourselves as the entities with the highest intelligence ever observed in the Universe. And as an extension of this, we like to see ourselves as rational beings who are able to solve a wide range of complex problems under all kinds of circumstances using our experience and intuition, supplemented by the rules of logic, decision analysis and statistics.
It is therefore not surprising that we have some difficulty to accept the idea that we might be a bit less smart than we keep on telling ourselves, i, simon nickerson phd thesis.
These are then cited as the specific elements of real intelligence, e. Bergstein, When exclusive human capacities become our pivotal navigation points on the horizon we may miss some significant problems that may need our attention first. To make this point clear, we first will provide some insight into the basic nature of both human and artificial intelligence, simon nickerson phd thesis.
This is necessary for the substantiation of an adequate awareness of intelligence Intelligence Awarenessand adequate research and education anticipating the development and simon nickerson phd thesis of A G I. For the time being, this is based on three essential notions that can and should be further elaborated in the near future. So why should we vigorously focus on human -like AGI?
How intelligent are we actually? The answer to that question is determined to a large extent by the perspective from which this issue is viewed, simon nickerson phd thesis thus by the measures and criteria for intelligence that is chosen. For example, simon nickerson phd thesis could compare the nature and capacities of human intelligence with other animal species.
In that case we appear highly intelligent. Thanks to our enormous learning capacity, we have by far the most extensive arsenal of cognitive abilities 2 to autonomously solve complex problems and achieve complex objectives. This way we can solve a huge variety of arithmetic, simon nickerson phd thesis, conceptual, spatial, economic, socio-organizational, political, etc.
The primates—which differ only slightly from us in genetic terms—are far behind us in that respect. We can therefore legitimately qualify humans, as compared to other animal species that we know, as highly intelligent.
For example, we could view the computational capacity of a human brain as a physical system Bostrom, simon nickerson phd thesis Tegmark, The prevailing notion in this respect among AI scientists is that intelligence is ultimately a matter of information and computation, and thus not of flesh and blood and carbon atoms. In principle, there is no physical law preventing that physical systems consisting of quarks and atoms, like our brain can be built with a much greater computing power and intelligence than the human brain.
This would imply that there is no insurmountable physical reason why machines one day cannot become much more intelligent than ourselves in all possible respects Tegmark, Our intelligence is therefore relatively high compared to other animals, but in absolute terms it may be very limited in its physical computing capacity, albeit only by the limited size of simon nickerson phd thesis brain and its maximal possible number of neurons and glia cells, simon nickerson phd thesis, e.
Kahle, To further define and assess our own biological intelligence, we can also discuss the evolution and nature of our biological thinking abilities. As a biological neural network of flesh and blood, necessary for survival, our brain has undergone an evolutionary optimization process of more than a billion years.
In this extended period, it developed into a highly effective and efficient system for regulating essential biological functions and performing perceptive-motor and pattern-recognition tasks, such as gathering food, fighting and flighting, and mating. Almost during our entire evolution, the neural networks of our brain have been further optimized for these basic biological and perceptual motor processes that also lie at the basis of our daily practical skills, like cooking, gardening, or household jobs, simon nickerson phd thesis.
Possibly because of the resulting proficiency for these kinds of tasks we may forget that these processes are characterized by extremely high computational complexity, e. Moravec, simon nickerson phd thesis, For example, when we tie our shoelaces, many millions of signals flow in and out through a large number of different sensor systems, from tendon bodies and muscle spindles in our extremities to our retina, otolithic organs and semi-circular channels in the head, e.
Brodal, simon nickerson phd thesis, This enormous amount of information from many different perceptual-motor systems is continuously, parallel, effortless and even without conscious attention, processed in the neural networks of our brain Minsky, ; Moravec, ; Grind, In order to achieve this, the brain has a number of universal inherent working mechanisms, such as association and associative learning Shatz, ; Bar,potentiation and facilitation Katz and Miledi, ; Bao et al.
These kinds of basic biological and perceptual-motor capacities have been developed and set down over many millions of years. Much later in our evolution—actually only very recently—our cognitive abilities and rational functions have started to develop. Petraglia and Korisettar, ; McBrearty and Brooks, ; Henshilwood and Marean, As a result, it should not be a surprise that the capacities of our brain for performing these recent cognitive functions are still rather limited.
These limitations are manifested in many different ways, for instance:. The capacity of our working memory is approximately 10—50 bits per second Tegmark, Mobile calculators can perform millions times more complex calculations than we can Tegmark, Our limited processing capacity for cognitive tasks is not the only factor determining our cognitive intelligence.
Except for an overall limited processing capacity, human cognitive information processing shows systematic distortions. These are manifested in many cognitive biases Tversky and Kahneman,Tversky and Kahneman, Cognitive biases are systematic, universally occurring tendencies, inclinations, or dispositions that skew or distort information processes in ways that make their outcome inaccurate, suboptimal, or simply wrong, e.
Lichtenstein and Slovic, ; Tversky and Kahneman, Many biases occur in virtually the same way in many different decision situations Shafir and LeBoeuf, ; Kahneman, ; Toet et al. The literature provides descriptions and demonstrations of over biases. Biased reasoning can result in quite acceptable outcomes in natural or everyday situations, especially when the time cost of reasoning is taken into account Simon, ; Gigerenzer and Gaissmaier, Biases are largely caused by inherent or structural characteristics and mechanisms of the brain as a neural network Korteling et al.
Basically, these mechanisms—such as association, facilitation, adaptation, or lateral inhibition—result in a modification of the original or available data and its processing, e. weighting its importance, simon nickerson phd thesis. For instance, lateral inhibition is a universal neural process resulting in the magnification of differences in neural activity contrast enhancementwhich is very useful for perceptual-motor functions, maintaining physical integrity and allostasis, i.
biological survival functions. For these functions our nervous system has been optimized for millions of years. McBrearty and Brooks, ; Henshilwood and Marean, ; Petraglia and Korisettar, Since cognitive functions typically require exact calculation and proper weighting of data, data transformations—like lateral inhibition—may easily simon nickerson phd thesis to systematic distortions, i.
biases in cognitive information processing. Examples of the large number of biases caused by the inherent properties of biological neural networks are: Anchoring bias biasing decisions toward previously acquired information, Furnham simon nickerson phd thesis Boo, ; Tversky and Kahneman,Tversky and Kahneman,the Hindsight bias the tendency to erroneously perceive events as inevitable or more likely once they have occurred, Hoffrage et al.
In addition to these inherent structural limitations of biological neural networks, biases may also originate from functional evolutionary principles promoting the survival of our ancestors who, as hunter-gatherers, lived in small, simon nickerson phd thesis, close-knit groups Haselton et al.
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