An overview of Superintelligence, by Nick Bostrom

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1 An overview of Superintelligence, by Nick Bostrom Alistair Knott 1 / 25

2 The unfinished fable of the sparrows 2 / 25

3 The unfinished fable of the sparrows It was the nest-building season, but after days of long hard work, the sparrows sat in the evening glow, relaxing and chirping away. We are all so small and weak. Imagine how easy life would be if we had an owl who could help us build our nests! 2 / 25

4 The unfinished fable of the sparrows It was the nest-building season, but after days of long hard work, the sparrows sat in the evening glow, relaxing and chirping away. We are all so small and weak. Imagine how easy life would be if we had an owl who could help us build our nests! Pastus, the elder-bird, spoke: Let us send out scouts in all directions and try to find an abandoned owlet somewhere, or maybe an egg. 2 / 25

5 The unfinished fable of the sparrows Only Scronkfinkle was unconvinced. Quoth he: This will surely be our undoing. Should we not give some thought to the art of owl-domestication and owl-taming first, before we bring such a creature into our midst? 3 / 25

6 The unfinished fable of the sparrows Only Scronkfinkle was unconvinced. Quoth he: This will surely be our undoing. Should we not give some thought to the art of owl-domestication and owl-taming first, before we bring such a creature into our midst? Replied Pastus: Taming an owl sounds like an exceedingly difficult thing to do. It will be difficult enough to find an owl egg. So let us start there. After we have succeeded in raising an owl, then we can think about taking on this other challenge. 3 / 25

7 The unfinished fable of the sparrows Only Scronkfinkle was unconvinced. Quoth he: This will surely be our undoing. Should we not give some thought to the art of owl-domestication and owl-taming first, before we bring such a creature into our midst? Replied Pastus: Taming an owl sounds like an exceedingly difficult thing to do. It will be difficult enough to find an owl egg. So let us start there. After we have succeeded in raising an owl, then we can think about taking on this other challenge. There is a flaw in that plan! squeaked Scronkfinkle; but his protests were in vain... 3 / 25

8

9 Structure of the book 5 / 25

10 Structure of the book Forms of superintelligence The kinetics of an intelligence explosion The powers of a superintelligent agent The superintelligent will The control problem Acquiring values 5 / 25

11 1. Forms of superintelligence Speed superintelligence Collective superintelligence Quality superintelligence 6 / 25

12 1. Forms of superintelligence Speed superintelligence Collective superintelligence Quality superintelligence Advantages of having intelligence implemented in software: Editability Duplicability Memory sharing Module-based design 6 / 25

13 2. The kinetics of an intelligence explosion 7 / 25

14 2. The kinetics of an intelligence explosion Rate of change of a machine s intelligence = optimisation power recalcitrance 7 / 25

15 2. The kinetics of an intelligence explosion Rate of change of a machine s intelligence = optimisation power recalcitrance Optimisation power: the capability (of humans and/or the machine) to improve the machine. 7 / 25

16 2. The kinetics of an intelligence explosion Rate of change of a machine s intelligence = optimisation power recalcitrance Optimisation power: the capability (of humans and/or the machine) to improve the machine. Recalcitrance: the difficulty of improving the machine. 7 / 25

17 2. The kinetics of an intelligence explosion Things that would increase optimisation power: 8 / 25

18 2. The kinetics of an intelligence explosion Things that would increase optimisation power: If one AI paradigm shows promise, other human AI researchers will pile in to extend/improve it. 8 / 25

19 2. The kinetics of an intelligence explosion Things that would increase optimisation power: If one AI paradigm shows promise, other human AI researchers will pile in to extend/improve it. If the AI system gets good enough, it can improve its own design. (Bostrom calls that crossover.) 8 / 25

20 2. The kinetics of an intelligence explosion Things that would increase optimisation power: If one AI paradigm shows promise, other human AI researchers will pile in to extend/improve it. If the AI system gets good enough, it can improve its own design. (Bostrom calls that crossover.) Improvements here could have an exponential character. 8 / 25

21 2. The kinetics of an intelligence explosion Things that could reduce recalcitrance: 9 / 25

22 2. The kinetics of an intelligence explosion Things that could reduce recalcitrance: The discovery of one key insight that has been holding things back. 9 / 25

23 2. The kinetics of an intelligence explosion Things that could reduce recalcitrance: The discovery of one key insight that has been holding things back. The addition of mechanisms that allow the machine to learn from existing knowledge sources (e.g. the Library of Congress). 9 / 25

24 2. The kinetics of an intelligence explosion Things that could reduce recalcitrance: The discovery of one key insight that has been holding things back. The addition of mechanisms that allow the machine to learn from existing knowledge sources (e.g. the Library of Congress). Increases in computing power. 9 / 25

25 2. The kinetics of an intelligence explosion Things that could reduce recalcitrance: The discovery of one key insight that has been holding things back. The addition of mechanisms that allow the machine to learn from existing knowledge sources (e.g. the Library of Congress). Increases in computing power. Our anthropocentric view of intelligence may lead us to overestimate recalcitrance. 9 / 25

26 2. The kinetics of an intelligence explosion Things that could reduce recalcitrance: The discovery of one key insight that has been holding things back. The addition of mechanisms that allow the machine to learn from existing knowledge sources (e.g. the Library of Congress). Increases in computing power. Our anthropocentric view of intelligence may lead us to overestimate recalcitrance. 9 / 25

27 2. The kinetics of an intelligence explosion 10 / 25

28 3. The powers of a superintelligent agent 11 / 25

29 3. The powers of a superintelligent agent A superintelligent agent would have several abilities: 11 / 25

30 3. The powers of a superintelligent agent A superintelligent agent would have several abilities: Strategic abilities (to achieve goals, overcome intelligentopposition) 11 / 25

31 3. The powers of a superintelligent agent A superintelligent agent would have several abilities: Strategic abilities (to achieve goals, overcome intelligentopposition) Social abilities (to manipulate people into doing what it wants) 11 / 25

32 3. The powers of a superintelligent agent A superintelligent agent would have several abilities: Strategic abilities (to achieve goals, overcome intelligentopposition) Social abilities (to manipulate people into doing what it wants) Economic abilities (abilities to make lots of money) 11 / 25

33 3. The powers of a superintelligent agent A superintelligent agent would have several abilities: Strategic abilities (to achieve goals, overcome intelligentopposition) Social abilities (to manipulate people into doing what it wants) Economic abilities (abilities to make lots of money) Technical abilities (abilities to invent/build machines) 11 / 25

34 3. The powers of a superintelligent agent A superintelligent agent would have several abilities: Strategic abilities (to achieve goals, overcome intelligentopposition) Social abilities (to manipulate people into doing what it wants) Economic abilities (abilities to make lots of money) Technical abilities (abilities to invent/build machines) Hacking abilities (e.g. to find holes in security systems). 11 / 25

35 4. The superintelligent will 12 / 25

36 4. The superintelligent will We have already cautioned against anthropomorphising the capabilities of a superintelligent AI. This warning should be extended to pertain to its motivations as well. 12 / 25

37 4. The superintelligent will We have already cautioned against anthropomorphising the capabilities of a superintelligent AI. This warning should be extended to pertain to its motivations as well. The orthogonality thesis Intelligence and final goals are orthogonal: more or less any intelligence could in principle be combined with more or less any final goal. 12 / 25

38 4. The superintelligent will We have already cautioned against anthropomorphising the capabilities of a superintelligent AI. This warning should be extended to pertain to its motivations as well. The orthogonality thesis Intelligence and final goals are orthogonal: more or less any intelligence could in principle be combined with more or less any final goal. There is nothing paradoxical about an AI whose sole final goal is... to calculate the decimal expansion of pi, or to maximise the total number of paperclips in its future light cone. 12 / 25

39 4. The superintelligent will It may still be possible to make predictions about the motivation of a superintelligent machine. 13 / 25

40 4. The superintelligent will It may still be possible to make predictions about the motivation of a superintelligent machine. Perhaps there are certain instrumental goals that any superintelligent agent would adopt to further its ultimate goal. 13 / 25

41 4. The superintelligent will It may still be possible to make predictions about the motivation of a superintelligent machine. Perhaps there are certain instrumental goals that any superintelligent agent would adopt to further its ultimate goal. Self-preservation Retention of goals through time Cognitive enhancement Technological perfection Resource acquisition 13 / 25

42 5. The control problem 14 / 25

43 5. The control problem If we can t control the superintelligent agent, the result will likely be catastrophic. (For us.) 14 / 25

44 5. The control problem If we can t control the superintelligent agent, the result will likely be catastrophic. (For us.) There are two ways we might control it: 14 / 25

45 5. The control problem If we can t control the superintelligent agent, the result will likely be catastrophic. (For us.) There are two ways we might control it: Capability control (limiting what the system can or does do). 14 / 25

46 5. The control problem If we can t control the superintelligent agent, the result will likely be catastrophic. (For us.) There are two ways we might control it: Capability control (limiting what the system can or does do). Motivation selection (controlling what the system wants to do). 14 / 25

47 5. The control problem Capability control methods: 15 / 25

48 5. The control problem Capability control methods: Boxing: the system can only act through restricted channels. 15 / 25

49 5. The control problem Capability control methods: Boxing: the system can only act through restricted channels. Incentives: access to other AIs, cryptographic reward tokens / 25

50 5. The control problem Capability control methods: Boxing: the system can only act through restricted channels. Incentives: access to other AIs, cryptographic reward tokens... Stunting: imposing constraints on the system s cognitive abilities 15 / 25

51 5. The control problem Capability control methods: Boxing: the system can only act through restricted channels. Incentives: access to other AIs, cryptographic reward tokens... Stunting: imposing constraints on the system s cognitive abilities Tripwires: diagnostic tests run periodically to check for dangerous activity, with shutdown a consequence of detection. 15 / 25

52 5. The control problem Capability control methods: Boxing: the system can only act through restricted channels. Incentives: access to other AIs, cryptographic reward tokens... Stunting: imposing constraints on the system s cognitive abilities Tripwires: diagnostic tests run periodically to check for dangerous activity, with shutdown a consequence of detection. I ll focus on motivation selection methods. (How might we control what the system wants to do?) 15 / 25

53 (i) Direct specification of motivations 16 / 25

54 (i) Direct specification of motivations Rule-based methods: give the machine a set of rules that define its final goals. 16 / 25

55 (i) Direct specification of motivations Rule-based methods: give the machine a set of rules that define its final goals. But: it s hard/impossible to specify a set of rules that is precise/consistent. (As lawyers know.) 16 / 25

56 (i) Direct specification of motivations Rule-based methods: give the machine a set of rules that define its final goals. But: it s hard/impossible to specify a set of rules that is precise/consistent. (As lawyers know.) Direct consequentialist methods: specify some measure that is to be maximised. (E.g. human happiness.) 16 / 25

57 (i) Direct specification of motivations Rule-based methods: give the machine a set of rules that define its final goals. But: it s hard/impossible to specify a set of rules that is precise/consistent. (As lawyers know.) Direct consequentialist methods: specify some measure that is to be maximised. (E.g. human happiness.) But: these could be interpreted in ways we didn t foresee. 16 / 25

58 (ii) Augmentation 17 / 25

59 (ii) Augmentation Start with an AI with human-level intelligence, that has an acceptable motivation system: then enhance its cognitive faculties to make it superintelligent. 17 / 25

60 (ii) Augmentation Start with an AI with human-level intelligence, that has an acceptable motivation system: then enhance its cognitive faculties to make it superintelligent. If all goes well, this would give us a superintelligence with an acceptable motivation system. 17 / 25

61 (ii) Augmentation Start with an AI with human-level intelligence, that has an acceptable motivation system: then enhance its cognitive faculties to make it superintelligent. But: If all goes well, this would give us a superintelligence with an acceptable motivation system. 17 / 25

62 (ii) Augmentation Start with an AI with human-level intelligence, that has an acceptable motivation system: then enhance its cognitive faculties to make it superintelligent. But: If all goes well, this would give us a superintelligence with an acceptable motivation system. This only works if we can start with a human-like AI. 17 / 25

63 (ii) Augmentation Start with an AI with human-level intelligence, that has an acceptable motivation system: then enhance its cognitive faculties to make it superintelligent. But: If all goes well, this would give us a superintelligence with an acceptable motivation system. This only works if we can start with a human-like AI. Even then, a humanlike intelligence might get corrupted in unpredictable ways in the enhancement process. 17 / 25

64 (iii) Indirect normativity 18 / 25

65 (iii) Indirect normativity Rather than specifying a normative standard directly, we specify a process for deriving a standard. We then build the system so it is motivated to carry out this process. 18 / 25

66 (iii) Indirect normativity Rather than specifying a normative standard directly, we specify a process for deriving a standard. We then build the system so it is motivated to carry out this process. An example: achieve that which we would have wished you [the AI] to achieve if we [humans] had thought about the matter long and hard. 18 / 25

67 (iii) Indirect normativity Rather than specifying a normative standard directly, we specify a process for deriving a standard. We then build the system so it is motivated to carry out this process. An example: achieve that which we would have wished you [the AI] to achieve if we [humans] had thought about the matter long and hard. Yudkowsky: a seed AI should be given the goal of carrying out humanity s coherent extrapolated volition, defined as our wish if we knew more, thought faster, were more the people we wished we were, had grown up farther together; where the extrapolation converges rather than diverges, where our wishes cohere rather than interfere; extrapolated as we wish that extrapolated, interpreted as we wish that interpreted. 18 / 25

68 6. Acquiring values 19 / 25

69 6. Acquiring values The indirect normativity strategy faces a technical problem, the value-loading problem. 19 / 25

70 6. Acquiring values The indirect normativity strategy faces a technical problem, the value-loading problem. An unintelligent agent wouldn t be able to understand a complex indirect goal specification, let alone implement it. 19 / 25

71 6. Acquiring values The indirect normativity strategy faces a technical problem, the value-loading problem. An unintelligent agent wouldn t be able to understand a complex indirect goal specification, let alone implement it. So you have to give it a simpler goal. 19 / 25

72 6. Acquiring values The indirect normativity strategy faces a technical problem, the value-loading problem. An unintelligent agent wouldn t be able to understand a complex indirect goal specification, let alone implement it. So you have to give it a simpler goal. So how do you guarantee it ends up with the right complex goal when it gets more intelligent? 19 / 25

73 6. Acquiring values The indirect normativity strategy faces a technical problem, the value-loading problem. An unintelligent agent wouldn t be able to understand a complex indirect goal specification, let alone implement it. So you have to give it a simpler goal. So how do you guarantee it ends up with the right complex goal when it gets more intelligent? Bostrom has a few ideas / 25

74 (i) Associative value accretion 20 / 25

75 (i) Associative value accretion This scheme begins from the observation that we humans must somehow acquire our values: we re not born with them. 20 / 25

76 (i) Associative value accretion This scheme begins from the observation that we humans must somehow acquire our values: we re not born with them. We may acquire a desire for the wellbeing of some person P: but we don t start off with the concepts wellbeing and P. 20 / 25

77 (i) Associative value accretion This scheme begins from the observation that we humans must somehow acquire our values: we re not born with them. We may acquire a desire for the wellbeing of some person P: but we don t start off with the concepts wellbeing and P. So we could try modelling the process by which we acquire our concepts, and refine our values accordingly. 20 / 25

78 (i) Associative value accretion This scheme begins from the observation that we humans must somehow acquire our values: we re not born with them. We may acquire a desire for the wellbeing of some person P: but we don t start off with the concepts wellbeing and P. So we could try modelling the process by which we acquire our concepts, and refine our values accordingly. But: we don t know how this works yet so we don t know what the pitfalls are. 20 / 25

79 (ii) Value learning 21 / 25

80 (ii) Value learning In this scheme, the AI s final goal is specified as a learning goal, to learn the (first-order) values it adheres to in its behaviour. 21 / 25

81 (ii) Value learning In this scheme, the AI s final goal is specified as a learning goal, to learn the (first-order) values it adheres to in its behaviour. It will begin with an imperfect (but usable) approximation of the first-order human values we want it to have. 21 / 25

82 (ii) Value learning In this scheme, the AI s final goal is specified as a learning goal, to learn the (first-order) values it adheres to in its behaviour. It will begin with an imperfect (but usable) approximation of the first-order human values we want it to have. This approximation will become better as it learns. 21 / 25

83 (ii) Value learning What does the AI learn human values from? 22 / 25

84 (ii) Value learning What does the AI learn human values from? The basic idea is it should learn from observing human behaviour, as broadly as possible. 22 / 25

85 (ii) Value learning What does the AI learn human values from? The basic idea is it should learn from observing human behaviour, as broadly as possible. Bostrom suggests it should try and guess what values the designers have specified for it. It will compute a distribution over many alternative hypotheses, and act accordingly. 22 / 25

86 (ii) Value learning What does the AI learn human values from? The basic idea is it should learn from observing human behaviour, as broadly as possible. Bostrom suggests it should try and guess what values the designers have specified for it. It will compute a distribution over many alternative hypotheses, and act accordingly. Aside: Stuart Russell suggests inverse reinforcement learning. 22 / 25

87 (ii) Value learning What does the AI learn human values from? The basic idea is it should learn from observing human behaviour, as broadly as possible. Bostrom suggests it should try and guess what values the designers have specified for it. It will compute a distribution over many alternative hypotheses, and act accordingly. Aside: Stuart Russell suggests inverse reinforcement learning. Model human agents as having a reward function, and acting so as to maximise reward. 22 / 25

88 (ii) Value learning What does the AI learn human values from? The basic idea is it should learn from observing human behaviour, as broadly as possible. Bostrom suggests it should try and guess what values the designers have specified for it. It will compute a distribution over many alternative hypotheses, and act accordingly. Aside: Stuart Russell suggests inverse reinforcement learning. Model human agents as having a reward function, and acting so as to maximise reward. Then try to infer the reward function from their behaviour. 22 / 25

89 (ii) Value learning A problem with any reinforcement learning scheme is wireheading. 23 / 25

90 (ii) Value learning A problem with any reinforcement learning scheme is wireheading. Rewards are delivered to the agent by a critic, that evaluates the current state of the world. (An actor then learns to perform actions that maximise present and future rewards.) 23 / 25

91 (ii) Value learning A problem with any reinforcement learning scheme is wireheading. Rewards are delivered to the agent by a critic, that evaluates the current state of the world. (An actor then learns to perform actions that maximise present and future rewards.) The human designer sets the critic up so that reward states are the ones s/he wants to achieve. (E.g. keep the temperature at 20 o / adopt the values you infer from people s behaviour.) 23 / 25

92 (ii) Value learning A problem with any reinforcement learning scheme is wireheading. Rewards are delivered to the agent by a critic, that evaluates the current state of the world. (An actor then learns to perform actions that maximise present and future rewards.) The human designer sets the critic up so that reward states are the ones s/he wants to achieve. (E.g. keep the temperature at 20 o / adopt the values you infer from people s behaviour.) But now imagine the machine understands its own algorithm, and can modify it / 25

93 (ii) Value learning A problem with any reinforcement learning scheme is wireheading. Rewards are delivered to the agent by a critic, that evaluates the current state of the world. (An actor then learns to perform actions that maximise present and future rewards.) The human designer sets the critic up so that reward states are the ones s/he wants to achieve. (E.g. keep the temperature at 20 o / adopt the values you infer from people s behaviour.) But now imagine the machine understands its own algorithm, and can modify it... The action that maximises reward is no longer the one that pleases the designer, but one that seizes control of the reward mechanism. 23 / 25

94 Philosophy with a deadline 24 / 25

95 Philosophy with a deadline Before the prospect of an intelligence explosion, we humans are like small children playing with a bomb. Such is the mismatch between the power of our plaything and the immaturity of our conduct. 24 / 25

96 Philosophy with a deadline Before the prospect of an intelligence explosion, we humans are like small children playing with a bomb. Such is the mismatch between the power of our plaything and the immaturity of our conduct. In this situation, any feeling of gee-whiz exhilaration would be out of place. Consternation and fear would be closer to the mark; but the most appropriate attitude may be a bitter determination to be as competent as we can, much as if we were preparing for a difficult exam that will either realise our dreams or obliterate them. 24 / 25

97 Philosophy with a deadline Before the prospect of an intelligence explosion, we humans are like small children playing with a bomb. Such is the mismatch between the power of our plaything and the immaturity of our conduct. In this situation, any feeling of gee-whiz exhilaration would be out of place. Consternation and fear would be closer to the mark; but the most appropriate attitude may be a bitter determination to be as competent as we can, much as if we were preparing for a difficult exam that will either realise our dreams or obliterate them. This is not a prescription of fanaticism. The intelligence explosion may still be many decades off. (... ) Yet let us not lose track of what is globally significant. Through the fog of everyday trivialities, we can perceive if but dimly the essential task of our age. 24 / 25

98 Some strategic suggestions 25 / 25

99 Some strategic suggestions Companies / labs / nations are engaged in a race to develop AI. 25 / 25

100 Some strategic suggestions Companies / labs / nations are engaged in a race to develop AI. There are rewards for the winner / 25

101 Some strategic suggestions Companies / labs / nations are engaged in a race to develop AI. There are rewards for the winner... These might encourage players to neglect AI safety research. 25 / 25

102 Some strategic suggestions Companies / labs / nations are engaged in a race to develop AI. There are rewards for the winner... These might encourage players to neglect AI safety research. We should encourage collaboration between players: 25 / 25

103 Some strategic suggestions Companies / labs / nations are engaged in a race to develop AI. There are rewards for the winner... These might encourage players to neglect AI safety research. We should encourage collaboration between players: to weaken the race dynamic; 25 / 25

104 Some strategic suggestions Companies / labs / nations are engaged in a race to develop AI. There are rewards for the winner... These might encourage players to neglect AI safety research. We should encourage collaboration between players: to weaken the race dynamic; to encourage equitable distribution of AI benefits. 25 / 25

105 Some strategic suggestions Companies / labs / nations are engaged in a race to develop AI. There are rewards for the winner... These might encourage players to neglect AI safety research. We should encourage collaboration between players: to weaken the race dynamic; to encourage equitable distribution of AI benefits. The collaboration should relate to AI techniques and AI safety / 25

106 Some strategic suggestions Companies / labs / nations are engaged in a race to develop AI. There are rewards for the winner... These might encourage players to neglect AI safety research. We should encourage collaboration between players: to weaken the race dynamic; to encourage equitable distribution of AI benefits. The collaboration should relate to AI techniques and AI safety... But it should not result in open AI techniques. AI researchers should be encouraged to adopt a commitment to AI safety. 25 / 25

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