As we have entered the age of artificial intelligence (AI), the Epilogue of this book asks the following questions: Is AI a threat to human excellence, especially with the prospects of superintelligence alleged to outsmart human intelligence soon? Is technological singularity near? What are its implications for human intelligence, talent, and creativity? Is it a harmless speculation of a technological turning point or an advocacy for technological hegemony? As a response to the sentiment that somehow silicon-based intelligence or life (whatever that means!) will replace carbon-based intelligence and life as a dominant force on earth, I point out three fallacies of AI superiority: the omnipotence of technical rationality in solving human problems; the doctrine of “the faster, the better”; and anthropomorphism. Consequently, I bring up the issue of alignment: how the AI community can align or realign AI technology with human purpose or telos. At the end of the day, the human spirit underlying the pursuit of excellence should prevail, so as to inspire new generations of masters and makers, who not only can make instrumental changes but also will preserve human aspirations and inspirations to create a better world, a main impetus of human excellence.

In this chapter, the law scholar Ebrahim Afsah outlines different implications of AI for the area of national security. He argues that while AI overlaps with many challenges to the national security arising from cyberspace, it also creates new risks, including the emergence of a superintelligence in the future, the development of autonomous weapons, the enhancement of existing military capabilities, and threats to foreign relations and economic stability. Most of these risks, however, Afsah concludes, can be subsumed under existing normative frameworks.

Over the coming decades, artificial intelligence and robotics will continue to grow rapidly in power and scope, exerting a transformative impact on our lives. Seven trends are likely: the machines will become progressively more versatile and multifunctional in their capabilities; they will be designed to improve their own software and hardware over time, as they interact with their environment; the line between AI and robots will gradually blur, as machines come to permeate our society at all levels; these machines will be entrusted with complex practical tasks that require them to develop sophisticated commonsense knowledge about the human social world; the machines will be interconnected in functional networks that multiply their powers; such machines will need to be able to refuse to obey certain kinds of human commands, raising fundamental questions about who controls their actions; and the logic of arms races will apply to such machines, impelling nations and corporations to develop increasingly powerful machines as quickly as possible, with only a secondary concern for caution and safety.

Whereas today’s “narrow” AI machines can be easily controlled, the probable advent over the coming decades of machines endowed with Artificial General Intelligence (AGI) will pose a far more difficult control challenge – for such machines will function as agents endowed with many human-like capabilities. The AI expert Stuart Russell has proposed a radical new design for the motivational architecture of AI machines, which he calls “humble AI.” Rather than programming AI machines to pursue specific goals, he argues, it would be much safer to give them a single broad goal, namely, maximizing the realization of human preferences. The key innovation here lies in programming the machines so that they can never be 100 percent certain about what those human preferences are – a gambit that restores ultimate control over the machines’ behavior to their human overseers. Russell argues persuasively that fundamental safety research in the field of AI needs to be pursued as a much higher priority than it currently receives today.

The answers that each political community finds to the law reform questions posed by AI may differ, but a near-term threat is that AI systems capable of causing harm will not be confined to one jurisdiction – indeed, it may be impossible to link them to a specific jurisdiction at all. This is not a new problem in cybersecurity, but different national approaches to regulation will pose barriers to effective regulation exacerbated by the speed, autonomy, and opacity of AI systems. For that reason, some measure of collective action is needed. Lessons may be learned from efforts to regulate the global commons as well as moves to outlaw certain products (weapons and drugs, for example) and activities (such as slavery and child sex tourism). The argument advanced here is that regulation, in the sense of public control, requires active involvement of states. To co-ordinate those activities and enforce global ‘red lines’, this chapter posits a hypothetical International Artificial Intelligence Agency (IAIA), modelled on the agency created after the Second World War to promote peaceful uses of nuclear energy, while deterring or containing its weaponization and other harmful effects.

As AI systems operate with greater autonomy, the idea that they might themselves be held responsible has gained credence. On its face, the idea of giving those systems a form of independent legal personality may seem attractive. Yet this chapter argues that this is both too simple and too complex. It is simplistic in that it lumps a wide range of technologies together in a single, ill-suited legal category; it is overly complex in that it implicitly or explicitly embraces the anthropomorphic fallacy that AI systems will eventually assume full legal personality in the manner of the ‘robot consciousness’ arguments mentioned earlier in the book. Though the emergence of general AI is a conceivable future scenario – and one worth taking precautions against – it is not a sound basis for regulation today.