Superintelligence: Paths, Dangers, Strategies - Chapter 1

Growth modes and big history

The author begins this Chapter by rightly identifying that each generation has been experiencing rapid growth with the speed of growth by each generation coming faster than the previous one. For instance, in earlier eras, people would have found it irrational to suggest that the world economy would one day be doubling several times in a single lifetime, something which is a reality in today's era. In light of this, it is not unreasonable to suggest that the potential for growth of superintelligence in future generations is unlimited. 

In fact, Moore's law suggests that the power of computers doubles every two years. 

Great expectations

Since the invention of computers in the 1940s, it has been imagined that machines could one day develop human-level intelligence with the capability to learn, reason and solve complex problems. As the mathematician I. J. Good asserted in 1965:

"Let an ultraintelligent machine be defined as a machine that can far surpass all the intellectual activities of any man however clever. Since the design of machines is one of these intellectual activities, an ultraintelligent machine could design even better machines; there would then unquestionably be an 'intelligence explosion,' and the intelligence of man would be left far behind. Thus the first ultraintelligent machine is the last invention that man need ever make, provided that the machine is docile enough to tell us how to keep it under control."

Seasons of hope and despair

As Bostrom rightly identifies, there were some eras when AI fell out of fashion and some eras where the experts were lauding about AI and being highly optimistic about it. Such instances occurred in the 1980s when Japan created its Fifth-Generation Computer Systems Project and funding in AI increased in various parts of the world. When the Fifth-Generation Computer Systems Project failed to meet its expectations, an era of drought in AI emerged and funding decreased. 

Additionally, Bostrom continued to state that the ideal machine would be like the concept of the Bayesian agent "that makes probabilistically optimal use of available information." However, "this ideal is unattainable because it is too computationally demanding to be implemented in any physical computer." 

State of the art

This sub-chapter describes the major milestones that AI has achieved so far in society. Accordingly, AI can beat the best chess player in the world and as experts in the late fifties once asserted: "If one could devise a successful chess machine, one would seem to have penetrated to the core of human intellectual endeavor.” However, it is suggested that it has not penetrated the core of human intellectual yet because as Bostrom points out, "It can play chess; it can do no other." Appropriately, common sense and natural language has turned out to be much more challenging to attain than expected. Bostrom rightly acknowledged that the success in chess-playing has been the result of a rather simpler than anticipated algorithm. If common sense, natural language and general reasoning manage to be established into a machine, then it would be very likely that that machine can do as much as a human can do or that it is very close in doing so. 

Furthermore, AI is already being involved in countless sectors and industries. As Peter Diamandis stated, "AI will affect every single industry on Earth." Examples of AI being utilised in several industries include finance (stock-trading systems), health (identifying diseases early, e.g breast cancer), law and self-driving vehicles, among others. The world also contains a population of 10 million robots. 

Opinions about the future of machine intelligence 

In recent years, there has been a dramatic rise of interest in AI and this is evident by the large amount of investment received by AI companies and the fact that numerous students are choosing to study AI at university or take AI-related courses as well as the very reading of this book and the summary of it on this blog. 

Correspondingly, there is also widespread interest from the public in knowing when 'human-level machine intelligence'(HLMI) will be achieved or if ever. As Bostrom reports, relevant research has concluded that there is a "10% probability of HLMI by 2022, 50% probability by 2040, and 90% probability by 2075".