A favourite joke involves two barefoot guys in the forest who spot a bear off in the distance, but lumbering towards them and closing fast. They have to run for it but one stops first to put on his running shoes. The other laughs and asks if the running shoes are going to enable him to outrun the bear. Finishing his lacing up, the first guys smiles and says: “I don’t need to outrun the bear; I just need to outrun you.”
As to the anticipated challenges of matching the powers of Artificial Intelligence when it supersedes the capacities of what humans can do – when it graduates to “Super” status and leaves humanity floundering in its dust: we may take a lesson from the bear joke and wonder if there is a middle way.
It appears from all the media commentary that we have little idea now as to how to design SAI that will not obliterate us, whether by accident, or through its own malign design, or by some combination possibly exacerbated by the purposeful interventions of thoroughly malign humans. Can we at least get smart enough to do what we cannot yet do, and find a way of programming SAI to help us solve this?
Without getting into the philosophical difficulties of bootstrapping something like intelligence, two things are clear. We must get smarter than we are if we are to solve this particular problem; and brains take a long time to evolve on their own. We need an accelerant strategy, and it will take more than brain-training. Research must proceed more quickly in brain-computer interfaces, nano-biotechnology and neuropharmacology, and in the sciences of gene editing and deep-brain stimulation. While research into several of these technologies has been driven by cognitive impairments such as movement disorders and the treatment of depression, their capabilities in areas of potential enhancement of cognitive function are attracting greater interest from the scientific and investment communities. It is definitely becoming a bear market.
Lively Stuff from Planet BAM!
- Augmenting today’s blogspot: Gizmodo reflects on our race against time
Essentially, software that can improve itself is going to learn increasingly quickly how to improve itself increasingly quickly. Humanity will have to rely upon the wisdom of whichever humans have the greatest influence in controlling the process. A useful companion link is an earlier Gizmodo article on this topic, referencing Bostrom.
- MIT algorithm proceeds from visual cues to the accurate prediction of sounds
Commentary is dominated by potential for film soundtracks and thoughts on human credulity
- Your brain on silence: you can be more at one with the world when it’s a quiet one
What started as a Finnish marketing exercise inspired some useful reflections on quietness