I have a new paper out where I describe the technological landscape leading to AGI and implications for AI safety (largely building on Bostrom's work in Superintelligence).
Here's the abstract:
In this paper, we contrast three major pathways to human level AI, also known as artificial general intelligence (AGI), and we investigate how safety considerations compare between the three. The first pathway is de novo AGI (dnAGI), AGI built from the ground up. The second is Neuromorphic AGI (NAGI), AGI based loosely on the principles of the human brain. And third is Whole Brain Emulation (WBE), AGI built by emulating a particular human brain, in silico. Bostrom has previously argued that NAGI is the least safe form of the three. NAGI would be messier than dnAGI and therefore harder to align to arbitrary values. Additionally, NAGI would not intrinsically possess safeguards found in the human brain – such as compassion – while WBE would. In this paper, we argue that getting WBE first would be preferable to getting dnAGI first. While the introduction of WBE would likely be followed by a later transition to the less-constrained and therefore more-powerful dnAGI, the creation of dnAGI would likely be less dangerous if accomplished by WBEs than if done simply by biological humans, for a variety of reasons. One major reason is that the higher intelligence and quicker speed of thinking in the WBEs compared to biological humans could increase the chances of traversing the path through dnAGI safely. We additionally investigate the major technological trends leading to these three types of AGI, and we find these trends to be: traditional AI research, computational hardware, nanotechnology research, nanoscale neural probes, and neuroscience. In particular, we find that WBE is unlikely to be achieved without nanoscale neural probes, since much of the information processing in the brain occurs on the subcellular level (i.e., the nanoscale). For this reason, we argue that nanoscale neural probes could improve safety by favoring WBE over NAGI.
If you want to read the full paper, here's a link: