Computing overhangOverhang refers tois a situation where new algorithms can exploit existing computing power far more efficiently than before. This can happen if previously used algorithms have been suboptimal.
As Yudkoswky puts it, human intelligence, created by this "blind" evolutionary process, has only recently developed the ability for planning and forward thinking - deliberation. On the other hand, the rest and almost all our cognitive tools were the result of ancestral selection pressures, forming the roots of almost all our behavior. On the other hand,As such, when considering the design of complex systems where the designer - us - collaborates with the system being constructed, we are faced with a new signature and a different way to achieve AGI that's completely different than the process that gave birth to our brains.
As Yudkoswky puts it, human intelligence, created by evolution, is characterized by its design signaturethis "blind" evolutionary process, has only recently developed the ability for planning and forward thinking - and it has developed poorly adapted to deliberation. On the other hand, almost all our cognitive tools were the result of ancestral selection pressures, forming the roots of almost all our behavior.
On the other hand, when considering the design of complex systems where the designer - us - collaborates with the system being constructed, we are faced with a new signature and a different way to achieve AGI.AGI that's completely different than the process that gave birth to our brains.
Though estimates of whole brain emulation place that level of computing power at least a decade away, it is very unlikely that the algorithms used by the human brain are the most computationally efficient for producing AI. This happens mainly because evolution had no insight, no deliberate plan in creating the human mind, and our intelligence didn't develop with the goal of eventually being modeled by AI. As Yudkoswky puts it, human intelligence, created by evolution, is characterized by its ownthis design signature - and it has developed poorly adapted to deliberation. On the other hand, when considering,considering the design of complex systems where the designer - us - collaborates with the system being constructed, we are faced with a new signature and a different way to creatingachieve AGI.
Though estimates of whole brain emulation place that level of computing power at least a decade away, it is very unlikely that the algorithms used by the human brain are the most computationally efficient for producing AI. This happens mainly because evolution had no insight, no deliberate plan in creating the human mind,our brains evolved during a natural selection process and our intelligence didn'thus weren't developdeliberatly created with the goal of eventually being modeled by AI. As Yudkoswky puts it, human intelligence, created by evolution, is characterized by its design signature - and it has developed poorly adapted to deliberation. On the other hand, when considering the design of complex systems where the designer - us - collaborates with the system being constructed, we are faced with a new signature and a different way to achieve AGI.
As of today, enormous amounts of computing power is currently available in the form of supercomputers or distributed computing. Large AI projects can grow to fill these resources by using deeper and deeper search trees, such as high-powered chess programs, or by performing large amounts of parallel operations on extensive databases, such as IBM's Watson playing Jeopardy. While the extra depth and breadth are helpful, it is likely that a simple brute-force extension of techniques is not the optimal use of the available computing resources. This leaves the need for improvement on the side of algorithmic implementations, where most work is currently focused.focused on.
Though estimates of whole brain emulation place that level of computing power at least a decade away, it is very unlikely that the algorithms used by the human brain are the most computationally efficient for producing AI. This happens mainly because evolution had no insight, no deliberate plan in creating the human mind, and our intelligence didn't develop with the goal of eventually being modeled by AI. As Yudkoswky puts it,it, human intelligence, created by evolution, is characterized by thisits design signature - and it has developed poorly adapted to deliberation. On the other hand, when considering the design of complex systems where the designer - us - collaborates with the system being constructed, we are faced with a new signature and a different way to achieve AGI.
Though estimates of whole brain emulation place that level of computing power at least a decade away, it is very unlikely that the algorithms used by the human brain are the most computationally efficient for producing AI. This happens mainly because evolution had no insightinsight, no deliberate plan in creating the human mind, and our intelligence didn't develop with the goal of eventually being modeled by AI. As Yudkoswky puts it, human intelligence, created by evolution, is characterized by its own design signature - and it has developed poorly adapted to deliberation. On the other hand, when considering, the design of complex systems where the designer - us - collaborates with the system being constructed, we are with a new signature and a different to creating AGI.