If you think things are fast now, remember Moore's Law!

 In the book Stolen Focus the case is made (page 31) that there is a limit on humans which is being stretched by the increasing volume and pace of information, decisions, consequences and responses. If you think things are fast now, remember Moore's Law, the principle that the speed and capability of computers can be expected to double every two years. Your life will be on auto-pilot because you dear human, are incapable of making the decisions for yourself.

Even more alarming we will plunder energy and mineral resources to fuel the systems and production of this world in which we are less present and participant.


Here is some data..

Processing power, decision speed, and energy consumption are important factors to consider in both humans and computers/AI systems.

HUMANS:

1.1. Processing Power: Humans have remarkable processing power in terms of information perception and processing. However, it's hard to quantify this processing power in a way that compares directly with computers, due to the difference in biological and digital systems. Nonetheless, the human brain is estimated to compute at approximately 10^16 (10 petaflops) to 10^19 (1 exaflop) operations per second.

1.2. Decision Speed: Human decision speed can vary significantly based on a multitude of factors like fatigue, stress, expertise, etc. However, for a simple reaction time task (such as pressing a button when a light appears), it is about 200-300 milliseconds. More complex tasks, requiring cognitive processing, can take significantly longer.

1.3. Energy Consumption: The human brain consumes about 20% of the body's total energy, approximately 20 watts, even though it makes up only about 2% of the body's weight.

COMPUTERS AND AI:

2.1. Processing Power:

Past (1980s-1990s): Home computers in the 1980s had processing power measured in millions of instructions per second (MIPS), for instance, the IBM PC (1981) operated at 0.64 MIPS.

Present: High-end processors in consumer desktops can perform at a level of hundreds of billions of instructions per second (gigaflops). For instance, the Apple M1 chip operates around 2.6 teraflops, which is trillions of operations per second. Supercomputers can perform at the level of exaflops, or a billion billion calculations per second, like the Fugaku supercomputer in Japan.

Future (2023 onwards): The trend of increasing processing power is expected to continue, with quantum computing potentially revolutionizing the field by performing complex calculations far more efficiently than traditional computers. However, there are also predictions about the end of Moore's Law, the principle that the speed and capability of computers can be expected to double every two years.

2.2. Decision Speed:

Past (1980s-1990s): Early computers and AI would have decision speeds that were limited by their hardware, which was considerably slower than today's hardware.

Present: Modern AI can make decisions at speeds that are often much faster than humans, typically in milliseconds or less. This is particularly true for well-defined tasks with clear inputs and outputs.

Future (2023 onwards): It's expected that decision speed will continue to improve with advancements in hardware and software. Quantum computing, for instance, could enable even faster decision-making for certain types of problems.

2.3. Energy Consumption:

Past (1980s-1990s): Early computers were not energy-efficient by today's standards. For example, the CDC 6600, a high-performance computer from the 1960s, used about 150 kilowatts of power.

Present: Modern computers and AI systems can be much more energy-efficient, but power consumption varies widely depending on the system. A modern server for AI processing might consume on the order of a few hundred watts. At a larger scale, data centers can use significant amounts of power, with the largest ones using on the order of tens of megawatts.

Future (2023 onwards): Energy efficiency is a major area of focus in computer and AI development. It's expected that future systems will use power more efficiently, but as systems become more powerful, their overall energy consumption may still be high. Efforts like AI model optimization and hardware advances aim to keep this in check.