An initial consciousness could through design, refactoring, and replication build up the universe without any further miracles beyond the initial consciousness in the first place.
Behind this messy argument is a deeper concern I have that we are doing a disservice to young people by presuming that they really do need more than a decade to learn advanced skills. We can subject young people to more intense education than we are now, and that they could have college-graduate level skills before they become 18 years old. Yet, we think that such an expectation is unwise as if it risks losing something more valuable. Perhaps we fear the young person’s loss to easy access to the presumption of innocence.
Just like the fact that I can’t interest an advanced piano teacher doesn’t diminish the fact that such teachers exist, the fact that science can’t engage the immaterial teacher says nothing about the existence of such a teacher. The teacher is simply uninterested in engaging, and have every good reason to not engage.
A data-driven economy is not a free economy. While there remains promise that algorithms acting on vast amounts of rapidly arriving data can produce a better economy, I am suspicious that such an economy will eventually languish because it robs the human actors of their ability to negotiate. The vitality of a free economy derives from individual freedom to negotiate terms of engagement. Eventually, A data-driven economy may prove to be superior but it will succeed only by suppressing natural human negotiation. Human actors negotiating in a data-driven economy must negotiate with machines. Applying approaches that work for other humans to machines instead is criminalized as cybercrime. Human negotiation involves coming to terms with weaknesses as well as strengths. Exploiting weaknesses of machines is a crime.
Current debate about artificial intelligence automating jobs usually consider that the jobs at risk are low-skilled jobs. The advancements in AI simply raise that lower level of jobs that can be more economically performed by machines. For example, there is now talk of autonomously driving trucks that will put truck-drivers out of work. Even…
Agile practices are shown to work. Such practices are integral to many modern successful businesses. Similarly, machine learning algorithms are showing their success. At the time of this writing, it appears that both will play a large role in the future economy.
Following the lessons from computer neural networks, we should recognize that intelligence in an organizational neural network arises within the network itself. It does not dependent on hierarchical decision makers. Neural-network organizations have no need for individually accountable human decision makers such as managers or officers. Such an outcome is consistent with the goals of evidence-based decision making that ideally obligate decisions based on the evidence alone and not on whim of a designated leader.