The future-tense science is the risk-based decision making with the goal of a future that progresses from the past. The goal needs to be optimizing the benefits enjoyed in the future given the facts on the ground in the present. Memories of the past is not as important to this planning as is the current facts and the future opportunities.
The current practice of ghost flights by airlines is a great analogy to what needs to happen in the workforce. To protect the nation from large numbers of hospitalizations and deaths, we could remove the people from their positions when they are over 50 or have one of the identified preexisting conditions. Eventually (hopefully), the crisis will abate where we can allow these people to return to their original roles.
One of the advantages of machine intelligence over human intelligence is that machines are not driven toward poetry. To me, poetry captures the scientific appreciation for the simplest explanations with the fewest number of terms. Humans are innately poets by nature, and even the objectivity of science can not escape the human delight in well-crafted poetry, or human disdain for inelegance in descriptions.
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…
What matters is the diversity of personal experiences of the individual team members, not the experiences of the broader populations represented by that individual team member. Even if the social-group experiences were relevant, the individual will have access to only a tiny fraction of the experiences we expect him to represent. The genome itself is not a communication channel for sharing intelligence among living humans.
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.
Except in Donald Trumps reality TV shows, most managers do not have the luxury of immediately declaring “you’re fired”, and certainly not in that short of a period of time. Again, I trust the instinct is correct and that no amount of coddling will obtain the desired performance. However, we work within an idealistic human resources department that demands a drawn out process that reserves a firing event only for overtly criminal behavior. We are usually stuck with our hiring decisions because our job is to be a hiring manager and a hiring manager’s job is to select the idealized competent candidate for the job opening.
Up through the mid 20th century, a large portion of employers had an employment model that expected life-long employment commitment from employees. These companies used fixed benefit retirement programs as an incentive to encourage long term commitment to a company and at the same time defer income expenses to be more competitive in the…