90 STASHED IDEAS
New studies reveal that customer’s patience levels increase when they are provided with a feeling of nostalgia, alleviating the negative effects of waiting long.
The experiment shows that being in a state of nostalgia relaxes us and puts us in a positive mood, as we remember a unique or favourite experience of the past, which we are unlikely to experience again. Example: A long waiting line at the checkout counter can benefit from 70s music playing in the background.
Any customer-service oriented company knows that managing wait times are crucial for their customer satisfaction and bottom-line. Whether on the phone, at the checkout counter, or in a restaurant, long wait times lead to a subpar customer experience and a drop in sales.
Customers' patience can be psychologically manipulated by selective colour choices (like blue), temperature, or the visuals that surround them during their wait times.
Making mistakes is fine, but making the same mistake over and over is not. We need to make mistakes, and learn from it, or else we will keep living the same year in repeat mode. If we don’t analyse and reflect on our actions, we will never learn from our mistakes and will be unable to calibrate our decision making.
The thumb rule here is to be less busy, and maintain a daily journal to think and reflect on your past actions.
We normally trust people and believe we have the right information. The resulting outcome is akin to chinese whispers, the phenomenon when information is passed on to a lot of people in a chain, leading to distortion and falsification.
The thumb rule here is to seek information from credible, first hand sources.
Our various cognitive biases make us behave irrationally, even though we believe we are acting logically. If we are tired, in a rush, or are distracted we tend to rush towards a bad decision. Other factors include working with an authority figure or in a group.
The rule to follow is to never make important decisions when one is emotionally weak, tired, distracted, or in a hurry.
The real problem is rarely visible in the first instance, as we only look at the symptom or the result. If we let someone else define the problem, we are far away from it. We might be too close to the problem and need an objective view.
The rule is to not let others define your problems
It is easier to portray being virtuous, to create the image, than to actually be virtuous. We are by default programmed to do what is easy and not what is right. If we get too focused on optics rather than outcomes or results, we will start being biased and selfish. We would then be away from our true nature, making decisions because of external factors.
The rule here is to act like you own the company.
Meta-learning is when you learn about how much you know and don’t know in a particular domain.
Meta-learning is important because it’s easy to delude yourself into believing you know more than you actually do.
Many people, who are otherwise perfectly healthy can create patterns and have an illusion that they are somehow in control of the external events that no one could influence.
The belief is so strong that it can affect their behaviour and make them do superstitious actions that make no sense.
Scientists studying the illusion of control phenomenon in many of us state that the exaggerated belief patterns are actually a useful tool for success, as the overconfidence of our actions influencing the outside environment can act as a catalyst.
Being in control does wonders to our self-esteem and the sense of power creates a chain reaction that helps us even if it is just a delusion.
It goes beyond traditional evolutionary approaches. It explains innovation. Instead of optimizing for a specific goal, it embraces the creative exploration of a diverse population of solutions.
The steppingstone’s potential can be seen by analogy with biological evolution: feathers likely evolved for insulation and only later became handy for flight.
Pursuing specific goals can be a hindrance to reaching those objectives.
Kenneth Stanley, a computer scientist, hoped to show that by following ideas in interesting directions, algorithms can produce a diversity of results and solve problems. Thus, ignoring an objective can get you to the solution faster than pursuing it. He showed this through an approach named novelty search.
Biological evolution is the only system to produce human intelligence.
If we want algorithms that can navigate the physical and social world as we do, we need to imitate nature's tactics. Instead of hard-coding for specific metrics, we must let a population of solutions blossom that may discover an indirect path or a set of stepping stones to allow them to evolve better than if they'd received those skills directly.
Neuroevolution is a form of artificial intelligence. It is a meta-algorithm, an algorithm for designing algorithms. It adopts the principles of biological evolution in order to design smarter algorithms. Eventually, the algorithms get pretty good at their job.
Traditionally, evolutionary algorithms are used to solve specific problems. For instance, the ability to control a two-legged robot. Solutions that perform the best on some metrics are selected to produce offspring.
In spite of successes, these algorithms are more computationally intensive than approaches such as "deep learning."