Maximalism with Random Effect

Let’s move to interaction. Building a give-and-take relationship between the AI and users is one of the biggest design challenges. To facilitate successful exchanges, we focus on the design improving the AI and keeping user interested by operant conditioning, where an antecedent stimuli is followed by a consequence of the behaviour through creating a reward (reinforcement).

B.F. Skinner in the 1950s suggests using unpredictable rewards called variable ratio model to keep behavior going and establish a habit. Skinner observed that lab mice responded well to random rewards. As he discovered over 50 years ago, variable rewards are a powerful inducement for creating habits in technology-induced behaviors.

Consider random variable effect in this relationship. It can dramatically alter the nature of conventional prebuilt UX flows. Less monotonous experiences with random feedback can make AI interaction more enjoyable and engaged. Besides, induced feedback loop help with machine learning by gaining the data AI needs to get smarter and be more relevant.