Executive Summary

Curiosity has been hailed as one of the most critical competencies for the modern workplace. As the workplace becomes more and more automated, it begs the question: Can artificial intelligence ever be curious as human beings? AIs desire to learn a directed task cannot be overstated. Most AI problems comprise defining an objective or goal that becomes the computers number one priority.At the same time, AI is also constrained in what it can learn. AI is increasinglybecoming a substitute for tasks that once required a great deal of human curiosity, and when it comes to performance, AI will have an edge over humans in a growing number of tasks. But the capacity to remain capriciously curious about anything, including random things, and pursue ones interest with passion, may remain exclusively human.

Curiosity has been hailed as one of the most critical competencies for the modern workplace. Its been shown to boost peoples employability. Countries with higher curiosity enjoy more economic and political freedom, as well as higher GDPs. It is therefore not surprising that, as future jobs become less predictable, a growing number of organizations will hire individuals based on what they could learn, rather than on what they already know.

Of course, peoples careers are still largely dependent on their academic achievements, which are (at least partly) a result of their curiosity. Since no skill can be learned without a minimum level of interest, curiosity may be considered one of the critical foundations of talent. AsAlbert Einstein famously noted,I have no special talent. I am only passionately curious.

How it will impact business, industry, and society.

Curiosity is only made more important for peoples careers by the growing automation of jobs. At this years World Economic Forum, ManpowerGroup predicted that learnability, the desire to adapt ones skill set to remain employable throughout ones working life, is a key antidote to automation. Those who are more willing and able to upskill and develop new expertise are less likely to be automated. In other words, the wider the range of skills and abilities you acquire, the more relevant you will remain in the workplace. Conversely, if youre focused on optimizing your performance, your job will eventually consist of repetitive and standardized actions that could be better executed by a machine.

But what if AI were capable of being curious?

As a matter of fact, AIs desire to learn a directed task cannot be overstated. Most AI problems comprise defining an objective or goal that becomes the computers number one priority. To appreciate the force of this motivation, just imagine if your desire to learn something ranked highest among all your motivational priorities, above any social status or even your physiological needs. In that sense, AI is way more obsessed with learning than humans are.

At the same time, AI is constrained in what it can learn. Its focus and scope are very narrow compared to that of a human, and its insatiable learning appetite applies only to extrinsic directives learn X, Y, or Z. This is in stark contrast to AIs inability to self-direct or be intrinsically curious. In that sense, artificial curiosity is the exact opposite of human curiosity; people are rarely curious about something because they are told to be. Yet this is arguably the biggest downside to human curiosity: It is free-flowing and capricious, so we cannot boost it at will, either in ourselves or in others.

To some degree, most of the complex tasks that AI has automated have exposed the limited potential of human curiosity vis-a-vis targeted learning. In fact, even if we dont like to describe AI learning in terms of curiosity, it is clear that AI is increasingly a substitute for tasks that once required a great deal of human curiosity. Consider the curiosity that went into automobile safety innovation, for example. Remember automobile crash tests? Thanks to the dramatic increase in computing power, a car crash can now be simulated bya computer. In the past, innovative ideas required curiosity, followed by design and testing in a lab. Today, computers can assist curiosity efforts by searching for design optimizations on their own. With this intelligent design process, the computer owns the entire life cycle of idea creation, testing, and validation. The final designs, if given enough flexibility, can often surpass whats humanly possible.

Similar AI design processes are becoming more common across many different industries. Google has used it to optimize cooling efficiency with itsdata centers. NASA engineers have used it to improve antennae quality for maximum sensitivity. With AI, the process of design-test-feedback can happen in milliseconds instead of weeks. In the future, the tunable design parameters and speed will only increase, thus broadening our possible applications for human-inspired design.

A more familiar example might be the face-to-face interview, since nearly every working adult has had to endure one. Improving the quality of hires is a constant goal for companies, but how do you do it? A human recruiters curiosity could inspire them to vary future interviews by question or duration. In this case, the process for testing new questions and grading criteria is limited by the number of candidates and observations. In some cases, a company may lack the applicant volume to do any meaningful studies to perfect itsinterview process. But machine learning can be applied directly to recorded video interviews, and the learning-feedback process can be tested in seconds. Candidates can be compared based on features related to speech and social behavior. Microcompetencies that matter such as attention, friendliness, and achievement-based language can be tested and validated from video, audio, and language in minutes, while controlling for irrelevant variables and eliminating the effects of unconscious (and conscious) biases. In contrast, human interviewers are often not curious enough to ask candidates important questions or they are curious about the wrong things, so they end up paying attention to irrelevant factors and making unfair decisions.

Lastly, consider a human playing a computer game. Many games start out with repeated trial and error, sohumans must attempt new things and innovate to succeed in the game: If I try this, then what? What if I go here? Early versions of game robots were not very capable because they were using the full game state information; they knew where their human rivals were and what they were doing. But since 2015something new has happened: Computers can beat us on equal grounds, without any game state information, thanks to deep learning. Both humans and the computers can make real-time decisions about their next move. (As an example, see this video of a deep network learning to play the game Super Mario World.)

From the above examples, it may seem that computers have surpassed humans when it comes to specific (task-related) curiosity. It is clear that computers can constantly learn and test ideas faster than we can, so long as they have a clear set of instructions and a clearly defined goal. However, computers still lack the ability to venture into new problem domains and connect analogous problems, perhaps because of their inability to relate unrelated experiences. For instance, the hiring algorithms cant play checkers, and the car design algorithms cant play computer games. In short, when it comes to performance, AI will have an edge over humans in a growing number of tasks, but the capacity to remain capriciously curious about anything, including random things, and pursue ones interest with passion may remain exclusively human.

More:

Can AI Ever Be as Curious as Humans? - Harvard Business Review