I just finished the book Robot-Proof: Higher Education in the Age of Artificial Intelligence written by Joseph Aoun, the President of Northeastern University. While the author’s intended audience is other individuals within the academic community, I found the model which he built his message around one worth sharing. The premise of the book is a call to arms to the higher learning community, stating the current situation in the world one where machines are competing directly against the wider human workforce at a magnitude and speed like never before. Indeed technology as destroyed jobs before, resulting in workers both willing and able needing to retool, and for workers not both willing and able being left out in the cold. What is pointed out is that at each of these pivotal moments in society, individuals sought education as a counter. Factory workers replaced by machines learned how to operate and repair the machines. Economists who before created models “by hand” , now utilize data science tools. Where I feel this book is applicable to our business is the framework he provides for the literacy and cognitive competencies, and his articulation of near and far transfer of learning.
The 6 LiteraciesMost of us are familiar with half of the list presented in the book; reading, writing, and arithmetic. The argument presented is that for the majority of explosion of the middle class in most of the world’s middle-class populations, these three knowledge areas were most closely correlated to the earnings potential, job stability, and an individuals demand in the market. The presence of technology however has threatened this relationship. Machines are not able to read books and even provide critiques of them. Machines are actually writing news articles to a level audiences can not tell they were not written by humans. Law firms are using software to read through documents to find necessary discovery information. Even the most complex math we are finding that computers are having little trouble handling. This leads to the necessity for individuals to possess levels of acumen in three additional literary areas; data, technology, and humanities.
DATA literacy centers around the current reality that we have more information being created, transmitted, and stored than ever before. Individuals today have an incentive to understand this body of information surrounding their everyday life. While everyone will not be expected to be an expert in time-series analysis, individuals would best serve themselves understanding better visualization formats for time, understanding relations of data sets across time. Basic univariate (one variable) descriptive statistics like mean, and the median. Bivariate statistics to describe relationships between two items. The big picture frameworks that previously were only required for management is really now a need for all individuals on the team.
TECHNOLOGY literacy centers around the need to have insight into how the tools around us work. When machines first began to be adopted into society, there was a closer relationship between being able to use a machine, and knowing how the machine works. Early cars required an understanding on how they worked in order to use them. The opportunity cost of how much technology has been democratized is the fact that an understanding of how technology works is beyond most individuals. Individuals today have an incentive to now just know how to use their cell phone, or Tensor-Flow 2.0, but have some level of understanding how these technologies work.
HUMANITIES literacy centers around the group of studies that previously were seen only useful to smaller subsets of society. Psychology, Philosophy, Politics, Economics, and History. Only a decade ago, I remember being told the joke that the fastest way to your parent’s basement was through an anthropology degree. If you the list of the top ten most in demand jobs that LinkedIn post every October, you will see that things have definitely changed. Data science, data analyst, and user experience (A job that didn’t even exist a decade ago) teams are filled with individuals with backgrounds in these areas. Organizations are finding that the humanities are needed more than ever as we navigate what Moore (1985) called the ‘policy vacuums’ that technology creates. Data privacy, data governance, market segmentation, net effects of tools more a part of societies’ everyday activities, how individuals can be expected to misuse technology. Internally to organizations, collaboration and communication are main premises of AGILE teams. Data ethics concerns requires individuals to have a respect for these areas of knowledge.
The literacy group of six presented above are the areas of knowledge that we need to know. The cognitive competencies following are not about what you know, but rather how you think and perceive,how you process information. While computers are more and more able to compete and actually beat humans in processing digital quantitative information with which they have previous experience, humans remain far superior in processing analog qualitative information with which they have never seen before. The following competencies assist an individual in this ability.
CRITICAL THINKING is the sole member of this list from ‘the old days’. Many of us can remember in college or maybe even high school, syllabi that had this category for the areas that were focused on for the education. A Big Picture definition is rational, logic based approaches to situations.
SYSTEMS THINKING is the ability to examine and perceive environments, situations, events, machines, and interactions holistically. Many best selling business books speak on the negative outcomes associated with “silo mentality”. The reality of the situation however is that it is not only management and C-level individuals who need to internalize this message. Agile brings teams the concept of “the definition of done”. Only a couple decades ago, the manufacturing community was given the book The Goal, talking about better processes for running a shop. Many of those same firms today are using, and embedding software into their products. This is the reason the development operations (DevOps) book “The Phoenix Project” was written. A recommended read, but premised around the idea is that firms that use to build hardware are more and more building software, and that this tech and software world is really no different than the manufacturing floor of yesterday, except now many of your stations are harder to see visually.
ENTREPRENEURSHIP is the ability to create something new. A traditional approach to the term entrepreneur may lead some to believe there is no context outside of a self employed or business owner setting. The companies of today and tomorrow actually require individuals who approach their tasking from a point of view of creativity. Technology is moving so fast that it is no longer reasonable to expect that a service or product provided today will be desired tomorrow, or that it will be brought to fruition in the same ways. It is estimated that for children in grade school in the world, 65% of the jobs they will take do not yet exist.
CULTURAL AGILITY is the ability to understand and interact with different environments. There is a difference between and international and a global firm. An international firm is one with operations in numerous countries, but centralized authority from their home office. A globalized firm allows autonomy at the various country branches to better address regional concerns. Such firms are needing people with these mindsets capable of adjusting to different areas, and more importantly arriving to today’s problem with insightful contributions and solutions from their diverse interactions and experiences from yesterday.
Near and Far Learning Transfer:
Another strong separation between our best machines and the average four year old; our ability to apply learning almost anywhere. This is a skill set that again is highly desirable and one an individual can work on and thus improve. Learning would not be very beneficial if we are only able to apply it in the specific situations that it was taught. This is often the critique of certain schoolhouse education approaches that do not correlate with workforce success as they do school house success. Our desire is that our learning can be transferred to other areas. A mathematics background being applied in economics or data science can be viewed as a near transfer; while there is not a 100% overlap, there is much in common between the original education and the destination application area. Far transfers are where humans outperform computers. It is a human ability to take an algorithm created in data science to predict fracture patterns of frozen ice, and adapt it to predict the “fracture” patterns of social media conversations between partisan political groups.
While technology today is impacting society at a rate and magnitude like never before, the action is still the same. Individuals can respond very much the way individuals have always responded; adopting new areas of knowledge, and provide as products and services the cognitive skills that machines to date have never been able to attempt. Whether building personal acumen, resumes, or creating a team of personnel to face the challenges of day, these frameworks indeed can provide a useful model to minimum start the race, if not get you across the finish line.
Below are links to the author’s book and some video presentations of his.
Robot Proof: Higher Education in the Age of Artificial Intelligence
I would love to hear your thoughts as well. Please feel free to reach out to me at email@example.com
Originally Posted in Medium July 9, 2019