Technology and the Jobs – “A series of tasks.”

In the last twelve months, I have drafted and created several contracts and agreements for personal and business use by utilising specialist legal services from a qualified lawyer whose career is based on contract knowledge and structure.  In the same period, I have also used online self-service products such as that provided by Wonder Legal Contracts. 

Typically, if a bespoke document, complex arrangement, or modification of an existing document is at issue, then a specialist contract lawyer is probably the best option.  However, many contracts are simple and aim to protect two or more parties in a standard personal or business scenario.  In each case, the product I have received has been excellent.  It appears that the contract automation seems to be freeing up lawyers to focus on unique and challenging work.

In another paradigm, the Australian Government estimates that 1,375 Australians will die from melanoma in 2020.  The technology behind early and accurate detection of melanoma from companies like Foto Finder is taking shape and starting to redefine the role and relationship dermatologists are having with their patients. 

From a personal perspective, melanoma has been prominent in my family, but we are lucky to live in Australia.  As a result of the high incidents of melanoma in Australia, this country leads the world in melanoma research.  My family has been well serviced by some of Sydney’s leading dermatologists and melanoma surgeons. 

However, I have often wondered about the considerable trust we have in our dermatologists and the proportionate responsibility they carry using their accumulated knowledge, sight, and illuminated magnifying glass.  As the AI-assisted automation develops for melanoma diagnosis, it must free up the dermatologist to operate at a higher level with less repetitive tasks and less subjective assessment.

The central premise in the video, “Humans Need not Apply”, is that ignoring technology and the related developments can be perilous for career development. The short video was posted on YouTube in August of 2013.  Just because technology is immature does not mean it will not mature.  Understanding the direction and probable impacts on current jobs and societal norms can be vital in keeping abreast of changes. 

Even though the video is now several years old, the main points are current.  As an example, self-drive cars and trucks are already safer than vehicles driven by people.  Therefore, it seems inevitable that self-drive is the future.  Government legislations and community acceptance are now being addressed.  Community attitudes and pre-conceived ideas are slowly changing.

An integral part of the change management and government decisions is the impact it will have on the economy.  The Australian Bureau of Statistic estimates that 8.4% of the Australian workforce are in transport-related jobs.  What happens to these roles when the self-drive vehicles come to pass? Regulatory authorities want time to adjust public policy, procedures and training and skill programs before 8.4% of the workforce is redeployed.

Keeping up with all leading-edge technology developments can be exhausting, confusing and consume your life.  However, keeping an eye on trends and emerging technology that will shape our life and the work landscape is essential for staying up to date.  Referring to the well documented Technology Adoption Life Cycle,  most of us are Early Majority or Late Majority adopters.  As the pace of change continues to increase, Laggards can expect to be increasingly unemployable.

In the popular vernacular, blue-collar roles are ripe for automation and supplanting by bots.  However, in the examples above, a range of roles across the spectrum are already impacted.

A series of Tasks

What is a job, and how does it relate to technology? During a recent discussion with a colleague, in the context of ways of working, we canvassed the makeup of a job.  In its simplest form, a job is a “series of tasks”. 

Preparing a legal contract, diagnosing for melanoma, and driving a vehicle is a series of tasks. 

“How can you say such a thing?”

I anticipate wailing and teeth-gnashing from all types of experts, but let us explore this idea for a moment. 

In many jobs, a series of tasks and the variable within are specialised, and the degree of complexity can vary considerably.  A professional role can require extensive training and development of expertise over several years and a long career.

However, if a series of tasks is the base distillate, then what is missing are the layers of complexity and variables required for informed advice and opinion.   This extra piece is the expertise & professional input on which our economy is based.

If this premise is accepted, then the next step could be to create a database of variables and knowledge which make up the informed professional advice and opinion.  However, this is where it gets interesting. 

Let us revert to the melanoma example. It is now possible to build a database and then an AI brain which uses all the melanoma diagnosis knowledge and experience from a single dermatological practice and start to combine it with the same experience across the state, country and then globally.  The technology can enable the analysis for the diagnosis to be drawing from globally accumulated knowledge and giving a rapid and accurate recommendation.   AI-assisted melanoma diagnosis is already happening.

What is the difference? 

When a student starts their career as a medical professional and dermatologist focusing on melanoma, they are learning from medical professors & academics with a depth of experience.  They are also studying all available and relevant published papers to enhance their knowledge and prepare for consulting with patients.  They become an intern and study under an established professional in surgery with regular patients.  Finally, after several years, they are deemed qualified to practice dermatology on their merit. 

The stark reality from this continuum is that, at the individual student level, they are learning from the beginning.  Starting from the beginning and working towards a level of mastery is very important. An essential human characteristic is to strive for a competence level.  Once attained, the series of tasks being performed every day by the professional contribute to a personal sense of achievement and happiness.  The achievement gives a feeling of contributing and succeeding and can be core to human contentment.

AI model

Establishing an AI solution is becoming easier.  It can take time and is only as good as the quality of the information and algorithms which are used for establishment.

Let us continue with the melanoma example. 

Once established, a melanoma diagnosis AI solution is available globally.  At some point, it will contain knowledge and research from all reputable sources from around the world.  There is a big difference between the learning of a professional and an AI solution.  The design of the AI solution ensures continuous system learning.  Combined knowledge and expertise in dermatology and melanoma diagnosis can increase for an indefinite period. The system can draw on the depth of knowledge 24 hours a day and gain in knowledge, speed, and accuracy.  The AI solution is not looking to retire at age 65.  It should have no knowledge of academic politics or human impediments to sharing knowledge or professional jealousy.

Curators

Of course, the successful development of many AI solutions takes time and expertise.  Who will create and curate AI solutions.?

The development and curation require the skills of data scientists who craft appropriate algorithms and analyse the data patterns and trends.  The rise of the data scientist as a business-critical profession is already well established but still increasing.  There is a worldwide shortage of data scientists.  Many training institutions are turning out skilled data scientists, but demand continues to be far greater than supply.

It is surprising how many times I have been in recent discussions where people were advocating the dropping of mathematics as a required skill for a modern workforce.  The premise is that there is enough technology to handle most mathematics requirements these days.  Given all the focus on STEM (Science Technology, Engineering & Maths) subjects, there seems to be a lack of logic to the premise.

What are the required skills of a data scientist?  Rashi Desai lists ten skills as:

•                 Probability & Statistics

•                 Multivariate Calculus & Linear Algebra

•                 Programming, Packages and Software

•                 Data Wrangling

•                 Database Management

•                 Data Visualization

•                 Machine Learning / Deep Learning

•                 Cloud Computing

•                 Microsoft Excel

•                 Development Operations

It seems like mathematics skills may be making a comeback.  It is a positive story.  The school of engineering at any leading university has several avenues available for students to follow.  The emergence of AI as a mainstream business requirement will add further complexity to this offering.  At the same time, it will increase the career opportunity for STEM graduates.

Technology and The Job Interview

A traditional and standard part of many job interviews has been some type of psychometric test.  These tests are many and varied and usually based on some type of academic research.  In most cases, the tests are relied on to back up a hiring decision.  It can also enable cutting corners and justify a candidate.  In my experience, the best hiring outcomes always result from human connection, cultural alignment and skilful interviewing.

Through casual discussions, I am increasingly coming across professionals who are using applications like “Crystal Knows” to create a profile and make a decision.  This solution scrapes the internet for information available on an individual. It presents a personality profile indicating how best to communicate with the individual’s persona and predicts their likely strengths and weaknesses.

A colleague I spoke to recently said he does not engage in a sales call unless he has run the profile through Crystal Knows for the person he is about to engage.  

Anyone looking for a new job now is likely to be asked to make a short video or be asked to be recorded while being interviewed.  The recording seems innocuous until a discussion with Michael Kollo and Richard George.

At the Australian AI HR Services technology company, Faethm, Michael Kollo is General Manager, and Richard George is the Chief Data Scientist.  Faethm AI supports its clients by using AI to predict which roles in an organisation are trending out of usefulness and which will be on the increase for a specified business and market space.

The AI systems available now are not just used for examining the content of the answers to questions. Part of the algorithm focuses on the analysis of the applicant’s emotions and an ability to predict the types of jobs to which the person is best suited.

An example of this could be for accountants.  Accountants are essential to a business.  Gradually over the last twenty years, the software solutions contained in ERPs, CRMs and SME centric accounting systems like Xero have changed the financial analysis and reporting function for accountants.  The “series of tasks” that define essential accounting practices has morphed into one of analysis and interpretation rather than calculation centric.  

The Faethm AI solution can distil the key competencies that a skilled accountant must possess and transform these onto possible emerging roles.  While this sounds clinical, the added value for the individual and the new role is the outline of the emerging vital skills and the required soft skills.

For the accountant example, the good news out of this development is that the AI solution could aid in predicting when the current role will be phased out, what possible skills need immediate training and development and what new job alternatives should be considered.

Technology can be daunting.  Companies which specialise in AI solutions are not necessarily strong on the human side of the equation. Human aspects include the stress and mental health issues that can be associated with a job change and a secure future.

Michael Kollo puts it like this: “As an individual, it is important to decide and understand what makes you optimistic about the future.  What do you need outside work?  Think about yourself, your family and friends.  Within work, what can you master?  What skills can you acquire? What does the world need from you?”