The relationship between humans and machines extends as far back as humanity itself, making work easier for us and being the speechless servant of mankind. Forever under control, was the premise on which our increasing reliance on machines came to rest and made us accept them as a part of our lives.
A notion, so deeply embedded in our psyche is hard to change. Machines worked like we directed them to. They never took decisions on their own. Our lives were made easier and trust flourished, but things underwent a revolution that no one thought would materialize, making us uneasy despite the advantages offered.
Automation, as we talk of it in the 21st century is immensely different than what the word meant in the Industrial eras. Back then machines were used to bring in more efficiency and effectiveness into any kind of work we wanted them to but they never thought on their own. Having a mind is not “Machine”, it’s a human trait which he hold on so dearly to. It feels like a new kind of creature is threatening us, whose mind remains unexplored, unlike our own.
Deep learning and artificial intelligence are the two different types of “minds” that our new automated machines have. The utilize data that they are fed with and process it to make informed decisions that were previously considered an impossible feat for machines to perform.
Despite the fanfare and the increasing interest in every new technology that pops up every now and then in the automation sphere, fear and mistrust deepen even more. Businesses and employees fear going out of work while people fear, whether these technologies are reliable and safe to use. This deep mistrust of the automated resources is going to impact its acceptance on a far wider scale and make it difficult for automation companies to sell their products.
Google, one of the forerunners in the race towards driverless cars, has been defending its automated technologies on the roads. A recent incident, where its automated car collided with an oncoming bus in California when changing lanes, garnered a lot of media attention and prompted Google to issue a whole statement related to the incident in its monthly report on the self-driving cars project.
Google testified that it had made changes to its software following the collision to avoid such mishaps in the future, but how many more updates should these cars warrant to make them achieve their objective of being “Safer” than humans is anyone’s guess at the moment.
The Autopilot feature on planes is handled by highly qualified pilots who have the expertise to use that software to get it right and make the planes they fly, more efficient and safer but will the general public be able to do the same, with each person differing on age, gender, reflexes and perception and with the streets more crowded than the air highways? The reason why the performance of automated systems on cars is being so much scrutinized is because they are the litmus test of how automation fares in the utterly human territory. Every collision that these cars make will make people resort more firmly to the idea of preferring humans at the controls as no one would want to be killed by a “software”.
Who would be held responsible? How many collisions would these cars cause? How will these systems be reprimanded? These are just some of the questions that impede the journey of automation in just a single field of interference and with a different kind of impact on human lives i.e. Cars and safety respectively.
While automation is safe or not, is a question that lies on the technology used and how good it gets with time, but the rising fear in humans that automation will replace everyone regardless of the skill set is another facet of the same hurdle that is public mistrust facing automation.
Enlitic, a deep learning-based diagnostic tool, is fast outpacing professional radiologists due to its prowess over what it does. Its website claims that it’s 10,000 times faster at interpreting medical scans and images than its human counterpart.
Enlitic’s technology was 50% more competent at identifying malignant tumors than a group of 3 expert radiologists and got a false negative rate when detecting cancers in medical scans as compared to an extremely high 7% error rate secured by human radiologists. Radiologists earn a median yearly income of $287,000 in the US each year, according to PayScale, reflecting the knowledge value risk that has come to haunt this profession due to the ongoing efforts at making radiology automated.
The concerns of widespread job loss due to the “fourth revolution” were also a part of the discussion at the recently concluded meeting of global elites at the World Economic Forum in Davos. The elites are highly concerned at the impending scenario of high job loss due to automation in the upcoming years.
But as automation continues to provide innumerable advantages to corporates, the case for its gaining more widespread acceptance continues to become stronger than ever. Firms are reaping real-time benefits by deploying various versions of automated technologies like SAP-based systems, software predicting consumer buying behaviors, big data analysis tools, etc. Even marketing departments and creative professionals are turning to automation, using AI technology and machine learning to automatically tag images with keywords in digital asset management systems. And as the influence of these process-based technologies grows, these corporate firms will definitely make themselves more inclined towards accepting top automation technologies to power their next cycle of growth.
But the public doesn’t seem too happy with this scenario as it envisions that in the case of widespread adoption of automated technology, the number of jobs available is going to go down to a level where it will be increasingly difficult for a person with mid-level skills to land a long term work gig.
Public mistrusts lies in the two grey areas of safety and job security where we do not have defined parameters of till where automation should be moved forward to if it wants itself to be accepted on a grander scale without any major rebuttals. The automation companies should realize that these two pressing issues will stand to affect their profitability in the upcoming years probably even more than how they perform as organizations.
Humans are at their strongest when the resisting change that they believe, threatens their lives, no matter how beneficial others tell them it to be.
In the case of automation, it’s not a group of people, it’s a global issue, making it even harder to come out with a complete solution to soften the deep mistrust that people have and pave the way for future to come to us and for humanity to progress.