Automation has delivered massive gains across various industries and is leaving an indelible impact on the future of work. Human labour is under pressure to adapt (and qualify) to work alongside these ‘smart’ machines. Occupation by occupation, field by field, intelligent automation is integrating itself into the daily aspects of our work.
So Why Intelligent Automation Now?
The rise of ‘ubiquitous computing’ has tightly integrated cloud-based software services into all aspects of personal and professional life, while the progress in distributed computation enables companies of all sizes to learn from this wealth of available data. Many professions are now focused mainly on laying the groundwork for the future of their own jobs with intelligent automation. As our capability to automate more and more cognitive aspects of work increase, we are no longer asking if it is possible to automate, but rather what the costs are — and if there are experts available to deliver it.
The Rise of a Two-tier Workforce
This gives rise to an interesting dilemma: Automation is ‘leveling up’ and competing with more and more complex tasks, shifting the mix of occupations, and requiring the workforce to move towards higher-level skills. At the same time, the skills necessary to deliver these solutions are being commoditized into “building blocks”, which can be bought and sold in the nascent “skill economy” — freeing human experts from traditional employment. Demand for these critical technical skills widely outweighs the supply, and human experts are hard-pressed to automate these highly technical skills in an effort to meet demand by scaling their abilities. Projects are quickly building around this nascent problem, pools of internal and external workers are assembling teams, and solutions are being built with scaling in mind.
The Future Experts
Human experts analyze problems, create solutions, and teach intelligent automation systems which then scale – and deploy the product to the market. Continuous improvement, collaboration, and innovation are the driving forces in a technological future that replace the manual creation of a system with a guided solution. Just eight years ago, software was written line-by-line in order to encode years of expert human knowledge for such applications as image analysis and speech recognition. Today, deep learning architectures surpass mere human domain knowledge in almost every practical application.
Andrej Karpathy coined the term ‘Software 2.0’ in 2017 to describe how we are now moving to develop systems by focusing on preparing the teaching material, having the model learn from it, and automatically generating the solution. The result is an abstract pattern of decisions that are applied to perform a task.
Creative Destruction
The creative destruction caused by technology is so pervasive that it’s practically a cliché. It’s difficult to ignore both the speed at which disruption affects society, but also the acceleration in the pace of this change. Technological innovations brought about through the development of new platforms are disrupting global markets from within years to months. Apps such as Uber have revolutionized long-standing, ossified global industries like taxi services. Now the arms race is to deploy autonomous vehicles to the streets (and the sky) and are quickly disrupting the private car-share economy. We are now seeing the disrupters become the disrupted.
Enabling the Experts
Artificial intelligence (A.I.) automation is only one piece of a much larger puzzle. As the need for innovation continues to accelerate, we will have to brace for the impact of continuous self-disruption. Knowledge is no longer as valuable as it once was (as it quickly goes out of date). The most sought-after skills are now ‘meta-skills’ that enable an expert to quickly adjust to change, coordinate the constant learning required, and self-guide their learning to reach proficiency in a new territory quickly and effectively.
Companies working in fields at the forefront of this societal and technological change have realized for years what is now becoming a commonplace reality – we have to change the way we work – by enabling flexibility to work across domains, time zones, in industries from anywhere, whenever it’s necessary.
The future of the team
Teams may no longer be bound by traditional ways of collaboration, as the pace of change requires us to adapt the process to the problem. Human experts must focus on their creativity, learning to interface with technology, and using modern tools to upgrade and scale their professional impact. Thus resulting in intelligent automation.
At DREAM, we believe in the power of people drawn to innovation in order to solve problems. Project managers benefit from the guidance of A.I. bots enabling them to overcome problems (some of which they might not even be aware of without the benefit of the system). Talent benefits from a more robust and accurate matching system of skills to job requirements, avoiding the pitfalls of human error and bias.
DREAM enables projects to quickly ‘level up’ their game by automated solutions drawn from a global talent pool. We work to provide a platform for the global community to scale their professional expertise via an expert system which deploys their solution to a larger audience. We envision a future of distributed expert teams working hand-in-hand with A.I. enabled bots to quickly deliver solutions. The revolution will be augmented!