Machine Learning: Evolution In The Age Of Acceleration

Doug Freud

On August 31, 1993, noted science fiction author William Ford Gibson stated in an NPR interview, “The future is already here, it is just not evenly distributed.” In 2017, the cloud, machine learning, Big Data, and scalable distributed computing should all compel us to reflect on his declaration more than ever. As these capabilities mature, an oligarchy of technology companies has emerged (e.g., Alphabet, Amazon, Apple, Facebook, and Microsoft) that is skewing the digitization of both our personal and work lives.

In Thank You for Being late: An Optimist’s Guide to Thriving in the Age of Accelerations, Thomas Friedman points out that the rate of change is not only accelerating within technology, but also in our markets and the environment. In this world, it’s easy to be overwhelmed by the rate of change, and many of us run the risk of being digitally disrupted. Our existential conundrum moving forward is: How do humans and organizations evolve and remain relevant in this age of accelerations?

Human adaptability

When we’re in a car that is rapidly accelerating, most people don’t feel comfortable until the speed levels off, and then they can adjust. In the age of accelerations, the issue is that we aren’t leveling off anytime in the foreseeable future. We must admit to ourselves that even the smartest among us can’t keep up with the pace of knowledge discovery.

Although our ability to adapt is improving, it can’t keep up with the rate of change from the new era of computing powered by artificial intelligence (AI). AI-powered systems can surpass human ability across a diverse set of tasks, including disease diagnosis, driving cars and trucks, and human rehabilitation performed via robots. Although it’s painful to admit, what is required is intelligence, and human consciousness often only gets in the way.

We’re falling behind, and it’s not only because of the limitations that are inherent in our biologically powered cognition. We’re also falling behind because our governance processes and social interactions are still old school. In a hyperconnected, real-time streaming world, we often interact and transact in batch mode. Are educational systems that infrequently update their curriculum preparing students for next-generation jobs?

Successful adaptation

The organizations and individuals who will evolve in this age of acceleration will be the ones who acknowledge what used to work in the past is unlikely to in the future. We need to adapt by:

  • Learning how to feel comfortable in a world that is constantly accelerating
  • Accepting the reality of lifelong learning
  • Adjusting to mass collaboration across departments and organizations
  • Adopting a process for continuous innovation that scales

As the digital disruption becomes more evident, we’re likely to try to control it (perhaps through legislation like taxing robots or universal basic income). But in the end, we can’t slow it down or stop it. We must adapt – and that requires acknowledging our inherent limitations and accepting new challenges.

Evolution of markets

One thing we can be confident about is that all businesses are subject to digital disruption in the age of accelerations. C-level executives lose sleep wondering what would happen if Google or Amazon decides it wants to enter their industry. The reality is that they should probably fear startups more than Amazon or Google. The cost of entry for many industries is significantly lower due to the emerging cloud infrastructure. Startups don’t need huge capital to invest in their IT infrastructure, like scaling up via the cloud when they need it.

The next generation of startups will be based on one of the following ideas:

  • Take an existing analog product or service, include sensors/data, and add intelligence via AI
  • Convert your existing product’s business model to provide access rather than ownership. For example, why sell cars when you can provide transportation as a service?

For incumbents, the challenge is to adopt a next-generation approach to innovation and embrace the accelerations. Buckle up – everything is about to speed up.

Increased data-processing power, the availability of Big Data, the Internet of Things, and improvements in algorithms are converging to power a renaissance in business intelligence. Learn Why Machine Learning and Why Now?

Doug Freud

About Doug Freud

Doug Freud is a global Vice President of Data Science for the SAP Customer Innovation and Engagement Platform team. His academic background is Industrial Organizational psychology, and he has worked in both GTM and professional service roles. He is a proven leader with ability to manage cross-functional teams that implement innovative solutions. His passion is using data and machine learning to change business processes and create new systems of innovation.