the Problem Solver
Augment the Problem Solver
- AI is a powerful tool for designers and developers, not a replacement
- Emerging technology is revolutionizing product development
- Be prepared for the exponential possibilities of the future
We live in a golden age of problem solving, a time of major innovative leaps such as the Space X Falcon rocket, custom gene therapy and self-driving cars. These product innovations are led by a generation of entrepreneurs who believe anything is possible and are raising the bar to realize their potential.
But what if designers and engineers had more time to innovate? What if they were not required to do things the way they have always been done? As these questions arise, companies need to engage in a comprehensive rethink of their product development and production processes. The goal is to out-solve the competition and deliver a larger range of compelling products with shorter lead times.
We see a constellation of increasingly digital “augmenters” such as collaborative bots, virtual simulators and generative design software that give designers and engineers “super- human” powers. These augmenters leverage AI to help write building blocks of code, suggest design options and aid in problem solving by predicting code or equipment failure, discovering new drugs and much more.
But despite popular belief, so-called “intelligent augmentation” is not about abdicating control to machines. Instead, it is a partnership—a joining of forces with machines for solving higher-order problems. The tools already exist for such collaboration. For example, Google’s general- purpose TensorFlow whips up predictive models with tens- of-lines of code—a task that would have required thousands of lines only a few years ago.
Today’s problem solvers are already comfortable being aided by automated processes that move rote and repetitive tasks to machines. Many of the manual developer activities such as testing, deployment and infrastructure setup are now automated. Each step of the product-development process has been adapted and optimized to eliminate waste, save time, improve quality and shorten cycle times in order to offset any cost of initial implementation. Companies like Amazon, GitHub, Jenkins and Ansible have reinvented what it means to manage and deploy software by embracing automation. But what happens when we look even further to bigger leaps in productivity ahead?
In order to out-solve the competition, companies must re-imagine the way problems are solved. Here are four product development examples that capture the potential of intelligence augmentation to speed progress:
- Collaborating with algorithms
- Utilizing Blockchain to accelerate product development
- Freeing developers to be creative by removing rote tasks
- Engaging more senses for a deeper understanding
It is hard to grasp the potential of a future where we are surrounded by artificial intelligence that helps us understand our environment and solves hard problems. But soon, we will have computers that can learn almost as well as humans. And in a world where processing power is unlimited, new and exotic compute architectures are already solving intractable problems by taking advantage of complex simulation, optimization and machine learning. There is no longer any room for incremental change in the way we innovate. Instead, we must be asking these key questions:
- What does it mean for an algorithm to learn how to learn?
- How can software evolve to tackle any intellectual task?
- How can programs start writing original code to solve a problem?
While the idea of fusion between machines and minds may seem otherworldly, development of brain-to-machine interfaces is evolving extremely fast and already has resulted in practical applications. In the automotive industry, Nissan is researching “brain decoding technology” that seeks to catch signs that a driver’s brain is about to initiate a movement, such as turning the steering wheel or pushing the accelerator pedal. Using driver-assist technology, the car can begin the action more quickly, which can improve reaction times.
Commercialization of these exponential possibilities is just over the horizon. However, to be truly beneficial and accepted by customers and consumers, brain-machine interfaces—or any other type of human augmentation—will need to be engineered and designed to fit within social norms and values.