In 1997, IBM’s Deep Blue computer won a six-game chess match against world champion Gary Kasparov. More recently, IBM’s Watson supercomputer defeated two of the greatest Jeopardychampions of all time during a three-day competition.
Despite these impressive exhibitions, machines cannot think like humans – yet. For example, given different sets of information, humans have a natural ability to detect patterns and perceive order. Computers, on the other hand, must be told what types of pattern structures to look for.
But Deep Blue and Watson have demonstrated that when the rules of the game are well defined and the desired patterns are known, humans can program computers to solve these problems better or faster than our own minds can solve them.
Another example is engineering design optimization, where mathematical computer algorithms are being used to optimize engineered systems and components. In this context, the roles of mind and machine are still being debated. Unlike in chess or Jeopardy, no one has proposed a suitable competition to determine whether machines can outperform humans on design optimization tasks.
In my opinion, it is not a question of mind versus machine. The more important issue is how we can best leverage mind plus machine. While there is some overlap in their skill sets, the most powerful capabilities of the human mind complement those of computers.
Let’s consider two different processes for designing a structural component. We’ll call them A and B. In each case, we will iterate on the geometry of the part to find a design that minimizes total mass while maintaining the stresses below a certain limit. We’ll use a finite element model to estimate the stress distribution for each design.
During each iteration of Process A, we use a graphical post-processor to visualize the distribution of stresses throughout the entire part. Relying on the human mind, we make design modifications based on intuition and knowledge about how stress flows. Process A is a typical manual design process, in which a human can modify a design based on intuition and knowledge about the problem.
In Process B, only the value of the maximum stress is available for each design. Relationships between shape changes and stress must be inferred solely on the basis of this single output value, since we do not know the distribution of stress throughout. Process B is a typical automated multi-disciplinary design optimization (MDO) search process, in which the mathematical MDO algorithm conducting the search has no intuition or knowledge about the physics of the problem.
When a problem is relatively simple, or similar to one that has been solved before, the human mind is often faster at finding a desirable solution than a computer algorithm would be. This is especially true when a full-field graphical solution is available to provide feedback during each design step. Our intuition and understanding of the problem are very valuable in these cases.
On the other hand, if a human attempted to solve even a simple problem using Process B, the lack of information about each design would make it extremely difficult to suggest useful design changes that meet the goals. A computer-based MDO process can usually solve this problem very effectively, even if it does require more design iterations than might be needed for Process A.
As the problem becomes more complex, the human mind has a diminishing ability to process the relationships between a large number of variables and several conflicting design criteria. In contrast, computer algorithms can search for patterns and complex relationships within very large data sets.
Greater complexity also makes it more difficult to define an appropriate design problem and to interpret the results of a design study. It is here that human knowledge, experience and intuition play their most important roles.
So trying to achieve an entirely automated design process is at best a foolish objective. Computer algorithms still only do what they are taught to do, and the human mind has way too much to offer in terms of creative insight – at any level of complexity. Rather than aiming for an entirely automated process, we should seek ways to creatively combine mathematical search and human intuition in a hybrid strategy that leverages the strongest attributes of each tool.
No computer can tell us how to design such a process.