Adding More Data Isn’t the Only Way to Improve AI
Sometimes an AI-based system can’t decipher the physical world with a sufficient degree of accuracy and the option of just adding more data isn’t possible. In many of these cases, however, this deficiency can be addressed by using four techniques to help AI better understand the physical world: synergize AI with scientific laws, augment data with expert human insights, employ devices to explain how AI makes decisions, and use other models to predict behavior.
Artificial intelligence (AI) gets its “intelligence” by analyzing a given dataset and detecting patterns. It has no concept of the world beyond this dataset, which creates a variety of dangers.
One changed pixel could confuse the AI system to think a horse is a frog or, even scarier, err on a medical diagnosis or a machine operation. Its exclusive reliance on the data sets also introduces a serious security vulnerability: Malicious agents can spoof the AI algorithm by introducing minor, nearly undetectable changes in the data. Finally, the AI system does not know what it does not know, and it can make incorrect predictions with a high degree of confidence.
Adding more data cannot always surmount these problems because practical business and technical constraints always limit the amount of data. And processing large datasets requires ever-larger AI models that are outpacing available hardware and growing AI’s carbon footprint unsustainably.
We have identified an alternative remedy: connecting data-driven AI with other scientific or human inputs about the application’s domain. It is based on our two decades of experience at the University of California’s Center for Information Technology Research in the Interest of Society and the Banatao Institute (CITRIS) in working with academics and business executives to implement AI for many applications. There are four ways it can be done.
We can combine available data with relevant laws of physics, chemistry, and biology to leverage the strengths and overcome the weaknesses of each. One example is an ongoing project with Komatsu where we are exploring how to use AI to guide the autonomous, efficient operation of heavy excavation equipment. AI does well in running the machine but not so well in understanding the surrounding environment.
Therefore, to teach the AI algorithm the differences between soft soil, gravel, and hard rock in the terrain being excavated we used physics-based models that describe size, distribution, hardness, and water content of the particles. Equipped with this knowledge, the AI-driven machine can apply just the right amount of force to grab a bucketful of earth efficiently and safely. Similarly, we use AI to operate a robotic surgical arm, and then combine it with a physics-based model that predicts how skin and tissue will deform under pressure. In both cases, whether earth or tissue, combining data-driven and physics-based models makes the operation safer, faster, and more efficient.
When available data is limited, human intuition can be used to augment and improve the “intelligence” of AI. For example, in the field of advanced manufacturing, it is extremely expensive and challenging to develop novel “process recipes” required to build a new product. Data about novel processes is limited or simply does not exist and generating it would require lots of trial-and-error attempts that may take many months and cost millions of dollars.
A more effective way is to have humans and AI augment and support each other, according to Lam Research, a leading maker of semiconductor equipment that supplies state-of-the-art microelectronics manufacturing facilities. Starting from scratch, highly experienced engineers usually do well in arriving at an approximately correct recipe, while AI is continuously collecting data and learning from those efforts.
