The history of AI shows that attempts to build human understanding into computers rarely work. Instead, most of the field’s progress has come from the combination of ever-increasing computer power and exponential growth in available data. Essentially, the ability to bring ever more brute computational force to bear on a problem-focused on larger data sets have given increasing usefulness. But, it’s limitations are also magnified in sharp relief more than ever. The bitter lesson is that the actual contents of human minds are tremendously, irredeemably complex…They are not what should be built into machines. Machine learning doesn’t live up to the hype. These systems are fundamentally brittle, and always break down at the edges where performance is essential and consequences much direr. There are many potential applications that can be effective and useful tools. They are simply much less ambitious than the current hype would indicate, but they are also far more realistic.
Productivity, expansion, and entrepreneurship were enabled through the adoption of new technology. Undeniably, the net economic benefit was substantial. But lives were disrupted, jobs were lost, and what would be seen with a historical perspective as an obvious beneficial choice, was anything but obvious to those so immediately and negatively impacted. Technological advancements produce net benefits for society. But for every advancement, there is a cost. Leadership and subsequent public policy must address this shortfall. As in the past, the solution has been training and education leading to economic inclusion and prosperous lives. and subsequent public policy must address this shortfall. As in the past, the solution has been training and education leading to economic inclusion and prosperous lives. History has taught us the net benefit of technological advancement, the turmoil it brings, and the solution required.
Medical Intelligence is a new discipline, converging human and artificial intelligence. Artificial intelligence will not replace human intelligence, especially in medicine. Diagnosis and treatment will remain a human endeavor. But AI will be an indispensable tool helping human intelligence effectively deliver better quality healthcare. The overwhelming benefit is that it raises the bar for all practitioners. A minimum level of quality medical care can available globally. The higher standard for diagnostic accuracy, therapeutic recommendations, and overall care from this mass of data gathering will improve overall health and wellness everywhere. Applied effectively, these tools also drive down overall healthcare costs, diagnostic errors, and unnecessary procedures. Greater accuracy eliminates needless testing and procedures significantly and delivers effective care more quickly. Diagnosis is more immediate, recovery times faster, care more available, and overall expenses reduced.
Distributed learning can enable machine learning for health care. With its unique privacy approach, it can very effectively overcome the greatest obstacle facing AI adoption in health care today. We no longer need to choose between patient privacy and the utility of the data to society. We can now achieve privacy and utility simultaneously.
A new technique, inspired by quantum cryptography, allows large medical databases to be tapped for causal links. This is a fundamental breakthrough in thinking, and this perspective has the potential to spot cause-and-effect, supercharge medical diagnoses, and use AI effectively.