Artificial intelligence (AI) has revolutionized numerous aspects of our lives, from digital assistants and data analysis to everyday health care. As AI continues to evolve and impact society, effective communication becomes crucial in improving its learning capabilities. Basically, the machine is only as smart as we make it, thus we need to “teach” it using the most accurate information.
Communication serves as a fundamental bridge between humans and AI systems. As much as we all wish it were magic, that is just simply not the case. Communication allows us to convey our intentions, objectives and knowledge to AI models, “training” them to respond accordingly. This interaction fosters an iterative learning process, facilitating AI’s ability to adapt and improve over time. Through communication, AI models can learn from human expertise and experiences, leading to enhanced decision making and problem solving capabilities.
The quality and diversity of the data fed to AI models can greatly influence learning outcomes. For example, some health care technology can ingest provider documentation from all over the world, and as you can imagine, what information is communicated from one provider may mean something totally different to another. A common example that comes to mind is the use of abbreviations or acronyms, and I have the perfect story.
A group of orthopedic providers could not understand why they were getting questions about kidney injuries. They asked around the office, found that everyone was getting the same messages, and thus brought their questions to the content team. “We are seeing these messages about not having clinical support for acute kidney injuries, and that is correct as our patients don’t have this condition, but it’s saying we documented that they did, why?”
The content team reviewed, found frequent documentation of “AKI” for this group of orthopedic providers, and asked, “what do you mean when you use the abbreviation AKI? Typically, in health care that abbreviation is used for acute kidney injury.” They seemed shocked and stated, “well of course it means artificial knee implant!”
As AI becomes more embedded in our lives, it is essential to establish clear guidelines and frameworks that govern its development and deployment. Transparent communication regarding AI’s limitations, potential biases and decision-making processes helps users understand the capabilities and limitations of the technology. Furthermore, responsible AI development ensures that the feedback received is used to improve the model while respecting user privacy and security.
Effective communication with AI is a cornerstone of its continued development and improvement. By actively engaging with AI systems and providing feedback, users contribute to the learning and refinement. The quality and diversity of the data used to train AI models are paramount, as it shapes the system’s understanding of the world. As we communicate and collaborate with AI, we can foster an environment that produces increasingly sophisticated and valuable AI products, ultimately benefiting society.
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