Without Adequate Monitoring, AI Can’t Reach Its Full Potential
The challenge of achieving effective monitoring can prevent businesses from fully embracing AI possibilities.
AI is one of the fastest growing industries in the world. Its rapid growth has been predicted for many years, and most of those predictions have proved accurate, with growth volume having outpaced even some of the most optimistic forecasts since the early 2000s. The artificial intelligence market is now expected to be worth more than $390 billion in the next five years, due to exponential growth in AI capabilities and big data research. The constant development of innovative technologies allows new players from nearly every industry to enter the market and take advantage of AI enablement each day.
This means that more and more industries are coming to rely on AI, and still more are hoping to introduce reliable AI into their businesses in efforts to increase profits and efficiencies and reduce their reliance on human labor. Many manufacturing and retail companies already depend on AI technologies for much of their production, marketing, and quality control efforts. As new industries like education and healthcare begin to explore the possibilities of artificial intelligence, the requirements and limitations of AI are being highlighted, most notably the ongoing need for monitoring to ensure the accuracy that makes AI so appealing to begin with. As more AI models are created, the need for monitoring and the resultant adjustments will only increase.
Some of the most promising applications of AI are in the healthcare industry, and particularly in the diagnosis of some of the most prevalent forms of cancer. There have recently been studies finding that the accuracy of breast cancer diagnoses can be improved with the use of some AI solutions, which are capable of finding more cancerous tumors and raising fewer traumatizing false positives than screenings by human radiologists. This application of AI is an excellent example of the crucial need for ongoing monitoring and tuning of models to maintain the kind of accuracy that makes the solutions useful to healthcare providers and potentially lifesaving to their patients.
Over time, AI models become obsolete because the data on which they are based is constantly growing and changing. As in the example of breast cancer diagnoses, a single AI model might be based on the medical data and imaging from an initial set of 1000 patients. Over time, the amount of potential data increases as more women are diagnosed or cleared of breast cancer. If the initial model cannot incorporate this new data on an ongoing basis, its accuracy will slowly decrease until it becomes no more accurate, or potentially even less accurate, than a screening completed by a human radiologist.
So why don’t companies utilizing AI simply engage in continuous monitoring and tuning of their solutions? Because although AI typically reduces the need for human labor, the process of monitoring and tuning requires significant human involvement. In order to measure the accuracy of a solution like a breast cancer screening tool, that tool must be compared with the accuracy of a human being: a radiologist, oncologist, or other specialized medical professional. The accuracy of the model versus the doctor’s opinion results in an accuracy score. For example, a tool that matches the diagnosis of the doctor 90 out of 100 times is 90% accurate. Another human element is needed for verification of the results. After all, we must then know whether the doctor made the right diagnosis in order to validate the accuracy of both the human and AI diagnostics.
This kind of complex monitoring, and the adjustments to AI tools that the monitoring is used to make, are both absolutely necessary and difficult to accomplish. The best solution to the problem is a hybrid tool:
- A performance monitoring dashboard to monitor different aspects of the solution
- A team of human validators and tuners to apply necessary adjustments and validate their efficacy
Few companies have the resources to maintain the number of skilled employees required to complete this sort of monitoring and tuning in-house.
This is where companies like CrowdANALYTIX come in. They leverage crowdsourcing to maintain a community of more than 25,000 expert data scientists who will not only develop customized initial AI models, but also monitor and tune them to maintain high levels of accuracy long-term. Crowdsourcing allows this work to be accomplished for far less than the high cost of an in-house team. Companies like CrowdANALYTIX use both a back-end team of data scientists and a front-end team of monitors to ensure the constant observation, analysis, and tuning of every AI model in their care.
The result is the ability to rely on the accuracy of time-saving, cost-saving, high-accuracy AI models across industries, from massive retail operations to healthcare diagnostics that touch individual lives.
This piece originally appeared on CrowdANALYTIX.