Medicine is one of the fields AI systems will have the greatest impact. Currently for example, diagnoses are done by doctors looking at patient data,using the knowledge gained over years of training. However, humans are limited in their ability to analyze data by their individual experience and education. Furthermore, humans are very prone to bias, if early tests point to a certain diagnosis, humans will often look for more evidence supporting that diagnosis overlooking evidence for others.
Machines are far less prone to bias and can use experience and knowledge from all available resources at once. With the recent advances in Deep Learning, algorithms have already shown to outperform humans on analyzing medical images and sensors, finding more accurate and less biased solutions. While we are not ready yet to let machines take over the job of a doctor the technology is clearly mature enough to aid doctors in their daily work. For example by indicating regions of interest in medical images. This optimizes the workflow of the medical experts while counteracting human biases.
The core strength of AI algorithms is finding patterns in large amounts of data. This includes patterns that arise from humans interacting with your business. Whether these are financial transactions, subscriptions or conversations, this data can often tell you much about the behavioral patterns of your customers. Patterns that can be used to perform detection, prediction, and personalization of your interaction.
For example, it’s possible to detect fraudulent transactions, predict a users likelihood of purchase and personalize a user’s recommended products. To achieve this we use Machine and Deep Learning algorithms on your historical data and build a model that evaluates each individual customer and finds the most optimal solution for this specific customer. This is where hyper-personalization comes in, instead of putting a customer in a category and using this category to personalize the system, we find a tailored solution based on this specific person's attributes.
When you are maintaining a process like a supply chain or an assembly line you will be familiar with bottlenecks and unexpected downtime. Besides causing annoyance, occurrences can cause great financial loss or inefficiency especially when it happens at critical steps in the process. Often these types of processes generate a lot of data, whether it is from machine’s sensors or process logs, data is generally abundant. While all this data from various sources can be hard to manage and almost impossible to interpret by humans, they are perfect for Machine Learning algorithms.
Time-series forecasting methods, especially those that employ Deep Learning, have booked great success in predictive maintenance. Here we predict machine failure based on its sensor data allowing you to perform maintenance at the optimal time instead of dealing with unexpected downtime.
Since online purchases are thriving, transport and storage of these goods are getting ever more important. This doesn’t only come with great financial costs but also has a large impact on the environment. Optimizing the management of warehousing, container layout, and traffic routes can and will be more significant than ever before.
In most cases a lot of historical data is present about how this was managed previously. However, many processes are often optimized separately and with outdated methods. With present-day AI technologies, like deep reinforcement learning, we can not only optimize the existing separate processes but also combine the different elements of the chain. Ultimately increasing the efficiency over the entire chain with great financial and environmental benefits.
Besides the mentioned industries and areas of application AI advancements have booked great successes in many other areas and has the potential to revolutionize many more.