The market for AI is expected to grow to $390.9 billion by 2025, and industries show a similar trend, with the use of AI in automotive, for example, expected to grow by 35% per year, and in manufacturing by $7.22 billion by 2023. However, ethical and responsible AI continues to be a barrier for many companies, which often lack the resources or the in-house talent to create unbiased models at a time when AI is making increasingly influential decisions. Companies are also struggling with scaling and automation.
Trust your data
Understanding the value of data is a key factor in the safe use of AI in organisations. To run effective models, you need high-quality training data.
- The first step in the process is data collection. You need to start with a clear data collection strategy. A diverse group of human annotators is needed to carry out the data annotation process. The more accurate their annotations, the more accurate the model predictions will be. The different perspectives allow the user to cover a wider range of use cases and edge cases. In the data collection and annotation phase, it is very important to have an appropriate plan for the tools.
- The next step in the process is data training. A very important step is to feed the ML machine with the right data. This affects the machine’s properties and the accuracy of the results.
- Once the model has achieved the desired accuracy, it is ready to use. Once the model is implemented, it starts to interact with real data. The user must continue to evaluate the output of the model; if it does not provide valid data, it must go through the validation phase again.
Those who have tried and won
In 2017, John Deere acquired Blue River Technologies, which together could revolutionize the use of crop protection products. Their artificial intelligence models use drones and computer vision algorithms to detect weeds on farms. This allows pesticides to be sprayed only on weeds, rather than on all crops. Spending on pesticides has been around $20 billion a year, but this effort would result in a 90% reduction in spending on pesticides. The methodology of this artificial intelligence project is precise image segmentation. This method requires pixel-level tagging data to determine which image element is a weed and which is a plant. It requires both a comprehensive tool interface and human segments with in-depth knowledge of segmentation.
Applications of artificial intelligence in other industries
The manufacturing industry uses AI to automate logistics and supply chains. Artificial intelligence can also track and monitor packaging as part of an intelligent factory tracking system to shorten delivery times and avoid oversupply or monitor throughput and downtime – factors that have a big impact on costs. Several AI trends are worth highlighting, including automation and safety, voice assistance, and personalization, among others.