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How artificial intelligence can be used for potential business value.

The use of the artificial intelligence market is expected to grow to $390.9 billion by 2025, and industries within the space show a similar trend that is automotive AI, for example, is expected to grow by 35% year over year, and manufacturing AI will likely increase by $7.22 billion by 2023. However, according to top industry analysts, most (about 80%) of AI projects stall at the pilot phase or proof-of-concept phase, never reaching production. In many cases, this is due to a lack of high-quality data. Ethical and responsible AI continue to be obstacles for many companies, which often lack the resources or internal talent to build unbiased models in a time where AI is making increasingly impactful decisions. Companies also face an uphill battle with scaling and automation.

Believe in Your Data

The main factor of using artificial intelligence confidently in one’s business is to understand the value of data. People need high-quality training data to launch effective models. So, defining the data strategy upfront, including what the data pipeline will look like, will be crucial to success. Many data scientists and machine learning engineers say that about 80% of their time is spent wrangling data.

• The first step of the process is to collect data. One must start with a clear strategy for data collection. They should think about the use cases they are targeting and ensure that their datasets represent each of them. They must have a clear plan for collecting diverse datasets. Implement the data annotation process would require a diverse crowd of human annotators. The more accurate their labels are, the more precise their model’s predictions will ultimately be. Various perspectives will enable the user to cover a broader selection of use and edge cases. At the data collection and annotation phase, it’s critical to have the right plan for tooling in place. Be sure to integrate quality assurance checks into your processes as well. Given that this step takes up most of the time spent on an AI project, it’s especially helpful to work with a data partner in this area.

• The next step of the process is to train data. Feeding the ML machine with the right data is a vital step. It affects the characteristics of the machines as well as achieving accuracy in the result.

• Once the model reaches the desired accuracy levels, it is ready to launch. Post-deployment, the model will start to encounter real-world data. The user should continue to evaluate the model’s output; if it fails to output the correct data, a loop that data back through the validation phases. It’s helpful to keep a human-in-the-loop to manually check a model’s accuracy and provide corrected feedback in the case of low-confidence predictions or errors.

The Ones Who Tried and Won

In 2017 John Deere acquired Blue River Technologies, and together they’re poised to revolutionize pesticide use. Their AI models use drones and computer vision algorithms to identify weeds on farms. Doing so enables pesticides only to be sprayed on the weeds, rather than all crops in a field. Spending on pesticides was around $20 billion per year, but these efforts, it is expected to lead to a 90% reduction in pesticide costs. The methodology for this AI project is precise image segmentation. This method requires labeling data at the pixel level to determine which component of an image is weed versus crop. As one might imagine, the annotation process is very complex and involved. It requires both a comprehensive tooling interface and human levelers with a deep level of expertise in segmentation.

Use of AI in other Businesses

The manufacturing industry is using AI to automate logistics and supply chains. Nokia, for example, uses machine learning to alert an assembly operator when quality deviates. Specifically, if there are inconsistencies in the production process. AI may also monitor and track packages as part of a smart factory monitoring system, reducing lead time and preventing overstocking, or it can monitor throughput and downtime, highly impactful factors from a cost perspective. There are many automotive AI trends worth highlighting, including automation and safety, voice assistance, and personalization, among others. Self-driving cars are perhaps receiving the most fanfare, as these have the power to most dramatically change our daily lives.

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