Dec 08, 2023
2 min to read
In this guest post from Akeneo partner Unifai, discover why AI requires expert training, ongoing supervision, and a long-term commitment for optimal performance. Explore the critical aspects of AI education and discover why dedicating time and resources to understanding this technology is paramount for enduring success in the ever-evolving business landscape.
Artificial intelligence (AI)
Artificial intelligence has profoundly revolutionized the world of business, offering the possibility of automating complex tasks and significantly improving their performance.
But, let’s face it, AI can be a bit mysterious and confusing; navigating the world of AI requires dispelling myths and addressing prejudices that often surround this groundbreaking technology.
With that in mind, let’s delve into the critical aspect of AI education, exploring why dedicating time and resources to it is paramount for achieving enduring success.
Contrary to popular belief, most AI systems lack neural capacity or consciousness; they are sophisticated computing tools operating based on received data and learned patterns. As one AI expert at Unifai wisely points out, “AI can’t think for us, it works with pattern recognition.”
One common misconception is that AI is entirely autonomous, capable of solving all problems without human intervention. This often leads to unrealistic expectations from customers, envisioning AI as a one-size-fits-all solution.
However, AI isn’t a “plug-and-play” magic wand; it needs training and ongoing supervision. According to our in-house AI expert, “In the first year, expect AI to handle about 50% of the workload, but it requires continuous commitment for optimal performance.”
The training of AI models relies on the essential involvement of human experts, and can be broken up into two distinct stages: initialization and re-training.
In the initialization phase, AI experts use customer-supplied data to train the models. This stage involves meticulous data collection, preparation, and annotation. These experts play a pivotal role in imparting the specific behavioral patterns essential for AI.
Re-training is a crucial stage where experts evaluate tags created by customers, ensuring AI models remain relevant to evolving needs. This collaborative effort between experts and customers is vital for the continual improvement of AI.
Following the training phase, the AI calibration stage allows customers to test results over varying time periods. Multiple iterations are conducted, analyzing and adjusting tasks based on results. It’s crucial to recognize that customers’ expectations of AI vary based on their maturity with the technology.
Contrary to the notion of AI’s automatic capabilities, the emphasis should be on understanding it as a tool requiring long-term commitment for optimal results.
Customers’ expectations of AI vary according to their level of maturity with this technology. However, it is essential to deconstruct the idea that AI can do everything automatically and to emphasize that it is a tool that requires a long-term commitment to achieve optimal results.
Artificial intelligence isn’t magic, and it isn’t a stand-alone solution. It needs to be initially trained with high-quality and reliable data, supervised and maintained by experts, and calibrated or adjusted based on performance.. AI training relies on the involvement of human experts and continuous collaboration with customers.
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