Most AI-branded applications are not leveraging it at its full potential. Connecting your application to the API of OpenAI is not enough. You’re constantly limited by the way the model has been trained.
In today’s article, you’ll understand what it truly means to ‘Train an AI model’, and how to leverage this to maximize the usage you do with AI. It is becoming a necessity for businesses wanting to leverage the full potential of AI technology.
Consider the story of Sarah, an e-commerce business owner. She invested in an AI chatbot to enhance customer support.
However, without proper AI Training, the chatbot was not able to understand the specific needs of her customers or the unique aspects of her business.
With AI Training, Sarah could personalize the chatbot to her business, improving customer interaction and satisfaction through the following steps:
1/ Acceed a model trained specifically on answering customers’ answers.
2/ Feed that model with all the needed information from her company.
3/ Continuously improve the model with trial and error.
The winners who take it all in this industry are people that understand that AI training is a necessary step in any process incorporating AI.
AI Training is not confined to one industry. It's a requirement across sectors aiming to maximize the power AI has on their day-to-day work.
Let’s dive into some examples of AI-trainings processes:
In the healthcare sector, AI Training enables personalized patient care. AI systems can be trained on individual patient data, allowing them to provide personalized health recommendations and care plans.
Retail businesses use AI Training for personalized product recommendations. By training AI on customer purchase history and browsing behavior, retailers can offer highly personalized shopping experiences.
In the finance sector, AI Training has proved instrumental in fraud detection and risk assessment. Financial institutions train their AI models on historical transaction data.
Once trained, the AI system is capable of identifying patterns or anomalies that may indicate fraudulent activity. This allows for quicker, more efficient detection of potential fraud, minimising financial loss.
The manufacturing industry leverages AI Training for quality control and predictive maintenance. AI systems are trained on data from the production line to detect defects and inconsistencies in the products.
Predictive maintenance is another area where AI Training is used. AI models are trained on machine performance data to predict potential equipment failures before they occur.
At its core, AI Training involves feeding data into an AI model and allowing it to learn and make predictions or decisions based on that data.
It's through this training that AI systems can learn to understand and cater to business-specific needs.
Let’s dive into 3 types of AI training:
In supervised learning, the AI model is trained on a labeled dataset. For example, an AI model for customer support might be trained on a dataset of customer queries and appropriate responses.
In unsupervised learning, the AI model is trained on an unlabelled dataset and learns to identify patterns and structures within the data.
In reinforcement learning, the AI model learns by interacting with its environment and receiving feedback in the form of rewards or penalties.
We understood that the key success factor is the personalization of AI models to the needed use case. As we aim to stay industry agnostic our AI-training systems are not based on specific industries but rather on horizontal layers that can be used by everyone.
When building an AI workflow on argil, you get access to various templates of fine-tuned actions. We do all the hard work for you so that you can focus on your strategic and action plan.
It's through AI Training that AI systems can truly understand and cater to business-specific needs, paving the way for personalized, effective AI solutions.
