AI-Powered machines can be integrated at various levels within the framework of understanding intelligence across three planes. At the lowest level is intelligence to understand other humans and their behaviours. Then comes understanding of the surrounding and greater universe. And at the highest level is the intelligence of ‘consciousness’ – a deep sense of understanding of thy self – emotions, righteousness, soul reflection and more.
Human beings are wired to think at all 3 planes. They have a deep understanding about their ‘consciousness’ and at the same time they understand the world and surrounding, which allows humans to reason about complex situations, make decisions based on intuition, and guide others.
Machines on the other end, completely lack ‘consciousness’. It has no understanding of itself. The intelligence that human’s program into developing AI machines can make machines super intelligent, but never achieve the stage of ‘consciousness’ where it can self-reflect on its existence. Apart from that AI can acquire a deep understanding of the surrounding and exhibit behaviour that resembles that of humans. Just like humans, AI-powered machines need to be trained such that they can learn to generalize their knowledge beyond the training data to handle new, unseen examples. Training exposes AI machines to variations, complexities, and diverse examples, enabling them to handle different scenarios and uncertainties commonly encountered in real-world situations.
In this blog, we will look into the various dimensions of training machines. While doing so we will keep a simple to understand tone for every reader type! The examples in this blog will be related to retail industry, as this industry has an exceptional scope to imbibe AI for an exponential growth.
Here are some of the prevalent ways in which AI-powered machines being trained to learn like humans do:
It is a powerful approach in which machines are provided with labelled examples, where each example consists of input data along with the corresponding correct output or label. By exposing AI powered machines to a diverse set of labelled data, they can learn to recognize patterns and relationships between the inputs and outputs.
Retailers leverage supervised learning to segment their customer base. By analysing customer data, such as demographics, purchase history, and browsing behaviour, machine learning models can identify distinct customer groups. This segmentation enables targeted marketing campaigns, personalized recommendations, and tailored shopping experiences.
This training method involves the discovery of patterns and relationships within data without explicit labelling. It can be useful in retail for extracting valuable insights from large volumes of unlabelled data, such as customer transaction records, browsing behaviours, or product descriptions.
This helps retailers understand which items are frequently bought together and can drive cross-selling opportunities, optimize product placement, and enhance promotional strategies.
It provides a framework by incorporating trial-and-error learning, feedback, and reward systems. It differs from supervised learning in a way that in supervised learning the training data has the answer key with it, so the model is trained with the correct answer itself whereas in reinforcement learning, there is no answer, but it decides what to do to perform the given task. In the absence of a training dataset, it is bound to learn from its experience.
It is what our dynamic pricing software-BRIO does by enabling machine itself to do price optimisation in real-time based on market dynamics and customer behaviour. By continuously monitoring market conditions, such as competitor prices, demand patterns, and customer preferences, machines adapt their pricing policies accordingly.
Neural networks have emerged as a valuable asset in the retail industry. And it is inspired by the structure and function of the human brain, consisting of interconnected layers of artificial neurons that process and transform input data. They excel at recognizing patterns and extracting meaningful information from large datasets. With this, it allows AI-powered machines in retail price automation for related tasks that typically necessitate human intelligence. AI-powered machines autonomously handle pricing tasks, optimizing strategies, and driving profitability. By leveraging neural networks, AI-powered machines can perform complex computations, make autonomous decisions, and carry out sophisticated tasks with speed and accuracy.
Deep neural networks can help retailers analyse and pick out unique characteristics of clothes. This helps small clothing stores find out which popular fashion brands are in high demand and fit their budget for future collections. It also helps the store owners predict and evaluate how much of each item they should have in stock based on the market’s needs, so they can come up with the best plan for selling their products.
Transfer learning involves pre-training a model on a large dataset or a complex task and then fine-tuning it on a target task with a smaller dataset. It is useful in cases where limited data is available for a new task or for a similar task that has already been well-studied. It also helps to reduce time and resources needed to train a new model from scratch.
When it comes to demand forecasting, retailers can train their model on historical sales data, market trends, and external factors can capture underlying patterns and dynamics. This learned knowledge can be transferred to new products or markets with limited sales data, enabling retailers to make reliable demand predictions and optimize inventory management.
Human-in-the-Loop (HITL) Learning
This is the training model where machine learning algorithm receives input from both a dataset and human experts, who provide additional feedback and guidance to the algorithm. It is a powerful tool that helps to address some of the limitations of machine learning algorithms, such as biased and limited data availability. By incorporating human expertise into the learning process, HITL improves the accuracy and effectiveness of machine learning systems and helps to ensure that they are aligned with human values and priorities.
Retailers often have vast catalogues of products that need to be accurately categorized. HITL learning can involve human experts reviewing and validating the initial categorization performed by the AI machines.
Of course, Machine and humans have more differences than similarities. But AI-powered machines have made significant strides in learning the human way. AI-powered Machines are providing retailers with improved efficiency, cost savings, Price skimming, enhanced customer experience, data-driven insights, and scalability. Additionally, AI-powered machines enable dynamic pricing strategies, allowing retailers to adjust retail pricing strategies in real-time based on factors like demand, competition, and market trends. As retailers continue to invest in machines, we can expect to see more transformative solutions that will revolutionize the retail industry.
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