How are Machine Learning solutions taught and tested?

Artificial Intelligence (AI) and Machine Learning (ML) strategies have gained substantial traction in IT investments, driven even further by the COVID-19 pandemic. These technologies have matured significantly within the areas of e-commerce, healthcare, finance, and education.

Machine Learning

Machine Learning is a way to apply AI to systems, allowing them to learn and improve automatically from experiences, eliminating the need for explicit programming. It revolves around the development of computer programs capable of accessing data and independently learning from it. The primary objective is to enable computers to learn and make decisions without human intervention, thereby adapting their actions accordingly. In today's business landscape, this technique is pivotal for any company or enterprise striving for digital transformation.

Recent studies reveal that 29% of global developers have actively worked on AI/Machine Learning software in the past year. It's projected that 25% of Fortune 500 companies will incorporate essential AI components, such as text analysis and machine learning, into their robotic process automation (RPA) endeavors, paving the way for new applications of intelligent process automation (IPA). Additionally, analysts predict that by 2021, about 15% of customer experience applications will offer hyper-personalized services, combining diverse data sources and reinforcement learning algorithms.

The foundation of Machine Learning rests upon the confidence in systems' ability to learn from data, discern patterns, and make decisions with minimal human intervention. However, the journey begins with algorithm training, a critical aspect of the Machine Learning process. This process, for example, is integral in developing virtual assistants or chatbots, commonly found in advanced e-commerce websites.

These chatbots also use Natural Language Processing (NLP) techniques to dissect, comprehend, and interpret human language, facilitating the annotation processes. These techniques also extend to text recognition, enabling the extraction and analysis of information from image-based documents.

How are machines trained under the HITL paradigm?

In the realm of machine learning, the developers are responsible for crafting systems or models equipped to address single or multiple questions. This model construction hinges on a process called "training." The core objective of this training process is to develop a model that is precise and capable of delivering accurate answers most of the time. The model needs to be “trained” by explicitly providing it with correct answers. In other words, in order to train a model, it is necessary to gather data that will help establish connections between data patterns and accurate responses, facilitating in this way predictions based on the acquired training.

The first step is to collect data that will be used to feed into the model. While quality is crucial, quantity also holds value. An automated data collection technique frequently employed is web scraping, which sources data from diverse outlets.

Following data collection,one must prepare the data by mixing it and ensuring that correlations don't bias the answers in a specific direction. Once again, human intervention plays a pivotal role in this phase.

But, what is HITL? And during what part of the process is it essential?

The Human in the Loop (HITL) paradigm involves integrating human feedback during the training phase (modeling and simulation) of machine learning algorithms. What's the purpose behind HITL? This approach has emerged as the optimal route to train more accurate models, as individuals test, refine, and continually feed the data in real time, constructively and virtually.

Given that machine-powered systems haven't yet achieved the desired levels of precision, human intervention is vital during the training process to enhance the precision of machine learning models. In simple terms, HITL describes the process that occurs when a machine or computer system cannot yield an answer to a problem and a person must intervene.

At Arbusta, we embrace the HITL paradigm to ensure the quality and accuracy of our machine learning models. Our approach centers on fostering collaboration between humans and machines, leading to superior outcomes that cater to our clients' needs.

Phases of Machine Learning: Training data, cross-validation, and testing

Data scientists typically divide their data into three categories: training data, cross-validation data, and testing data.

1. Training Data: In the machine learning training process, training data serves to enable the machine to recognize patterns. This data contributes to training and adjusting the model's criteria, with the objective of minimizing bias and enhancing performance.

2. Cross-validation data: they are used to ensure higher accuracy and efficiency of the algorithm during machine training. It's used for hyperparameter tuning and for enhancing the model's capacity for generalization.

3. Testing data: it is instrumental in evaluating the performance of the trained model when predicting new responses. This data offers an impartial assessment of the final model, as they weren't used during the training phase, providing insight into how the model may perform new data.

All these data components coexist within the model, emphasizing the importance of ensuring that validation and testing data stem from the same distribution, and that the same training data isn't used for evaluation. This measure prevents the machine from memorizing responses and facilitates a more precise evaluation. A model achieving an accuracy rate of 90% or higher instills a high level of confidence in its responses.
Upon completion of training and evaluation phases, the model is primed to answer questions and make assumptions in context.

Final adjustment: If the accuracy of prediction percentages falls short, it is necessary to adjust or reconfigure the parameters (also known as hyperparameters). However, it's equally important to establish the criteria for what a sufficiently accurate model is, as obsessing over parameter adjustments could be time-consuming.

The Implementation of Artificial Intelligence and Machine Learning presents a lot of opportunities and advantages for businesses. At Arbusta, we are ready to assist you in maximizing these technologies and tailoring them to meet your distinct needs. Feel free to reach out to us at [email protected] to start a conversation. Explore the potential of training models through data with our Data Services.