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Artificial Intelligence

20 November 2023

Machine Learning: the data behind your strategy

Machine Learning promises to be a far-reaching technological revolution that will profoundly influence various market sectors.

It is a discipline that enables Artificial Intelligence (AI) solutions to be adaptive. It was defined by A. Samuel in 1959 as ‘the field of study that gives computers the ability to learn without being explicitly programmed’. Each ML algorithm is generally designed to perform a specific task. The entire learning process is based on data: thanks to statistical techniques, machines can use and exploit data to extract the information and instructions needed to solve the task.

In other words, they learn from data. An interesting aspect of Machine Learning is that machines can improve their performance each time they complete the task, as they gain experience and more data.
The goal of Machine Learning is to enable machines to learn ‘stuff’, allowing them to analyse huge amounts of data, identify patterns and make predictions based on what they have learnt.

Today, organisations need sophisticated tools and intelligent applications that can talk, listen, interact, analyse and make decisions with unprecedented speed and accuracy. This is because, on the one hand, organisations have access to huge amounts of information and data, while on the other, their users demand a faster and more personalised service. This is why Machine Learning has become one of the most efficient answers to contemporary business challenges.


Three pillars of Machine Learning

Machine learning techniques can be divided into three main categories: supervised learning, unsupervised learning and reinforcement learning. In supervised machine learning, machines are provided with some examples or data that have been previously annotated and tagged by humans correctly and completely.

Data annotation consists of the activity of labelling or tagging specific elements within the training data, such as text, images, audio or video, in order to enable machines to understand its contents and identify crucial information. The machines use these examples to learn to make predictions by identifying patterns and associations between the data. Once trained, it can apply this knowledge to new, unseen data, accurately predicting outcomes based on the learned patterns.

The annotated data is subsequently used to train the models, playing a crucial role in ensuring the quality of data collection, as the annotated datasets serve as a reference or benchmark standard for evaluating model performance and the overall quality of other datasets.

Unsupervised learning, on the other hand, deals with unlabelled data. The model attempts to identify patterns or relationships within the data without explicit guidance. It uncovers hidden structures and groups data according to similarities.

Reinforcement learning adopts an interactive approach, similar to a persistent learner who progresses by trial and error. The model receives feedback in the form of rewards or penalties based on its actions, which guide it to make better decisions over time. This dynamic learning process allows the AI to continuously improve and make optimal choices in complex environments.


Not just research and analysis: commercial applications of Machine Learning

Machine Learning has numerous applications in various fields. For example, in the healthcare sector, ML models can analyse medical data to help diagnose diseases and predict patient outcomes. In the financial sector, ML algorithms are used for fraud detection and stock market forecasting. In marketing, ML can analyse customer behaviour to provide personalised recommendations and targeted ads.

As the field of Machine Learning continues to advance, its potential for solving complex problems and simplifying our lives becomes increasingly evident. The availability of powerful computational resources such as Graphics Processing Units (GPUs) has enabled the execution of increasingly complex and sophisticated models, facilitating the transition from Machine Learning to Deep Learning. These two disciplines share the same principles, but the capabilities of deep learning have made it possible to tackle and solve increasingly intricate problems (an example of this is the incredible results achieved by systems based on Large Language Models such as GPT chat).

At Almawave, we take a state-of-the-art approach by seamlessly integrating different machine learning techniques (combined with symbolic reasoning techniques) to perform natural language processing tasks. Our goal is to leverage the strengths of each technology while mitigating the limitations, ensuring the delivery of tailor-made solutions for each use case. This is our Composite AI approach, which allows us to deliver the optimal, tailor-made results to meet the unique requirements of each context.