Machine Learning
Artificial Intelligence
Data Science
Integration is Key
Over the past two decades, we have witnessed substantial growth in the industrial sectors dedicated to technology production, resulting not only in the emergence of new applications but also in the popularization of various terms and concepts that were previously restricted to a few professionals in the field.
These decades were characterized by the redefinition of the society we live in by integrating and spreading advanced technologies in our daily lives, such as the creation of machine learning algorithms, predictive models aimed at increasing operational efficiency in companies, and applications that use Artificial Intelligence, like Chat GPT, Copilot, and MidJourney. Never before in the history of human civilization have we been so immersed in and surrounded by tools, applications, technologies, and concepts that until recently seemed possible only in movies or a distant future.
With this transformation, it has become essential to understand the functioning and differences between key concepts such as Machine Learning, Artificial Intelligence, and Data Science.
In this article, we will explore these three terms and understand their applications and importance in the current scenario.
Statistics, the foundation of everything
Before detailing the particularities of each of these concepts, it is important to recognize that statistics established the initial mathematical and methodological foundations for the development of fields like Machine Learning, AI, and Data Science.
Statistics is the discipline that seeks to describe and infer from historical data the probability of a particular future event occurring. Over time, statistical techniques such as Hypothesis Testing, Probability Distribution, Statistical Modeling, and Confidence Interval were absorbed by computer science and applied in the development of machine learning algorithms.
This allowed systems to learn patterns from data and develop more accurate and intelligent decision-making capabilities, leading to exponential progress in computer sciences and resulting in the emergence of fields like Data Science, Artificial Intelligence, and Machine Learning.
Understanding Applications and Concepts:
Machine Learning:
Machine Learning is a branch of computer science focused on developing algorithms that learn from data and make predictions. Considered a subfield of AI, it allows computers to learn without being explicitly programmed for each task. A definition of the role of machine learning models was given by MIT professor Thomas Malone, who says:
- "A Machine Learning system can be descriptive, meaning the system will use data to explain what happened."
- "Predictive, meaning the system will use data to predict what will happen."
- "Prescriptive, meaning the system will use data to make suggestions about which action to take."
Main Models:
- Supervised Machine Learning: In this model, the algorithm is trained with a labeled dataset, meaning the data inputs are already associated with the correct outputs, allowing the model to learn and become more accurate over time. For example, the algorithm can be trained with various photos of cats, learning to identify patterns and particularities of the images to autonomously identify cats in the future. It is the most widely used machine learning model.
- Unsupervised Machine Learning: Here, the model is trained with unlabeled data, meaning there is no explicit association between inputs and outputs. This allows the model to discover patterns or trends that are not explicitly sought. For example, the algorithm can be trained to analyze social media data and identify different user groups based on their posting behavior.
- Reinforcement Learning: Just as human behavior can be guided by reinforcement and punishment, machine learning can be conditioned similarly. In this case, the training consists of conditioning which is the best action to take based on a reward or punishment system. If the decision made is correct, it is reinforced; otherwise, it is punished.
Main Applications:
- Fraud Detection: Machine learning algorithms can detect anomalous patterns in financial transactions, identifying fraudulent activities in real-time and preventing financial losses.
- Demand Forecasting: Machine learning models can analyze historical sales data and other external variables to predict future product demand, aiding in inventory management and production planning.
- Product Recommendations: Recommendation systems based on machine learning can suggest products or services to customers based on their purchase history and browsing behavior, increasing sales and improving customer experience.
- Ad Campaign Optimization: ML algorithms can analyze the performance of different ad campaigns, identifying which strategies are most effective and optimizing budget allocation to maximize return on investment (ROI).
Artificial Intelligence:
Artificial Intelligence (AI) is a field of computer science that seeks to develop systems and algorithms capable of performing tasks that are exclusive to human capabilities. These systems use different strategies and techniques to simulate skills such as problem-solving, learning, perception, decision-making, understanding, and generating natural language and human speech recognition. AI allows the manipulation and discovery of patterns that would be difficult to find by human effort alone, generating unimaginable commercial benefits. For example, with the use of AI, Netflix achieved a level of individual personalization that resulted in a 25% increase in its customer base.
There are three main types of AI according to the functions each can perform:
- Weak or Narrow AI (ANI): These can store a large volume of data and are trained to solve specific problems, but without general reasoning and human understanding capabilities. It is the most commonly used type of AI today, found in applications like Alexa, Siri, chatbots, and facial recognition. Although highly effective in their specific functions, they cannot perform tasks outside their programmed scope.
- Strong or General AI (AGI): This is a hypothetical and theoretical AI, still in development, but capable of learning, understanding, and applying knowledge equivalent to human beings, that is, performing cognitive tasks.
- Artificial Superintelligence (ASI): Like AGI, it is also theoretical, but the prediction is that its capacity and intelligence would be superior to humans, capable of executing tasks impossible for us. Although a fascinating concept, it also raises significant ethical and security questions, especially about the control and intentions of these machines.
It is worth noting that in recent years, a specific type of AI has been gaining traction: Generative AI. You can find a comprehensive article on this topic here.
Main Applications:
- Customer Service: Chatbots and virtual assistants use AI to provide 24/7 customer service, answer frequently asked questions, resolve basic issues, and direct complex queries to human agents.
- Inventory Management: AI algorithms can predict product demand, optimize stock levels, and reduce waste, helping companies manage their resources more efficiently.
- Recruitment and Selection: AI systems can analyze resumes, evaluate candidates, and even conduct preliminary interviews, helping companies find the best candidates faster and more efficiently.
- Predictive Maintenance: In sectors like manufacturing, AI can monitor equipment in real-time, predict failures, and schedule maintenance before major problems occur, reducing downtime and repair costs.
- Personalized Marketing: Using AI to analyze purchasing behaviors and customer preferences, companies can create personalized marketing campaigns, increasing campaign effectiveness and customer satisfaction.
Data Science:
Data science is a multidisciplinary field that seeks to solve complex problems using data. It combines mathematical, statistical, artificial intelligence, programming, and machine learning principles and concepts to conduct descriptive, diagnostic, predictive, and prescriptive analyses to guide future decision-making in a strategic and optimized manner.
Unlike machine learning and artificial intelligence, which are subfields used in the implementation and development of tools, applications, and algorithm training, Data Science uses these subfields as working tools.
For example, a data scientist will implement data analysis strategies, data preparation, and algorithm construction using machine learning models to identify trends and patterns to solve complex problems.
Some techniques used in data science are:
- Classification: A supervised learning technique where the goal is to predict the category or class a new observation belongs to, based on a training dataset with already categorized observations. An example would be diagnosing whether an email is spam or not or determining if a transaction is fraudulent or legitimate. Among the different types of classification, neural network classification is one of the most used within data science.
- Regression: A statistical analysis technique used to model the relationship between a dependent variable (the one you want to predict) and one or more independent variables (those you use to make the prediction). The goal is to find a mathematical function (usually a straight line or curve) that best describes this relationship, so we can use this function to make predictions about new data. Example: predicting future sales of a product based on its sales history.
- Clustering: An unsupervised learning technique that groups a dataset into clusters, where data within the same cluster are more similar to each other than to data in other clusters. The similarity grouping occurs because, in clustering, data cannot be accurately classified into fixed categories. An example would be segmenting customers into clusters based on their purchasing behavior.
Main Applications:
- Data-Driven Decision Making: In various industries and sectors, data science is used to inform and guide decision-making. The analysis of large volumes of data using statistical strategies, machine learning, and data visualization makes data science one of the most important areas in the technology sector today.
- Product and Service Improvement: Companies use data science to better understand customer needs and preferences, allowing for the creation of optimized products and services to meet that demand.
- Resource and Process Optimization: It allows for better management of available resources, reducing waste and optimizing operational efficiency. Examples include demand forecasting and production planning to supply chain management and sales performance of a particular company.
Integration is Key:
Although Machine Learning, Artificial Intelligence, and Data Science are distinct areas, their roles and applications are highly interdependent. The integration between them is not only necessary but also essential to develop efficient and innovative technological solutions that allow us to face complex challenges and explore new opportunities in the information age.
NCS Consultancy has a multidisciplinary team of highly skilled professionals with expertise in all stages of the data process, ready to offer personalized analytical solutions to boost your organization with the most advanced technology on the market.
Contact us so that our Data Science & Analytics team can help your organization extract the most from your data and position itself competitively and strategically in the job market.