Unlocking Business Potential with Artificial Intelligence and Machine Learning
Artificial Intelligence is not just a passing trend; it is become an integral part of modern businesses. Those who can leverage AI to address challenges, enhance efficiency, and reduce costs will gain a competitive edge. Although you may not need to become a data scientist, understanding AI and Machine Learning concepts and how they can improve your organization’s offerings is crucial. Effective utilization of these tools requires aligning them with business objectives to solve specific problems. This article aims to explore the key concepts of AI and Machine Learning that enhance business operations and performance.
Introduction
In today’s business world, Artificial Intelligence and Machine Learning are revolutionizing how companies operate and make decisions. By analyzing vast amounts of data, these technologies help businesses predict trends, optimize processes, and improve customer experiences. AI and machine learning can turn raw information into valuable insights. These insights drive strategic decisions, such as predicting customer behavior or optimizing supply chains, leading to increased efficiency and revenue. Even though adopting AI and machine learning brings significant benefits, it requires careful consideration of ethical, legal, and operational factors. By overcoming these obstacles and making the most of these technologies, businesses can stay ahead and succeed in today’s ever-changing market.
Machine Learning and Predictive Analytics
Machine Learning(ML) involves using algorithms to analyze data to uncover patterns or correlations among various data items. Once these relationships are identified, they can be used to make predictions about the behavior of new cases. This process mirrors how humans learn from experience to make decisions.
In fact, one application of machine learning is object recognition, where systems are developed to identify everyday objects from images. This involves using labeled datasets, which includes pictures of various objects like chairs, umbrellas, and washing machines. Each image is labeled to specify the type of object it contains. Object recognition systems analyze features such as shapes, colors, and textures in these images to learn to distinguish among different objects. This technology has wide-ranging uses in fields, such as autonomous driving, retail, and robotics. Machine learning algorithms recognize patterns in images to differentiate among objects based on their distinctive features, such as chairs having legs and a backrest, washing machines being cube-shaped with knobs, and umbrellas being long and slender, often black.
Another common uses of machine learning is prediction, where algorithms use existing data to make predictions about future outcomes. For instance, analyzing individuals’ purchasing history can reveal patterns in their behavior, and allow predictions about their future purchases. This information could then be used to target them with tailored promotional offers for certain products.
Machine learning, particularly in the context of predictive modeling or Predictive Analytics (PA), is commonly used to predict future behaviors of individuals. However, it can also be utilized to other situations where there is a need to determine or predict an unknown event or thing, whether it is in the past, present, or future.
Machine Learning is the extraction of knowledge from data.
For example, doctors utilize patient data, including symptoms, test results, and medical history, to diagnose current health conditions. While this is not strictly predicting the future behavior of the patient, it involves determining the patient’s current state based on gathered evidence and medical knowledge. Similarly, machine learning can be able to analyze detailed information about symptoms and known illnesses to estimate the probability of someone having a specific medical condition based on the symptoms they show.
Machine learning involves creating predictive models that capture patterns in data to generate predictions, often represented by scores. These models are widely used in various business applications, like credit scoring, target marketing, personalized pricing strategies, and preventative healthcare. For instance, credit scoring models predict individuals’ creditworthiness based on their financial history, while target marketing models analyze demographic and behavioral data to determine which customers are likely interested in specific products. Besides, machine learning is increasingly applied in preventative healthcare to predict individuals’ likelihood of developing certain conditions and recommend appropriate interventions.
Machine Learning and Predictive Analytics share key characteristics, such as analyzing patterns in data, relying on large datasets, and aiming for predictive modeling (particularly in industries) like manufacturing and finance. However, they are distinct approaches with different methodologies and capabilities. Predictive analytics uses mathematical and statistical models to generate predictions based on historical data, whereas, machine learning automates predictive modeling by training algorithms to detect patterns in data without explicit instructions. Machine learning models evolve and enhance with more data, in contrast, predictive analytics relies solely on past data. Recognizing these differences is vital in determining the appropriate approach for specific tasks and decision-making processes. For instance, a number of companies use machine learning to customize website experiences for users, and to adapt content based on their behavior. Predictive analytics, meanwhile, helps forecast outcomes of campaigns by analyzing past data to predict future trends, which is useful in decision-making and planning. Both are valuable tools for improving customer engagement and optimizing marketing strategies.
Machine Learning enables computers to learn from experience. It involves using computational methods to extract insights directly from data, without relying on predefined equations. These algorithms improve their performance over time as they learn from more data.
Artificial Intelligence
Essentially, Artificial Intelligence (AI) is a term used to describe computer systems that can perform tasks that typically require human intelligence. While there are various definitions of AI, it generally involves machines mimicking cognitive functions, such as learning, problem-solving, and decision-making. Machine learning, a subset of AI, focuses on developing algorithms that enable computers to learn from data and improve their performance over time without being explicitly programmed. Nevertheless, AI encompasses more than just machine learning; it includes fields. like natural language processing, robotics, and computer vision, all working towards creating systems capable of human-like intelligence.
In practice, a lot of AI applications we face today heavily rely on machine learning techniques to analyze vast amounts of data and make predictions or decisions. These uses range from virtual assistants like Siri and Alexa to recommendation systems used by online retailers. Even though these systems can be highly effective within their specific domains, they are still considered Narrow AI, lacking the general intelligence and adaptability of humans. Achieving General AI, which would involve machines being able to learn and perform tasks across various domains similar to humans, remains a distant goal. Nonetheless, AI continues to advance, offering exciting possibilities for solving complex problems and improving efficiency in various industries.
Machine Learning (ML) is a branch of Artificial Intelligence (AI) and computer science that focuses on the using data and algorithms to enable AI to imitate the way that humans learn, gradually improving its accuracy.
In most AI and machine learning applications, several core components are essential for their functioning as follows:
- Data input: Gathering data from sources like cameras, microphones, or online forms.
- Data preprocessing: Converting raw data into a format suitable for computer analysis.
- Predictive models: Generated using historical data to make predictions or classifications.
- Decision rules (rule sets): Guiding interpretation of predictions and decision-making processes, derived automatically or by human experts.
- Response (output): Taking actions based on decisions made, like issuing credit cards or sending job offers.
Most AI applications, like Siri heavily rely on making educated guesses based on patterns they recognize. For instance, when they hear something, they break it down into smaller parts and try to figure out what was said. They use fancy math to calculate the likelihood of different words being spoken. It is like if there is a small chance it is “Hello,” a bit more chance it is “Yell-O,” and a higher chance it is “Mellow,” they will go with “Mellow.” These systems get even smarter by using layers of different guesses to understand what is happening around them. In speech recognition, they do not just focus on individual words but also look at how words are put together in sentences. Then, they use these guesses along with some rules to decide what to do next based on what was said.
Scores in Predictive Model
In business, most AI and machine learning applications rely on predictive models, which produce scores indicating what is likely to happen based on the input data. These scores fall into two categories:
- Probability scores, which predict the likelihood of specific events occurring. For instance, whether a customer will make a purchase. These are known as classification models.
- Magnitude scores, which forecast the quantity or size of something. For example, the amount a customer might spend in-store or the time until an action occurs. These are referred to as regression models.
Classification models are significant tools in business, predicting the likelihood of different events. They generate scores indicating the probability of an event happening. These models are used in various areas like healthcare for diagnosing illnesses, on social media to identify inappropriate content, and in predicting customer behavior and fraud detection. They also help in assessing dating compatibility, making product recommendations, estimating staff retention, and predicting machine breakdowns for preventive maintenance. Through their predictive capabilities, classification models empower businesses to make informed decisions and optimize their operations efficiently.
Classification Models generate scores representing the likelihood of events occurring, typically ranging between 0.0 and 1.0. For example, a score of 0.65 from a product recommendation model indicates a 65% chance of the individual buying the product. In credit scoring, scores are scaled from about 300 to 700, where a score of 300 suggests a very low likelihood of loan repayment, whereas, a score of 700 indicates almost certain repayment. Most business classification models yield a single score for simple yes/no events, but for problems with multiple outcomes, separate scores are generated for each possibility. Advanced models can produce thousands of scores, like in object recognition systems where each score represents the probability of a specific object being present in an image. Although users do not directly interact with these scores, decision rules are applied to interpret them to provide meaningful outcomes through user-friendly interfaces.
However, Regression Models are all about quantities the, like magnitude of an event. For instance, they predict life expectancy for setting insurance premiums, estimate journey times based on driving behavior, forecast credit losses when loans default, predict spending in local supermarkets, determine intervals between product purchases for marketing timing, forecast call lengths for call center resource planning, predict occupancy patterns to optimize visit times, and estimate response times after actions, like sending letters or emails. Basically, most businesses tend to favor classification models over regression models because they are more popular and effective for everyday applications.
There exists a third possibility. Imagine my primary objective is to retain valuable customers, with less concern for losing those who contribute little revenue. In this scenario, it might be advantageous to construct two separate predictive models: First, an attrition model to forecast customers most likely to switch to a competitor’s product (Classification). Second, a revenue model to identify customers with the highest spending patterns (Regression). As a result, by applying these two models in combination, a tailored set of retention strategies could be utilized and significant cost savings reached.
The models offer predictions about customer behavior, but they do not dictate what actions should follow those predictions. Deciding on the appropriate strategies for customer retention based on these predictions is an important part of the organization’s machine learning project. It is crucial because many organizations develop accurate predictive models but fail to utilize them efficiently in practice. So, making correct decisions and acting on them appropriately is key for the success of a machine learning projects.
The Nature of Machine Learning
As mentioned earlier, most machine learning approaches use predictive models to drive decision-making and subsequent actions. There are different types of predictive models, with three of the most popular being Scorecards (linear models), Decision Trees (Classification And Regression Trees or CART), and Artificial Neural Networks (ANNs), commonly known as Neural Networks (NNs). Scorecards and decision trees are straightforward for non-technical individuals to comprehend, as the model scores are calculated transparently. Nevertheless, Neural networks are more complex and can seem like a black box due to the difficulty in understanding why they arrive at certain scores and decisions. Despite this complexity, neural networks are increasingly favored for their ability to provide more accurate predictions compared to scorecards or decision trees in many scenarios. Advanced neural network forms, like deep learning, are leading the way in AI research, making neural networks the preferred choice for solving intricate AI problems like object identification, speech recognition, and language translation.
Consider a scenario where the government aims to screen every member of the population for heart disease as a preventive measure. To build predictive models for identifying individuals at risk of heart disease, historical patient data is necessary. This data includes various factors, like age, gender, BMI, blood pressure, lifestyle habits, and medical history. By analyzing this data alongside health outcomes over a five-year period, the development sample for the predictive model is formed. It is important to have data on both individuals who developed heart disease and those who did not, as the machine learning process relies on identifying differences between these two groups to generate accurate predictions. The process of building predictive models involves using complicated algorithms to the development sample data to identify relationships that correlate with heart disease events or non-events. Even though specialized computer software can perform much of this mathematical analysis, understanding the underlying techniques and parameters is beneficial for interpreting model diagnostics and ensuring the creation of predictive models that meet business limits. Iterative model building may be necessary, as well as requiring expertise to guide the process towards the most effective and accurate model. Suppose the data scientist initiates the process by exploring a scorecard-style model. Notably, Linear Regression and Logistic Regression stand out as the primary algorithms used for developing such models. Once an appropriate software tool is chosen, the algorithm is applied to the development sample. This process results in the generation of the predictive model. Initially, each person is assigned a “starting score” of 350. Then, points corresponding to relevant factors are added or subtracted from this starting score. For example:
In spite of the fact that the scorecard model depicted in that table forecasts the likelihood of an individual developing heart disease within the next 5 years, the translation this score to probabilities are remained to address. To establish this relationship, we should create a “score distribution” table as follows:
Understanding the significance of the score is significant, but the next question is: How accurately does the scorecard model forecast heart disease?
Decision Trees
Decision trees represent another commonly used predictive model. Similar to scorecards, they represent a user-friendly and straightforward nature. All popular decision tree algorithms are variations on the style of repeatedly splitting the development sample into smaller and smaller sections.
A decision tree is a non-parametric supervised learning algorithm, which is utilized for both classification and regression tasks. It has a hierarchical, tree structure, which consists of a root node, branches, internal nodes and leaf nodes.
The following is a simple description of a decision tree algorithm:
Step-1) Start with the tree with the root node, says S, which contains the whole dataset.
Step-2) Determine the most suitable attribute in the dataset using Attribute Selection Measure (ASM).
Step-3) Divide the S into subsets that includes possible values for the best attributes.
Step-4) Create the decision tree node, which consists of the best attribute.
Step-5) Repeat the process recursively by constructing new decision trees using the subsets of the dataset generated in step 3. Continue this iteration until a point is reached where further classification is not feasible, designating the final node as a leaf node.
For example, the decision tree model from the heart disease development sample is:
There are 13 shaded nodes, each numbered, which correspond to 13 different scores. Individuals with a score of 1 have the lowest probability of developing heart disease, while those with a score of 13 have the highest probability. Then, a score distribution table produced showing how the scores are distributed.
The difference in the data used by scorecard and decision tree models comes from how they are built. Each model relies on specific rules and methods to decide which factors are important and how much weight to give them. As a result, they have different strengths and weaknesses when it comes to predicting different cases.
Neural Network and Deep Learning
Neural Network is a form of artificial intelligence that mimics the human brain’s data processing. It is part of a machine learning technique, called Deep Learning, where interconnected nodes, or neurons, are organized in layers similar to the brain’s structure. This setup enables computers to learn from mistakes and enhance their performance over time. This makes them as adaptive systems. Artificial Neural Networks address complex tasks, like document summarization and facial recognition with improved accuracy. Neural networks can help computers make intelligent decisions with limited human assistance. This is because they can learn and model the relationships between input and output data, which are nonlinear and complicated. For instance, they can do the following tasks.
Neural networks can understand unstructured data and make general observations without explicit training. For example, they can recognize that two different questions, like “Can you tell me how to make the payment?” and “How do I transfer money?”, convey similar meanings.
Neural network architecture draws inspiration from the human brain, where interconnected neurons communicate through electrical signals to process information. Artificial neural networks replicate this by using artificial neurons, called nodes, which collaborate to solve problems. These nodes are software modules within neural network algorithms, which means using computing systems to perform mathematical calculations.
The neuron in the above picture operates as follows:
- Observation data, like Age and BMI, serves as inputs to the neuron.
- Each input is multiplied by a weight, which can be positive or negative. For non-numeric data such as gender or smoker, 0/1 flags represent each condition (e.g., 1 for female, 0 for male).
- The inputs, along with their relating weights, are summed up to calculate an initial score.
- This initial score undergoes a transformation, often adjusted to fall within the range of 0 to 1. While not strictly necessary, this transformation is considered good practice. It ensures that all neurons in a neural network produce values within the same range.
- The transformed score becomes the output produced by the neuron.
Eventually, to produce a neural network model a number of neurons are connected together. For example:
Neural network training is the process of teaching a neural network to perform a task. Neural networks learn by initially processing several large sets of labeled or unlabeled data. By using these examples, they can then process unknown inputs more accurately.
The process for determining the weights in a neural network involves several steps, which typically follow these principles:
- Initially, each weight is assigned a random or zero value.
- The network calculates scores for all cases in the development sample using these initial weights.
- The accuracy of the final score is assessed, often by evaluating properties like lift and gain.
- Based on this assessment, the weights are adjusted to enhance the predictive accuracy of the model.
- Steps 1 through 4 are repeated iteratively until either no significant improvement in model performance is observed or a predetermined amount of time has elapsed.
Neural networks are popular because they can find subtle patterns in data that simpler methods might miss, like scorecards and decision trees. So, this leads to more accurate predictions.
Deep learning, a branch of artificial intelligence, mimics the human brain’s way of processing data. It uses neural networks, which are interconnected nodes arranged in layers, to recognize complex patterns in data such as images, text, and sounds. This technology enables computers to make accurate predictions and generate valuable insights. The neurons between the input and output layers of a neural network are referred to as hidden layers. The term “deep” usually refers to the number of hidden layers in the neural network. Deep learning models can include hundreds or even thousands of hidden layers.
Obviously, sophisticated artificial intelligence applications often based on complex deep learning models built upon neural networks, nevertheless, for many problematic issues, this level of complexity is unnecessary. This mean: “You do not need a sledgehammer to crack a nut”. Overly complex models may not yield better results, especially for simpler problems. In some cases, employing a simpler model may actually produce superior outcomes.
Unsupervised and Reinforcement Learning
The machine learning methods discussed so far have relied on having both observation and outcome data in a development sample. For instance, in the heart disease problem, each patient’s data was labeled to indicate whether they developed heart disease later on. This approach, where labeled data guides the learning process, is known as supervised learning. Many real-world AI applications, like target marketing or fraud detection, fall under this category.
In contrast, there are scenarios where outcome data is scarce or absent, leaving only unlabeled observation data. In such cases, supervised learning is not feasible. Instead, unsupervised learning techniques come into play. Unsupervised learning focuses on knowledge discovery by identifying patterns or interesting features within the data, rather than predicting outcomes, like in supervised learning. Unsupervised Learning involves training a machine using a dataset that lacks labels or tags. In this approach, the learning algorithms are unaware of the data’s meaning or context. It is similar to listening to a podcast in a foreign language without understanding it. Without a teacher or dictionary, it is like trying to make sense of the podcast on your own. While listening to just one podcast may not provide much benefit, spending hundreds of hours listening to similar content allows your brain to gradually form a model of the language, recognize patterns, and anticipate certain sounds. Initially, there are some techniques that are used in Unsupervised Learning such as self-organizing maps, nearest-neighbor mapping, and k-means clustering. The main goal is to explore the data and find some structure within.
Reinforcement Learning is a type of machine learning paradigm where an agent learns to make decisions by interacting with an environment. The agent learns to achieve a goal or maximize a cumulative reward through a process of trial and error. In other words, in reinforcement learning, the agent learns by interacting with the environment, receiving feedback (rewards or penalties) based on its actions, and adjusting its strategy to maximize cumulative rewards over time. By repeatedly playing games or performing tasks in the environment and receiving feedback, the agent learns which actions lead to better outcomes and gradually improves its decision-making policy.
Big Data and Machine Learning
Big Data refers to extremely large and diverse collections of structured, unstructured, and semi-structured data that continues to grow exponentially over time. These datasets are so huge and complex in volume, velocity, and variety, that traditional data management systems cannot store, process, and analyze them.
Big data analytics mines existing data to identify emerging patterns and trends that inform decision-making. In contrast, machine learning uses available data to enable a machine to self-teach and improve its capabilities. The relationship between Big Data and machine learning is that data serves as the fuel for machine learning processes. The tangible benefit to a business is derived from the predictive model, customer clustering or other analytical outputs, which are delivered at the end of the process, not the data itself. Although having more and better quality data can improve the effectiveness of machine learning solutions, it is not essential to have gigabytes or terabytes of data for practical application.
The Machine Learning Checklist for Business Success
This checklist ensures that critical aspects of machine learning projects are addressed. In addition, it helps to maximize the chances of business success and diminish potential risks in your organization.
· Identify the Business Problem:
· Define the specific problem or objective that machine learning will address.
· Define Metrics for Optimization:
· Determine key metrics to optimize, such as time saved, increased profit, reduced cost, customer satisfaction, or lives saved.
· Decision Rules and Actions:
· Specify decision rules and actions based on the output from the machine learning process.
· Data Sources:
· Identify where the data for machine learning will come from and ensure enough examples are available for training.
· Operationalization:
· Determine how predictive models and decision rules will be implemented within existing systems or processes.
· Allocate resources (time and money) for implementation and identify responsible parties.
· Ethical and Legal Considerations:
· Assess ethical and legal risks associated with automation, considering factors such as customer impact, data immutability, beneficiaries, and data protection laws.
· Develop mitigation strategies to manage identified risks.
· Data Availability:
· Ensure all required data is available operationally.
· Create a checklist to verify the availability of every data item needed for the models.
· Active or Passive Implementation:
· Determine whether the solution will be implemented in an active or passive way.
· Establish controls to ensure that overriding occurs only when appropriate to avoid devaluing the system.
· Assessment of Success:
· Develop a monitoring process to assess the system’s performance in real-life scenarios.
· Measure actual benefits realized against those promised during the analytical phase.
· Consider ongoing costs of maintaining the system, including updates for new data, regulations, and changing business requirements.
· Include costs for regulatory compliance and annual audits in the assessment.
In Conclusion
It is clear that understanding Artificial Intelligence and Machine Learning basics is vital for businesses to succeed. By using AI and ML effectively to solve problems and boost efficiency, companies can stay ahead of the curve. Although not everyone needs to be an expert in this area, grasping the fundamentals of AI and aligning them with business goals is essential for achieving business success, specially for leaders.
Finally, addressing legal challenges related to Artificial Intelligence is another controversial issue, such as who is responsible if AI systems make mistakes, ensuring fairness and transparency in decision-making, safeguarding privacy and data, and tackling algorithm biases. Therefore, maintaining laws updated to match rapid technological progress is also a significant hurdle. It requires collaboration among various stakeholders to keep a balance between increasing AI’s benefits and mitigating its risks.
