By using a combination of statistical modelling, Machine Learning (ML), and Artificial Intelligence (AI) technologies, we can supply users with the very latest insights on ongoing incidents.

With future-proofed predictions, users can reach a greater level of preparedness in the face of a wide range of critical developments – from CBRNE threats to maritime incidents.

When compared to common data analysis solutions – historical data visualisation and manual inferences – ML and statistical modelling provide an intuitive and forward-facing approach based on the very latest streaming data available.

This results in an ability to continuously supply our users with concise and responsive intelligence, which reduces their time to insight and enables the greatest level of preparedness possible.

Machine Learning and Statistical modelling: what’s the difference?

The terms ML, AI, and statistical modelling can often be misused, or misinterpreted, which can often lead to further confusion or potential errors at a later stage (such as scope or budget creep). To help clarify this, we’re defining these terms below:

What exactly is statistical modelling?

Using a variety of techniques such as logistic regression and statistical inferences, statistical modelling can create a visualised representation of analysed input data.

Rather than moving into the realms of recommendation engines, unsupervised machine learning and speech recognition, statistical modelling is confined solely to the application of historical data. In this capacity, statistical models are able to analyse trends, risks, and outliers with extreme accuracy.

To shift into the proactive intelligence that more advanced analytics offers, we need to turn to Machine Learning algorithms.

Operating by training an algorithm to function without human intervention, Machine Learning tests models based on sets of training and labelled data. Once models are tested and verified, they are then performed on unlabelled data to map and predict future outcomes. In the current world, ML use cases can involve fraud detection, and computer vision.

Learn more about the differences between Machine Learning and statistical modelling, as well as how we build our models, here.

What are some statistical modelling techniques?

To understand how statistical models perform and function, it can be insightful to dive into the various techniques that can be involved. Some of the most popular statistical modelling techniques are linear regression, classification, and resampling.

As each technique is optimised for different use cases, preferred outcomes, and datasets, it is important to understand how each technique will affect inputted data.

Linear regression

Linear regression is used to analyse and understand the knock-on consequences of an independent variable on a dependent variable. It can also then be used to understand the relationships between the two.

By understanding this cause and effect, users gain a unique perspective on previously collected data and a deeper understanding of conflicting outcomes throughout. In the realm of incident modelling, this understanding of consequences and the ripple effect throughout of incidents, helping users to more safely navigate ongoing risks.

Classification

Classification can authorise and group datasets into distinct sub-categories to make more accurate comparisons and insights.

Common types of classification techniques include discriminant analysis (where multiple clusters are grouped and analysed) and logistic regression (which can determine if a dependent variable is dichotomous or binary).

Resampling

Resampling involves taking repeated pieces of samples from a wider sample of input data, before analysing and comparing the results.

Read more: How we use AI, ML, and statistical models at Riskaware

How are Machine Learning models trained?

Just as statistical modelling processes utilise different techniques to reach the desired results, so too can Machine Learning outcomes be affected by how they’re trained – a process that brings an ML model to full sophistication and independence.

By training ML models in different ways, they will interact with given data differently. This leads to a wide array of use cases. While reinforcement training may enable functionalities such as self-driving cars, supervised Machine Learning may lead to facial recognition and computer vision capabilities.

The most common ways to train Machine Learning models are:

Supervised learning

When training a Machine Learning model using supervised learning algorithms, an ML specialist takes on the role of a supervisor.

This role involves collecting and labelling training data, before ‘collaborating’ with the ML model to ensure recognition. Once achieved, the supervisor will then repeat the test to guarantee that any errors have been correctly navigated. If so, they can move on to the next set of training data or make further amendments as needed.

Supervised learning use cases are ideal for work in complex analysis environments, such as stock price prediction tools.

Unsupervised learning

On the other hand, unsupervised Machine Learning algorithms remove the supervisor from the process. Here, ML models are trained by interacting with a set of unlabelled data, along with instructions to independently find patterns and connections.

Between supervised and unsupervised algorithms, we can find a hybrid of the two: semi-supervised. Here, algorithms are left unsupervised yet are supported with a small, labelled set of training data. Semi-supervised learning tools include object localisation, while unsupervised algorithms usually take on the form of recommendation engines – for example.

Reinforcement learning

If the goal of Artificial Intelligence and Machine Learning is to replicate the processes of a human brain, the most effective way of doing so currently is through reinforcement learning.

Reinforcement learning trains ML models by introducing trial and error – providing positive reinforcement when the model reaches a specified target. In the real world, automated real-time bidding and workflow optimisation are both key examples of reinforcement learning in action.

What are the challenges of using Machine Learning models?

While there are many benefits to introducing Machine Learning capabilities, as well as statistical modelling, implementing models can be a daunting task. It may be fraught with technical challenges and time-consuming training.

By anticipating these challenges, users may be able to navigate issues carefully and with agility, to mitigate any chance of disruption. Some of the most common challenges when building and interacting with Machine Learning models include:

Not choosing the correct learning method: We’ve already explored how the different variations in training methods lead to different use cases and applications. While one use case may call for supervised learning, another may call for unsupervised, and so on. By choosing the incorrect learning method for your algorithm, your ML model may be ineffective when interacting with your training data, causing a needless waste of resources, time, and cost.

Lack of sufficient data quality: Data quality directly affects the potential of your ML models. With gaps in datasets, ‘noisy’ training data, or low-fidelity data, your models will be unable to accurately represent real-world characteristics and capture behaviours that they are designed to emulate. What’s more, training models using data that has too much detail in it can even restrict ML algorithms and lessen their usability.

Unknowingly introducing unwanted bias: Bias doesn’t just happen when ML models are trained with poor quality training data. If critical datasets are removed or excluded from analysis, models will be unaware of result-changing data, which can skew perception and performance equally.

An inability for algorithms to scale as datasets naturally grow: As any organisation scales, so too will its datasets. What may begin as isolated datasets will soon evolve into an entire ecosystem of data points and sources.

As this evolution continues, it’s important to ensure that ML models can take advantage of any incoming data easily and efficiently to avoid longer time-to-insights and unreliable results.

Discover more about the challenges of working with Machine Learning models here.

How are Machine Learning models and statistical modelling advancing?

It’s no secret that Machine Learning, Artificial Intelligence, and statistical modelling capabilities are on a consistently changing trajectory.

As users increase their sophistication and demands, solutions and techniques must adapt to meet their requirements – an age-old relationship that promises innovation. Looking to the future of Machine Learning and statistical modelling capabilities, we expect to see three distinct outcomes:

Greater speed and processing

The most important insights are the most responsive and the most recent. Efforts have focused on combining the power of Machine Learning models with advanced technology capabilities such as quantum computing, allowing models to be made from billions of rows of data in potentially real time. However, while these benefits are incredible, GPU-integrated ML algorithms currently hold a high error rate, which, on the other hand, should reduce in frequency with consistent focus and innovation.

Improved cognitive functionality

As Machine Learning processes develop, their cognitive abilities (designed to learn through experience and draw conclusions) are expected to continue evolving, allowing them to take on more advanced capabilities to enhance available intelligence.

Heightened accessibility

While users get more sophisticated in their demands, another aspect of Machine Learning that is only improving is accessibility features – opening up the ability to engage with Machine Learning models throughout the training and deployment lifecycle.

Here, we find innovative features such as Natural Language Processing – which aims to allow users to interact with – and generate – models using simplistic and natural language. Taking our use cases as an example, this may involve a user asking a model, “what will this oil spill look like three weeks from now?” or, “how will this particular threat linger after initial contact?”

Committed to our ML capabilities

Machine Learning, Artificial Intelligence, and statistical modelling bring relevant, and necessary insights to many challenging environments both at Riskaware and beyond.

We continue to deploy Machine Learning models to equip our users with intelligence capable of mitigating risk, damage, and disruption – and are constantly developing new tools to aid our users even further.