AI, ML, and statistical models reinforce much of the work that we do here at Riskaware.
Enabling our users to reach previously invisible strategic insights, our modelling capabilities are designed to be used and deployed across a wide range of situations for true operational awareness – from oil spill responses to cybersecurity.
But how and why do we use and interact with artificial intelligence and statistical models – and what is the advantage of using these advanced analytics tools instead of traditional data analysis solutions?
Below, we explore the common use cases of AI and statistical modelling at Riskaware, and the importance of obtaining proactive, preventative insights. Read on to learn more.
Why do we use AI?
While traditional data analysis processes may equip users with comprehensive insights into a developing incident or event – such as marine oil spills – they do not equip users with the full scope of intelligence. While traditional analysis processes are confident in supplying user insight, they can’t provide information on the probability of other solutions.
Through deploying a combination of AI, Machine Learning, and statistical modelling processes, we aim to provide proactive and predictive solutions to real-world incidents.
AI and statistical modelling transforms raw data points into actionable intelligence to give users the flexibility and agility needed to support strategic decision-making based on the very latest data as it develops.
AI, Machine Learning, and statistical modelling – knowing the difference
Before exploring how we use and deploy Machine Learning, AI, and statistical modelling for a variety of use cases and solutions at Riskaware, it may be beneficial to examine and define how these disciplines differ from one another.
As a subset of AI, Machine Learning tools operate by training algorithms through supervised learning, unsupervised learning, or other methods to interpret data without being specifically programmed to do so.
On the other hand, Northeastern University writes that statistical modelling ”is the process of applying statistical analysis to a dataset.” which allows users “to identify relationships between variables,” using traditional data analysis fitted to a given model.
At Riskaware, we use a combination of AI, ML, and statistical modelling to provide our users with sophisticated and accurate insights, capable of informing real-time decisions.
Gaining an insightful overview
Following the identification of real-world challenges in need of greater situational awareness, we introduce statistical modelling techniques that enable greater situational awareness. We then integrate results into a dashboard for accessible insights at a glance.
With enhanced preparedness, teams can select the very best response to outcomes before they happen, mitigating risks and possible costs, while ensuring the safety of teams.
Using these tools for prevention and response can be critical in a wide range of environments, explore them in action below.
AI and modelling for prevention
Often when incidents occur, the longer the response time the greater the damage. This is why we adapt the information provided by our AI and statistical modelling-enabled outcomes for the challenge at hand.
In our MarineAware Operations platform, these models are designed to provide insights into the ongoing developments of oil spills and other marine accidents, facilitating a rapid and strategic response that can reduce the impact on the environment, and the health and wellbeing of people and animals.
Our UrbanAware tools, on the other hand, demonstrate the flexibility of modelling capabilities by providing the same principles to project outcomes of chemical, biological, radiological, nuclear, and explosive threats (CBRNE) in urban environments.
Enabling strategic responses that mitigate risks to safety and public health, such as evacuation planning, UrbanAware’s reporting capabilities provide critical and forecasted intelligence designed to save lives.
Read more about AI and Statistical modelling in the face of CBRNE threats
Statistical modelling for security
Just as our focus on statistical inferences and modelling provides elevated insights to key decision-makers when combatting physical threats and risks, so too can it help reinforce and heighten security throughout the cyber landscape.
At Riskaware, we also realised the potential of AI and statistical modelling processes to inform users of security risks and vulnerabilities – allowing them to better plan and secure their infrastructure. By using AI and statistical modelling processes, we can forecast the most likely attack paths as well as predict an attackers next move in real time, enabling defensive measures to be taken that minimise the impact of an attack.
With an accessible web-based platform producing at-a-glance insights based on comprehensive learning outcomes, users can heighten their security and awareness of the vulnerabilities of their infrastructure.
AI and health monitoring
AI tools bring a wide range of applications outside of strategic decision-making and incident handling – helping to improve everyday living and provide personalised intelligence to users.
To see this in action, we only have to turn to the world of wearables, where diverse AI tools are transforming how we interact with health data. With advanced AI capabilities, health wearables may report back to the wearer and other recipients of shared data on a vast array of critical health data – from dehydration and blood pressure to UV exposure and detected heart defects.
Possessing the unique skills to make calculated strategic assumptions based on real-time data, these AI capabilities may save lives by flagging when conditions are reaching, and surpassing, critical conditions, before suggesting countermeasures.
Our AI, ML, and statistical modelling-enabled tools are designed to add value and insights to a wide range of use cases, from personal health to cybersecurity.
To learn more about our applications, read the Riskaware guide to AI, ML, and statistical modelling, here.