Data is the lifeblood of Artificial Intelligence. AI/ML models develop the capability to perform tasks based on the quality and quantity of training data consumed. As emerging technologies, Artificial Intelligence and Machine Learning can ingest large amounts of relevant Big Data and provide incredible accuracy and efficiency with their solutions. For this piece, we’ll be exploring how AI is harnessing the power of real-time data, data that is providing the accuracy, speed, and opportunity to scale that companies and world governments are looking for.
WHAT IS REAL-TIME DATA ANALYSIS?
Real-time data is any information received immediately after discovery. Such data could be static or dynamic in nature, and it can be used for navigation and tracking purposes. It can also be used for analysis purposes, and research, after a large sum of such real-time data has been collected.
Real-time data analysis involves analyzing the data immediately on reception. Users draw quick conclusions with the data and publish their findings, or use obtained insights to improve processes.
A popular example of real-time data that is being consumed daily is the coronavirus infection spread/cases across the globe. South Korea flattened its curve by introducing an app that could monitor citizens’ whereabouts and warn them about nearby infection hotspots. China has used active surveillance techniques for increased testing. Computer vision systems have helped the Chinese government identify unhygienic practices specific to this pandemic (such as crowded zones and citizens avoiding facemasks) in real-time and assess the risk such practices bring.
Coming back, customer relationship management (CRM), fraud detection, and credit scoring, among other popular use cases, take advantage of real-time data.
AI AND REAL-TIME DATA
With real-time data available in the petabytes, it shows that there is no shortage of such data’s availability. AI/ML models require volumes of training data to achieve the highest accuracy possible. Real-time data allows these models to learn from the latest information, thus increasing the model’s relevancy with time.
From agriculture to banking, here are some interesting industries wherein AI and real-time data are aiding innovation:
50% of the world’s population depends on agriculture for their survival. For farmers to succeed in this space, they need to monitor their crops and ensure that only the highest quality of produce is being delivered. With the help of AI, farmers now have a higher guarantee of growing uninfected and market-ready fruits and vegetables.
Farmers can monitor the moisture level of soil using soil and water level sensors. Such sensors can alert farmers about low water levels, and action can be taken accordingly. Microsoft’s agriculture project (FarmBeats) has been able to use AI, Edge, and IoT to reduce water intake by 35% while increasing farm productivity by 45%.
Computer vision systems can detect insects and pests that are threatening to ruin crops. This allows farmers to sprinkle pesticides only on the affected crops, thus saving resources. AI today can also help farmers with weather prediction. Having such information can help farmers to mitigate potential damage to their crops.
The human body has multiple measurable parameters that provide insights. Real-time patient data is a boon for surgeons looking to monitor parameters such as blood loss and oxygen levels. Using such information, medical professionals can determine the necessity for high-level treatments and take a specific action.
Also, real-time data collected from multiple patients can understand trends and identifying health patterns. Using such insights, doctors can detect health issues at the earliest stages and recommend the appropriate treatment. For example, doctors are able to review mammograms 30 times faster with AI, with accuracy levels of 99%. This allows hospitals to save on resources by eliminating unnecessary biopsies.
MILITARY AND DEFENCE
Nations can use computer vision for real-time surveillance of sensitive locations, borders, ports, etc. We can train computer vision systems with the relevant training data (that depicts illegal activities and national threats) to understand when to alert authorities. By monitoring such locations, governments will know when to call for military assistance. This will ensure the military will be used only when absolutely necessary thus ensuring reduced national security risks. Attacking moving targets using a combination of computer vision and advanced artillery.
Such technology allows militaries to flip the cost curve. For example, if a $1m missile takes down an F-35 jet, it becomes an expensive loss for the air force that lost the jet. But what if the same air force sends a low-cost unmanned vehicle (such as the Octatron Skyseer, which costs $25k-$35k)? If it gets destroyed by a similar missile, the army that launched that missile bears the loss, because it’s more expensive for them to use their missile. Hence, the cost curve gets flipped.
The United States, the world’s most powerful military force, introduced Project Skyborg for developing drones and UAVs (Unmanned Flying Vehicles). China is also working towards weaponizing their AI capabilities with military robotics and drone technology. Recently at the UN, the US, Russia, South Korea, and Israel expressed their desire to explore AI-inspired weaponry, also known as “killer robots” to the opponents of such technologies.
In a fast-paced industry such as banking, real-time data aggregation happens in volumes every second. There are multiple parameters and customer touchpoints that banks can use to their advantage. Real-time banking data will allow Machine Learning algorithms to understand what each customer is like and accordingly create a personalized experience, thus improving customer engagement. For example, if a customer is reviewing his/her personal investments regularly, banks could offer investment advice.
Fraud detection is another important application of AI in banking. When a bank’s ML model notices activity that has often ended up in fraud, they can alert authorities to take action. Using AI-backed fraud detection solutions, banks have reported an increase in detecting real fraud by 50%. Such systems help banks build trust with their new customers and retain loyal ones.
I mentioned this earlier under AI’s influence on real-time data in agriculture. Meteorologists construct and operate several models and data sources to track the activity of clouds (shapes, movements, density, etc.). With numerous real-time data points obtained from weather analysis, it’s humanly impossible to cover all aspects of weather effectively. Artificial Intelligence becomes the only viable solution for handling such large data volumes, due to its ability to consume Big Data with ease.
While monitoring cloud sizes and other weather parameters with AI, weather forecasters have the luxury of reporting more accurate information. For example, such AI systems can detect comma-shaped clouds from satellite imagery. Comma-shaped clouds are a strong visual indicator of severe weather events. Also, satellite imagery of the earth’s ozone layer with AI-backed insights can allow lawmakers to create policies surrounding climate change and climate protection strategies.
Real-time data is voluminous, and with each passing year, we are collecting almost double the amount of the previous year. AI/ML models are the only systems out there that can handle such data, and developers are discovering numerous applications for implementation. Real-time data offers solutions for quick decision making and AI models are here to make that process faster.
This article was originally published on TechPluto.
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