What is Machine Learning
Machine learning, as the name suggests, refers to teaching machines to “think” and improve. This is achieved through software that allows machines to learn by exposing them to new data. These machines perform tasks, not by being programmed, but as a result of the patterns they learn to discern from existing data. Machine learning falls under the umbrella of Artificial intelligence.
Machine Learning is now widely used in the IT industry serving various real-world applications. New age learners are keen to learn the technology by enrolling in machine learning course.
What is Artificial Intelligence
Check out the video to understand the Artificial Intelligence.
Sectors where ML has created an impact and why companies are adopting it
Machine learning has been adopted into many industrial sectors, with drastic impact. This article is about the application of machine learning in the defense sector. However, before we move to that, below mentioned are other sectors that have benefited from machine learning:
While human teachers still have a place in the classroom, the use of technology to enhance the learning experience is very much a reality for today’s students. However, even the most dedicated teacher cannot give undivided attention to all his/her students. As long as a school has access to computers, tablets, and the internet, they can use machine learning to complement their teachers’ work in the following ways:
- Learning plans can be customized to each students’ needs and style of learning.
- By collecting and analyzing a student’s work, machine learning algorithms can identify learning disabilities
- Generating relevant assignments and tests by sifting through the countless study material.
- Guiding students through the learning process and giving feedback.
With machine learning, student/teacher ratios will cease to be a concern as each student will receive a quality education and undivided attention.
Some applications of machine learning in healthcare include:
- Use of computers to compare symptoms against a vast database of possible diagnoses, leading to faster diagnoses and accuracy of treatment.
- Use of robo-doctors, such as the Da Vinci, to assist in surgical procedures, greatly reducing the margin of error.
- Use of predictive and cognitive analytics to identify “at risk” individuals and give them preventive care before they develop a disease like diabetes.
- Providing follow up care at home using wearable gadgets.
The financial sector has over the last few years experienced a wide adoption of Machine learning and AI. This is because the finance sector is high risk, deals with a lot of data and its operations demand accuracy. Machine learning algorithms tackle all three problems: machines can sort through a large set of data faster than a human, they are more accurate, and, hence, they minimize risk.
Some use examples of machine learning in the financial sector include:
- Vetting customers during the loan application process
- Using Robo advisors, for example, Wealth front or Best Robo Advisors, to manage investment portfolios and recommend “hot” investment opportunities to customers.
- Customer service and query handling.
- Fraud detection by monitoring and detecting unusual transactions.
The self-driving car is the most widely recognized use of machine learning in transportation. Waymo, which began as Google’s self-driving car, is equipped with software and hardware designed to learn the environment and driver behavior.
Uber is also in the process of developing self-driving vehicles, while Tesla already fits its vehicles with an autopilot that allows the car to self-drive. The autopilot feature is especially useful for navigating difficult weather conditions.
The impact of Machine Learning in the defense sector
Machine learning has created quite an impact in the defense sector, particularly in the military. This is in areas such as
Modern warfare is becoming heavily reliant on AI and machine learning. Unlike conventional systems, machine learning software can process a lot of data. This improves the decision making and accuracy of combat systems.
Governments that adopt machine learning in their military sectors will have an edge in warfare scenarios. China and the US are the most prominent of AI and machine learning for military use. In 2017, the US Department of Defense spent $7.4 billion on AI and Big data. China has also made huge investments and is projected to be the leading AI nation by 2030.
New warfare systems and weapons now come embedded with AI, making them more efficient and less dependent on human operation. This has also resulted in more synergy of operations and systems as well as reducing the maintenance needed for equipment and weapons.
A cyber-attack on military facilities can result in the loss of highly sensitive data and cause damage to military bodies. The use of machine learning in military facilities helps to prevent these types of attacks, by protecting networks from unauthorized intrusion. By relying on established patterns, security systems can preempt attacks and develop countermeasures.
An efficient transportation system is important for success in the military. Ammunition, food, weapons, troops and other goods need to get to their destination on time and in good condition. Integrating machine learning into the military’s logistical process will reduce human effort, minimize errors and exemptions, leading to shorter lead times, and detect anomalies faster.
Combat environments are complex. Terrains, weather conditions, and enemy behavior can make target recognition difficult. Machine learning techniques can assist in such situations by analyzing geographical data, news feeds and intelligence data, to give a better understanding of the target. For instance, DARPA’S TRACE program relies on machine learning to identify targets using radar.
In 2018, the University of North Carolina received a $1.6 million grant from the US Department of Defense, like funding for a project meant to develop machine learning and AI solutions for handling injuries in combat situations. The system will analyze data and predict the skills and patient care techniques that apply in a particular scenario. Having such a tool to guide decision making will ensure each outcome is the best possible one.
The military uses computer-generated environments to train their personnel. For these simulators to be effective, they have to create realistic conditions and also be adaptive. This means that they have to adapt their behavior to accommodate a variety of situations.
This is achieved through reinforcement learning, where virtual or human agents learn by receiving reward or punishment signals after performing certain actions. This approach helps to improve combat training for virtual agents and human soldiers.
Intelligence and reconnaissance missions are important for threat awareness. For stealth purposes, defense bodies have taken to using unmanned systems such as drones. These are fitted with software that allows them to identify borders, recognize potential threats and report or mount an attack. Drones are especially useful in remote areas.
As industries and governments continue to invest in getting certification in machine learning courses, there will be a lot more machine involvement in tasks that are repetitive, tedious or threaten human lives. For the defense sector, this will mean higher security within, and outside borders, a high conviction rate for crime, and, for the military, soldiers will no longer need to put their lives in danger unnecessarily.