Knowing the Difference: Artificial Intelligence VS Machine Learning
You’ve probably heard of Artificial Intelligence (AI) and Machine Learning (ML). The quest to develop machines that mimic human abilities, like thinking and decision making, led us to the world of AI. AI is already shaping our daily life and is only set to enhance its capabilities in the future.
Before learning about the differences between AI and ML, read our article on how AI came into being:
AI and ML are the most trending terminologies used to refer to the intelligent systems being developed nowadays.
Despite their closeness, these two terms are often confused for synonyms. While AI is a broader idea that aims to produce intelligent systems that can replicate human thinking capabilities and behaviour, ML is a branch of AI that allows machines to learn from data without being explicitly programmed. AI and ML can be distinguished under the following categories:
- Goals & Tasks
— The primary goal of AI is to create a smart computer system that can solve complicated problems more efficiently than humans. AI makes it easy to represent human knowledge in a form that machines can understand. Requiring no human intervention, AI is a technology that can acquire and manipulate data to give desired results. Some of the tasks that can be performed with AI are processing and translating human languages, enabling robots to behave like humans, developing interactive personal assistants like Siri and Cortana, and diagnosing abnormal conditions and performing surgeries in the medical field. AI is being developed as a system that can support humans to improve productivity, efficiency and accuracy.
— The goal of ML is to create new and/or use existing algorithms to learn from data in order to create generalizable models that make accurate predictions or to detect patterns, especially with new and previously unexplored data. ML is used to automatically model and detect patterns in data, with the purpose of projecting a specific output or reaction. Statistics and mathematical optimization are heavily used in these algorithms. ML algorithms can be used to perform tasks such as clustering, classification, regression, anomaly detection and build recommendation systems.
2. Applications
— AI has a wide range of applications. For example, In astronomy, AI is being used to understand various aspects of the universe, such as its origin and how it works. In healthcare, AI is being used to diagnose diseases and simplify medical procedures. In finance, AI is being leveraged in the form of automation, chatbots, adaptive intelligence and algorithm trading. AI is being used to ensure data security through existing technologies such as the AEG bot and the AI2 Platform. The travel and transport industries use AI to completely manage and arrange travels by using chatbots to interact with humans. The automobile industry is leveraging AI to implement self-driven cars. In the field of Robotics, humanoid robots are the latest addition that can behave and interact from experience. AI can be used in the field of education to develop virtual personal tutors to enhance digital learning.
— ML finds applications in many daily tasks. One such popular application is image recognition, where ML is used to identify objects, persons, places in digital images. This can be seen in automatic face tag suggestions in popular social media platforms like Facebook. ML is used in speech recognition, where it is now possible to search by voice and interact with personal assistants like Siri and Cortana. ML is being used in Google maps to predict traffic conditions in real time and suggest the best navigation route. Recommendation systems are built using ML. Entertainment websites such as Netflix and Amazon Prime use recommendation systems to suggest movies/shows based on a user’s recent views. ML is also used for product recommendation on e-commerce websites. ML is being used to monitor credit card and online transactions to identify frauds. ML algorithms are also used in stock market trading to avoid losses.
3. Types
— AI technologies are categorised by their capacity to mimic human actions. The types are Artificial Narrow Intelligence (ANI), Artificial General Intelligence (AGI) and Artificial Superintelligence (ASI).
- ANI, also referred to as weak AI, is the only type of artificial intelligence that has been successfully realized to date. Narrow AI is goal-oriented and operates under a narrow set of constraints and limitations. It doesn’t mimic or replicate human intelligence, and merely simulates human behaviour based on a narrow range of parameters and contexts.
- AGI, also known as strong AI or deep AI, is the concept of a machine with general intelligence that can learn and apply its intellect to solve any problem. It can emulate human intelligence and/or behaviours. In every given context, AGI can think, understand, and act in a manner that is indistinguishable from that of a human.
- ASI is the hypothetical AI where machines become self-aware and surpass the capacity of human intelligence and ability. The muse of dystopian science fiction, in which robots overrun, overthrow, and/or enslave humanity, indicates superintelligence. Artificial superintelligence is the idea that AI will evolve to be so similar to human emotions and experiences that it will not only understand them, but will also elicit emotions, wants, beliefs, and goals of its own.
— ML algorithms are of three types: Supervised Learning, Unsupervised Learning, and Reinforcement Learning.
- Supervised Learning is a way to train a machine learning model wherein a lot of training samples are provided to help a machine learn and predict outputs accordingly. Regression and classification are achieved through supervised learning.
- Unsupervised Learning is used to make machines learn by finding patterns in data. This is used to perform classifications and groupings of data that may not be very obvious to humans.
- Reinforcement Learning is a method that is closest to the way humans learn. Here, machines are made to learn through continuous interactions with data.
Future of Artificial Intelligence and Machine Learning
AI as a whole has made significant progress toward replicating human intelligence’s usefulness in performing particular jobs, but there are still significant limitations. AI algorithms, in particular, are often limited to “specialised” intelligence, which means they can only solve one problem and do one activity at a time. Despite how promising the capabilities of AI sound, its development is hindered by a major drawback that it frequently necessitates “learning,” which might include large volumes of data, raising questions about the availability of the right kind of data, as well as the requirement for categorization and privacy and security concerns. The issue of computing and processing power limitations that is gradually being overcome to enable more efficient AI.
While AI is still in its infancy, and the pursuit of strong AI has long been considered science fiction, recent advances in machine and deep learning suggest that we should be more serious about the potential of achieving artificial general intelligence in our lifetimes. In terms of Artificial Superintelligence, it’s frightening to think about a world in which machines outperform humans at the fundamental things that make us human. We can’t predict all of the effects AI will have on our world, but the abolition of diseases and poverty isn’t out of the realm of possibility.
Artificial Intelligence in Pipeline Monitoring
Pipeline companies monitor pipelines using a number of techniques, ranging from cutting-edge equipment to patrolling pipelines directly. Regular visual inspections are carried out — by strolling, flying, or using drones — and the business also employs electronic monitoring via high-tech control centres and pipeline patrols. Advancements in technology and the rapid digitisation of the oil and gas industry have enabled the use of AI to simplify the process of pipeline monitoring.
SuperVision Earth’s AI based innovation ensures safety and integrity of pipeline infrastructure. SuperVision Earth combines various monitoring technologies to analyse and detect threats along pipeline routes and to ensure the safety of pipeline systems and operations. Furthermore, SuperVision’s AI innovation analyses the detected risks and immediately reports risks to the relevant pipeline operators. The SuperVision Space (SVS) app uses earth observation and remote sensing technology to monitor threats along pipeline routes and transmission lines resulting in the creation of resilient infrastructure networks.
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References
- https://www.javatpoint.com/difference-between-artificial-intelligence-and-machine-learning
- https://www.analyticsinsight.net/artificial-intelligence-vs-machine-learning-vs-data-analytics-what-you-need-to-know/
- https://www.analyticsinsight.net/the-difference-between-artificial-intelligence-and-machine-learning/
- https://nareshit.com/goals-of-artificial-intelligence/
- https://www.innoarchitech.com/blog/machine-learning-an-in-depth-non-technical-guide#:~:text=Machine%20Learning%20Defined&text=Its%20goal%20and%20usage%20is,new%20and%20unseen%20similar%20data.
- https://www.javatpoint.com/application-of-ai
- https://medium.com/app-affairs/9-applications-of-machine-learning-from-day-to-day-life-112a47a429d0
- https://www.javatpoint.com/applications-of-machine-learning
- https://codebots.com/artificial-intelligence/the-3-types-of-ai-is-the-third-even-possible
- https://www.forbes.com/sites/ashleystahl/2021/03/10/how-ai-will-impact-the-future-of-work-and-life/?sh=67dcd9a079a3
