Learning 5 Domains of AI
"The creation of real artificial intelligence could herald the end of the human race,"- Stephen Hawking
That would be alarming, wouldn’t it? But can that be true? Artificial Intelligence is a brilliant concept and is providing multiple solutions today along with a large unexplored scope for tomorrow. But is there a real threat from AI or is it also just fiction? The answer to that query is in every invention/discovery by the human race. “Half knowledge is worse than no knowledge at all.”
That has been the core reason behind every problem that the world faces today. Mankind half thinks and half assumes. Not much is done to do proper research at first before implementing knowledge. It is the same with Artificial Intelligence. There are a lot of advantages that AI can bring to the world in the forms of development, better health, better lifestyles, better security, research, access to education, and lots more. But people only underline the disadvantages because of half-knowledge.
This problem is resolved by SchoolForAI (SFAI). This is India’s first hybrid learning platform meant for AI and Data Analytics. SchoolforAI features online mentor-led & self-paced e-learning. We combine direct tutoring activities with online sessions for students of grade 7 and above to gain edge over the emerging technologies. Our project-based learning approach encourages students to master the concepts and challenge themselves towards solving real-life problems.
We give you a proper understanding of AI and then how it can be implemented. For a start, AI programs copy humans and learn g in the same way that we do, through interaction with our surroundings. The entire concept revolves around 5 key AI domains- Data, Computer Vision, Natural Language Processing, Audio, and Automated Machines. Let us take a look at each of them:
The data could be numerical, graphical, or an audio stream, for example. However, we're referring to the quantitative and qualitative data that is utilized in machine learning. Businesses are particularly interested in this type of data, and most AI applications are based on it. This could possibly be related to Data Science, where the goal is to extract insights from past data. In today's world, Data Science and AI have a long way to go before they can be considered a unified force.
Most businesses use AI systems for prescriptive and predictive purposes in this domain, which is the workhorse of AI. In this domain, we employ a lot of statistics to forecast the outcome. Every managerial decision is based on data and predictive analytics.
AI can be defined as mimicking the human vision system to see the world around it using computer vision applications. It is the branch of science concerned with gathering, processing, analyzing, and comprehending images from the real environment in order to generate data in the form of decisions. Deep learning and neural networks are used in one of the most powerful and popular AI applications. The three main components of Computer Vision are:
- Optics – It consists of gear such as a digital camera or an infrared camera, as well as lenses that allow for frame or continuous recording of events. If you observe a robot or a surveillance system, the first aspect of acquiring information about the environment is the cameras. Pre-processed digital data is sent into the picture processing stage.
- Digital Image Processing – Analogue-to-Digital converters and signal processing techniques are used to prepare the image for machine learning in the proper shape and format. The accuracy of machine learning is directly influenced by the effectiveness of digital picture processing.
- Machine Learning – Once the data is ready in the required appropriate format, the same could be used for training and testing purposes.
Natural Language Processing
It is a branch of Artificial Intelligence in which computers intelligently analyse, comprehend, and infer meaning from human language. In simpler words, natural language processing (NLP) allows humans to speak with computers in their own language. The goal is to simulate human ability to read, write, interpret, and translate text inputs. The ability will make the machine more humane in terms of identifying the topic of conversation, the user's sentiment, sarcasm, and so on.
Speech and text could be used as input and output for NLP applications. Please notice that we have classified Speech or audio as a separate domain for clarity. As a result, part of the NLP only cover text input and output. Text classification, translation, and natural language generation are just a few of the activities that NLP is employed for.
Despite the fact that speech is traditionally classified as part of NLP, the improvements in audio applications demand that it be classified as a separate area. Speech recognition, speech generation, speech to text, and other sound signals such as machine or animal sounds are all examples of audio. "Speech recognition researches and develops methods for recognizing and translating spoken language into writing." We could utilize natural language processing techniques to analyse the text once the audio signal has been translated to text.
Siri, Alexa, and Google Home are the best examples of applications where the user speaks directly to the app and receives an audible response. The goal here is to duplicate humans' abilities to speak and listen. This streamlines machine interaction and makes it more natural than traditional text-based input. Within a few years, it is predicted that voice searches would account for half of all searches.
Other than speech signals, such as environmental sounds, animal calls, and so on, audio applications are not confined to speech. Examples are industrial applications, where devices continuously monitor the numerous sounds produced by essential machines and processes in order to predict any unwelcome breakdowns or process problems. This would also be utilized for preventative maintenance, increasing machine availability.
Personal assistants like ALEXA, machine health monitoring, voice-based translators, and audio redaction are just some of the applications. The voice stream might be divided into several frequencies and targeted with specific frequencies that could be transcribed to text or processed directly depending on the application. This results in a more human-like understanding of natural interactions to generate insights.
Autonomy is required for machines to resemble humans. It refers to the ability to work independently while completing a task. In terms of applications, the autonomous domain emphasizes devices that operate independently for a specified goal. This is an interesting area, particularly because there has been a lot of advancement in the realm of transportation.
When we think of autonomous robots, we immediately think of self-driving cars. Actually, we most likely use multiple domains to develop autonomous devices. Autonomous cars rely on computer vision, natural language processing (NLP), audio, and other computer science and hardware integration to complete a specified task, such as getting from point A to point B without human intervention.
The machine's complexity determines its reliance on other areas or AI domains, but humans' ultimate goal appears to be to construct machines that can work independently as if they co-existed with humanity. Examples of this include AI-powered robot cleaners that can map your home and steer themselves while anticipating your cleaning needs.
As a conclusion, did you like the way in which the give domains of AI were narrated? This is just the tip of the iceberg. More can be explored by properly understanding the concepts in a tone that you understand. Technology is advancing at a very fast pace and you must keep up with it for a better future professionally as well as in personal skills. But for this you need to love what you learn and how you learn. How do you do that?
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