Artificial Intelligence and Machine Learning, today, are taking the commanding position in almost all the important spheres of life. It may be the business or education, be it research or parliamentary electoral system. We will make the reader aware of the basics of “What is Artificial Intelligence and Machine Learning?”.
What is Artificial Intelligence (AI)?
While answering the question “What is Artificial Intelligence and Machine Learning?”, at first we have to understand the basic concept of Artificial Intelligence. Artificial Intelligence (AI) gives machines the capacity to learn from experience, accommodate new inputs, and perform human-like tasks. Artificial Intelligence performs its tasks by assimilating large amounts of data with fast, repetitious processing with the help of intelligent algorithms, engaging the software to learn automatically by recognizing patterns in the data. Forms of AI in use today include machine learning, data analytics, robotics, digital assistants, chatbots, etc.
What is Machine Learning(ML)?
Machine Learning is a special kind of learning in which machines can learn on their own without being exactly and vividly programmed. Machine Learning is nothing but an application of Artificial Intelligence. AI provides a system with the ability to automatically learn and improve from experience. It generates a program by integrating input and output of that program. Machine Learning (ML) uses neural networks and statistical analysis to find ‘hidden information’ in data. It automates building analytical models.
What is Natural Language Processing (NLP)?
It is a sub-field of artificial intelligence. It is concerned with the interactions between computer and natural human languages. It can be defined as the ability of computers to process, analyze, understand a large amount of natural language data and generate human language, including speech.
What is Deep Learning (DL)?
It is a variation of machine learning – it involves the ability of machines to develop self-learning capabilities from large amounts of data using huge neural networks with many layers of processing units. Common applications of Deep Learning include image and speech recognition.
Though artificial intelligence is the most popular subject today, its study was started in the 1950s. Artificial intelligence and machine learning are the terms used in computer science. The terms are inter-connected but still have separate domains. Artificial intelligence signifies intelligence having artificial character. That means intelligence is not natural but developed by human beings.
Definition of Artificial Intelligence:
If we go through the textbooks of Artificial Intelligence for definition, we get the subject as the study of “intelligent agents”. It denotes that devices which perceive its environment and take actions in favor of maximization of the chance of successfully achieving its goals. The term “artificial intelligence” is, sometimes, used to narrate machines (or computers) that imitate “cognitive” functions as the human mind, such as “learning” and “problem-solving”. Though academic studies on Artificial intelligence was started in the year 1955, it was not a case of gradual advancement in the related field. A lot of ups and downs happened in the last 65 years. Still today, there is no widely accepted definition of Artificial intelligence. There are many sub-fields of Artificial intelligence, like robotics, Machine Learning, data analytics, etc. Artificial intelligence is not a system itself. It is to be imparted in a system (maybe in Computer or any other system)
Artificial Intelligence is conceived as computer systems, which have similarities with the human mind, though a computer and a human mind cannot be identical in all aspects. As intelligence signifies the mental capability produced by the human brain, the most dependable way to reproduce it is to sincerely simulate the brain. This idea was manifested as: “the ultimate goals of AI and neuroscience are quite similar” and “At a more fundamental level, any computational model of learning must ultimately be grounded in the brain’s biological neural networks”.
How AI works?
Is it a fact that Artificial Intelligence is trying to do exactly what the human brain does? It is a very important question. Is AI equivalent to brain modeling or emulation? The question can be posed in another way: whether “mind” can be completely reduced into “brain.” If the answer is ‘no’, then a model of the brain and a model of the mind are not the same, and the indicating essence of intelligence is related more to mind than to the brain.
Current Machine Learning Studies
In the current machine learning studies, “learning” has been commonly specified as the process of using a meta-algorithm (learning algorithm) to produce an object-level algorithm (model for a domain problem) according to the training data.
Some important Uses of Machine learning
Machine learning is a popular term for today’s technology. We are being habituated with machine learning in our daily life even without knowing it, such as Google Maps, different language translator, Alexa, etc. Some of the most significant real-world applications of Machine Learning are given here:
1. Speech Recognition: For Google application, Search by voice
– is actually speech recognition. Speech recognition converts voice instructions into text, and it is also known as “Speech to text”, or “Computer speech recognition.”Some popular applications of speech recognition technology are Alexa, Siri, and Cortana.
2. Traffic prediction: While visiting a new place when we take help of Google Maps, it gives us the possible correct path with the shortest route and a prediction of traffic conditions: whether traffic is cleared, slow-moving, or heavily congested. Everyone who uses Google Maps, actually helps this app to perform even better. It takes information from the user and stores it in the database to develop the performance.
3. Image Recognition: Used to identify objects, persons, places, digital images, etc. The popular use of image recognition and face detection is Automatic friend tagging suggestions
4. Product recommendation: Machine learning is widely used by various e-commerce and entertainment companies such as Amazon, Netflix, etc., for product recommendation to the user. Whenever we search for some product on Amazon, we started getting an advertisement for the same product. Google understands the user interest using various machine learning algorithms and suggests the product as per customer interest.
5. Virtual Personal Assistants: When you sit in front of a computer after the office-hour, usually a list of pending tasks emerges. A few work-related assignments, a meeting you need to set, important officials to meet, emails to send, somebody in the office celebrating a birthday.etc. While the digital age seems to add to our daily tasks and challenge our concentration, it also delivers a solution: the smart virtual assistant. Virtual Personal Assistant is ready to manage all my tasks/problems systematically, one by one. Siri, Alexa, Google Now are some of the popular examples of virtual personal assistants.
And, Samsung’s Bixby—the latest Virtual assistant. What are the capabilities of VPA? These are: (a) The ability to understand natural language and learn the dialect, (b) The ability to maximize the smartphone’s capabilities, (c) The ability to adapt to your lifestyle and requirements.
These assistants record our voice instructions, send it over the server on a cloud, and decode it using ML algorithms and act accordingly.
6. Self-driving cars: One of the most exciting applications of machine learning is self-driving cars.
7. Email Spam and Malware Filtering: Whenever you receive a new email, it is filtered automatically as normal, and spam. What is operating behind this? It is Machine learning technology. Machine Learning algorithms like Multi-Layer Perceptron, C 4.5 Decision Tree Induction and Naïve Bayes classifier are used for email spam filtering and malware detection. Over 400,000 malware is detected every day and each piece of code is 95–98% similar to its previous versions. The system security programs, powered by machine learning, understand the coding pattern. Therefore, they detect new malware with a 2–5% variation and offer protection against them.
8. Videos Surveillance: It is the system by which it is possible to track the unusual behavior of people. When that kind of incidents is reported to be true, those help to improve the surveillance services. This becomes possible because Machine Learning is operating at the backend.
9. Stock Market trading: Machine learning is widely used in stock market trading.
10. Medical Diagnosis: In medical science, machine learning is used for disease diagnoses.
11. Automatic Language Translation: Machine Learning helps us by converting the text into our known languages. Google’s GNMT (Google Neural Machine Translation) provides this feature, which is a Neural Machine Learning that translates the text into our familiar language.
12. Online Transportation Networks: Booking a cab, the app estimates the price of the ride.
13. Social Media Service: personalizing the news feed to better ads targeting, social media platforms are utilizing machine learning for their own and user benefits.
14. Online Fraud Detection.
15. Online Customer Support
16. Search Engine Result Refining: Google and other search engines use Machine Learning to improve technique to extract useful information from images and videos.
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