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Basic Use Cases of AI, ML, Deep Learning and Internet of Things by@emily-daniel

Basic Use Cases of AI, ML, Deep Learning and Internet of Things

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Emily Daniel HackerNoon profile picture

Emily Daniel

Emily is a tech writer, with expertise in entrepreneurship, & innovative technology algorithms.

The world’s most influential companies and technologies are influenced by the efficiency of Artificial intelligence and similar technologies. Whether it is Facebook or Amazon, Google or Microsoft, all firms are harnessing AI techniques and algorithms to introduce high-level performance and streamlined operations. 

Not only Artificial Intelligence but its other technology variants are also utilized by the IT industry. These are Machine Learning (ML) and Deep Learning (DL). Extending them more, these technologies are intersected with others or we should say that they rely on other technologies for application development. Internet of Things (IoT), a growing technology is used in combination with the aforementioned ones to set a foundation of useful applications in this digital world. 

Artificial Intelligence

In 1956, AI was coined by John McCarthy. Artificial Intelligence corresponds to the replacement of human intelligence by machines. The tasks and real-world problems that are done by humans are performed by machines and human effort is reduced. In Artificial Intelligence, machines need to learn, understand and respond accordingly. For this, machine training is done in such a way that they will be able to plan, recognize the sounds and objects, learn the language, and be able to respond in a particular situation or exception. 

AI can be categorized into two; one is General AI and the other is narrow AI. in general AI, all the qualities and characteristics of humans are present according to which machines respond. They think like humans, they act like them, they are able to make decisions in real-time. Narrow AI corresponds to faces of human intelligence, machines that learn to identify the images of humans lie in the category of narrow AI. 

Machine Learning

Machine learning is an application of AI that enables the system to learn automatically and improve the experience. This is done without explicitly programming the system. In machine learning, the machine learns on its own, computer programs are developed such that data is accessed from it, they earn that data for themselves. However, Artificial intelligence models can be developed without AI, but they won’t be efficient or generic as for it, explicitly million lines code will be required and the system would need to be programmed using complex decision trees and programming rules. 

To eliminate the overhead of hard-coding software in which explicitly all instructions need to provide, machine learning helps to teach the instructions to the machine itself. Machine learning, adjust the weights itself and give results accordingly. 

For example, machine learning is used to make improvements in the field of computer vision in which machine is enabled to recognize an image, object or video. For this, millions of sample images are collected and objects are tagged in them algorithms are used to build AI models and they are tested against various inputs. In this way, the accuracy of the model is checked.

Deep Learning

An approach to machine learning. Deep learning involves Artificial Neural networks just like the working of the brain in which neurons are connected with each other in such a way that they make a network. In deep learning, other approaches are logical inductive programming, decision trees, reinforcement, clustering, Bayesian networks, etc. However, deep learning is used to mimic the biological characteristics and structure of the brain, the way neurons are connected and how they transfer information and do prediction. In Neural Networks, there are discrete connections and layers to other “neurons”. Among those, each layer learns specific features, trained, sets weights and gives output accordingly. 

What role does the Internet of Things play?

Artificial intelligence is highly connected with the Internet of Things technology. Just the way our body used to collect the sensory inputs such as touch, sight, and sound, all the information passes through the brain, the brain interprets it and asks the user to take proper actions for that time in the form of body movements. 

Similarly, all connected sensors make an IoT environment. Data is collected by the IoT devices, using Artificial Intelligence techniques and algorithms, the data is filtered, processed and based on it, neural networks learn and act accordingly. Both Machine learning and deep learning have collectively led to huge leaps for the development of AI in recent years.

Machine learning and deep learning require huge data to operate. The data is first collected and then split into a training data set and testing data set. The ratio of the training dataset should be larger than the testing dataset. To train the models and cover entire perspectives, an abundant amount of data is required. Using IoT, millions of devices are collected that collect data and store them in one place. This data is then used to train AI models. 

More the data, the more efficient will be the model. 

Application of Artificial Intelligence

Artificial intelligence is helping various industries with their remarkable applications and use-cases. Real-world problems are better solved with the help of Artificial intelligence algorithms and techniques. Following are some of the applications of Artificial Intelligence:

Email filtering: Artificial intelligence is used to filter out the incoming emails and categorize them based on the content if they are spam or not. With a huge amount of emails, the system is trained in such a way that they learn how to differentiate the spam emails from non-spam. 

Personalization: To personalize the online experience, artificial intelligence is used. AI models learn from your browsing history and interests and show you the ads accordingly. Services like Netflix and amazon learn and show relevant information.

Fraud detection: Banks and financial institutions use artificial intelligence techniques and algorithms to identify the suspicious entities, fraud and activities in the financial system. Moreover, authorized access is ensured by identifying entities using facial recognition technology in which Artificial intelligence is used. 

Speech recognition: speech recognition functions are deployed using artificial intelligence that detects the vocals of individuals and identifies a person. Most of the applications are now available that have speech recognition feature such as Siri and Alexa. 

Emily Daniel HackerNoon profile picture
by Emily Daniel @emily-daniel.Emily is a tech writer, with expertise in entrepreneurship, & innovative technology algorithms.
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