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WHAT IS DEEP LEARNING, WITH REFERENCE TO ARTIFICIAL INTELLIGENCE (AI)

What Is Deep Learning, With Reference To Artificial Intelligence (AI)

WHAT IS DEEP LEARNING, WITH REFERENCE TO ARTIFICIAL INTELLIGENCE (AI)

Excerpt: John McCarthy (Computer and Cognitive Scientist) is behind this concept of brilliant technological advancement. He is often called the father of AI. The human race has always been amazed and obsessed with hi-tech technology. After mobile phones to computers in today’s era of gen z, Artificial intelligence (AI) has become a part of our daily lives. 

Table of contents: 

  • Introduction
  • Role and impact of artificial intelligence in today’s era
  • What is deep learning
  • Machine Learning
  • Categories of Machine Learning
  • Most important Neutral Networks and Algorithms
  • Deep learning vs machine learning
  • Top 5 uses of deep learning with artificial intelligence
  • Benefits of using deep learning
  • Future of deep learning and AI
  • Conclusion

Introduction: 

Everything you do in a day, whether it is personalized shopping, online fraud prevention, administrative tasks, voice assistants (Siri, Alexa), autonomous vehicles, etc., is powered by the concept of Artificial Intelligence (AI).  

Also, the biggest movie of the year 2022, “A V A T A R,” was made with the help of Artificial Intelligence.

In simple words, Artificial Intelligence can be described as advanced technology involving computers that can simulate human intelligence and perform tasks assigned to them. It assists the human race in some aspects of life, from gaming – entertainment to administrative-educational advancements. AI can perform various tasks assigned to it, but in some functions, it even matches human intelligence these days. And, of course, there are debates worldwide that AI may someday overpower the human race. But, we know that somewhere, AI is an amazing and lot more interesting technological achievement than we have achieved till now.

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Briefly Explain the Role and Impact of Artificial Intelligence in Today’s era. 

● Making use of data science and artificial intelligence is crucial, and its potential growth trajectory is unbounded

● A crucial aspect in business in many ways and will continue to be global communication and networking

● Banking online driving aids, including route planning, traffic updates, and weather information shopping for leisure time like Netflix and Amazon for movies and television shows. Today, AI permeates every part of our online personal and professional lives

● When we turn on our gadgets, we immediately connect to AI features like image recognition and facial ID email clients via social media. Use Google to look up voice-activated devices like Apple’s Siri and Amazon’s Alexa

● Healthcare (for example, several uses in genetic sequencing research, the treatment of tumors, and the creation of instruments to expedite diagnostics of conditions like Alzheimer’s disease)

● Academia (major universities in AI research include MIT, Stanford, Harvard, and Cambridge) (leading universities in AI research include MIT, Stanford, Harvard, and Cambridge)

● Robotics and autonomous/self-driving vehicles (such as Waymo, Nissan, and Renault) 

● Billion-dollar industry of gaming and VR reality 

Now let’s go through the hi-tech and most interesting concept of Deep Learning. 

What is Deep Learning?

According to Oxford languages, Deep learning is “a type of machine learning based on artificial neural networks in which multiple layers of processing are used to extract progressively higher level features from data.” 

Now to breakdown this complex definition into a simpler form, let’s define 

Machine learning first: 

In machine learning, algorithms are trained to learn from data and make predictions or choices based on that data. Through the identification of patterns and links in data and the use of those findings to guide deliberations, it enables computers to learn and enhance their performance without human input.

And it is categorized into four categories:

1) Supervised Learning 

2) Unsupervised Learning 

3) Reinforcement Learning 

4) Semi-supervised learning 

Therefore, it is easy to understand now that “the structure and operation of the brain, especially the neural networks that make up the brain, are models for the field of deep learning, a branch of machine learning. It involves using a large dataset to train artificial neural networks so they can learn and decide for themselves“. In simple words, Deep learning is a field of machine learning that helps form neural networks like a human brain for artificial intelligence using algorithms.  

Complex concepts can be built by computer using simpler ideas with Deep learning. 

Neural Networks – Artificial neurons, often called nodes, make up a neural network organized similarly to the human brain. These nodes are arranged in three layers close to one another:

● The output layer

● The hidden layer(s)

● The input layer

Most Important Neural Network types or Algorithms 

–   Multilayer Perceptron (MLP):

MLPs are a feedforward neural network with many layers of artificial neurons with activation capabilities. MLPs are composed of completely coupled input and output layers. They can be used to create speech, picture, and machine translation software because they have the same number of input and output layers but could also have numerous hidden levels.

Perfect to start learning with. 

–   Convolutional Neural Networks (CNN):

CNNs have multiple layers that process and extract features from data

It is often referred to as ConvNets, has several layers, and is mostly used for object detection and image processing. CNN is used in the analysis of images received from satellites, detecting anomalies, and processing medical images.

–   Recurrent Neural Networks (RNN):

The outputs from the LSTM (Long Short Term Memory Networks) can be sent as inputs to the current phase thanks to RNNs’ connections that form guided cycles.

Due to its internal memory, the LSTM’s output can remember prior inputs and is used as an input in the current phase. Natural language processing, time series analysis, handwriting recognition, and machine translation are all common applications for RNNs.

Here, at time t, the input receives feed from the output at time t-1. Similarly, the work at time t provides the information at time t+1. RNNs can process inputs of any length. 

Deep learning v/s Machine learning. 

Machine learning and deep learning are methods for teaching artificially intelligent (AI) systems to identify patterns and make judgments. But there are a few recognizable variations between these two:

Abstraction level: Artificial neural networks with numerous layers of interconnected nodes are trained as part of deep learning. As incoming data moves through these layers, it is processed and transformed, and at each layer, more complex features are extracted. Contrarily, machine learning can utilize several techniques to learn from data and is not always dependent on neural networks.

Standards: Since the neural network learns to recognize patterns and characteristics in the data, deep learning often requires a huge amount of labeled data to train its models. Inversely, machine learning frequently uses smaller amounts of data and can also include expert knowledge in its decision-making process through rules and heuristics.

Computing criteria: Since deep learning models must analyze a lot of data and learn numerous levels of abstraction, their training can be computationally demanding. Machine learning models require fewer computer resources depending on the complexity of the model and the volume of data it is trained on.

Deep learning is machine learning that performs various tasks, especially speech and picture recognition. Deep learning is one of many different methods and techniques that are included in the larger topic of machine learning.

Top 5 uses of Deep learning with AI?

❖   Computer vision: Deep learning algorithms can accurately recognize and classify objects in images and videos.

❖   Speech recognition: Deep learning algorithms can transcribe and translate spoken language into text, enabling applications such as voice-to-text dictation and language translation.

❖   Natural language processing: Deep learning algorithms can be used to understand and generate human-like text, enabling applications such as language translation and chatbots.

❖   Fraud detection: Deep learning algorithms can identify patterns in transaction data that may indicate fraudulent activity.

❖   Stock price prediction: Deep learning algorithms can analyze financial data and predict future stock prices.

Overall, deep learning has the potential to revolutionize a wide range of industries by enabling computers to learn and make intelligent decisions on their own without the need for explicit programming.

Benefits of Using Deep learning

High accuracy: Deep learning algorithms can learn and make sense of complex patterns and relationships in data, resulting in increased accuracy for tasks such as image and speech recognition.

Automation: Deep learning algorithms can learn and improve their performance without human intervention, enabling the automation of tasks such as data analysis and decision-making.

Scalability: Deep learning algorithms can be trained on large datasets, making them well-suited for handling large data.

And many more on the way. Keep counting as Deep learning techniques are getting better day by day. 

Future of AI and Deep learning 

Deep learning and artificial intelligence (AI) are topics that are quickly developing and have the potential to change a variety of applications and industries. There are a lot of interesting advancements coming our way, and it’s hard to say what the future will hold. But some of the other possibilities are listed here; 

  1. Health Industry: AI and deep learning techniques can be used to analyze medical images, predict patient outcomes, and personalize treatment plans.
  2. Transformation in vehicles: Self-driving cars and other autonomous vehicles are making significant progress and are likely to become more widespread in the coming years.
  3. Robotics and Automation: AI and deep learning can improve robots’ capabilities, making them more versatile and able to perform a wider range of tasks.
  4. Natural language processing: AI and deep learning are making it possible for computers to understand and generate human-like speech and text, which could have a huge impact on customer service, language translation, and other applications.
  5. Predictions through analysis: AI and deep learning can be used to analyze large amounts of data and make accurate predictions about future outcomes, which could be useful in fields such as finance and marketing.

Conclusion:

Soon, we are stepping into a faster and smarter version of Artificial Intelligence and Deep learning, which will bring a new era in the history of human civilization. In this article, the concept of Deep learning is briefly explained in parallel to the other aspects like Artificial Intelligence and machine learning for the best understanding of this great superpower of Deep learning that will develop simulations of a human-like virtual world of the future.

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WHAT IS DEEP LEARNING, WITH REFERENCE TO ARTIFICIAL INTELLIGENCE (AI)