Exploring the Layers of Intelligence: From Artificial to Deep Neural Networks
Even though artificial intelligence, machine learning, deep learning, and neural networks are all buzzwords in the realm of technology, there are substantial misconceptions about what these terms mean. Within the context of this blog, we are going to get an understanding of the distinctions that exist between artificial intelligence, machine learning, deep learning, and neural networks.
Comparing AI, Machine Learning, Deep Learning, and Neural Networks
Amidst the turbulence of technological jargon that surrounds artificial intelligence (AI), machine learning, deep learning, and neural networks, have you ever felt a little scattered? Let’s understand each terminology in detail to understand what differentiates them.
What is Artificial Intelligence?
Artificial intelligence (AI) may be thought of as a computer system that is capable of doing activities that need human intelligence. It is analogous to the brainpower that computers possess since it enables them to learn from data and carry out tasks independently without the need for explicit programming. Today almost 35% of global businesses are using AI. Smart Cars and Google’s predictive search algorithms are examples of Artificial Intelligence.
What is Machine Learning?
One subfield of artificial intelligence is known as machine learning, and its primary objective is to create algorithms that enable computers to carry out particular tasks without being explicitly taught to do so. This indicates that machine learning algorithms can learn from data, spot patterns, and make judgments with minimum interaction from humans. Email automation and spam filtering, Face recognition systems are examples of it.
Types of Machine Learning:
| Type | Description |
| Supervised Learning | It entails training a model using data that has been labeled, in which the algorithm learns to turn inputs into outputs with increasing accuracy. |
| Unsupervised Learning | The model is given unlabeled data and tasked with finding patterns and relationships. |
| Reinforcement Learning | The algorithm learns by trial and error, receiving rewards or penalties based on its actions |
What is Deep Learning?
Deep learning is a subfield of ML that uses artificial neural networks to replicate how the human brain organizes and processes information. In the same way that our brains process information. These networks are made up of layers of nodes that are interconnected and are called neurons. Each neuron processes and transforms input to recognize patterns. Deep learning is highly effective at solving increasingly difficult issues, such as audio recognition, picture recognition, natural language processing, and other similar tasks. Today, the market size of deep learning is expected to reach around $71 billion by 2027
What are Neural Networks?
Neural Networks is an essential component of AI that mimics human actions to process the information. They are generally the subset of ML and form the backbone of DL.
The Basics of Neural Networks
| Layer | Function |
| Input Layer | Receives the data for processing. |
| Hidden Layers | Layers where computations take place, transforming the input into something that the network can use |
| Output Layer | Generates the final results or predictions based on the processed data. |
Differentiating AI, Machine Learning, Deep Learning, and Neural Networks
Let’s look at this table to understand the differences between AI, Machine Learning, Deep Learning, and Neural Networks:
| TERM | DEFINITION | RELATIONSHIP |
| Artificial Intelligence (AI) | The broader umbrella term encompasses systems that can mimic human intelligence to perform tasks. | N/A |
| Machine Learning | A subset of AI focused on algorithms that learn and make decisions from data. | Under the umbrella of AI |
| Deep Learning | A subfield of machine learning that uses neural networks to simulate human-like decision-making. | Subset of Machine Learning |
| Neural Networks | The fundamental components of deep learning systems, are inspired by the structure and function of the human brain. | Core components of Deep Learning systems |
Certifications in AI and Machine Learning
Acquiring the best AI certifications from reputed companies is essential for those who are interested in enhancing their knowledge and advancement opportunities in their careers. The top AI ML Certification programs by the United States Artificial Intelligence Institute (USAII®), Harvard University, and Stanford University are particularly noteworthy examples of distinguished qualifications. The United States Artificial Intelligence Institute (USAII®) provides the Certified Artificial Intelligence Engineer (CAIE™) certification for those who are interested in a career in Artificial Intelligence. The purpose of these “Top AI ML Certification programs” is to authenticate the abilities and knowledge of AI professionals, so guaranteeing that they are prepared to face the difficulties that come with an artificial intelligence career.
Let’s understand CAIE™ certification in detail –
| Feature | Details |
| Certification Name | Certified Artificial Intelligence Engineer (CAIE™) |
| Instituting Organization | United States Artificial Intelligence Institute (USAII®) |
| Program Duration | 4-25 Weeks |
| Hours of Learning | 8-10 Hours per Week |
| Program Format | Self-Paced |
| Payment Options | Flexible (Pay in Full or Installments) |
| Program Fee | US $691 (All Inclusive) |
| Eligibility Criteria | ● Associate Degree/Diploma in any discipline, preferably STEM, with 2 years of programming experience. ● Bachelor’s Degree (completed or pursuing) in any discipline, preferably STEM, with basic programming proficiency. |
| Curriculum Highlights | ● Artificial Intelligence ● Essential Machine Learning ● Deep Learning ● Deep Reinforcement Learning ● Computer Vision and GANs ● Natural Language Processing ● Reinforcement Learning |
| What You Gain | ● Deep understanding of AI and ML. ● Skills to contribute effectively to AI projects. ● Ability to run machine learning tests, select datasets, transform data science prototypes, etc. |
| Certification Benefits | ● Distinguishes you in the job market as an AI Engineer. ● Enhances AI skills and efficiency. ● Provides a robust foundation for a career in AI. |
| Resources Provided | ● Study books, eLearning materials, and workshops. ● Hands-on videos with Practice Code. ● Sharable digital badge. |
Bottom Line
Currently, the average salary for an artificial intelligence engineer in the United States is roughly $1,41,142 per year (Source: Glassdoor). Therefore, obtaining certifications such as the Certified AI Engineer (CAIE™) provide you with a wealth of career opportunities to begin in the AI industry. AI Experts and those who are interested in pursuing a career in artificial intelligence should keep in mind that the field of artificial intelligence and its subsets is large and constantly evolving.