Thanks to the development of technology that is making everything so easy and convenient in our daily lives. If you take a moment to observe, then you will come to know that there are dramatic changes in machines like refrigerators adjusting the temp itself, computers are performing smarter logic itself, cell phones are getting smarter, and many more things are being digitized and getting ahead of themselves. Seeing all of these changes, all I can say is betterment, and that is only possible because of the development of these technologies.
Modern technologies like AI – artificial intelligence, ML- machine learning, DL – deep learning, DS – data science have become the buzzwords that everybody talks about to meet the current market opportunities, but no one fully understands that what it is. All of these terms seem very complicated at first. There is a misconception related to the words as most of people think these things are the same as they directly relate to, Machine Learning or Artificial Intelligence. However, that’s not true.
So, I am writing this blog to washout the illusion by elaborating and stating the significant difference between AI vs. ML vs. DL vs. DP and how they are used in business. Let’s deep dive into these technologies.
Before digging into insightful differences, let’s first start with a quick introduction.
What is Artificial Intelligence?
AI has become one of the widely used technologies all over the world. If we talk about what actually AI is, then Artificial intelligence is the intelligence that is demonstrated by machines. This technology is programmed to think like humans and mimic their actions. Today AI is used to build smart machines that think like human brains and are capable of performing problem solving, decision making tasks.
The core purpose of AI is to impart human intelligence to machines. It specifically focuses on making the devices more intelligent and thinks as well as act like humans. Such devices are being trained to resolve problems and learn in a better way than humans do.
Today this technology has expanded its roots in almost every sector right from healthcare, retail, manufacturing, food & beverages, real-estate to infrastructure.
Self-driving cars and robots are the best examples of AI. At present, AI deals with the following issues;
- General Intelligence
- Knowledge representation
- Learning
- Motion and Manipulation
- Natural Language Processing
- Perception
- Planning
- Reasoning and Problem Solving
- Social Intelligence
AI Market Size
According to Statista– The global artificial intelligence (AI) software market is forecast to grow rapidly in the coming years, reaching around 126 billion U.S. dollars by 2025.
What is Machine Learning?
ML is a subset of AI that exclusively focuses on making predictions based on buyer experiences. It enables the computer to make a data-driven decision rather than explicitly program for carrying out a specific task. The algorithms are designed in a specific way that learns and improves over time and helps the user to make a better decision.
Let’s understand it with an example-
“Hey Siri, can you please explain what Machine Learning is?” I am sure you might have purchased any product from Amazon. So, while browsing the products, it recommends similar products that you might be interested in. You might have also noticed that the combination of products is also being suggested. So, have you ever wondered how this recommendation happens? This is machine learning my friend.
Machine Learning Market to Grow by USD 11.16 Billion During 2020-2024.
Types of machine learning
- Supervised and semi-supervised learning
- Unsupervised learning
Machine Learning deals with the listed below issues:
- Analyze data
- Collect data
- Filter data
- Train algorithms
- Test algorithms
- Use algorithms for future predictions
Common examples are image recognition, improved search engine result, personal assistant, and redefined product recommendation.
Difference between Artificial Intelligence and Machine Learning-
| Artificial Intelligence | Machine Learning | |
|---|---|---|
| Goals | The goal of AI is to simulate human intelligence to perform tasks like problem-solving and decision-making. | The goal of ML is to discover and learn the patterns of data and then make predictions based on those patterns to solve business questions. |
| Subsets | Machine learning and Deep Learning are the two key subsets of AI. | Deep Learning is the primary subset of Machine Learning. |
| Nature | AI is decision making. | ML allows machines to learn new things from data and detect and analyse trends to solve problems. |
| Types | On the basis of capabilities, AI is further divided into 3 categories- known as Weak AI, General AI, Strong AI. | ML divides into Supervised learning, Unsupervised learning and Reinforcement learning. |
| Applications | Applications like Facebook, Instagram, Facebook are using AI. | Applications like Image recognition, speech recognition, traffic prediction and more. |
Now let’s take a look at-
What is Deep Learning?
Deep Learning is an approach to Machine Learning that is recognized via neural networks. A neural network is a set of task-specific algorithms that makes use of deep neural networks that are specifically inspired by the structure and function of the human brain. Deep learning is motivated by theoretical arguments from circuit theory, current knowledge, intuition, and empirical results neuroscience. Deep Learning algorithms can be classified by various types and identified by patterns to provide the desired output when it receives an input.
Deep learning is the best example in this regard; it’s associated with but not interchangeable with the border category of machine learning. Deep learning sits inside machine learning that sits inside artificial intelligence.
Let’s discuss how these technologies differs-
Difference between Machine Learning and Deep Learning
| Machine Learning | Deep Learning | |
|---|---|---|
| Data Dependencies | Superior performance on a small and medium dataset | Performs excellent on a big dataset |
| Hardware dependencies | Performs on a low-end machine | Preferable requires a machine with GPU. Deep Learning performs on a noteworthy matrix multiplication |
| Feature engineering | Carefully understand the features of how it represents the data | Required to understand the specific best functionality that represents the data |
| Execution time | From a few minutes to hours | It requires a time of up to 2-3 weeks. |
| Interpretability | Some algorithms are easy to interpret like, logistic and decision tree. Whereas some are almost impossible like, SVM and XGBoost |
Difficult to impossible |
When it’s ideal to use ML or DL?
| Machine Learning | Deep Learning | |
|---|---|---|
| Training dataset | Small | Large |
| Choose features | Yes | No |
| Number of algorithms | Many | Few |
| Training time | Short | Long |
Moving on to let’s take a look at another prominent technology- Data Science
What is Data Science?
Data science is a multidisciplinary term for a whole set of tools and techniques of data inference and algorithm development to solve complex analytical problems. It makes use of scientific processes, methods, and algorithms to make it happen. Initially, the goal was to identify hidden patterns in raw data to help a business to enhance and expand their profits. The term Data Science became a buzzword when Harvard Business referred to it as “The Sexiest Job of the 21st Century”. The data science life cycle has six different phases:
1. Discovery
2. Data preparation
3. Model planning
4. Model building
5. Communicating results
6. Operationalizing
You Might Like to Read: Data Scientist Vs Data Engineer 2022 – When to Hire Guide?
How is Data Science Associated with AI, ML, and DL?
Data Science is interdisciplinary that can be used in various fields such as machine learning, visualization, statistics and more. It’s a process as well as a method that analyzes and manipulates the data. It also enables us to find the meaning and appropriate information from the large volumes of data. It makes it convenient to use data for making viable business decisions in science, business, technology as well as in politics.
Data Science is interdisciplinary that can be used in various fields such as machine learning, visualization, statistics and more. It’s a process as well as a method that analyzes and manipulates the data. It also enables us to find the meaning and appropriate information from the large volumes of data. It makes it convenient to use data for making viable business decisions in science, business, technology as well as in politics. If you are looking to hire data scientists for your business, we are a one-stop solution for you. At Bacancy, we have a team of certified data scientists who are skilled at algorithmic, data mining, artificial intelligence, machine learning, and statistical tools.
Summary
AI – Artificial Intelligence is a comprehensive term; it is conveying a cognitive ability to a machine. Earlier AI systems were using the pattern to match and expert systems. The core idea behind machine learning is that the machine itself learns and responds without human intervention. Whereas, Deep Learning is the breakthrough innovation in the field of artificial intelligence. As it enables many applications of machine learning by the overall extension in the field of AI. AI is the present and has a bright future with deep learning’s help. If there is enough data to train, then deep learning delivers impressive results, for text translation and image recognition.
I have briefly described Machine Learning vs. Artificial Intelligence vs. Deep Learning vs. Data Science. We have clearly understood what each term is explicitly specified for. At Bacancy Technology, our focus is on developing cutting-edge solutions that help you resolve today’s real-world problems faced by businesses. If you are looking for an organization that can assist you with Machine Learning Development services or can give you in-detail insights on AI Consulting that matters to your business, then feel free to get in touch to leverage our top-of-the-line expertise.
