AI vs. ML vs. DL: What’s the difference?
We all have seen AI do amazing things and know what it can do. AI in our everyday life pretty much surrounds us. Recommending movies on Netflix and music on Spotify, navigating roads in Google Maps, controlling smart home devices and speakers using Alexa to cab booking apps like Uber, we use AI to help make our daily lives easier and improve customer experience. AI has offered many different benefits across multiple industries like healthcare, retail, manufacturing, banking, and many more.
So what is AI? What is ML? What is DL? There are many popular terms around this area, such as Artificial Intelligence, Machine Learning, Deep Learning, Data Science, etc. There has been a lot of confusion around these terms. Knowing and differentiating artificial intelligence (AI) vs. machine learning (ML) vs. deep learning (DL) has now become more critical than ever. Although these terms might be closely related, there are differences between them. See the illustration below.
What is Artificial Intelligence (AI)?
Artificial Intelligence is a broader term that refers to the replication of humans, the way it thinks, work, and function. It has the ability of computers and machines to mimic the human mind’s problem-solving and decision-making capabilities. It anticipates problems and deals with issues as they arise. It performs three cognitive skills just like a human:
- logical reasoning
It enhances human performance and augments people’s capabilities.
What are the types of AI?
AI-based systems fall into four categories:
- Reactive AI- These are systems that only react. They respond to identical situations in the same way, every time. They do not learn from past experiences to make decisions.
- Limited Memory AI- These systems learn from past experiences and build experiential knowledge by observing actions or data. However, as the name suggests, the referenced information is short-lived, not saved in the long-term memory.
- Theory of Mind AI- These systems can understand and remember the emotions of humans, then adjust behavior based on their emotions accordingly and how they affect decision-making.
- Self-aware AI– These systems are designed to be aware of themselves. They understand their internal states, predict other people’s emotions, and act accordingly.
As AI uses computers and machines to mimic problem-solving, machine learning uses computers to mimic human actions, performs predictions, automation, and make decisions as AI applications.
Where is Artificial Intelligence (AI) used?
AI is used in different domains to provide insights into user behavior and recommendations based on past data. For example, Google’s predictive search algorithm used past user data to predict what a user would type next in the search bar. The uses of artificial intelligence fall under the data processing category, which includes:
- Searching within data and optimizing the search to give the most relevant results
- Logic-chains for if-then reasoning that is applied to execute a string of commands based on parameters
- Pattern-detection to identify significant patterns in extensive data set for unique insights
- Applied probabilistic models for predicting future outcomes
What is Machine Learning (ML)?
Machine learning is one way to achieve artificial intelligence that uses statistical methods and algorithms. It enables the machines/computers to learn automatically from their previous experiences and data and allows the program to change its behavior accordingly. The ML systems can automatically learn and improve without explicitly being programmed.
Why is machine learning important?
Machine learning is essential nowadays as it helps to automatically build models quickly and accurately analyze large and complex datasets with access to enormous volume and variety of data and affordability of computational power.
There are multiple use cases where machine learning can be applied to cut costs, mitigate risks, and improve the overall quality of life, including recommending products/services, detecting cybersecurity breaches, and enabling self-driving cars.
What are the types of machine learning?
The three different types of machine learning algorithms are as follows:
- Supervised Learning- Uses labeled datasets to train or supervise the model to classify the data and accurately predict outcomes. The model can measure its accuracy and learn over time using labeled inputs and outputs.
- Unsupervised Learning- Uses machine learning algorithms to analyze and cluster unlabeled data sets. These algorithms discover hidden patterns in data without the need for human intervention.
- Reinforcement Learning- Train machine learning models to find an optimal solution to maximize reward in a particular situation. This algorithm finds the best possible behavior or path to a specific situation.
ML provides many different techniques such as Decision trees, Random Forests, Support Vector Machines, K Means Clustering, etc., to make the computer learn. ML models are used in various use cases such as demand forecasting sales of products, predicting customer behavior, gauging customer sentiments from their social media behavior.
What is Deep Learning (DL)?
Deep learning is a subset of AI and machine learning inspired by the brain’s structure and the function called artificial neural networks. These neural networks attempt to simulate the behavior of the human brain, allowing it to learn from large amounts of data. Deep Learning systems help a machine learning model filter the input data through layers to predict and classify information. While a neural network with a single layer can still make approximate predictions, additional hidden layers can help to optimize and refine for accuracy. It drives many AI applications and services that perform analytical and physical tasks without human intervention and improves automation.
How does deep learning work?
Deep learning networks learn by discovering intricate structures in the data they experience. By building computational models composed of multiple processing layers, the networks can create multiple levels of abstraction to represent the data.
For example, a deep learning model known as a convolutional neural network can be trained using large numbers (as in millions) of images, such as those containing cars. This type of neural network typically learns from the pixels contained in the images it acquires. It can classify groups of pixels that represent a car’s features, with groups of features such as headlights, tyres, and rear mirrors indicating the presence of a car in an image.
One of the significant differences between deep learning and machine learning is how data is presented to the machine. Machine learning algorithms usually require structured data (a specific set of features to identify the car in the image). In contrast, deep learning networks work on multiple layers of artificial neural networks (a large number of car images and the system can autonomously learn the features that represent a car).
Where are we with AI today?
With AI, machine learning, and deep learning techniques, many industries such as manufacturing, fintech, e-commerce and retail, telecom, transportation, etc., try to solve actual problems and get answers in real-time. AI gives you the ability to sift through all your data and make logical connections between past actions and different criteria.
According to IDC, 90% of enterprises will insert AI into their processes and products. It is also expected that in 2022, traditional businesses will adopt an AI-first approach to platform and digital transformation, says Forrester research. The more AI inside, the more enterprises can shrink the latency between insights, decisions, and results.
In fact, in the coming years, AI will be democratized and become accessible to everyone across an organization. According to Gartner’s research, 50% of enterprises will devise AI orchestration platforms to operationalize AI, and 65% of application development will be done on low-code/no-code AI platforms.
Artificial intelligence has many applications in the world that are changing the face of technology. While creating an AI system as intelligent as humans remains a dream, ML and DL algorithms already allow the computer to outperform us in many areas such as computations, pattern recognition, object detection, and anomaly detection.
Do you agree with the points mentioned in the blog? Is there any specific difference that we’ve missed? If yes, do let me know your comments in the section below.
Eliminate AI Model bias with HyperSense AI Studio
Payal is a Product Marketing Specialist at Subex, who covers Augmented Analytics. In her current role, she focuses on CIO challenges with data management, and potential solutions to these challenges. She is a postgraduate in management from Symbiosis Institute of Digital and Telecom Management, with analytics as her majors, and has prior engineering experience in the Telecom industry. She enjoys reading and authoring content at the intersection of analytics and technology.