Machine Learning VS AI
Machine learning and AI are two of the most popular technologies ruling most industries. Though most sectors are on a mission to make the most of the technology, many of them fail to understand the differences. Though there are only minute differences between machine learning vs AI, it is important to understand them.
Introduction
Comparing machine learning vs AI, both are equally accurate and the appropriate technologies to analyze, assess, and create bulk data within a few seconds. Though the terms machine learning and AI are popular among users, not many of us know the differences.
In fact, according to a report by Statista, the global AI market is expected to reach $1.81 trillion by 2030, driven largely by advancements in machine learning and deep learning technologies. Moreover, Gartner predicts that by 2026, 80% of emerging technologies will have AI foundations.
This article explains the terminologies in detail and also highlights the differences between machine learning vs AI. In addition, we shall also discuss everything you should know about both the technologies and everything you should know about them.
We’ll start by understanding the differences in these terminologies.
What is Artificial Intelligence(AI)?
Artificial intelligence is the technology that helps extract bulk data from machines in different formats (audio, visual, and textual) in an accurate manner using human intelligence. AI experts in technology train computer systems to think and respond according to human requirements.
What is Machine Learning?
Machine learning enables computer systems to learn from data and produce outputs based on human requirements. These outputs include predictive data models, images, sorting of data, and evaluation of bulk data.
Artificial Intelligence vs Machine Learning
Now that you’ve got a clear understanding of both terms, we shall see some of the differences in machine learning vs AI.
| Feature | Artificial Intelligence (AI) | Machine Learning (ML) |
|---|---|---|
| Definition | Simulates human intelligence to solve problems | Enables systems to learn from data and improve over time |
| Goal | To create smart systems that can perform human tasks | To learn from data and make accurate predictions |
| Approach | Decision-making and reasoning-based | Data-driven learning and pattern recognition |
| Dependency | Less dependent on structured data | Highly dependent on large amounts of data |
| Flexibility | Can work with minimal data or rules | Requires detailed datasets and feature training |
| Development Complexity | More complex due to broad functionality | Comparatively simpler and more focused |
What are the Similarities Between AI and Machine Learning?
Here are some of the similarities between machine learning and AI:
- Both AI and ML rely on human commands for input and operation, though ML needs more detailed instructions.
- These technologies are designed to meet human needs—AI offers direct answers, while ML simplifies complex data and makes data easier to analyze.
- Both provide similar feedback by sourcing information from large datasets, though AI often gives more user-focused results.
- AI and ML need initial data to function. However, AI typically requires less monitoring than ML, which demands ongoing verification.
Benefits of using AI and ML Together
Here are some of the benefits of using AI and ML together:
Real World ML Applications
Machine Learning is reshaping various industries by enabling data-driven decisions, automation, and intelligent systems that adapt over time. It plays a vital role in modern innovation detailed mention below.
ML in Healthcare
✡ Use of ML algorithms to predict the conditions of patients and suggest treatments.
✡ Patient history collection and documentation through ML algorithms.
✡ Drug discovery through research using ML algorithms
Machine Learning enhances diagnostics, predicts disease, and personalizes treatment. In fact, AI in healthcare is expected to reach $187 billion by 2030 (Statista)
ML in Transport
✡ Machine learning algorithms predict speed and routes based on the locations.
✡ Suggest shortcuts and routes for quick and easy travel.
✡ Helps in pre planning routes to avoid congestion.
The global AI in transportation market is projected to grow from USD 1.21 billion in 2017 to USD 10.30 billion by 2030, reflecting a CAGR of 17.87%
ML in Finance
✡ Machine learning helps in quick fraud detection and elimination.
✡ Quick credit score evaluation with less data.
✡ Study the marketing and trading trends.
ML in E-commerce
✡ ML algorithms help in making purchase predictions.
✡ Analysis of customer behaviour, purchase patterns, and history.
✡ Reduce spam or fraud by installing ML software.
ML in Other Fields
✡ ML enhances face, feature, and iris recognition.
✡ ML algorithms in cyber fields help reduce cyberattacks.
✡ Students can get customized lesson plans depending on their learning patterns.
Deep Learning vs Machine Learning: What’s the Difference?
Here’s another set of terms that look similar but are different from each other. There are quite a few differences between deep learning vs machine learning. Read ahead to understand them:
| Points of Difference | Deep Learning | Machine Learning |
|---|---|---|
| Complexity | Deep learning creates data sets based on human commands. However, they are convertible only into certain formats, like images and texts. | Machine learning is more complex. This technology aims to simplify large data sets into usable formats. |
| Data usage | The more the data, the better the output. | Requires fewer inputs but produces more outputs. |
| Processing capacity | Needs more input and computational commands to generate written output. | Runs based on graphics processing units to generate images, videos, and visual data. |
| Interpretability | Engineers need to know computations and layered structures to interpret data. | Can be understood and decoded without any specific training. |
| Applications | Used for processes that require more research and bulk data, such as for medical research, self-driving cars, and voice recognition | Used to eliminate spam or fraud. It is mostly used in email filtering, internet searches, and predictive texts. |
These are some of the main differences between machine learning vs deep learning. It is important to understand that deep learning is also a sub-category of machine learning. Also, both of these are evolving technologies and require expert practical and theoretical knowledge. Acquiring a master’s in AI or higher helps to work in these fields.
If you’re interested in pursuing a bachelor’s in AI or ML, we guide you to top universities and colleges offering the best training and placement support. With AI-related job openings projected 170 Million new jobs by 2030, 92M roles displaced globally, finding the right program is crucial for future success in this high-demand field.
FAQs
Is machine learning and AI the same?
No, artificial intelligence is the process of making technology think from a futuristic human perspective. On the other hand, machine learning is a part of AI where data sets are derived using certain algorithms and formulas.
What is an AI that is not machine learning?
While machine learning is a part of AI, AI is the base technology. It operates based on certain algorithms, formulas, languages, and a specific set of data.
Can machine learning exist without AI?
No, machine learning is a part of AI. However, AI is an independent technology. Most of the commands and prompts of ML are conveyed to the software in an AI language.
Does AI always use machine learning?
No. However, machine learning uses AI prompts, techniques, and technologies to extract specific data from sources.
Can AI replace machine learning?
No, machine learning cannot exist without AI because it is a subfield of AI. While AI includes various approaches, machine learning specifically focuses on enabling machines to learn from data, making it a part of the broader AI system.
