Often, individuals in the technology world use the terms Artificial Intelligence and Machine Learning interchangeably. However, while AI and ML are closely related, they’re different in almost every aspect. Want to know the difference between AI and Machine Learning or how they’re different? Read along.
The internet is full of definitions that fail to define AI and ML properly, which is why the confusions arise. However, in this blog, you’ll find a detailed differentiation of AI vs. Machine Learning. So much so that even a non-tech-savvy person will be able to differentiate these terms.
In addition, you’ll also find the advantages or use cases/applications of AI and ML in different business domains, which will further help deepen your understanding. So, read in full.
Some individuals think of AI as creating a conscious sentient being via programming, which is often termed artificial general Intelligence. On the other hand, data scientists use AI at a practical scale for solving real-world problems such as customer service, data analysis, manufacturing, etc.
So, we can conclude that Artificial Intelligence is an umbrella term for technology that helps individuals autonomously solve problems by simulating human Intelligence. And this is done using algorithms that analyze input data and produce outputs.
Take a chatbot, for example. Whenever a customer types in his/her query, the bot comes up with a suitable answer (output) based on the question (input).
What’s more amazing is that AI algorithms can react to input data in different forms. Take virtual assistants or personal assistants like Google or Siri, for example.
Whether you say, “What’s the weather like” or “What’s the temperature” or maybe “How’s the weather” the assistant is going to notice the keyword and provide you with the correct answer.
Note. AI has numerous subsets such as Machine Learning, deep learning (common application: virtual assistants), neural networks (common application: facial recognition), computer vision (common application: image recognition or object recognition), and natural language processing.
Machine Learning, as stated above, is one of the subsets of Artificial Intelligence.
However, unlike AI, Machine Learning as a term doesn’t mean creating smart machines or computer programs that can simulate human behavior to solve problems. It means creating computer systems that learn and improve on their own, using experiences (huge volumes of data) without human intervention.
We can cite the example of voice assistants to understand Machine Learning.
Earlier, the assistants used only to recognize specific statements like “What is the weather outside” and give an output. If anything else was said, the assistant wasn’t able to produce the desired output.
However, because of Machine Learning, assistants can now recognize and respond to a variety of different statements having the same meaning.
Another example of ML is predictive analysis.
Several companies, such as Netflix and Amazon, use predictive analysis to predict user behavior and deliver a better user experience. What’s more, banks use predictive analysis to predict fraudulent patterns and stop illicit transactions from happening.
In a nutshell, if AI is the grand vision of creating intelligent computer programs or machines, ML consists of the models, technology, and processes that are being used by experts to reach there.
Machine Learning and Artificial Intelligence can be differentiated based on several factors such as their scope, primary goal, application, the data type they deal with, etc. Here’s a table that can help you differentiate Machine Learning and Artificial Intelligence better:
| Criteria | Artificial Intelligence | Machine Learning |
|---|---|---|
Primary Goal | To create computer systems that simulate human intelligence for solving problems. | To enable machines to learn automatically from data and improve the accuracy of outputs |
Hierarchy | AI sits at the top with Machine Learning, deep learning, computer vision, neural networks, and natural language processing being its subsets. | ML is a subset of AI and deep learning is the subset of Machine Learning that involves deep learning algorithms, vigorous training data, and multilayered neural networks. |
Scope | AI has a pretty broad scope. | Limited scope, as the focus is on making machines more accurate. |
| Applications | Chatbots, voice assistants, humanoid robots, etc. | Google search algorithms, recommendation engines, etc. |
Types | Weak AI, Strong AI, and General AI. | Supervised learning, Unsupervised learning, and Reinforcement learning |
AI and ML are revolutionizing the finance industry because of predictive analysis. ML can help finance companies, especially banks, with fraud pattern recognition and raise the alarm whenever there’s something fishy, thus preventing fraud from happening.
Please Note. While there’s already a rule-based system that’s used by different banks, it’s accused of causing false positives. And this costs the banks a lot of time as the employees have to mitigate the issue only to find it was a false alarm. However, fortunately, as an ML system learns from its experiences, the chances of false positives reduce which helps save time and resources.
With AI in place, companies can easily automate redundant tasks and improve efficiency. And this is true for every domain across the globe. Here are some examples:
Teachers have to manually process attendance and create questions or exam papers redundantly, which requires a lot of time and effort. However, with AI, teachers can use attendance tools or biometric systems to mark attendance and use tools such as PrepAI for generating intuitive question papers within minutes.
Manufacturing is highly labor intensive and has a lot of moving parts which increase the likelihood of making mistakes.
However, AI makes it easy. Using automation tools or robots at a manufacturing facility, companies are drastically improving their delivery potential and reducing the error margin while ensuring worker safety and innovation.
Machine Learning algorithms work behind the voice assistants such as Google’s Google Assistant, Apple’s Siri, or Microsoft’s Cortana for speech recognition using NLP or Natural Language Processing to provide you with an answer to your query.
And several companies integrate voice assistants or chatbots on their apps/websites to provide a quick resolution of the customers’ queries cutting down human labor and improving the overall customer experience.
For decision-making to be accurate, it should be data-driven. However, using traditional means, companies are unable to use data to the fullest and make a decision. That’s where AI and ML come into the picture.
Using AI and ML and big data, companies can analyze huge volumes of data within no time and create a visually immersive report or dashboard which is easy to read and thus makes an informed or smart decision.
By processing the existing data using predictive analysis, healthcare individuals can determine the risk of any other possible viral outbreak, which can save numerous lives. Also, using AI robotics, surgeons can stay well equipped during operations where wasting a second can cost someone their life.
What’s more, telemedicine is a popular domain that helps medical professionals diagnose patients who’re sitting thousands of miles away. This improves healthcare access and improves efficiency.
According to a research by MIT, it was found that around 75% of the healthcare facilities that used Artificial Intelligence claimed that it improved their ability to cure diseases. And around 4 in 5 facilities said that AI helped them avoid job burnout.
Earlier, it took years for scientists merely to structure the data, let alone analyze it. However, with AI, one can both structure and analyze hundreds of thousands of GBs of data easily and analyze the same using visualization techniques and business intelligence tools.
AI in the stock market is becoming increasingly popular. Just like companies use ML to detect patterns for reporting fraud, ML is also used to detect patterns to determine whether the market or a stock will rise or fall. However, whether the prediction is right or wrong is still a matter of debate.
Companies such as Uber, Ola, Lyft, etc., have made public transport more accessible to a wider range of individuals across the world. And they use AI and Machine Learning both to find the best route for the destination and to decide the optimum price based on location, distance, demand, and weather.
The chances of making small mistakes with big outcomes are higher in redundant tasks such as data entry and data processing. It’s because humans are prone to errors and not machines. However, using Robotic Process Automation, companies can automate redundant jobs, which not only reduces the likelihood of error but also improves efficiency.
Self-Driving Cars are a fascinating creation of humankind that makes use of all three aspects of ML, i.e., supervises, unsupervised & reinforcement learning. Such cars can detect objects around the car and find the distance from the car in front, the location of the pavement, and traffic signals which ensures driver safety and smooth travel.
According to Simplilearn, AI and ML jobs have jumped by above 75% in the past 4 years. Also, the AI software market is expected to touch $126 billion dollars by 2025, according to Statista.
The above statistics and the AI vs. Machine Learning use cases show one thing, and that is how deeply rooted AI and ML have become in today’s world. Whether it’s the marketing industry, manufacturing industry, or the medical and healthcare industry, AI and ML are everywhere, and the market is only expected to grow. So, make sure to integrate AI and ML into your Company to outgrow your competitors.
And this is when DataToBiz comes into the picture. DataToBiz is a team of data scientists and AI experts who can understand the needs of your businesses and devise a solution accordingly. Whether you want AI platform customization, AI pilot implementation, or want to make the most of your data, you can reach out to DataToBiz and get the job done.
Q1. Which is Best, Machine Learning or Artificial Intelligence?
In the AI vs. Machine Learning battle, no one is a winner or a loser. After all, both of them are useful, right? It’s just that AI has a broader scope while Machine Learning focuses on something limited.
Q2. Are AI and Machine Learning Similar?
Yes, AI vs. Machine Learning are similar, but they’re not the same. AI translates to intelligent machines, while ML translates to machines/programs that process data and learn from the same on their own.
Q3. Is Artificial Intelligence just Machine Learning?
Nope. There’s a lot more to it. In the AI Vs. Machine Learning battle, AI encompasses deep learning, natural language processing, neural links, and computer vision while Machine Learning encompasses deep learning.
So, in a nutshell, all of ML is AI, but not all of AI is ML.
Q4. Are AI and Machine Learning the same?
While AI vs. Machine Learning is very closely connected, they are not the same.
By now, you’d know how AI vs. Machine Learning are two different terms that are closely related and how extensively they’re being used around you.
Companies are rapidly opting for AI in one way or the other to improve their business offerings, enhance customer support, ensure process efficiency, make informed decisions, and finally boost their business revenue. You can also be one of those companies. Contact DataToBiz right away to get the process started.
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