The technology industry is expanding immensely at a rapid pace. Sometimes it’s hard to keep up with technological advancements as they are subjected to constant reinvention. As Newer technologies get invented, the present technologies also simultaneously get enhanced. All we have to do is to sit back and see the magic unfolds itself. Let’s look at the differences between ai and ml.
If you keep up with technology related news regularly, you definitely would have come across terms like AI and ML many times in your time. These terms get thrown around a lot when talking about technological advancements. So the curious kid in you must have always wondered what these actually are. But never really clearly understood it. Well no more, you’ve come to the right place. so fasten your seatbelts and let’s go for a short ride.
Neural Networks in AI and ML
Neural networks have been the key technology that enabled computers to think and learn things the way humans normally do. A neural network is designed to read and recognize patterns and data and classify the information accordingly. They recognize unlabelled data and classify each data according to their structures and functions. The data is not limited to multimedia file types, but it can even detect faces, voices and body movements of individuals and recognize and classify them accordingly.
What is Artificial Intelligence?
Artificial Intelligence is one such topic that has been extensively discussed and addressed in the field of technology for decades. Actually, science fiction novels and films had a great role in popularizing the term.
For a long time, it has been floating around as a very futuristic and far fetched technological idea. But now we can safely say it’s very much a reality. The applications of AI are really common in our daily lives in multiple forms.
AI, as the words suggest is an artificially created intelligence by humans as opposed to the natural intelligence of humans and animals. Artificial Intelligence is an umbrella term denoting the application of artificially created simulation to minimize human involvement required in both intellectual and physical tasks. So AI is essentially a computer programmed human simulation that can think and speak even act like humans. So it can potentially replace humans or assist them in tasks that usually require a human involvement such as
- Learning
- Analyzing
- Reasoning
- Predicting
- Decision making
- Problem-solving
There are many applications of Artificial intelligence that are knowingly or unknowingly associated with our daily technology experience. Apple’s Siri, Amazon Alexa, Google Home, Microsoft Cortana are some of the examples of AI-powered Applications.
What is machine learning?
Machine learning is a subcategory of AI. It’s defined as the ability of a machine to automatically learn and perform tasks and solutions from a pattern of previous examples and experiences instead of preprogrammed instructions.
Machine learning is actually not that complex. You must have encountered its application many times but never would have realized it’s actually machine learning.
OK, I’ll give you an example, while you are watching a video or browsing online shopping products, you must have come across some other video or product that are actually similar or related to what you were originally watching. Have you ever wondered how these sites knew your preferences? Well, that’s a simple form of machine learning. These sites essentially study your previous browsing history and learn about your preferences and favourite topics. And they recommend a few other videos or products that also fit your choices and inclinations. Just as simple as that.
So in broader terms, a machine or algorithm goes through the previous sample data, patterns and algorithms that a computer system uses to perform a task, then the machine’s artificial intelligence builds a logical model to make predictions and decisions to perform the given tasks without being given preprogrammed instructions.
Machine learning applications are able to read text, images, audio and video files and figure out if the user is going to like it or dislike it. It can even predict if these data can change the user’s mood to different levels. It can essentially act like a human if the right data is fed into its system. Many successful mobile applications are using ai and ml technology. They are continuously testing these technologies in their mobile app ideas.
Artificial Intelligence VS Machine learning
There is a bit of confusion around pinpointing and differentiating both ai and ml. Some people use both ai and ml interchangeably. But they are actually not quite the same in their structure and functions. Machine learning is a subset of artificial intelligence. That means ml can’t exist without ai. But an A.I can exist in different forms without a machine. The future of ai will be very innovative.
AI’s functions and actions are achieved by preprogrammed instructions and directions that are manually fed to the system beforehand. So the AI system can only work around these instructions to deliver the solutions. The purpose of AI is to maximize and accelerate the success rates of certain tasks by minimizing human involvement and errors. So the programs and instruction that are fed to the system have to be foolproof to provide successful results.
On the other hand, In machine learning, the machine itself learns and applies final decisions based on empirical data and patterns without prior instructions. The ML plays a more active role in actually performing the job. The efficiency of ML depends upon the quality and quantity of the data that is fed to the system. So often times it can only work with the available data, So it’ll not always end up in successful results. ML’s focus is on gaining the most accurate results irrespective of the success rate.
It’s also worth noting the relevance of deep learning while you are at it. Deep learning is part of machine learning that is almost identical to the human brain in its functions and applications. Deep learning can be defined as the ability of a system to recognize and learn things from unstructured and unlabelled data with or without supervised learning.
