Machine Learning and Artificial Intelligence are often confused in the modern world. However, the difference lies simply in that the latter is a broader category under which the first is housed. While artificial intelligence(AI) is defined simply as the knowledge and learning programmed into a system, machine learning(ML) is a process wherein the machine acquires knowledge itself without being explicitly programmed.


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AI is all about decision making and success. AI is utilized to find the solution to a particular problem, to come up with the best possible result, to trouble shoot data for particular issues. When all is said and done, AI is rather like a smart calculator, an automated mathematician, which with cold precision and stiff articulation works to riddle out the solution to every problem equation.

ML was born when people determined that instead of simply teaching computers everything they ever needed to know, it might be better to teach them how to think like people, thus allowing them to learn on their own; hence the term: Machine Learning. While AI seeks for the best possible solution, ML is much more about people pleasing. It allows a system to analyze data and create new algorithms based on that decision. ML does not necessary try to come up with the best possible solution, only a solution that follows based on the data. ML will continue to modify its algorithms based on incoming data.

Of course, while all ML is AI, AI refers to a much broader subject. AI is built off of a series of If-then statements, each individually programed by people. AI has been programed to create some of these statements itself based on the data it receives. Thus, while a piece of AI can remain static, he continually morphs and changes. AI can be taught to recognize the difference between an angry letter and a cheerful one. It can listen to music and discern between moods, as well as recognize facial expressions and different tones of voice.
All in all, ML and AI are both advancing in their fields and being used more and more in the public sphere. Then of course there’s deep learning which is a further subset of ML but that’s another topic for another day.

So, now that you understand what ML and AI are capable of, let’s run through what we’re capable of. Using AI and ML we can create neural network solutions such as: Recognizing and classification, Decision making and management, Clustering, Prediction, Approximation, Data compression and associative memory, Data analysis, and Optimization. The possibilities are limitless. Don’t make it harder on yourself when our AI and ML programs can help make running a business so much easier.


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