Machine Learning vs. Artificial Intelligence: What’s the Difference?

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Classified Description

Machine Learning Development Service
 
ML is technically a subset of AI. Essentially, ML provides systems the ability to automatically learn and improve from experience without being explicitly programmed; focusing on the development of computer programs that can access data and use it learn for themselves.

In other words, Machine Learning Development Service</a> relies on processing big datasets, while detecting trends and patterns within that data and essentially “learning” about these trends along the way.

Like people, machines have the ability to “learn,” acquiring knowledge and/or skills through their unique experiences. For example, say you have an ML program with  lots of images of skin conditions, along with what those conditions mean.

The 
machine learning algorithm examines the images and identifies patterns, allowing it to analyze and predict skin conditions in the future.

When the machine learning algorithm is given a new, unknown skin image, it will compare the pattern in the current image to the pattern it learned from analyzing past images. In the instance of a new skin condition, however, or if an existing pattern of skin conditions changes, the algorithm will not predict those conditions correctly.

This is because one must feed in all the new data so that the algorithm can continue to predict skin conditions accurately.


Artificial Intelligence (AI)
 
Unlike machine learning services, AI learns by acquiring and then applying knowledge. The goal of AI is to find the most optimal solution possible, by training computers a response mechanism equal to or better than that of a human being.

In the instance of adaptation in new scenarios, Artificial Intelligence services is perhaps the most ideal.

Let’s take a simple video game, for example, where the goal is to move through a minefield using a self-driving car. Initially, the car does not know which path to take in order to avoid the landmines.
 
After enough simulated runs, large amounts of data are generated concluding which path works and which paths do not. When we feed this data to the machine learning algorithm, it is able to learn from the past driving experience and navigate the car safely.


But, what if the location of the landmines has changed? The machine learning algorithm does not know these individual landmines exist, rather, it only exclusively knows the all it knows the pattern resulting from the initial data.

Unless we feed the algorithm the new data so it can continue learning, it will continue to guide along that (now incorrect) path.

Enter, AI – capable of analyzing new data in an algorithm to determine multiple factors; answering questions like, why did the paths change? Which direction is most ideal, given the new circumstances, and where are the new hot-spots? It will then codify rules of those hot-spots where the land mines exist.

Slowly, AI will begin to avoid them altogether by following the new trails – just like people, learning and adapting to new boundaries and environmental challenges. 

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