The term Artificial intelligence or AI is now amongst the most-used technological jargon on the market, and there is a very good reason for that. AI is being implemented across various industries. The technology is useful for various applications, from creating chatbots to turning unstructured data from videos into insights ready for further analysis.
Artificial intelligence is not the only technology that has been gaining in popularity. Machine learning is also becoming a prominent part of the tech industry. What is machine learning exactly? How does it differ from AI? We are going to do a deep dive into the topic in this article.
People often mistake AI and machine learning as being virtually the same, but the two terms actually represent very different things. Artificial intelligence is not a new technology. It is fundamentally a way for machines to be logical in mimicking the human brain and its thinking process. AI is programmed, but it is programmed in such a way that it can adapt to different situations.
Machine learning takes things a step further. Instead of being programmed to do specific things, machine learning is the actual process of learning from data sets and coming up with decisions based on those data sets. Machine learning can be used to develop artificial intelligence, but the process is different than what we understand as conventional AI.
The two keywords to capture from these explanations are adapt and learn. Applied AI can adapt to variations of a similar situation. It doesn’t just repeat a certain function, but rather it focusses on achieving a specific goal while taking into account variations in its operating environment. It adapts.
Machine learning, as the name suggests, learns. It takes a series of data, uses annotations, and other tools to develop a model—usually in an assisted way—and then utilizes that model to perform further analysis of different data sets, coming up with new conclusions. The cycle gets repeated and the AI utilizing machine learning improves with every cycle.
While AI represents a machine’s ability to acquire knowledge and adapt, machine learning is the actual process of knowledge acquisition. This means machine learning is a subset of AI, but not the other way around. Still, there are some interesting differences that can be seen in the way these two technologies approach independent and artificial intelligence.
For starters, applied AI’s main goal is success. It adapts in order to achieve a specific set of goals. Machine learning, on the other hand, focuses more on accuracy. It learns and improves its analysis model in order to generate a better, more accurate analysis of future data sets. It doesn’t have to be successful in achieving its primary goals.
The same is true with the way applied AI works compared to the machine learning process. Applied AI takes data from various inputs in a specific way. When you see a self-driving car analyze its surroundings based on predefined parameters, you are seeing applied AI at work. Machine learning doesn’t restrict itself when it comes to how it processes data, but the accuracy level of the model depends highly on the quality of data sets fed into it, as well as the way those data sets are annotated.
Applied AI handles decision making based on data. Machine learning takes care of the learning process itself. Machine learning can be used to make AI more capable in decision making. This is why machine learning is used to train AIs for specific purposes. While AI is the bigger function of data analysis, machine learning is its very foundation.
AI is more accessible than ever thanks to cloud computing platforms like AWS offering their own environment for AI development and machine learning. GPU-intensive cloud environments are not only more affordable but also more accessible. At the same time, we’re seeing technologies like TensorFlow making advanced machine learning applications possible.
This is where both AI and machine learning needs to take a step further, and that step is deep learning. Unlike machine learning, which relies on predetermined annotations to understand data sets and learn from them, deep learning simulates the human neural network and its learning process. It is capable in processing complex, often unclear data with greater accuracy.
Machine learning and deep learning have their own use cases, with the latter now powering some of the most advanced artificial intelligence entities. AIs like Google Duplex and AlphaGo can adapt to their environment and solve complex problems—which are normally very simple to humans—thanks to a strong neural network behind them.
Even so, machine learning will still be used in some situations. It is an inseparable part of AI development and it is a proven way to allow machines to learn from data. Where AI excels is in its ability to use methods like machine learning and deep learning to grow exponentially.
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