The Difference Between Artificial Intelligence and Machine Learning Algorithms
Artificial intelligence, as defined by Wikipedia, is "the ability of computers to simulate human behavior." It's an incredibly important aspect of computer science that deals with the creation of artificial intelligent machines, humans and reasoning like humans. Deep learning is also one of the large branches of artificial intelligence, just like reinforcement learning. Deep learning frameworks are increasingly being used in many areas such as speech recognition, computer vision, language processing, content management, task solving and image processing. Although these frameworks have a lot in common, they are still very different from each other. Below I'll discuss briefly some differences that I believe are of great importance when discussing the two topics.
The main difference is that whereas machine learning uses deterministic or strictly Algorithmic approaches in its learning models, Deep Learning makes use of non-deterministic or semi-deterministic methods. A nondeterministic machine learning models rely on the principle of backpropagation, whereas in the learning of Deep Neural Networks (DNN) they rely on the principle of Recurrent Neural Networks. Both of these types of models are relatively intuitive; however, the most accurate representations for both these types of algorithms are still hard to translate into machine learning. DNN allows for the extraction of data from a large number of inputs, while the recurrent neural network allows for the generation of new relevant data via backpropagation.
As discussed earlier it's a fairly straightforward difference however what makes it significant is the fact that DNN are significantly more accurate than any other representation of the backpropagation algorithm.
Another main difference is the use of labeled data in applications. Many companies who apply machine learning techniques in their product development apply them in the training phase, but label-based services and data mining can't be as easily applied in this phase. Labeling are of course an important tool in all applications, as they make the training process much easier, and help us understand the underlying software better. However, the labeling process also makes the training process rather rigid, as we all have experienced in the past when faced with a black box full of numbers or text. We have a hard time devising a good training program without any labels.
Another significant gap between the two is in the area of natural language processing. Despite the fact that many people think artificial intelligence will replace human language processing in the near future, this is not the case. Human languages are much more complex than machine learning languages, primarily because human languages are structured according to the rules and grammar, whereas machine learning machines are typically more abstract, and allow for more creativity. In fact, recent studies by Oxford University have shown that up to 90% of all translation mistakes can be attributed to poor translation skills, and the same goes for machine translation, which is often more accurate than human translations.
Machine learning and artificial intelligence are not mutually exclusive. There are use cases for both. For example, while most companies have moved from direct programming by humans using machine learning algorithms to train their systems, companies like Netflix have taken the opposite approach. They have developed a set of tools called Netflix's natural intelligence engine, which is used to guide the recommendation and placement of its video content, and is thus able to improve the quality of user experience.
While machine learning algorithms are useful for delivering personalized service, they are not perfect. They are too error prone. Furthermore, artificial intelligence poses certain risks. If large-scale use is put to use, it may lead to the manipulation or abuse of these tools, which is why companies like Facebook use machine learning algorithms only to a limited extent.
BY THE WAY, every sentence of this article was generated by a software that uses deep learning (a subset of machine learning) to generate high quality articles. This is one of the capabilities that can be achieved with technologies labeled as AI. The true difference between artificial intelligence a machine learning I believe is the mentioned by Mat Velloso in 2018.
Just kidding, we will talk more of these technologies and their applications specially in healthcare in the next blog posts, obviously in a more serious manner, although the article is quite accurate.
