The Difference Between Artificial Intelligence and Machine Learning

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Time to Read | 7 mins


Catchphrases that you will hear a lot today include artificial intelligence (AI), machine learning (ML), and neural networks. They are often used interchangeably, but are actually very different things. If you want to know the differences between Artificial Intelligence and Machine Learning, then please let me explain. We will cover different aspects of the topic to provide a better understanding of both systems and how they work.

Machine Learning

ML is the way that machines learn on their own without explicit programming. The application of AI provides a system with the ability to learn automatically as well as improve its learning through experience. Many wonder about the possibility of a computer system improving knowledge via its experience as well as the laws that govern this learning process.

For example, if you provide a model of machine learning with videos that you enjoy watching, with the video statistics given, it will automate and generate a recommendation system that would suggest videos that you might enjoy in the future along with other related features. This is something that many companies do today with regards to suggesting videos and audios.

Types of Machine Learning

There are three types of machine languages:

  • Supervised learning

Supervised learning, as the name suggests, is a learning scenario in which learning is guided by a teacher. This teacher is a dataset that has a role in training the machine. At the point when this machine or model becomes trained, it becomes conversant with the operations of things, which makes it possible to make decisions or predictions when new data is introduced into the system.

  • Unsupervised learning

This is an ML model that processes information and learns through observation. This learning model formulates structures using data received from every experience. Once a dataset is given to the model, it inevitably finds relationships and patterns in the dataset by creating clusters. The only setback with this type of ML is its inability to set labels to clusters, which means that it cannot group; however, it can differentiate.

  • Reinforced learning

This is the ability of an agent to relate with the environment and come up with the best outcome to a specific situation. This sort of ML follows the concept of trial and error with the system being rewarded or punished for a correct or incorrect result, respectively. On the basis of these rewards, the model trains itself on that scenario. Once it becomes self-trained, it gets ready to predict the next set of data presented to it.

Artificial Intelligence

The word artificial here refers to things that are not human or living things, and the word intelligence refers to the ability to think or understand. Many people believe that Artificial Intelligence is a system when, of course, it is not. AI is a factor that is implemented into a system but is not the system itself. This definition of can help us understand what AI is:

Artificial intelligence is the ability of a machine or computer to think and learn. This is a field of study that makes computers smart.

AI systems are designed to have a mind of their own and are not limited to specific sections of learning. This means that the concept of AI is making a system that thinks about the information presented to it without being limited to the specifics, thinks about the information that is presented, and gives output regarding its understanding of the situation. We can say that the AI system is designed to model the human mind.

Types of Artificial Intelligence

There are four types of AI systems:

  • Reactive Machines

This is the most basic type of AI system. It is purely reactive and does not have the ability to use past experiences or memories to make decisions in the now. This sort of AI has the ability to make decisions based on what it perceives the world to be at the moment rather than on its conceptual perspectives of how things work or should work.

  • Limited Memory

This type of AI system can specifically learn from historical data. Many might say this is what we highlighted as a difference between Artificial Intelligence and Machine Learning, but let’s look at things from another perspective. Take self-driving cars, which are being developed, as an example; the concept of limited memory is put into play. These cars observe many things as they drive such as the direction and speed of other cars, and as time goes by, it becomes part of a programmed representation of the world. The reason that AI is different to machine learning is that information is only temporary. This is not part of the cars learning brochure as most are programmed with a certain level of information, compared to humans who learn more as they drive. These skills, which are learned as these self-driving cars move, are temporary additions that help with driving in certain conditions; we should now understand the difference between Artificial Intelligence and Machine Learning.

  • Theory of Mind

There is an important difference between machines we use at the moment and those that are still being developed. These machines are more advanced and form representations of the world as well as the world’s entities and agents. These machines are known as the theory of mind, which understand that people in the world today have emotions and feelings that affect their behaviour as well as the way they react to things and situations.

These AI systems are built on the concept of humanity, which forms the bedrock of our relationships and social interactions. They can be compared to other systems that do not understand the concept of intentions and motives; these systems are built in such as way that they take into consideration other people’s feelings and the environment, which makes coexistence possible.

This is imperative, because if AI systems are to exist amongst us, they must have an understanding of human behaviour and intention. If they do not, they would act based on their programming; therefore, there would be many problems in processing information correctly.

  • Self-Awareness

This type of AI development builds a representation of itself and not just people. This means that this machine would have a consciousness of self as well as the things around it. This is a type III AI system and an extension of the theory of mind AI systems. Consciousness makes it possible for these machines to be self-aware and have an understanding of their state of overall well-being; this also gives them the capacity to predict how others are feeling.

Because of this awareness of self, whenever someone does something, there is an understanding of motive based on what they would do if they found themselves in that position, rather than simple programming that places reaction based on an action.

Let’s look into the differences between Artificial Intelligence and Machine Learning:

Based on the definitions of AI and ML, here are some of the differences you should bear in mind:

  1. Learning Patterns: when it comes to learning patterns, AI allows a machine to simulate human behaviours; in other words, the machine has the capacity to copy a certain type of human behaviour via programming. However, machine learning is a different ball game entirely as it is a subset of AI. ML has a learning pattern that allows a computer to learn from past experiences. Rather than giving it a strict learning algorithm, you give it billions of data to analyse, thereby giving it a chance to learn as new information comes in.
  2. The Goal Behind the Technology: using the goals behind the technology as a means of comparing AI and ML, it is clear that the aim of creating AI in the first place was to make smart computers – computer systems smart enough to solve complex or complicated problems like humans. However, the goal of ML was to create machines that can learn from data and give output based on their analytic capabilities.
  3. Capabilities: the capabilities of AI and ML differ greatly. Although AI does not learn from experience, we know that the central reason for creating these systems is for them to be as smart as humans. Since they are created for this purpose, we can say that versatility – its ability to perform various complex tasksis a core factor. This is not the case with ML because, even with the capacity to learn, machines only have the capacity to accomplish the specific tasks they are trained to complete.

The versatility of the topic of AI and ML is one that most cannot deny is a complex and yet growing topic. The reason for this article is to bring you the subject in simple, yet understandably rich, terms. Most of the time, the majority complicate matters for themselves when it comes to AI and ML; we believe that, with the information provided in this article, we have been able to shed light on how these similar systems differ.

Bottom Line

One thing is certain; as technology continues to grow, things will continue to intertwine making it more difficult to understand and when there is no proper foundation with regards to information, it would be difficult to pinpoint an AI or ML system and how they can be used by you.

The unending journey of the AI system with regards to building different technologies and machinery with human intellect, is one that all enthusiasts of technological advancements love to embrace. Copying the human mind for systematic operations is an unlikely approach, which means that proper implementation of the programming for AI might take a long time to perfect. With regards to ML, the majority of work can begin with small subcategories of data for task screening and adoption at the initial stages. Since ML is a subset of AI, it would still take a lot of work to implement and use it properly, since that is the case with the AI system per se.

Now that you know the difference between the systems, you can decide on which of these systems are of interest to you and how they can be implemented for your business and personal use. One thing you have to keep in mind is that AI, as well as ML, have specific purpose sets that are based on their main category of use. It is, therefore, advisable that for the implementation of any of these systems based on your decisions about their purpose, you should hire and make use of a professional AI developer from a reputable AI company to achieve the best results.

On a final note, we believe that this information has been useful for most people who have had the need for useful information regarding both systems. However, we believe that there is more to be covered in relation to this topic.

If you have any thoughts or questions about what we have discussed, please leave a comment below.

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