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Do you know the difference between Artificial Intelligence and Machine Learning?
Learn more about these two important terms in this comprehensive guide.
Artificial Intelligence (AI) and Machine Learning (ML) are buzzwords you’ve probably heard lately. But what exactly are they, and how do they work together?
These two terms are synonymous with anything related to automation in the IT world and are often spoken about in the same breath, and for a good reason.
Even though people use these concepts interchangeably, they are two very different technologies despite sharing a few common attributes.
At its core, Artificial Intelligence is the overarching digital phenomenon to which Machine Learning belongs. While relatively new to implementation in the real world at scale, both are pretty old concepts first introduced in the 1950s.
Artificial Intelligence focuses on creating machines that can perform tasks that typically require human intelligence, such as understanding natural language and recognising objects. However, machine learning is a subfield of AI that allows computers to learn from data.
Rather than being explicitly programmed to carry out a task, a machine learning algorithm will automatically improve its performance by increasing its data exposure.
Both AI and machine learning are undergoing rapid development and are beginning to significantly impact a wide range of industries. As these technologies continue to evolve, likely, their impact will only grow.
This article will detail these two broad categories of digital innovation so that by the end, you’ll know everything you need about AI and Machine Learning.
Artificial Intelligence is the process of programming a computer to make decisions for itself. This is done in several ways, but algorithms are the most common approach. AI algorithms are designed to mimic the human brain’s decision-making process, and they are used to solve complex problems that would be too difficult for a human to solve on their own.
The science and engineering of artificial intelligence are focused on the core idea of making computers behave in ways we thought required human intelligence. This, in turn, allows us to use machines to perform tasks more efficiently and effectively through automation and data-driven smart decision-making.
AI does not require to be pre-programmed; instead, it uses algorithms that can work with their intelligence. These algorithms, also called AI algorithms, are computer programs designed to learn and improve on their own through a continuous input of data and information.
AI algorithms are used in various fields, including computer vision, natural language processing, and robotics. AI algorithms are based on machine learning, which, as we’ve already established, is a type of AI that focuses on making computers better at completing tasks using data and experience.
There are many different types of AI algorithms, each with its strengths and weaknesses. Some AI algorithms are better at completing specific tasks than others as they are application-specific. For example, some AI algorithms are better at recognising objects in images, while others are better at understanding human speech.
AI algorithms also tend to vary in how they learn. While some AI algorithms are designed to quickly learn from a lot of data, others do the same slowly and incrementally.
The best AI algorithm for a given task is often specific to that task. For example, the best AI algorithm for playing chess is different from the best AI algorithm for driving a car.
Essentially, the critical point here is that there is no one-size-fits-all AI algorithm that is going to be the best at everything. Instead, AI developers often use a variety of AI algorithms to build systems that are good at many tasks.
AI has developed and changed form tremendously over the years. This computer technology has come a long way since its humble beginnings in the 1950s. What started as a simple concept has evolved into a complex field of study constantly evolving. AI is now used in various areas, from medicine to finance, and its applications are only limited by the imagination and creativity of the developer.
One of the most recent AI advances has been machine learning. These advancements have led to significant inroads in AI and are the driving engine for many of the AI applications we use today. As machine learning algorithms become more sophisticated, AI will continue to evolve and become even more powerful.
In the real world, AI is used in voice assistants such as Siri and Alexa, which power our smartphones and devices to make simple tasks like setting the alarm, for example, even more, simpler and more convenient.
Ultimately, AI is a constantly evolving technology. In a few years, the current use of AI will look relatively primitive and unexplored compared to the evolution made by then.
Machine learning is the process by which machines learn through experience. This digital phenomenon is comparable to how humans learn specific skills like a new language or playing an instrument through practice and experience.
As mentioned previously, machine learning is a subset of AI that refers to the ability of machines to learn from data and improve their performance over time. In other words, machine learning algorithms, like AI algorithms, help computers automatically improve given more data.
The beauty of machine learning lies in its differences from traditional hand-coded software, which is static and fixed once it has been written.
Like AI, Machine learning uses its own set of algorithms powered by data. The more data they have, the better they can perform. Therefore, part of the reason machine learning has taken off in recent years is that we now have more data than ever due to the ubiquity of technology worldwide.
Furthermore, we have become better at storing and processing large amounts of data. This is important because machine learning algorithms require lots of data to be effective, which is why they are often used in conjunction with big data techniques.
Another critical ingredient for machine learning is the concept of Compute power. Compute power in machine learning is the ability of a machine to perform calculations quickly and accurately. This is often measured regarding floating-point operations per second (FLOPS). The more FLOPS a device can achieve, the faster it can learn. However, raw compute power is not the only factor determining how quickly a machine can learn.
The efficiency of the algorithms used also plays a role. For example, an algorithm that can learn from small data sets will require less computing power than an algorithm that requires large data sets. Nevertheless, compute power is an essential consideration when designing machine learning systems.
Finally, machine learning algorithms are complex and require significant processing power to run effectively. This is a big reason why many machine learning applications are deployed in the cloud, where they can fully utilise the scalability of cloud computing platforms such as Microsoft Azure, Amazon Web Services, and Google Cloud, to name a few.
Programmers can build programs that continuously learn from a supplied data set and produce an anticipated output through the machine learning concept. In the real world, this is how software is programmed to understand voice, make sense of images, recognise text and so on.
Being a sub-category of AI, machine learning relies on working with small to large datasets by examining and comparing the data to find common patterns and explore nuances.
This is why companies such as Netflix and Spotify use machine learning in their web applications to recommend movies and music that their users might enjoy based on their search habits and preferences. These recommendations improve as the user engages more with the platform because the machine learning algorithm is fed more data to understand and interpret.
The Machine learning ideology can be categorised into three types, and they are:
Supervised learning is a machine learning technique where data is labelled and used to train the algorithm. The algorithm can subsequently make predictions on new, unlabeled data. Supervised learning is typically used for classification and regression-based tasks. A great example of a classification task would be identifying whether an email is a spam or not. In contrast, a regression task can be like predicting a house’s price based on size.
Supervised learning is a powerful machine learning technique that can be used to solve a wide variety of problems. However, it is essential to remember that the accuracy of the results depends heavily on the quality of the training data. Thus, to achieve good results, it is vital to have an extensive and representative training data set.
On the opposite end of the spectrum, we have unsupervised learning, a type of machine learning that allows algorithms to learn without explicit instructions. Either Cluster analysis or Dimensionality reduction can do this.
Clustering is a technique of grouping a set of objects so that objects can be differentiated from one another and grouped based on their similarities. This way, you end up in a situation where objects in the same group are more similar than objects in other groups.
On the other hand, dimensionality reduction is reducing the number of random variables under consideration by obtaining a set of principal variables. This is done to reduce noise and remove irrelevancies that can clutter judgement and output accuracy.
Unsupervised learning algorithms are used to find patterns in data. They can group data points into clusters, identify unusual data points, and detect outliers.
Unsupervised learning is potent and can be used for a variety of tasks. It is often used for anomaly detection, identifying data points that do not conform to the rest of the data.
Reinforcement learning is a machine learning technique that allows systems to learn by trial and error. By providing positive or negative feedback, reinforcement learning algorithms can automatically improve the performance of machine learning models over time. Essentially, the algorithm continuously learns from its environment using iteration.
This makes reinforcement learning an efficient and effective way to train machine learning models for various tasks. Reinforcement learning has been used successfully in several domains, including robotics, natural language processing, and video game playing.
As machine learning evolves, reinforcement learning will likely become increasingly important in many applications.
Machine learning is a process of teaching computers to make predictions or recommendations based on data. This data can be anything from a history of user interactions to weather patterns. On the other hand, AI is a more general term that refers to the ability of machines to perform tasks that would typically require human intelligence, such as natural language processing or image recognition.
It is evident that even though these two technologies are different from one other, they still have a close relationship and are used in conjunction to develop highly innovative digital solutions. But, how do they work together?
We can understand the synergy of these two digital ideologies by going through the steps involved in building and deploying an AI program.
First and foremost, an AI system is built using machine learning and other techniques as the driving force. The Machine learning algorithms are created by studying patterns in the supplied data set. Data scientists and engineers then further optimise the machine learning models based on patterns and outliers in the data. This process is repeated several times and continued until a high level of accuracy is achieved consistently.
Machine learning and AI are concerned with making computers more thoughtful and capable of doing things humans can do to facilitate automation for enhanced efficiency.
However, while machine learning is mainly focused on creating algorithms that can learn from data, AI focuses on building systems that can reason and act autonomously. Despite these differences, machine learning and AI are often used interchangeably because they both deal with ultimately making computers smarter and gaining intelligence, much like how a human brain does.
But is that the only difference between these two concepts? The sections below have broken down the various categories where machine learning and AI differ.
The core difference between AI and machine learning comes in what they mean, as these two technologies are similar in some aspects but also very different in others.
Machine learning is used to create algorithms or rules that can automatically sift through data to find patterns. These patterns can then be used to make predictions or recommendations. For example, use a food delivery application that uses machine learning. It will analyse your ordering habits and recommend new restaurants and cuisines it thinks you might like.
AI, on the other hand, takes these algorithms one step further by not only finding patterns but also understanding and acting on them. For example, an AI-powered chatbot could not only understand your questions but also provide helpful answers.
Machine learning is a subset of AI used to make machines learn from past data to produce an intended output. At the end of the day, they become a small part of the overarching AI system.
Interestingly enough, Machine learning and Deep learning are the two main branches of AI, and if we dig even further, deep learning is a subset of machine learning.
The concept of Artificial intelligence is vast and vague, making it much broader in scope compared to machine learning. It is safe to say that we are still in the burgeoning stages of the AI revolution, and there is much more to learn and grow in this fascinating field.
AI is used to create an intelligent system that can perform various complex tasks. These tasks include problem-solving, learning, and planning, achieved by analysing data and identifying patterns to replicate those behaviours.
AI is used in various fields, including healthcare, finance, manufacturing, and logistics. In healthcare, AI diagnoses illness, plans treatment protocols, and predicts patient outcomes.
In the world of finance, AI serves a different purpose, mainly used to identify financial fraud, predict stock market movements, and recommend products based on growth potential. In manufacturing, AI is used to optimise production lines and schedule maintenance.
And in logistics, AI is used to route vehicles and optimise delivery schedules. In essence, AI has the potential to transform almost every industry, and it already significantly impacts our world.
Machine learning is working to create machines that can perform only those specific tasks for which they are trained. It is mainly concerned with accuracy and patterns.
This is why machine learning is used chiefly when computing estimation in our daily lives. For example, Google Maps uses machine learning to recommend the fastest routes to the desired destination based on the location data acquired from the user’s smartphone.
In summary, much like AI, machine learning is also used in various industries ranging from email intelligence and financial services to academic evaluation and social media.
The most common applications using AI at their core are Siri, customer support using chatbots, expert systems, online game playing, and intelligent humanoid robots, to name a few.
On the flip side, some applications using machine learning as their driving technology are online recommender systems, Google Search algorithms, Facebook’s auto-friend tagging suggestions, and more.
And finally, a special mention must be given to John Deere, one of the leading manufacturers of agricultural machinery in the world. The company, headquartered in the United States, is committed to environmental sustainability, invests heavily in research and development, and has introduced numerous groundbreaking products, including farming equipment that uses precision agriculture technology.
One such product, the See & Spray Ultimate is revolutionising weed killing through the combined application of machine learning and computer vision.
This equipment uses machine learning technology paired with an advanced camera system to constantly scan the field and distinguish between crops and weeds. Their specialised sprayers then selectively target spray just the weeds, which has been shown to reduce non-residual herbicide use by more than two-thirds.
This effective weed control strategy means crops are protected from potential damage and provided ample nutrients and moisture for productive yields. Furthermore, John Deere’s See and Spray Ultimate uses a dual-tank system that allows target spraying and fungicide broadcasting at the same time, saving both time and money.
Ultimately, this one-of-a-kind equipment pays off the initial investment costs reasonably quickly, with the amount of herbicide saved, not to mention the improved crop yields and better cost-efficiency. John Deere’s commitment to quality and innovation is evident in this product, which is sure to benefit any farmer who uses it.
Through such innovations, John Deere is consistently leading the way in the agricultural industry utilising technology to the fullest.
So there you have it – a whistle-stop tour of the basics of artificial intelligence and machine learning. Both are remarkable technologies changing how we live and work and will only become more critical in the future.
If you want to learn more about how AI and machine learning can benefit your business, or if you simply have more questions about what all this means, book a discovery chat with us today. We live and breathe this stuff and are more than happy to discuss all the unique possibilities technology offers and help make it all easier to understand. Hopefully, we get a chance to speak with you very soon!