Artificial Intelligence and Deep Learning are two (2) of the hottest trends in machine learning right now, but they are also confusing due to their overlapping features and names.
So what exactly do they mean? Is one better than the other? Which should you choose? If you’ve got questions like these, we’ll answer them in this article as well as provide examples of each so you can see which might be more applicable to your situation.
Artificial Intelligence (AI) and Deep Learning (DL) are two of the hottest buzzwords in the world of technology right now. They’ve become so inextricably linked that they’re often treated as one and the same, but they’re not.
In fact, they have key differences that will affect the types of businesses that can benefit from them, as well as their current uses. Which one will give your business the best edge? Read on to find out!
What is Artificial intelligence (AI)?
Artificial intelligence (AI) is a term that has been around since the late 1950s but was popularized in the 1980s. Artificial intelligence (AI) is any system that exhibits behavior which mimics intelligent human beings, such as problem solving and learning.
Artificial intelligence (AI) is often used to refer to a broader set of things than just machine intelligence. As it stands today, artificial intelligence (AI) is one of the most significant forces shaping our lives and will continue to do so in the future.
Artificial intelligence (AI) can be broken down into two basic types: General artificial intelligence (AI), which refers to a computer’s ability to think abstractly, plan for future outcomes based on current inputs and make decisions about what action to take; and Narrow artificial intelligence (AI), which refers to computers’ ability to carry out specific tasks without general intelligence.
The latter type is most commonly discussed when discussing advances in robotics because machines are designed with very specific tasks they are able to complete well while still being able to learn new skills.
What is Deep Learning (DL)?
Deep Learning is a subset of Machine Learning, which is an application of artificial intelligence (AI). Deep Learning uses neural networks and is able to teach itself how to do tasks by analyzing large sets of data. Deep Learning (DL) has been used in many cases, from teaching machines to recognize objects in images to translate languages.
The advantage of deep learning (DL) over other forms of Machine Learning is that it can learn more efficiently by being able to go through the different iterations needed to reach its goal without human input.
How are they different?
Artificial intelligence, also known as AI, and deep learning are two different types of computer algorithms. They both use computational power to analyze data and create models based on the findings.
The main difference between artificial intelligence and deep learning is the level of sophistication in the algorithm. One distinction that can be made between AI and DL relates to how they function with imperfect data.
With AI, a model will likely generate inaccurate predictions if there are any errors in the input data. In contrast, deep learning (DL) can learn from this information and adjust its calculations accordingly. Therefore, it may not produce perfect results at first but will become more accurate over time as it continues to refine itself by analyzing more data.
Another key difference is how each type functions without a complete dataset. For example, when an AI cannot understand incomplete datasets due to a lack of context, it will often draw incorrect conclusions.
Conversely, deep learning has been shown to identify patterns that might not have otherwise been noticed. It's worth noting that these differences depend heavily on the algorithm being used; for example, neural networks (which are a subset of DL) typically require large amounts of training data whereas other algorithms do not need much training before producing accurate results.
A final important consideration involves software implementation and design.
What are the benefits of each?
AI is a type of data-driven machine learning that can be used to automate high-level cognitive tasks. AI often comes with the benefit of not needing to understand the context of a problem, which allows it to work on many different problems at once.
DL, on the other hand, works by understanding the context of a problem and takes more time and effort to train. DL can be thought of as providing custom solutions while AI provides generalized solutions. DL works on one specific problem at a time, but produces higher quality results in comparison to AI.
Both have their benefits and drawbacks; it is up to the company/organization to decide what will be best for them!
What are the drawbacks of each?
Artificial intelligence is a broad term used to describe machines that are programmed to carry out human-like tasks. AI can be helpful in a variety of ways including customer service, sales and even product development.
While AI has its benefits, it also has drawbacks. For example, AI often takes things at face value without considering the context of the situation. As such, it can lead to inaccurate assumptions about people and their needs.
Furthermore, one of the major limitations of AI is lack of understanding for nuance and subtleties in natural language.
Deep learning relies on neural networks made up of layered structures with an input layer, hidden layers and an output layer. Unlike artificial intelligence which uses traditional programming languages like C++ or Python, deep learning employs machine learning algorithms that can learn on their own by processing data sets with many features simultaneously.
The main drawback of deep learning is training time: it requires significant computing power to train these algorithms.
So, which one should you choose for your business?
AI is best used for automating tasks, with the goal of being able to provide a service with minimal human intervention. DL can be used for many things, but excels in those that require an understanding of the data, such as analyzing credit card transactions. Deciding which one you want to use depends on your business model and goals.
If you're looking to maximize profits by reducing costs, then artificial intelligence might be right for you. But if you are looking to increase revenue through increased efficiency and productivity then deep learning would likely suit your needs.
The more resources (time, money) that a company has will determine whether they should invest in AI or DL first.
Conclusion
Both AI and DL are good tools, but they each have their own advantages and disadvantages. AI can be programmed to solve specific problems, while DL requires more human input.
However, DL may be a better option if you don't have the resources to train AI on specific data sets or when you need to deal with an infinite number of outcomes.
