In our blog, we’ve highlighted the most important factors to help you understand Machine Learning algorithms. If you are still wondering what is Machine Learning (ML) and how it compares to Artificial Intelligence (AI), you have landed on the right page.
Although this specific topic isn’t new for us, we’ve never really dedicated time to actually answer the question “What is Machine Learning.” With AI technology moving so fast, it seems difficult to characterize ML; one runs the risk of being outdated quickly. This is not surprising if one considers that Machine Learning is the hottest trend in the AI job market, making it one of the most dynamic and fastest-growing fields of computer science.
We’ve decided to go for the challenge, so in this post we will discuss the core Machine Learning theory and the algorithmic paradigms it offers. We will show what Machine Learning is capable of and how learning algorithms can help your business grow. We will also review some of the general things it can do. Lastly, we will discuss how app development can play a major role in the Machine Learning industry.
Machine Learning Is a Subset of Artificial Intelligence
A definition of Machine Learning should start by stating that it is a subset of Artificial Intelligence. This means that anything that fits within Machine Learning and its algorithmic processes is necessarily part of the broader concept of AI, but not the other way around. This is because, in practical terms, ML is a particular type of AI. Although many people tend to use the terms interchangeably, they are not the same, even if they sometimes seem to be.
AI involves structured output learning, automated decision making, and task execution. In other words, it is a tool for rational decision making and execution based on a series of ‘intelligent’ algorithms. AI is still far away from being similar to human consciousness, although that’s probably a comparison one shouldn’t even be doing. As with any tool, its outcomes depend on how it is used.
The major differentiator between ML and AI lies in how each technology fulfills its purpose. ML’s capability to learn is what sets it apart from other AI technologies. The general AI concept does not necessarily have this algorithmic paradigm; it can be programmed to make decisions without learning throughout the process. This is why it is not uncommon to say that AI pursues intelligence while ML pursues knowledge. We’ve discussed some of the most important differences between both technologies in a previous post.
Because learning occurs through experience, ML relies on repetition analysis. ML algorithms use a sort of trial and error process in which data is generated and contrasted against results in order to ‘learn’. It sounds really simple, but ML can get really complicated, especially as tasks become more complex.
Just as ML is a subset of AI, there are also subsets within ML. This is the case of Deep Learning (DL), one of the most popular ML uses. DL allows algorithms to learn by establishing its own parameters rather than through predefined ones. As we also discussed in a past post, one of the most powerful uses of DL is in the field of quality control. By using this technology, companies can learn to detect defects in their value chains, helping optimize processes while generating efficiencies.
How Machine Learning Helps Applications
The idea of algorithmic paradigms that rely on supervised learning is really amazing, but we should not forget that this would not be possible without the use of data. Data is the key ingredient to powerful ML algorithms. One can even say that the success of ML is only as good as the data it is fed. Given this fact, I can assure you that apps will play a major role in specific ML uses, particularly those that impact the customer journey.
Although apps are by no means the only way to collect data, they are amongst the most significant ones. They provide companies with ways to interact easily with users in order to collect data and develop new ML solutions to problems. If companies want to use apps for this purpose, they should be accompanied by a great UX design that has the power to engage users.
As ML becomes more common and companies decide to experiment with it, tons of data will be necessary to find solutions. IoT will definitely play a major role in this process, as it allows collecting data through many different devices, particularly smartphones. Thanks to IoT technologies, companies can now insert data points into their systems in ways that were impossible just a couple of years ago.
Important sectors that seem promising in terms of ML technological transformation are MedTech and FinTech. As companies are able to gather data, new applications will be developed. This is already happening, and its pace might accelerate in the coming years.
More than Statistics, Computer Science, and Mathematics
Machine Learning is often associated with complex mathematical calculations and algorithms. This can certainly be the case, but it doesn’t necessarily have to. I’ve personally always considered that computational thinking is more important than knowing tons of math. As this form of thinking becomes more popular and people start adopting it to develop solutions with the use of ML, we will see a breaking point in how companies operate and how we think about problems.
As with most new technologies, there will be some companies that delay Machine Learning’s implementation, while others will take the risk and jump into the unknown. If you are on the latter, you might start thinking about how to use ML to benefit your company. No matter the industry you are in, there is much to gain, especially if you have the first-mover advantage.
Considering an app for your business, if you don’t have one, should be the first step. At Koombea we have experience developing world-class HiTech apps that help you reap the benefits of powerful technologies like ML and IoT. Sooner or later your competitors will do the same, so don’t hesitate to contact us. Now is the moment to leap towards the next generation of game-changing technologies.
