It has become very common that you will hear people use the words Artificial Intelligence and Machine Learning interchangeably.
Yes, Artificial Intelligence and Machine Learning are highly related, but the truth is that Machine Learning is a subset of Artificial Intelligence.
So as more and more companies and businesses start offering “intelligent” tools and solutions, it is very important to clearly comprehend the differences between both Artificial Intelligence and Machine Learning.
It’s quite possible that one day you will be in a situation where you’re having a discussion with somebody or a group who specializes in AI and Machine Learning, trust me, you would really want to know what you are talking about.
From detecting climate change, predicting customer behavior, and detecting cybercrime even before they happen, there are tons of ways Artificial Intelligence can be applied.
But the feature that will really make it possible to effectively use these applications of AI is Machine Learning, which will go a long way in helping to improve different informatics.
Any business can benefit from AI, even if such business is not fully or partially invested in cutting-edge technologies.
Simple marketing features, for example, Email marketing can be immensely upgraded with principles derived from AI and Machine Learning.
AI and ML are 2 of the most disruptive technologies in the world today, and both will no doubt change the future, this makes it quite important to understand the 2 concepts and related terms that you might just start using every day.
What Exactly is Artificial Intelligence?
Artificial Intelligence is not an entirely new concept, in fact, AI has been a mainstay of sci-fi books for several decades. Be that as it may, regardless of its long-standing existence, many people in the world today still do no understand what AI means.
Even though there are various definitions of Artificial Intelligence, the easiest to understand definition is: “Artificial Intelligence is the training and teaching of computers to carry out intelligent tasks, in a similar way that human beings currently do, but in a more efficient way.
Most people still view Artificial Intelligence as a futuristic technology, but that’s not the case. AI is currently being deployed in many facets of our day to day life.
For example, we can only enjoy a steady power supply in our homes today because the power grid systems that supply us power are being protected from threats like cyberattacks by powerful AI technology.
There tons of other AI applications that have less severity and we have more day to day encounters with but not many are conscious of it. Such AI applications are, YouTube and Facebook utilizes AI Algorithms to sort the home feeds and recommend videos and posts to users.
Many people today conceptualize Artificially Intelligence as a solitary concept, fact is that AI is much more comprehensive than that. Think of AI as a field of science such as physics or computer science, there are many other sub-fields within AI that people can specialize in.
Similarly, companies and businesses can specialize in a single subset of AI, one of those subsets is Machine Learning.
Understanding Machine Learning
As we have stated earlier in this article, Machine learning is a subset of Artificial Intelligence, however, ML focuses specifically on a certain field.
Once more, ML is a very complex field, but the most basic definition of Machine learning is giving systems the capacity to consequently learn and improve based on past experience.
At the end of the day, a machine is fed big data, and afterward, the machine gives an outcome dependent on gigantic measures of data, which can involve understanding patterns and trends.
Similarly, as with Artificial Intelligence, Machine Learning is likewise utilized in various regular applications that you are presumably acquainted with and are already disrupting various industries.
Tracking errors, sorting and categorizing transactions in cloud-based accounting systems, and understanding user behavior are just a few ways that many businesses are currently utilizing ML to run tasks. Social media sites currently use machine learning to track the behavior of their users for more effective ad targeting.
Fintech companies such as PayPal use Machine Learning to detect fraudulent transactions by comparing millions of transactions and learning patterns of legitimate transactions, then using that knowledge to fish out transactions that are illegitimate.
One of the more popular and recent cases of Machine Learning application that hit the airwaves was when a ML software was used to build up blurred images of people’s faces.
The application was hit with controversy when it built up the faces of white people in images, whereas the original images were of black people.
This is a model that shows how ML isn’t generally great yet, but on the other hand, it’s not really a defect with the idea itself.
A software is just as good as it’s algorithm and the data that it is fed with. In the event that the fed-in data is imperfect in any capacity, at that point the result will be defective, as well.
At the point when an ML algorithm functions effectively, the outcomes for businesses and organizations can be very significant.
With regard to web development, ML applications can help businesses to have a better understanding of users’ and customers’ behavior.
While transactions and purchases can seem very random, and effective ML algorithm can help discover patterns and give valuable insights, for example, when customers are well on the way to visit a store and additionally purchase an item on the web.
Amazon is one company which currently makes use of Machine Learning & AI to predict when customers will most likely order a product, so what Amazon does is to make the product available at a nearby warehouse to where the order will be made, this helps them to deliver the products very fast while also increasing operational efficiency and customer satisfaction.
It might appear to be complicated, particularly in the event that you don’t have a comprehensive understanding of AI.
Luckily, there are online courses for better understanding AI and ML, this can help you to effectively implement them in your business processes.
Understanding the Difference Between Artificial Intelligence and Machine Learning
Both of these concepts are mainly focused on easing the workload on humans, which of course is error-prone, and move it to machines, which can hypothetically accomplish the work impeccably and at a faster rate. It doesn’t generally turn out to be as perfect though, but as time goes on, the future of AI and ML is unimaginably brilliant.
The rise of AI and ML represents an aspect of the huge digital transformation that the world is currently going through.
Organizations have been exploiting this transformation and most digital businesses that you can start today integrate AI and ML-driven processes into them, including blogging, podcasting, web design and development, digital and social media marketing.
If you run a small or online business yourself, it’s best to foremost comprehend what each of these concepts can do. Once more, AI is the more extensive field while ML is a subset of AI. Artificial Intelligence is likewise more focused on greatly increasing success rates but isn’t too focused on precision. While, ML is focused on improving precision, not really success rates.
AI and ML are also quite different as far as their objectives are concerned. The objective of Artificial Intelligence is to simulate natural intellgence, similar to that of a human, to help break down information and take care of complex issues.
While the objective of Machine Learning is to just gain knowledge and improve from data related to a particular task to increase the performance of that task.
Machine Learning is quite a narrow concept, while Artificial Intelligence comprises of various concepts, and ML is just one of them. Deep learning and neural networks are some other subsets of Artificial Intelligence,
Artificial intelligence is a fast-growing field that in the next few years, will have a wide range of applications. Much the same as human beings, AI and ML will never be perfect, since they are being fed with human-generated data.
However, as more exploration is done, the outcomes will only improve. Before long, every business will have the opportunity to profit by integrating some type of AI application into their business model and operations.