It's no secret that the entire world is going through global digitalization. Companies everywhere keep introducing new technologies to improve efficiency and boost performance. You must have already run into terms such as AI, machine learning, data science, IoT, etc.
All of these terms define different technologies, even though they work together to provide the most benefits. They can work independently, but they work much better in synergy. One thing is for sure, AI and data science are the powerhouses behind Industry 4.0, and they are redefining the industry as we know it. Let's define all of these terms and their differences.
As you probably already know, data science works with data that help business owners make better decisions. The amount of generated data keeps growing every day. Filtering through endless piles of data is a must if you want to improve your business and make informed decisions, but crunching all of that data is what makes things hard.
Like it or not, your website collects data, but without the right data science approach, you can't use it to your advantage. Just imagine how long it would take to filter through terabytes of data manually. Moreover, if you want to increase your efficiency with AI, you first need an ML system to filter the data and learn how everything works.
Data is the equivalent of a digital gold mine that can generate business insights you can use to make a fortune. Constant online user behavior monitoring and similar practices allow businesses to make more accurate predictions, which means more sales and higher revenue. You would be amazed at what you can learn about customers, their goals, and their habits by simply analyzing available data. That’s why AI and data science have to work together.
The process of data analytics to improve business aspects is called data science. Companies use all kinds of approaches to create engines that find the data that matters the most. Once it knows the details, it can predict user behavior, find current trends, and help you make the best decisions in the future. Of course, you have to use multiple data algorithms to find opportunities and increase accuracy. Everything is possible with the right approach, and that's up to the data scientists to find out.
Other practices such as prescriptive analytics can give you more insights into every individual customer based on their online behavior. That will give you the chance to customize offers according to what your customers expect, which will lead to more sales and higher customer satisfaction. Yes, AI and data science often sound similar, but they have a few major differences.
They might offer similar results, but unlike data science is a practice by itself. AI, on the other hand, requires the use of machine learning that not only analyzes incoming data but also learns how to find new patterns. AI then uses what the ML model learned to propose the best solutions.
AI has been around for about 70 years, but it's come a long way since its early beginnings. The lack of computing power has kept the concept of AI as a potential future solution to all kinds of problems. As computing power kept increasing, the need for AI also spiked. There are many extremely powerful computers and advanced data algorithms available today. Together, they can generate insights AI models need to make accurate predictions.
Artificial intelligence quickly found its way into many everyday applications. It can now work on all devices and offer many different possibilities. Some AI solutions are used for voice recognition, others can help improve business operations, but there's an AI solution for almost everything. That brings us to the main question - what is AI exactly?
Well, it's not something you can touch or see. It's a computing ability that reads and understands data. It then uses machine learning to learn how data works, identifies patterns that are impossible for humans to find, and finally proposes solutions based on what it found. It practically gives the machine the power to mimic human behavior but with an emphasis on data. As you can see AI and data science are very closely tied together.
If you want a more accurate definition, AI is a mix of various mathematical algorithms that work together to help computers create a bigger picture using small pieces of data. Once it understands how everything works, it can make decisions based on these hidden data patterns. It's like a virtual brain that connects different cells into one big, highly capable neural network composed of billions of small data pieces.
Just like humans need energy for their bodies to work correctly, AI needs data. It's got an endless appetite for data, and it's only as good as the amount of available data. The more data you feed into AI, the better results you'll get. Every AI model uses data generated by multiple pipelines to find patterns and offer accurate solutions. If you limit the data input, the AI model can't find patterns, so it won't be able to propose working solutions. That's exactly why AI and data science have to work together.
As mentioned earlier, even though the concept of AI has been around for decades, scientists simply didn't have the technologies needed for it to work. Computers today can complete billions of actions simultaneously, which led to machine learning and modern AI.
The largest companies in the world such as Facebook, Amazon, and Google, use machine learning and AI to constantly improve their operations. They keep employing data scientists, ML, and AI experts to improve artificial learning even more.
Machine learning or ML is the backbone of every AI solution. It's a subset of AI, and it shares the same goals. ML is a process of serving machines data allowing them to learn new things. Once it identifies patterns, it applies the new knowledge to other data fed directly to the system.
There are a few different approaches to machine learning. Some ML models need supervision, others are able to find patterns independently. Sometimes the model needs a data scientist to provide instructions, while other times ML models find patterns without any human intervention. The goal of every ML model is to identify the differences between data input and output.
It reads all variables in the data and organizes it in a way that improves system performance. When the model proves useful, it's usually fed with unknown data to test its efficiency. If the results are accurate, the model can be used to improve production and predict future problems.
ML models use various algorithms to predict, classify, and regress problems. Some involve complex decision trees, others use a random approach. Some are linear, while others are polynomial. These are just some of the so-called clustering and regression algorithms ML models use to identify various problems.
There are dozens of algorithms every ML model uses to read incoming data accurately. Data scientists still have to prepare and organize it so these ML algorithms can actually learn something in the process. They usually use ML libraries to build ML models from scratch. Most of them are simple and easy to use, allowing complete beginners to build, train, and deploy their ML models in real-world applications. Of course, understanding how these algorithms work will provide better results.
AI and data science, along with ML and deep learning are shaping the future of industries everywhere. They work together to find problems, predict issues and trends, and offer the best solutions and business applications. As computing power keeps growing, these technologies will soon find a role in every industry, and will be the driving force behind all successful businesses.
Now that you understand the differences between AI, ML, and data science, you can start working on your own solutions to improve your operation on all fronts and levels.