AI, Machine Learning, and Data Science: Essential Features, Differences, and Relationships

Technological solutions based on data science (DS), artificial intelligence (AI), and machine learning (ML) become ingrained in our everyday lives. Though, these terms can’t substitute for each other. The article will tell you about these technologies and provide you with necessary information about their differences and relationships.

Data Science: What is It? 

Data science is a field of knowledge that helps obtain and process new information from databases. DS also selects, analyzes, and prepares structured and unstructured data according to solid analytical evidence. 

Data science is also necessary for:

  • Business research and questionnaire creation
  • Predicted analytics, represented as event and demand projections
  • Tactical optimization, including business process and marketing campaigns improvement
  • Recommendation systems, such as Amazon and Netflix ones
  • Automatic decision systems, like drones or facial recognition

For example, Netflix processes its data stores for viewing templates. As a result, it helps to understand users’ preferences better and conclude about the future Netflix series.

DS is always ruled by experts in this field. Data scientists manage the technology by observing the figures and providing data insights. Though, the data science specialist should be able to:

  • Process the data
  • Be experienced in statistical analytics
  • Understand the analysis tools (such as SAS and others)
  • Use the programming languages and tools (like SQL, R, Python, or RapidMiner) properly

But, it is only a drop in the ocean. The experts might also be well-informed in financial computing, quality assurance, or model building to understand the working domain more precisely and create qualitative DS solutions.

The Matter of Artificial Intelligence

It is no more a problem to create intelligent devices that can function and reflect like humans. AI can do almost everything, like playing chess in apps or recognizing the speaker’s voice through unique recognition systems. For instance, Amazon used such one for Alexa digital voice assistant, which replies to questions and identifies speech.

AI devices are also highly skilled for dealing with problems better than humans. Apps based on such technology have the following features: 

  • Optimization (e.g., Google Maps creating a pathway to the destination point)
  • Play-acting algorithms (such as Deep Blue)
  • Control theory and robotization (as walking a robot)
  • Reward training
  • Intelligent speech, etc. 

Thanks to the functionalities above, robots and self-driving vehicles started existing. For example, this way Amazon built up robots that deliver products from warehouses right to customers’ houses. The AI algorithms permanently upgrade, and it allows the users to improve their business profitability and solve different issues connected to staff costs and timing.

During the development of AI-based solutions, you may need such tools as Caffe, Chainer, TensorFlow, and others.

Machine Learning as a Subset of Artificial Intelligence

Among all AI tools, machine learning deserves a particular focus. It is a technology that allows the computer self-education, analyzing the obtained data with the help of specific algorithms.

In brief, computers can code themselves, thanks to ML. If you compare it with programming, you can see that the last is an automation procedure, while machine learning is dual automation. Thus, it can help the users achieve better results in minimal time.

You can see the perfect example of machine learning experts’ activity in Netflix’s improved movie recommendations for users. Therefore, people are engaged in more dynamic site utilization due to predictive analytics mechanisms. The machine algorithms analyze visitors’ preferences and offer films to watch according to the last ones.

ML engineers work with MALLET, Open Source/Apache Tomcat, C++ or Python programming, and other languages and tools. With the help of such tools they can create a streaming service like TikTok, Netflix, and Hangouts, or similar intelligent solutions.

Data Science vs. ML vs. AI

We have studied data science, machine learning, and artificial intelligence separately. Now it is time to check out the differences and connections among all these terms.

The Relations and Differences Between Data Science and Machine Learning

ML is also tightly connected to DS. Machine learning patterns depend on data, utilizing it as a training set to present more precise business predictions. Though, data science can be adapted not only to machine learning. The info may be received from mechanical or machine processes or other ones. 

The main distinction is that data science is not limited to statistics or machine algorithms but includes the whole data processing set. Thus, it contains such spheres as data-driven decisions, data engineering, or deployment in production mode.

AI vs. Data Science

Let’s look at the differences between artificial intelligence and data science spheres. 

DS manages data, using AI as an instrument for forecasting, predicting, pattern identification, and historical data interpretation. In this case, ML and AI assist DS specialists in gathering insights about the concurrences. 

Data science includes models, which process statistical data, visualize them, or make predictions. Artificial intelligence, in its turn, implements patterns that make machines behave like people, predict the future, and obey the algorithms.

Distinctions of AI and Machine Learning

Now let’s analyze the differences between artificial intelligence and machine learning.

AI allows the computer to imitate human behavior and make the processes automated. The bright examples of AI use are such popular solutions as Siri, Alexa Voice, and Google Home.

Machine learning technology is a set of techniques that permits computers to make conclusions from the info, send it to AI applications, and automate DS processes. Such popular audio/video prediction systems like YouTube, Amazon, Netflix, and Spotify can serve as samples of ML implementation in everyday life.  

How can Data Science, AI, and ML work together 

Despite the distinctions, data science, artificial intelligence, and machine learning technologies collaborate in vehicle automation processes (self-driving automobiles) and customer mechanization (voice assistants). Moreover, these methodologies also allow companies to save money and human resources to send them both to more urgent tasks. 

Thus, some solutions, like building up a driverless vehicle, need to use all three approaches simultaneously. Let’s see how these three methodologies work in combination:

  • With the help of machine learning, you can build up an information set with streetside photos and make the algorithm identify the stop signs on them. It can be done using the cameras of the vehicle.
  • Artificial intelligence solves the problem of vehicle control. The car should not only identify the signs but also put the brakes on at a required time. Moreover, you should bear in mind the abnormal weather conditions (slippery roads, etc.) and make the automobile stop according to such situations.
  • If your car sometimes misses the stop signs, you need to investigate the test data. After doing it, you will see that the number of incorrect results falls at night time, and the training data contains only streetside pics taken in the daylight. So, with the help of data science analysis, such an error can be revealed. To solve it, you should add some nighttime photos and go back to training.

Thus, you see that all three technologies go shoulder to shoulder. ML and AI can’t be separated in creating self-learning solutions or adaptive systems because machine learning makes the algorithms, and artificial intelligence acts based on them. 

The same goes for DS and ML. Machine devices can’t learn without information, so data science better goes with machine learning. 

Conclusion

We looked briefly at the essential features of data science, artificial intelligence, machine learning, the differences among these three technologies, and their relationships. 

But if you want to understand the matter, you will need help from field-oriented specialists. Data scientists and AI developers will help you create an innovative software product, enhancing your business value. 

Frequently Asked Questions

What are the distinctions between AI and ML?

Artificial intelligence makes computers copy human behavior. Thanks to that, the machines can function like ordinary people and complete their tasks.
Machine learning is a branch of AI represented by a set of methods. They help machines make conclusions from the given info and improve it.

Is data science related to ML?

Yes! Machine learning algorithms function thanks to the info given by data science. This way, they turn into more important and smarter ones. 
Though, it is not the same. DS is responsible for the whole data range and isn’t limited to statistics or analytic algorithms, like machine learning.

What is the distinction between data science and AI?

AI is a set of tools and methodologies that allows machines to imitate human actions and intellectual processes with the help of models based on solution trees and logic.
Data science is a field that works with various statistical techniques helpful for data analysis, visualization, and making predictions for AI systems. 

Author

Vitaly Kuprenko is a writer for Cleveroad. It’s a web and mobile app development company with headquarters in Ukraine. He enjoys writing about technology and digital marketing.

Write A Comment