How AI, Machine Learning, and Data Science Are Intertwined?

Today all the world is engaged with AI, machine learning, and data science. All these technologies bring significant benefits to businesses and accelerate digital transformation. However, these are the terms that have confused a lot of people and if you’re one among them, let me resolve it for you.

Machine Learning vs. AI vs. Data Science

All these technologies are just a subset of each other. AI is a broader umbrella under which data science and machine learning go. Besides, machine learning is a branch of AI. This is just a short state of how the technologies are intertwined. But before diving deeper, take a brief look at each technology separately.

Artificial Intelligence 

Artificial intelligence is a technique that teaches machines to act as humans do. AI makes it possible to learn from the experience. The machines just respond to the new input. AI can be trained to fulfill specific tasks by analyzing a vast amount of information and identifying their patterns. 

There are many examples of AI like self-driving cars, smart assistants,  face recognition systems, etc. One of the best examples of an AI appliance is chatbots. Companies can get substantial benefits by building a chatbot since it provides clients instant responses. For example, US-based fast-food enterprise Taco Bell created its own chatbot to automate sales processes. TacoBot enables users to make orders in the restaurant using Slack. If the customers don’t want to stand in line or look for a needed answer in the FAQ, they can utilize TacoBot to receive answers immediately.

Machine Learning 

This technology was introduced as an answer to the following questions:

  • How to train the big complex model most efficiently? 
  • How to teach more robots to imitate human behavior?
  • How to create an operational model of the human brain?

ML is a subgroup of artificial intelligence which employs statistical methods to make machines to perform human tasks and improve with experience automatically. Using machine learning, you don’t need to write code. You transmit information to the generic algorithm, and its design logic based on submitted data.

Data Science 

There are many misconceptions about data science. Data science isn’t about creating complicated models, visualization, or writing code. It is about utilizing data to make as much impact as possible for your business. This impact can be in the form of multiple things such as insights, data products, or product recommendations. The core task of data science is to solve real business-related problems using data. Since warehouses store a large volume of raw information, DS allows business owners to identify the needed information from a sea of data.  

The Connection Between Data Science and Machine Learning

In the basics of machine learning algorithms lies data submitted by data science. Thus, ML provides more accurate and well-informed business predictions.

However, data science encompasses more than the machine learning field. For example, in DS, information can be gathered automatically or manually (e.g., survey data). Furthermore, data science applies to a whole range of data processing (e.g., data integration, distributed architecture, data engineering, etc.). In turn, ML is limited to the statistical side.

The Relation Between Artificial Intelligence and Data Science

Data science takes care more of the tech part of data management. It applies AI to translate historical information, identify patterns, and claim short-term and long-term predictions. In this matter, AI assists data scientists in obtaining data about their core competitors through insights.

Data science employs a vast amount of statistical techniques to analyze, visualize information, and make forecasts. While artificial intelligence carries out the models to forecast coming events and engage in algorithms. Data science helps to design different models utilizing statistical insights. AI interacts with these models to teach machines to imitate human intelligence. 

Artificial Intelligence vs. Machine Learning

Artificial intelligence aims to develop machines that will execute human-like tasks. Machine learning is a branch of artificial intelligence involving multiple methods that enable computers to make conclusions based on data and submit them to AI-based apps. AI was developed to solve various business challenges and improve customer services. ML is taking data science to the highest automation level. 

It’s better to understand the essence of both AI and ML through their use cases. Every day, we deal with smart assistants like Alexa or Google Home. So it’s an AI service area. In turn, adaptive video systems like YouTube or Netflix are considered to be ML-powered.

Even though AI and machine learning have their own service area, they can cooperate to deliver personalized and predictive customer experiences. As a result, these technologies help various-sized businesses save thousands of dollars by automating routine processes and reducing costs due to predictive maintenance.

How Data Science, AI, and Machine Learning Can Act Jointly 

Let’s imagine we want to program the driverless car to stop at stop signs. In this case, ML, data science, and AI go hand in hand. Now, let’s see what part of functionality each technology is responsible for.

Machine learning. The car should identify the stop signs utilizing its cameras. Thus, the dataset with various streetside objects pictures need to be made and an algorithm to be trained to recognize stop signs on multiple photos.

Artificial intelligence. Once the car identifies the stop sign, it needs to start to brake. The car should be programmed to brake just in time without delays. Besides, various road conditions, like traffic jams, slippery roads, icy roads, should be included in the test.

Data science. Due to the complexity of the process and many factors included, self-driving cars do not always recognize the stop signs.  This is where data science comes in handy. For example, one of the reasons why the cars miss the signs is the time of day. Commonly, autonomous cars are trained to recognize the stop signs at all times, while in most cases, the training data encompass objects in the daytime. The task of DS is to find out the causal relationships. The further changes depend on obtained data.

That’s a prime example of how these technologies can work together. You can’t just apply data science or machine learning. Machines require data to be trained, and DS goes better with machine learning algorithms.  As well as, machine learning can’t be used for self-learning or predictive systems without AI. Artificial intelligence designs devices to think like humans,  ML empowers algorithms to be taught from data.

Summing Up

As you can see, these advanced technologies are connected, affecting certain parts of the whole process. In the current world, data is everything. So it’s better to use AI, machine learning, and data science in tandem to strengthen business’ possibilities and satisfy customers’ expectations.

Author’s bio:

Vitaly Kuprenko is a technical writer at Cleveroad, a web and mobile app development company in Ukraine. He enjoys writing about tech innovations and digital ways to boost businesses.

Lavanya Rathnam

Lavanya Rathnam is an experienced technology, finance, and compliance writer. She combines her keen understanding of regulatory frameworks and industry best practices with exemplary writing skills to communicate complex concepts of Governance, Risk, and Compliance (GRC) in clear and accessible language. Lavanya specializes in creating informative and engaging content that educates and empowers readers to make informed decisions. She also works with different companies in the Web 3.0, blockchain, fintech, and EV industries to assess their products’ compliance with evolving regulations and standards.

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