Machine learning, data science, and artificial intelligence are common terms in modern technology. These terms are often used interchangeably but incorrectly, which is understandable.

After all, hundreds of millions of people use the advantages of digital technologies. Yet only a small percentage of those users are experts in the field.

AI, data science, and machine learning represent valuable assets that can be used to great advantage in various industries. However, to use these tools properly, you need to understand what they are. Furthermore, knowing the difference between data science and machine learning, as well as how AI differs from both, can dispel the common misconceptions about these technologies.

Read on to gain a better understanding of the three crucial tech concepts.

Data Science

Data science can be viewed as the foundation of many modern technological solutions. It’s also the stage from which existing solutions can progress and evolve. Let’s define data science in more detail.

Definition and Explanation of Data Science

A scientific discipline with practical applications, data science represents a field of study dedicated to the development of data systems. If this definition sounds too broad, that’s because data science is a broad field by its nature.

Data structure is the primary concern of data science. To produce clean data and conduct analysis, scientists use a range of methods and tools, from manual to automated solutions.

Data science has another crucial task: defining problems that previously didn’t exist or slipped by unnoticed. Through this activity, data scientists can help predict unforeseen issues, improve existing digital tools, and promote the development of new ones.

Key Components of Data Science

Breaking down data science into key components, we get to three essential factors:

  • Data collection
  • Data analysis
  • Predictive modeling

Data collection is pretty much what it sounds like – gathering of data. This aspect of data science also includes preprocessing, which is essentially preparation of raw data for further processing.

During data analysis, data scientists draw conclusions based on the gathered data. They search the data for patterns and potential flaws. The scientists do this to determine weak points and system deficiencies. In data visualization, scientists aim to communicate the conclusions of their investigation through graphics, charts, bullet points, and maps.

Finally, predictive modeling represents one of the ultimate uses of the analyzed data. Here, create models that can help them predict future trends. This component also illustrates the differentiation between data science vs. machine learning. Machine learning is often used in predictive modeling as a tool within the broader field of data science.

Applications and Use Cases of Data Science

Data science finds uses in marketing, banking, finance, logistics, HR, and trading, to name a few. Financial institutions and businesses take advantage of data science to assess and manage risks. The powerful assistance of data science often helps these organizations gain the upper hand in the market.

In marketing, data science can provide valuable information about customers, help marketing departments organize, and launch effective targeted campaigns. When it comes to human resources, extensive data gathering, and analysis allow HR departments to single out the best available talent and create accurate employee performance projections.

Artificial Intelligence (AI)

The term “artificial intelligence” has been somewhat warped by popular culture. Despite the varying interpretations, AI is a concrete technology with a clear definition and purpose, as well as numerous applications.

Definition and Explanation of AI

Artificial intelligence is sometimes called machine intelligence. In its essence, AI represents a machine simulation of human learning and decision-making processes.

AI gives machines the function of empirical learning, i.e., using experiences and observations to gain new knowledge. However, machines can’t acquire new experiences independently. They need to be fed relevant data for the AI process to work.

Furthermore, AI must be able to self-correct so that it can act as an active participant in improving its abilities.

Obviously, AI represents a rather complex technology. We’ll explain its key components in the following section.

Key Components of AI

A branch of computer science, AI includes several components that are either subsets of one another or work in tandem. These are machine learning, deep learning, natural language processing (NLP), computer vision, and robotics.

It’s no coincidence that machine learning popped up at the top spot here. It’s a crucial aspect of AI that does precisely what the name says: enables machines to learn.

We’ll discuss machine learning in a separate section.

Deep learning relates to machine learning. Its aim is essentially to simulate the human brain. To that end, the technology utilizes neural networks alongside complex algorithm structures that allow the machine to make independent decisions.

Natural language processing (NLP) allows machines to comprehend language similarly to humans. Language processing and understanding are the primary tasks of this AI branch.

Somewhat similar to NLP, computer vision allows machines to process visual input and extract useful data from it. And just as NLP enables a computer to understand language, computer vision facilitates a meaningful interpretation of visual information.

Finally, robotics are AI-controlled machines that can replace humans in dangerous or extremely complex tasks. As a branch of AI, robotics differs from robotic engineering, which focuses on the mechanical aspects of building machines.

Applications and Use Cases of AI

The variety of AI components makes the technology suitable for a wide range of applications. Machine and deep learning are extremely useful in data gathering. NLP has seen a massive uptick in popularity lately, especially with tools like ChatGPT and similar chatbots. And robotics has been around for decades, finding use in various industries and services, in addition to military and space applications.

Machine Learning

Machine learning is an AI branch that’s frequently used in data science. Defining what this aspect of AI does will largely clarify its relationship to data science and artificial intelligence.

Definition and Explanation of Machine Learning

Machine learning utilizes advanced algorithms to detect data patterns and interpret their meaning. The most important facets of machine learning include handling various data types, scalability, and high-level automation.

Like AI in general, machine learning also has a level of complexity to it, consisting of several key components.

Key Components of Machine Learning

The main aspects of machine learning are supervised, unsupervised, and reinforcement learning.

Supervised learning trains algorithms for data classification using labeled datasets. Simply put, the data is first labeled and then fed into the machine.

Unsupervised learning relies on algorithms that can make sense of unlabeled datasets. In other words, external intervention isn’t necessary here – the machine can analyze data patterns on its own.

Finally, reinforcement learning is the level of machine learning where the AI can learn to respond to input in an optimal way. The machine learns correct behavior through observation and environmental interactions without human assistance.

Applications and Use Cases of Machine Learning

As mentioned, machine learning is particularly useful in data science. The technology makes processing large volumes of data much easier while producing more accurate results. Supervised and particularly unsupervised learning are especially helpful here.

Reinforcement learning is most efficient in uncertain or unpredictable environments. It finds use in robotics, autonomous driving, and all situations where it’s impossible to pre-program machines with sufficient accuracy.

Perhaps most famously, reinforcement learning is behind AlphaGo, an AI program developed for the Go board game. The game is notorious for its complexity, having about 250 possible moves on each of 150 turns, which is how long a typical game lasts.

Alpha Go managed to defeat the human Go champion by getting better at the game through numerous previous matches.

Key Differences Between Data Science, AI, and Machine Learning

The differences between machine learning, data science, and artificial intelligence are evident in the scope, objectives, techniques, required skill sets, and application.

As a subset of AI and a frequent tool in data science, machine learning has a more closely defined scope. It’s structured differently to data science and artificial intelligence, both massive fields of study with far-reaching objectives.

The objectives of data science are pto gather and analyze data. Machine learning and AI can take that data and utilize it for problem-solving, decision-making, and to simulate the most complex traits of the human brain.

Machine learning has the ultimate goal of achieving high accuracy in pattern comprehension. On the other hand, the main task of AI in general is to ensure success, particularly in emulating specific facets of human behavior.

All three require specific skill sets. In the case of data science vs. machine learning, the sets don’t match. The former requires knowledge of SQL, ETL, and domains, while the latter calls for Python, math, and data-wrangling expertise.

Naturally, machine learning will have overlapping skill sets with AI, since it’s its subset.

Finally, in the application field, data science produces valuable data-driven insights, AI is largely used in virtual assistants, while machine learning powers search engine algorithms.

How Data Science, AI, and Machine Learning Complement Each Other

Data science helps AI and machine learning by providing accurate, valuable data. Machine learning is critical in processing data and functions as a primary component of AI. And artificial intelligence provides novel solutions on all fronts, allowing for more efficient automation and optimal processes.

Through the interaction of data science, AI, and machine learning, all three branches can develop further, bringing improvement to all related industries.

Understanding the Technology of the Future

Understanding the differences and common uses of data science, AI, and machine learning is essential for professionals in the field. However, it can also be valuable for businesses looking to leverage modern and future technologies.

As all three facets of modern tech develop, it will be important to keep an eye on emerging trends and watch for future developments.

Related posts

Wired: Think Twice Before Creating That ChatGPT Action Figure
OPIT - Open Institute of Technology
OPIT - Open Institute of Technology
May 12, 2025 6 min read

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  • Wired, published on May 01st, 2025

People are using ChatGPT’s new image generator to take part in viral social media trends. But using it also puts your privacy at risk—unless you take a few simple steps to protect yourself.

By Kate O’Flaherty

At the start of April, an influx of action figures started appearing on social media sites including LinkedIn and X. Each figure depicted the person who had created it with uncanny accuracy, complete with personalized accessories such as reusable coffee cups, yoga mats, and headphones.

All this is possible because of OpenAI’s new GPT-4o-powered image generator, which supercharges ChatGPT’s ability to edit pictures, render text, and more. OpenAI’s ChatGPT image generator can also create pictures in the style of Japanese animated film company Studio Ghibli—a trend that quickly went viral, too.

The images are fun and easy to make—all you need is a free ChatGPT account and a photo. Yet to create an action figure or Studio Ghibli-style image, you also need to hand over a lot of data to OpenAI, which could be used to train its models.

Hidden Data

The data you are giving away when you use an AI image editor is often hidden. Every time you upload an image to ChatGPT, you’re potentially handing over “an entire bundle of metadata,” says Tom Vazdar, area chair for cybersecurity at Open Institute of Technology. “That includes the EXIF data attached to the image file, such as the time the photo was taken and the GPS coordinates of where it was shot.”

OpenAI also collects data about the device you’re using to access the platform. That means your device type, operating system, browser version, and unique identifiers, says Vazdar. “And because platforms like ChatGPT operate conversationally, there’s also behavioral data, such as what you typed, what kind of images you asked for, how you interacted with the interface and the frequency of those actions.”

It’s not just your face. If you upload a high-resolution photo, you’re giving OpenAI whatever else is in the image, too—the background, other people, things in your room and anything readable such as documents or badges, says Camden Woollven, group head of AI product marketing at risk management firm GRC International Group.

This type of voluntarily provided, consent-backed data is “a gold mine for training generative models,” especially multimodal ones that rely on visual inputs, says Vazdar.

OpenAI denies it is orchestrating viral photo trends as a ploy to collect user data, yet the firm certainly gains an advantage from it. OpenAI doesn’t need to scrape the web for your face if you’re happily uploading it yourself, Vazdar points out. “This trend, whether by design or a convenient opportunity, is providing the company with massive volumes of fresh, high-quality facial data from diverse age groups, ethnicities, and geographies.”

OpenAI says it does not actively seek out personal information to train models—and it doesn’t use public data on the internet to build profiles about people to advertise to them or sell their data, an OpenAI spokesperson tells WIRED. However, under OpenAI’s current privacy policy, images submitted through ChatGPT can be retained and used to improve its models.

Any data, prompts, or requests you share helps teach the algorithm—and personalized information helps fine tune it further, says Jake Moore, global cybersecurity adviser at security outfit ESET, who created his own action figure to demonstrate the privacy risks of the trend on LinkedIn.

Uncanny Likeness

In some markets, your photos are protected by regulation. In the UK and EU, data-protection regulation including the GDPR offer strong protections, including the right to access or delete your data. At the same time, use of biometric data requires explicit consent.

However, photographs become biometric data only when processed through a specific technical means allowing the unique identification of a specific individual, says Melissa Hall, senior associate at law firm MFMac. Processing an image to create a cartoon version of the subject in the original photograph is “unlikely to meet this definition,” she says.

Meanwhile, in the US, privacy protections vary. “California and Illinois are leading with stronger data protection laws, but there is no standard position across all US states,” says Annalisa Checchi, a partner at IP law firm Ionic Legal. And OpenAI’s privacy policy doesn’t contain an explicit carve-out for likeness or biometric data, which “creates a grey area for stylized facial uploads,” Checchi says.

The risks include your image or likeness being retained, potentially used to train future models, or combined with other data for profiling, says Checchi. “While these platforms often prioritize safety, the long-term use of your likeness is still poorly understood—and hard to retract once uploaded.”

OpenAI says its users’ privacy and security is a top priority. The firm wants its AI models to learn about the world, not private individuals, and it actively minimizes the collection of personal information, an OpenAI spokesperson tells WIRED.

Meanwhile, users have control over how their data is used, with self-service tools to access, export, or delete personal information. You can also opt out of having content used to improve models, according to OpenAI.

ChatGPT Free, Plus, and Pro users can control whether they contribute to future model improvements in their data controls settings. OpenAI does not train on ChatGPT Team, Enterprise, and Edu customer data⁠ by default, according to the company.

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LADBible and Yahoo News: Viral AI trend could present huge privacy concerns, says expert
OPIT - Open Institute of Technology
OPIT - Open Institute of Technology
May 12, 2025 4 min read

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You’ve probably seen them all over Instagram

By James Moorhouse

Experts have warned against participating in a viral social media trend which sees people use ChatGPT to create an action figure version of themselves.

If you’ve spent any time whatsoever doomscrolling on Instagram or TikTok or dare I say it, LinkedIn recently, you’ll be all too aware of the viral trend.

Obviously, there’s nothing more entertaining and frivolous than seeing AI generated versions of your co-workers and their cute little laptops and piña coladas, but it turns out that it might not be the best idea to take part.

There may well be some benefits to artificial intelligence but often it can produce some pretty disturbing results. Earlier this year, a lad from Norway sued ChatGPT after it falsely claimed he had been convicted of killing two of his kids.

Unfortunately, if you don’t like AI, then you’re going to have to accept that it’s going to become a regular part of our lives. You only need to look at WhatsApp or Facebook messenger to realise that. But it’s always worth saying please and thank you to ChatGPT just in case society does collapse and the AI robots take over, in the hope that they treat you mercifully. Although it might cost them a little more electricity.

Anyway, in case you’re thinking of getting involved in this latest AI trend and sharing your face and your favourite hobbies with a high tech robot, maybe don’t. You don’t want to end up starring in your own Netflix series, à la Black Mirror.

Tom Vazdar, area chair for cybersecurity at Open Institute of Technology, spoke with Wired about some of the dangers of sharing personal details about yourself with AI.

Every time you upload an image to ChatGPT, you’re potentially handing over ‘an entire bundle of metadata’ he revealed.

Vazdar added: “That includes the EXIF data attached to the image file, such as the time the photo was taken and the GPS coordinates of where it was shot.

“Because platforms like ChatGPT operate conversationally, there’s also behavioural data, such as what you typed, what kind of images you asked for, how you interacted with the interface and the frequency of those actions.”

Essentially, if you upload a photo of your face, you’re not just giving AI access to your face, but also the whatever is in the background, such as the location or other people that might feature.

Vazdar concluded: “This trend, whether by design or a convenient opportunity, is providing the company with massive volumes of fresh, high-quality facial data from diverse age groups, ethnicities, and geographies.”

While we’re at it, maybe stop using ChatGPT for your university essays and general basic questions you can find the answer to on Google as well. The last thing you need is AI knowing you don’t know how to do something basic if it does takeover the world.

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