Artificial Intelligence, Machine Learning, and Deep Learning Explained: How They Impact Your Business

In today’s fast-changing tech world, AI, ML, and DL are revolutionizing industries and business operations. We often hear these terms, but what do they actually mean? If you’re unfamiliar, this post is for you. At Bitmetric, understanding the differences is key to helping clients unlock their potential for business intelligence. Here, we’ll break down and explain each concept.

Artificial Intelligence

AI is a broad term that refers to the development of intelligent systems capable of performing tasks that typically require human intelligence, such as speech recognition, decision-making, and problem-solving. The goal of AI is to create machines that can operate autonomously and adapt to changing environments. AI seeks to emulate human cognitive functions by processing data, understanding context, and making informed decisions autonomously. Its applications are broad, spanning industries from healthcare to finance and beyond.

Despite the growing interest from academia, industry, and public institutions, there is no universally accepted definition of AI. Since measuring human intelligence is a complex task and its definition is subjective, various attempts have been made to quantify it. The objective definition of something as subjective and abstract as intelligence has falsely led to an impression that a well-defined definition cannot be achieved. As a result, definitions found in literature, policies, and market reports are vague and focus on the ideal rather than a measurable research concept.

The research of the European commission has identified the following common features in AI definitions:

• Environmental awareness: recognizing and understanding the complexities of real-world environments.
• Information processing: gathering and analysing input data.
• Decision making (including reasoning and learning): with a certain level of autonomy making informed decisions and performing tasks, which includes adapting and responding to environmental changes.
• Achievement of specific goals: the primary purpose of AI systems is to successfully achieve predefined objectives. 

The European Commission has established a High-Level Expert Group (HLEG) on Artificial Intelligence to aid in executing the European AI Strategy. One of HLEG’s primary tasks was to develop a concrete, foundational definition of AI. Based on a review of 55 documents, including institutional reports, policy papers, research publications, and market studies, the HLEG created the following definition:

 HLEG definition of AI is: “Artificial intelligence (AI) systems are software (and possibly also hardware) systems designed by humans (2) that, given a complex goal, act in the physical or digital dimension by perceiving their environment through data acquisition, interpreting the collected structured or unstructured data, reasoning on the knowledge, or processing the information, derived from this data and deciding the best action(s) to take to achieve the given goal. AI systems can either use symbolic rules or learn a numeric model, and they can also adapt their behaviour by analysing how the environment is affected by their previous actions.”

Source: AI WATCH. Defining Artificial Intelligence

Machine Learning

Machine learning, a subset of AI, enables computers to learn from data and improve their performance over time without explicit programming. It focuses on developing algorithms that can recognize patterns in data and make predictions or decisions based on those patterns. Machine learning is particularly valuable for business intelligence (BI), as it automates the process of extracting insights from vast datasets.

There are three types of machine learning:

1. Supervised Learning: The model is trained using labeled data, meaning the input comes with corresponding output labels.
2. Unsupervised Learning: The model works with raw, unlabeled data, learning to identify patterns and relationships.
3. Reinforcement Learning: The model learns from feedback provided by its environment, improving its decision-making through trial and error.

The use of machine learning for business applications—such as sentiment analysis, speech recognition, and computer vision—has grown exponentially and is expected to keep growing with organizations increasingly adopting it to streamline processes and drive innovation.

Deep Learning

Deep learning is a more advanced subset of machine learning, characterized by its use of artificial neural networks with multiple layers to process vast and complex datasets. The “deep” in deep learning refers to the depth of these layers, which allow the network to recognize complex patterns in unstructured data, such as images, audio, and text.

Unlike traditional machine learning models, which rely on structured data and feature engineering by human experts, deep learning models can automatically extract and learn hierarchical features from data. This makes deep learning particularly powerful for tasks such as image recognition, natural language processing, and autonomous systems.

Deep learning models are more computationally intensive than machine learning models, often requiring large amounts of data and computational power to achieve high accuracy. However, their ability to handle unstructured data opens up new possibilities for businesses to leverage previously untapped sources of information, such as internal documents, social media content or customer reviews, for predictive analytics and decision-making.

AI vs. ML vs. DL: What sets Them Apart?

To visualize the relationship between these three concepts, think of AI as an umbrella term encompassing both machine learning and deep learning. AI represents the overarching goal of building intelligent machines, enhance decision-making processes, automate routine tasks, and drive efficiencies across business operations. While ML and DL are the technologies that help us achieve that goal. Machine learning with its strong alignment to BI, enables businesses to better understand their structured data and make informed decisions. Deep learning pushes these capabilities further, uncovering insights from unstructured data that would be impossible to extract using traditional methods.

Unlocking the potential of AI: Your business’s next step

As AI continues to advance, machine learning and deep learning will play increasingly important roles in shaping the future of data analytics. These technologies empower businesses to unlock insights, automate processes, and make data-driven decisions at unprecedented speed and scale. For many businesses, the idea of adopting AI, machine learning, or deep learning might seem overwhelming. But it doesn’t have to be. At Bitmetric, we can help you explore how these technologies can be used in your business whether it’s starting small with machine learning models or diving into more advanced AI projects. For businesses looking to stay ahead of the curve, the adoption of AI, machine learning, and deep learning is can be the right way forward. Whether it’s improving customer experience or optimizing supply chains, or optimizing procurement using predictive analytics, these technologies are already reshaping the business landscape. As a data analytics consultancy, we are committed to helping you explore and implement these transformative technologies. Whether you are just beginning your AI journey or looking to scale your machine learning capabilities, we can provide the expertise and guidance you need to succeed. Let’s discuss how AI, machine learning, and deep learning can unlock new opportunities for your business. Get in contact with us.

By understanding the differences between AI, machine learning, and deep learning, businesses can strategically implement the right technology for their needs, maximizing the potential for growth and innovation. Reach out to our team to explore the possibilities and start leveraging the power of data today.

Milan Bratu

Milan joined Bitmetric in June 2024, after graduating from the master programme information systems in Finland. He is a junior BI professional with a diverse skill set in ETL, data analysis, project management, back-end, and front-end visualizations. His keen attention to detail, results-oriented mindset, and ability to work independently enable him to consistently deliver high-quality, impactful solutions. Milan’s methodical approach to solving complex challenges helps businesses transform data into actionable insights that drive success.

When he’s not diving into data, you’ll find Milan either scaling a bouldering wall, practicing Brazilian Jiu-Jitsu (BJJ) or MMA, playing padel, or playing the piano.