ML and AI play distinct roles

In today's technologically advanced era, the terms artificial intelligence and machine learning are often intertwined along with data science. While they're all closely related, they are not synonymous with each other.

To understand the journey from ML to AI, it’s important to first grasp their distinctions and the crucial role ML plays in shaping the AI systems that power technology we use today, such as self-driving cars, virtual assistants like Amazon Alexa and Google Home devices, and customer service chatbots — to name a few.


What is machine learning?

Machine learning is a subset of AI that focuses on algorithms and statistical models to enable machines to perform specific tasks without using human intervention. Instead, the AI systems rely on patterns and inference. In simpler terms, ML is about ‘teaching’ the machines to analyze and learn from data.

In relation to data science, which is a subfield of the broader computer science, data scientists create the machine learning algorithms and also are a key part of the data analysis workflows, including ‘cleaning’ the data, which we’ll talk about later.

Netflix's show recommendations, email spam filters, and even platforms like TensorFlow (an open-source library for numerical computation and machine learning) use ML models to optimize their AI-based systems. In other words, these systems will utilize the machine learning algorithms to ‘learn’ how to adequately sift through vast and evolving data, recognize patterns, and make data-driven decisions — often in real-time.


What is artificial intelligence?

Artificial Intelligence is a subset of data science and is all about the machines or computer systems capable of carrying out desired tasks in ways we'd deem 'smart' or simulate human intelligence. This includes use cases spanning from Google's search and IBM's Watson to autonomous cars and robotics.

Machine learning pattern-recognizing algorithms and the more complex neural networks form the backbone of many AI systems, enabling the systems to learn and optimize tasks like computer vision and natural language processing (NLP).


The role of ML in AI

So, how do we progress from ML to AI? It begins with data.

  1. Data collection: At the heart of every AI system lies data. Quality, vast data sets are vital for training robust machine learning models.
  2. Data processing: Post collection, data undergoes cleaning and processing by data scientists, ensuring the information fed into ML algorithms is accurate, relevant, and unbiased.
  3. Choosing the algorithm: Depending on the desired task, an appropriate algorithm is chosen — be it classification, regression, or even a more complex series of algorithms to make up an ML neural network — for training tasks like computer vision or NLP. The algorithm is designed to identify patterns in the vast data.
  4. Training the model: Here, the system ‘learns’ from the data, using techniques, such as reinforcement learning, unsupervised learning and others, to optimize the algorithm, fine-tune pattern recognition, and make output or forecasting decisions.
    This phase is resource-intensive, often requiring specialized hardware for tasks like deep learning, which takes on a grander scale of data engineering and neural networks.
  5. Evaluation and testing: After training, the model is evaluated with unseen data, determining its accuracy and reliability.
  6. Deployment: Once satisfactory, it integrates into the AI system, ready for real-world tasks.

Through these steps, ML offers AI systems the ability to learn from data and evolve. As more data becomes available and algorithms become more advanced, the AI systems improve, becoming more efficient at completing the desired outcomes.


The symbiotic relationship

In the world of data science, AI and machine learning share a symbiotic relationship. While ML provides the tools and techniques, AI offers the broader framework, giving context and purpose. Imagine AI as a vehicle with ML as its engine. Neural networks, automation techniques, and platforms like TensorFlow are the intricate parts making the engine run, driving the vehicle closer to its ‘human-like intelligence’ destination.


Future prospects

The boundaries of AI and machine learning continue to expand. With advancements in quantum computing, big data, and research in neural networks, the journey from ML to AI is accelerating. Automation is becoming more prevalent, with systems capable of self-learning and adapting. Computer vision is allowing machines to ‘see’ and understand their surroundings, while natural language processing lets them understand human language, bridging the communication gap.

As we delve deeper into the realms of data science, artificial intelligence and machine learning, understanding the journey from the fundamentals to advanced AI applications becomes paramount. With AI tools and machine learning techniques, like complex neural networks that power functionality from speech recognition to healthcare applications, the future promises innovations that will reshape how we perceive technology.