AI Science and Philosophy Series: AI vs. ML

Apr 26, 2023

In this second article of our AI Science and Philosophy series, we will explore some tech fields such as AI, ML, Deep Learning, Neural Networks, Data Science which tend to be used interchangeably in conversation and will demystify their relationship with each other.

Each is essentially a component of the prior term. That is, machine learning is a subfield of artificial intelligence. Deep learning is a subfield of machine learning, and neural networks make up the backbone of deep learning algorithms. In fact, it is the number of node layers, or depth, of neural networks that distinguishes a single neural network from a deep learning algorithm, which must have more than three.

How is AI different from Machine Learning?

Artificial Intelligence (AI) is a field which encompasses computer science, logic, rules, probability, continuous mathematics, perception, reasoning, learning, and action in order to build intelligent solutions for given problems.

Philosophers staked out some of the fundamental ideas of AI, but the leap to a formal science required a level of mathematical formalization in three fundamental areas: logic, computation, and probability. Bayes’ rule underlies most modern approaches to uncertain reasoning in AI systems.

Deep Blue, the chess-playing computer

Before the development of machine learning, artificially intelligent machines or programs had to be programmed to respond to a limited set of inputs. Deep Blue, a chess-playing computer that beat a world chess champion in 1997, could “decide” its next move based on an extensive library of possible moves and outcomes. But the system was purely reactive. For Deep Blue to improve at playing chess, programmers had to go in and add more features and possibilities.

For a machine or program to improve on its own without further input and rules from human programmers, we need machine learning.

Machine learning (ML) is the field devoted to understanding and building methods that let machines "learn" – that is, methods that leverage data to improve computer performance on some set of tasks.

With simple AI, a programmer can tell a machine how to respond to various sets of instructions by hand-coding each “decision.”

With machine learning models, computer scientists can “train” a machine by feeding it large amounts of data. The machine follows a set of rules—called an algorithm—to analyze and draw inferences from the data. The more data the machine parses, the better it can become at performing a task or making a decision.

IBM Watson, the machine learning cousin of Deep Blue

In 2011, IBM Watson beat two Jeopardy champions in an exhibition match using machine learning. Watson’s programmers fed it thousands of question and answer pairs, as well as examples of correct responses. When given just an answer, the machine was programmed to come up with the matching question. If it got it wrong, programmers would correct it. This allowed Watson to modify its algorithms, or in a sense “learn” from its mistakes. By the time Watson faced off against the Jeopardy champions, in a matter of seconds, it could parse 200 million pages of information and generate a list of possible answers, ranked by how likely they were to be right—even if it had never seen the particular Jeopardy clue before.

The basic concept of machine learning in data science involves using statistical learning and optimization methods that let computers analyze datasets and identify patterns. Machine learning techniques leverage data mining to identify historic trends and inform future models.

The typical supervised machine learning algorithm consists of roughly three components:

1. A decision process: A recipe of calculations or other steps that takes in the data and “guesses” what kind of pattern your algorithm is looking to find.
2. An error function: A method of measuring how good the guess was by comparing it to known examples (when they are available). Did the decision process get it right? If not, how do you quantify “how bad” the miss was?
3. An updating or optimization process: A method in which the algorithm looks at the miss and then updates how the decision process comes to the final decision, so next time the miss won’t be as great.

What is a neural network?

Neural networks, also known as artificial neural networks (ANNs) or simulated neural networks (SNNs), are a subset of machine learning and are at the heart of deep learning algorithms. Their name and structure are inspired by the human brain, mimicking the way that biological neurons signal to one another. Artificial neural networks (ANNs) are comprised of a node layers, containing an input layer, one or more hidden layers, and an output layer. Each node, or artificial neuron, connects to another and has an associated weight and threshold. If the output of any individual node is above the specified threshold value, that node is activated, sending data to the next layer of the network. Otherwise, no data is passed along to the next layer of the network. Neural networks rely on training data to learn and improve their accuracy over time. However, once these learning algorithms are fine-tuned for accuracy, they are powerful tools in computer science and artificial intelligence, allowing us to classify and cluster data at a high velocity. Tasks in speech recognition or image recognition can take minutes versus hours when compared to the manual identification by human experts.

How is deep learning different from neural networks?

The “deep” in deep learning is referring to the depth of layers in a neural network. A neural network that consists of more than three layers—which would be inclusive of the inputs and the output—can be considered a deep learning algorithm.

Special shout out to Data Science

Data science is an interdisciplinary academic and business field which combines math and statistics, specialized programming, advanced analytics, artificial intelligence and machine learning with specific subject matter expertise to uncover actionable insights hidden in an organization’s data. These insights can be used to guide decision making and strategic planning.

Conclusion: These fields and concepts are not mutually exclusive

In fact, they are all related to each other. Understanding the relationships between them is important for anyone who wants to work in these fields or wants to collaborate with researchers and experts in these areas. By demystifying them, we can better understand the technology that is driving innovation and transforming our world.