Artificial Intelligence and Machine Learning: What’s the Difference?

We live in an era of exponential growth, particularly when it comes to information. Hundreds of millions of terabytes of data are created, captured, copied, and consumed annually, and the volume increases every year.

For data scientists, sifting through such massive troves of unstructured information is unimaginable. That’s why many companies, governments, and academic institutions have begun using artificial intelligence and machine learning to improve computing efficiency and information processing.

Despite the rise in popularity of artificial intelligence (AI) and machine learning (ML), they remain widely misunderstood. They aren’t just futuristic sci-fi concepts from popular media; they’re real, useful tools in modern data science.

Many use the terms “AI” and “ML” interchangeably, often leading to more questions than answers. What is artificial intelligence? What is machine learning? How are they connected?

We’ll dispel the confusion surrounding these technologies by exploring the difference between AI and ML. We’ll also examine the benefits of these tools for algorithmic trading and how you can leverage AI and ML in your own trading strategy. 

What is artificial intelligence (AI)?

Artificial intelligence, or AI, refers to the capacity of machines to engage in problem-solving, decision-making, knowledge representation, and learning.

Although the founding theories of artificial intelligence trace their origins back to philosophers and logicians of antiquity, the study of artificial intelligence did not begin in earnest until the development of electronic computers in the 1940s. In 1956, American computer scientist John McCarthy and his colleagues first coined the term “artificial intelligence,” and the field of AI research was born.

AI employs various computational tools designed to accomplish specific goals. Search tools use planning algorithms and mathematical optimization to find the shortest route to a target goal or solution. For reasoning problems, AI uses formal logic statements and knowledge bases to create inferences from previously known information. It can also handle uncertainty using probabilistic algorithms and learn to recognize patterns by assigning labels to observations. 

You can find artificial intelligence at work in various digital applications, such as recommendation systems, image and language recognition, search engines, and autonomous vehicles. Analysts expect AI technologies to revolutionize numerous industries in the coming years, including energy, healthcare, finance, education, and logistics. 

What is machine learning (ML)?

Machine learning is the process through which machines discover optimal solutions without explicit instruction from a human. This mimicry of human learning trains computers to refine their abilities over time by studying the connections and insights derived from data. 

Machine learning applications typically fall into one of three approaches:

• Supervised learning systems teach computers to map connections between a set of inputs and outputs—known as training data—that is defined by a human.

• Unsupervised learning provides only unlabeled inputs, which forces the computer to identify the structure and discover patterns in the data.

• Reinforcement learning systems present a computer with a goal and then provide “rewards,” incentivizing it to maximize the desired outcome. 

How AI and ML are connected

If artificial intelligence were a tree, machine learning would be one of its many branches. ML is one of the most prominent subsets of AI, but not the only one. Here are some of the most notable uses of ML within AI: 

1. Deep Learning: A part of machine learning, deep learning uses layers of artificial neural networks to derive meaning from unstructured data. 

2. Robotics: Robotics engineers use artificial intelligence and machine learning algorithms to create robots that can learn to manipulate real-world objects.  

3. Natural Language Processing: Resting at the border of computer science and linguistics, natural language processing teaches machines to recognize, mimic, and manipulate speech. 

4. Computer Vision: Computer vision focuses on the ability of machines to analyze, identify, and interpret images, videos, graphs, and other visual formats.

Primary differences between AI and ML

We’ve covered how AI and machine learning are connected, but this doesn’t explain their differences. Although all machine learning systems are classified as artificial intelligence, not all artificial intelligence applications utilize machine learning. 

The field of artificial intelligence is broadly focused on developing software that can function without direct human input. Machine learning zeroes in developing a computer that can independently optimize its ability to perform a specific function. 

There are AI systems that don’t engage in ML. For example, chatbots and expert systems operate on complex sets of human-provided rules to decide how to respond to input, but they don’t learn from one interaction to the next.

Using AI and ML together for algorithmic trading

Artificial intelligence has limitless applications, but its best uses involve processing massive troves of data to provide analysis and make decisions. That’s why it has been an invaluable tool for modern trading.

AI and ML are revolutionizing the market in the following ways:

Recognizing trends

Most industries require actionable insight to make well-informed decisions. Given the complexity of financial markets, traders often struggle to find meaning within mountains of data. 

Traders that incorporate artificial intelligence and machine learning into their strategy gain a powerful tool to generate data-driven insights. Machine learning algorithms can scan market reports and pricing data to find hidden trends. An automated trading system can then incorporate this data into a new or existing strategy.

Improving performance and accuracy

Experienced traders know no trading strategy is perfect. There’s always room for improvement; the challenge lies in knowing where and how to enhance your technique. 

Together, AI and ML can take the guesswork out of evaluating trading strategies. Deep learning algorithms can magnify overlooked errors in analysis by using backtesting to consider historical factors.

This analysis allows for more accurate evaluations of strategies, improving performance and enabling better adaptation to market changes.

Reducing human errors

Even the best traders make mistakes. They are subject to human emotions, which can cloud their judgment and lead to poor decision-making. 

An algorithmic trading strategy reduces the chance of committing irrational errors. Algorithms are less prone to bias, making them ideal for risk assessment. Although machine learning algorithms can’t completely eliminate mistakes, they can learn to detect irregularities and more predictably assess risk. 

Harness AI and ML in your trading with Composer

If you want to incorporate AI and ML into your trading strategy, Composer can help.

Powered by ChatGPT-4’s large language model, Composer’s no-code automated trading platform allows you to create personalized strategies using natural language. Simply explain your goals in plain English, and you’ll receive AI assistance crafting the ideal approach.

Enhance your process with advanced AI algorithms that make trading smarter, not harder. You can focus on perfecting your strategy while the app handles the rest. Start your journey into the world of AI trading today.