With the proliferation of virtual assistants, responsive chatbots, and computer-assisted vehicles, artificial intelligence (AI) has become an integral part of our daily lives. Yet, we have a long way to go in understanding the nuances of this technology.

Take three AI buzzwords: artificial intelligence, machine learning, and deep learning. You may have heard these used interchangeably; the latter two really describe subsets of AI technology as a whole. Learning the distinctions between artificial intelligence vs. machine learning vs. deep learning can help us better understand AI as a whole — and how it can benefit us in everyday life.

Artificial Intelligence vs. Machine Learning: What’s the Difference?

As a field of computer science, artificial intelligence uses a wide range of algorithms to teach computers to think and act like humans. Machine learning is just one family of algorithms.

To better distinguish between artificial intelligence vs. machine learning, think of it this way: All ML is AI, but not all AI is ML. Machine learning is excellent for solving specific problems based on patterns in data sets, while AI can perform a wide range of human tasks.

How Does Deep Learning Differ?

When comparing artificial intelligence vs. machine learning vs. deep learning, think of deep learning as another aspect of AI, nested within machine learning. In comparing deep learning vs. machine learning, it’s helpful to think of deep learning as a multi-layered, more complex version of machine learning.

Deep learning requires much larger data sets and longer training sessions than machine learning. Because it’s modeled after the structure of the human brain, it’s better equipped for non-linear decision-making and can learn independently of ongoing human input. For instance, deep learning is what powers Automated Speech Recognition (ASR) tools that can recognize the nuances of human speech.

What Is Artificial Intelligence?

Artificial Intelligence is a type of intelligence that enables computers to mimic human intelligence to varying degrees. By recognizing patterns, adapting to changing inputs, and engaging in complex problem-solving, AI can learn and navigate real-world scenarios somewhat independently of human input.”

To put it more simply, in the words of computer scientist and one of the early developers of AI technology, John McCarthy, “AI is the science and engineering of making intelligent machines.”

That’s an overarching definition, but AI isn’t monolithic. In fact, AI is often described in three levels, each describing a different set of abilities. The three levels are:

• Artificial narrow intelligence: Also known as weak AI, this can only perform basic, singular tasks with narrowly defined parameters. Think voice assistants and speech recognition.
• Artificial general intelligence: Also known as strong or deep AI, this is more similar to human intelligence by learning and applying knowledge to solve problems. This is the type of AI we’re only beginning to see glimpses of as tools like generative AI take off — but we’re still not entirely there.
• Artificial superintelligence: This is only theoretical at this point — sentient AI that surpasses human intelligence. Think Data on “Star Trek.”

When used effectively, AI exponentially increases human productivity. Whether you’re generating content ideas, writing computer code, deploying assistive robots, or transcribing lengthy interviews, AI can help you achieve it in a fraction of the time. To do that, AI relies on a web of interconnected technologies. Two of the most important are machine learning and deep learning.

What Is Machine Learning?

Machine learning (ML) involves using algorithms to analyze large amounts of data, recognize patterns, and make decisions. It’s a subset of AI, meaning ML is a fundamental tool for most AI technology but only one small facet of comprehensive artificial intelligence.

In one sense, machine learning is a method of achieving AI. It’s a paradigm shift away from traditional computer programming in which we now teach computer programs by feeding them data. The more data you feed to a computer program, the more it learns and adapts.

How Is Machine Learning Applied in the Real World?

Machine learning with AI is constantly evolving, and companies and organizations leverage it for a host of tasks:

• Conducting deep data analysis: Today’s businesses rely on data for invaluable insights, but they’re drowning in metrics, KPIs, and granular customer details. With the help of ML, businesses can sift through all this data more quickly and accurately, yielding critical insights on everything from sales forecasts to customer churn.
• Enhancing efficiency: ML allows businesses to teach robots and computers to perform complex tasks that would otherwise demand hours of highly trained human labor. Insurance companies can automate claims processing. Banks and credit card companies can immediately detect fraudulent transactions. And companies can automatically transcribe online trainings to provide participants with important records and notes.
• Optimizing performance: Business owners are constantly looking for ways to progress in key areas of business performance. When they’ve chosen a process to improve, they can leverage ML to examine key metrics around that process and define steps to improve and optimize it.

What Is Deep Learning?

Deep learning is a subset of machine learning that relies on artificial neural networks. These networks mimic the human brain and are capable of recognizing more complex correlations and non-linear relationships.

Because of its complexity and ability to consume and analyze larger data sets, deep learning AI can operate and learn more independently — without human intervention — than basic machine learning models. Virtual assistants, customer service chatbots, self-driving cars, and even Netflix or Amazon recommendation algorithms, rely on deep learning to constantly improve.

With its ability to operate more independently and analyze an abundance of raw data, deep learning allows businesses to take the benefits of ML even further.

How AI, Machine Learning, and Deep Learning Work Together

To understand how deep learning, machine learning, and AI work together, consider an order fulfillment center. The facility uses AI to process and plan fulfillment, which involves collecting and reviewing orders to create a plan for workers to pick and pack items as efficiently as possible.

The entire process utilizes AI, but it likely relies on machine learning to continually improve its processes by analyzing order fulfillment times, inventory location, order volume, and other data to streamline fulfillment planning.

Deep learning may come into play with more complex orders or customer queries. For instance, what if a customer requests an order be shipped on a certain day or requires follow-up for an item that’s out of stock? These scenarios require non-linear processes and more extensive training for the AI to plan the proper steps.

Benefits and Drawbacks of This Tech

AI powered by machine learning and deep learning comes with several advantages and disadvantages. Before deploying this technology, organizations should consider the following:

Pros

With the following capabilities, AI is like a power-up for human potential:

• Faster and more efficient: AI far exceeds human capabilities in terms of processing speed.
• More accurate: When properly trained, AI can produce consistently accurate results, free from human error and the influence of emotions or biases.
• Always available: AI is available 24/7, always ready to perform automated tasks.
• Takes over repetitive tasks: AI is best suited for monotonous, time-consuming tasks that would take a person much longer and may not be the best use of their skills or time.

Cons

That said, AI also threatens to undermine humans in important ways:

• Could replace human jobs: As with any new technology deployed at scale, AI could replace some jobs.
• Lacks human qualities: As impressive as AI and its machine learning capabilities are, it still can’t replicate human emotions and empathy. This is especially clear in customer service.
• Not completely error-free: While highly accurate, AI is not flawless. Large-language artificial intelligence models like ChatGPT have produced inaccurate content, and systems can be unreliable or easily influenced by the biases of their human trainers.
• Expensive to implement: Costs of AI deployment vary significantly depending on the application and scope but may present a significant barrier.

The ASR of It All

One of the foremost applications of AI with machine learning and deep learning is Automatic Speech Recognition (ASR) technology, the conversion of spoken word to text. You can read an excellent primer on this in “Robust Automatic Speech Recognition: A Bridge to Practical Applications,” but we’ll cover it briefly here. While the field has been around for over 60 years, huge strides have been made in ASR in the last decade with the introduction of deep-learning language models.

ASR technology has become incredibly accurate and has a plethora of uses. It powers the dictation features on your smartphone or smart home manager. ASR algorithms can automatically transcribe the content of a legal deposition or a police officer’s body camera footage. It can even provide automatic live captions for a marketing webinar or a college professor’s lecture to make note-taking easier.

Plus, developers are now testing new ASR systems that can produce accurate results from low-quality or long-distance audio.

Cloud computing has brought massive amounts of data to industry leaders, presenting exciting opportunities to improve ASR accuracy. This influx of data has driven developers to make deep-learning training algorithms more efficient. And, as ASR technology has improved, customers have come to expect more from it — just think about what you expected from Siri 10 years ago compared to today.

What Can AI Do For You?

Artificial intelligence is at the vanguard of consumer and corporate technology today, and the businesses that come out ahead will be those that understand how to deploy machine learning, deep learning, and other aspects of AI. ASR, in particular, can make countless tasks easier and more efficient, from live dictation to quick video captures.

With Rev, you can capture speech in an instant — with incredible accuracy. Learn more about how Rev’s industry-leading AI can help you get more done.