Artificial Intelligence vs. Machine Learning vs. Deep Learning (AI vs. ML vs. DL)
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Artificial intelligence (AI), machine learning (ML) and deep learning (DL) are a few of today's most commonly used buzzwords that people often mix up.
What does it mean when an application uses AI technology? How does ML work? Where does DL fit into the picture? When having these types of discussions, it's helpful to understand some related terms and concepts.
In a nutshell, DL is a subset of ML, which lives under the umbrella of AI. As such, when we talk about DL, we're referring to a specific branch of AI.
AI has become an integral part of many technological solutions. From business process automation (BPA) to data analytics, AI-driven technologies are helping organizations analyze vast amounts of information, streamline time-consuming and error-prone processes, support more accurate decision-making based on data, and enhance customer experiences.
At the same time, it's becoming more challenging to navigate the complex and fast-evolving AI landscape. Simply using the wrong terminology in the wrong context can derail a business or technical conversation and lead to misunderstandings that are frustrating, inefficient and even costly.
To ensure everyone is on the same page, let's revisit the fundamentals to understand how these terms relate to each other.
AI technologies enable computers, robots and machines to exhibit human-like intelligence and mimic the perception, learning, problem-solving and decision-making capabilities of the human brain. They use a set of stipulated rules (i.e., algorithms) to direct machines to complete tasks and solve problems. With AI software, computers can learn to recognize objects, process data, understand text, respond to language, make decisions and perform tasks in ways akin to the human mind.
Besides ML and DL, other sub-fields of AI include evolutionary computation, robotics, expert systems, natural language processing (NLP) and fuzzy logic.
AI systems are generally categorized by their ability to approximate human behaviors. They differ based on their hardware, applications and their theory of mind (e.g., their ability to attribute mental states).
3 types of artificial intelligence
These are generally three recognized types of AI, only one of which exists today:
- Artificial Narrow Intelligence (ANI): Also known as "weak AI," these goal-oriented systems are programmed to perform a single, specific task using information from a predetermined dataset. ANI technology is not conscious or sentient, and it does not demonstrate emotion. Nor can ANI perform any processes outside of the ones they're specifically designed for. ANI is the only type of AI that exists today.
- Artificial General Intelligence (AGI): Also known as "strong AI," AGI technology will enable machines to learn, think and act in ways identical to humans while exhibiting emotions, consciousness and self-awareness. AGI will be able to perform various tasks in uncertain conditions by integrating past experiences to inform decision-making and problem-solving.
- Artificial Super Intelligence (ASI): ASI represents another hypothetical scenario in which AI-driven machines will exhibit multi-faceted intelligence surpassing that of humans, demonstrating superior problem-solving and decision-making capabilities. Like AGI, ASI doesn't yet exist.
10 common artificial intelligence applications
Today, organizations in various sectors use AI technology to facilitate a variety of use cases and applications involving big data, computer vision, data mining and speech recognition, among others. AI solutions can help improve cost-efficiency, business processes, customer experience and more.
For example, you'll find AI used in:
- Chatbots: Instantly respond to customer inquiries or route them to the right human agent, reducing time-to-resolution and improving customer experience.
- Personalization: Deliver targeted recommendations and a personalized user experiences by comparing user data with big data.
- eCommerce: Tag, organize and automatically handle large amounts of inventory, and help customers discover products using visual search (e.g., images or videos).
- Programmatic advertising: Analyze customer behavioral data to deliver highly targeted content that drives conversion.
- Intelligent virtual assistants: Handle manual and repetitive administrative tasks, saving time while avoiding unnecessary errors and delays.
- Human resource processes: Automate time-consuming tasks such as screening, scheduling, paperwork and data entry, which can streamline processes and improve employee experience.
- Healthcare applications: Improve documentation and prevent medical errors while increasing the reliability and predictability of procedures to deliver better patient outcomes.
- Cybersecurity: Analyze large amounts of data to detect vulnerabilities or anomalous user behavior that might signal a cyberattack.
- Logistics and supply chain management: Leverage real-time data to optimize resource allocation, identify best shipping routes, and shorten delivery timelines.
- Manufacturing: Analyze data collected along a production line (e.g., using IoT devices) to predict where failures may occur and minimize unscheduled downtime.
Machine learning is a subset of AI that learns by itself. An ML application consists of neural networks in which statistical learning algorithms automatically learn and improve from experience. It does so by simulating the thought processes of the human brain, and reprogramming itself as it ingests more data to increase the accuracy of its performance.
A basic neural network consists of an input layer through which data enters the system, one or more hidden layers where the data is processed, and an output layer in which conclusions are drawn. ML algorithms apply weights, biases and thresholds to interpret the data in the hidden layer(s) to reach conclusions with varying degrees of confidence.
ML technologies are designed to train machines to learn on their own, using prior data to draw conclusions and make predictions. Training involves supplying the algorithm with large amounts of information and teaching it how human users would draw conclusions from the data. As such, the success of any ML system hinges upon the availability and accuracy of the data supplied during the training process.
3 types of machine learning algorithms
ML algorithms are broadly classified into three categories:
- Supervised learning: Supervised learning ML algorithms develop a regression/classification model using a known input dataset and known responses. They can then generate predictions or draw conclusions for a new dataset based on the model.
- Unsupervised learning: Unsupervised learning ML algorithms learn about unsorted or unlabeled data by inferring patterns in the dataset without referencing known output. They're mostly used to identify data clusters and support dimensionality reduction when labeled data isn't available.
- Reinforcement learning: Reinforcement learning ML algorithms involve a trial-and-error process in which the algorithms learn through ongoing interactions with the environment. They use feedback from previous actions and experiences to adjust future responses.
Common machine learning applications
We encounter ML algorithms almost every day. They're widely used by platforms and websites such as Netflix, YouTube, Spotify and Amazon to provide personalized recommendations based on user preference and history as well as data gathered from users who exhibit similar behaviors.
For example, the ML algorithm in Google Maps processes current and historical data to predict upcoming traffic conditions and offer route recommendations. Facebook uses ML technology to support face detection and image recognition in its automatic tagging feature. Ride-share applications (e.g., Lyft and Uber) and the airline industry both use ML to execute dynamic pricing based on real-time supply and demand.
Personal virtual assistants, such as Siri, Alexa, Google Home and Cortana, offer ML-driven features such as speech recognition, speech-to-text conversion, text-to-speech conversion, and natural language processing. Meanwhile, financial institutions use ML technologies to detect fraudulent transactions and prevent cybercrime.
Deep learning is a subset of machine learning. DL models are built on deep neural networks with multiple hidden layers, each of which refines the conclusions drawn by a prior layer through a process called "forward propagation." Meanwhile, another process called "back propagation" identifies errors, assigns weights and routes the process back to previous layers to train the model.
Most DL models work with unlabeled data and are capable of unsupervised learning during which they detect patterns with minimal human involvement. This technique mimics how the human brain filters information and learns from examples to label, predict and classify data.
DL models are often defined as multi-neural network architectures since they contain a large number of layers and parameters.
DL can be considered an evolution of ML technologies. For instance, while ML requires features to be provided to perform classification, DL models can automatically discover these features. However, due to the vast amounts of data required to train the algorithm and generate accurate results, DL demands a lot of computing power.
4 types of deep learning neural networks
There are four types of neural networks:
- Convolutional neural networks: Mostly used for analyzing and classifying images, the architecture of a convolutional neural network is analogous to the organization and connectivity patterns of the visual cortex in the human brain.
- Recurrent neural networks: Used in the field of NLP and sequence prediction problems, the algorithm captures information about previous computations to influence subsequent output.
- Recursive neural networks: These neural networks process input hierarchically in a tree fashion. For example, a recurrent neural network algorithm would analyze a sentence by parsing it into smaller chunks of text or individual words.
- Generative adversarial networks: These networks can generate photographs that appear to be authentic to the human eye by taking photographic data and shaping the elements into realistic-looking objects (e.g., people, animals, locations).
Common deep learning applications
DL applications are becoming more common thanks to an exponential increase in computing power. Self-driving cars use DL algorithms to recognize road signs and differentiate pedestrians from lamp posts. DL models can help detect fake or biased information (e.g., on social media platforms). They enable virtual assistants to handle increasingly complex administrative tasks like taking notes, creating document summaries and booking appointments.
Visual recognition software built on DL algorithms can sort a large number of images based on criteria such as location, faces and a combination of people, events, dates and more. Deep video analysis can perform the time-consuming tasks of syncing audio and video as well as testing, transcriptions and tagging. Instant visual translation, built on convolutional neural networks, can provide automatic translation of text and images.
In the medical field, DL technologies are used to process vast amounts of information and perform complex tasks, such as medical imaging and genomic analyses. They can also help identify language and speech disorders in children to detect developmental delays.
There you have it. An in-depth breakdown of what AI, ML and DL mean, how they differ from each other, and common application we're seeing in the real world.
As the exciting field of AI evolves in today's increasingly data-driven environment, we expect to see more applications that touch every aspect of business operations. New technologies will no doubt continue to arise and evolve along with the established categorizations described above. That's why it's important to understand the fundamentals and stay on top of future developments that might effect your business.
By doing so, you'll be prepared to discuss and leverage AI, ML and DL technologies effectively to achieve business goals and stay one step ahead of the competition -- today and well into the future.