What Is Artificial Intelligence in Simple Terms?
Computers are very good at making calculations — taking inputs, manipulating them, and generating outputs as a result. But in the past, they have not been able to do other kinds of human tasks, such as understanding and generating language, identifying objects by sight, creating art, or learning from past experiences.
Today, many computer systems have the ability to communicate with humans using ordinary speech. They can recognize faces and other objects. They use machine learning techniques, especially deep learning and neural networks, in ways that allow them to learn from the past and make predictions about the future.
Much of this technology is still being developed and advanced every day, but now, even the average consumer can access AI models to generate content, solve problems, and handle a number of other advanced tasks.
What Is Generative AI?
Generative AI is a specific, emerging form of artificial intelligence that relies on big data training sets, neural networks, deep learning, and some natural language processing to create original content outputs. Although the most commonly used generative AI tools currently generate text and code, generative AI solutions can also generate images, audio, and synthetic data, among other outputs.
Generative AI is perhaps the most popular and fastest-growing type of AI today, especially with the global popularity of OpenAI’s ChatGPT and GPT-4. Other popular examples of generative AI include Google Bard, Jasper, Stable Diffusion, DALL-E, Microsoft and GitHub Copilot, and DreamStudio.
Artificial Intelligence vs. Machine Learning
At the simplest level, machine learning (ML) is a subset of artificial intelligence. While the greater artificial intelligence umbrella is dedicated to all kinds of approaches to human-like problem-solving, machine learning involves developing a specifically trained model that focuses on teaching machines to complete focused tasks and identify data patterns. In many cases, machine learning is used in conjunction with other forms of artificial intelligence.
Machine Learning vs. Deep Learning
Just as machine learning is a subset of artificial intelligence, deep learning is a subset of machine learning. Machine learning as a whole is about deriving insights from big datasets and making decisions based on the information these solutions find. It is an algorithmic, data-driven approach to decision-making. Deep learning is also an algorithmic approach to decision-making, but it’s a bit more complex; instead of working with one or a small number of algorithms, deep learning models work with multiple layers of algorithms — known as a neural network. This structure is designed to help deep learning models mimic the functions of human brains.
Types of Artificial Intelligence
Computer scientists have proposed different ways to classify the types of AI. One popular classification uses three categories:
1. Artificial Narrow Intelligence
Artificial Narrow Intelligence (ANI) is designed to complete one task or set of tasks with high competence and skill. Apple’s Siri, IBM’s Watson, and Google’s AlphaGo are all examples of Narrow AI. Narrow AI is fairly common in the world today.
2. Artificial General Intelligence
Artificial General Intelligence (AGI) is a form of AI that performs many intellectual tasks on par with a human. Many researchers are currently working on developing general AI. One of the best early examples of AGI is GPT-4, which is able to solve a variety of problems and has performed well on a number of standardized human tests.
3. Artificial Superintelligence
Artificial Superintelligence (ASI), which is still theoretical, has intellectual capacities that far outstrip those of humans. This kind of artificial intelligence is not yet close to becoming a reality.
Another popular classification uses four different categories
1. Reactive Machines
Reactive machines take an input and deliver an output, but they do not have memory or learn from past experiences. The bots you can play against in many video games are good examples of reactive machines.
2. Limited Memory
Limited memory machines can look a short distance back into the past. Many vehicles on the road today have advanced safety features that fall into this category. For example, if your car issues a backup warning when a vehicle or person is about to pass behind your car, it is using a limited set of historical data to come to conclusions and deliver outputs.
3. Theory of Mind
Theory of mind machines are aware that human beings and other entities exist and have their own independent motivations. Most researchers agree that this kind of AI has not yet been developed, and some researchers say that we should not attempt to do so. However, some of the latest generative AI models are performing well in theory of mind tasks and tests.
Self-aware machines are aware of their own existence and identities. Although a few researchers claim that self-aware AI exists today, only a handful of people share this opinion. Developing self-aware AI is highly controversial.
While these classifications are interesting from a theoretical standpoint, most organizations are far more interested in what they can do with AI.
AI Use Cases: What Can AI Do?
The possible AI use cases and applications for artificial intelligence are nearly limitless. Some of today’s most common AI use cases include the following:
Generative AI models are being used to generate content in a variety of formats: not just text but also code, synthetic data, audio and music, images, video, and voice. Content generation models are currently applied to a variety of industries and use cases, including marketing and sales, customer service, employee coaching, cybersecurity, computer vision, healthcare and pharmaceuticals, entertainment and gaming, and legal and government.
Whether you’re shopping for a new sweater, looking for a movie to watch, scrolling through social media, or trying to find true love, you’re likely to encounter an AI-based algorithm that makes suggestions. Most recommendation engines use machine learning models to compare your characteristics and historical behavior to people around you. The models can be very good at identifying preferences even when users aren’t aware of those preferences themselves.
Natural language processing
Natural Language Processing (NLP) is a broad category of AI that encompasses speech-to-text, text-to-speech, keyword identification, information extraction, translation, and language generation. It allows humans and computers to interact through ordinary human language (audio or typed), rather than through programming languages. Because many NLP tools incorporate machine learning capabilities, they tend to improve over time.
AI can not only understand human language, but it can also identify the emotions underpinning human conversation. For example, AI can analyze thousands of tech support conversations or social media interactions and identify which customers are experiencing strong positive or negative emotions. This type of analysis allows customer support teams to focus on customers that might be at risk of defecting and/or extremely enthusiastic supporters who could become advocates for the brand.
Voice synthesis and assistance
Many of us interact with Siri, Alexa, Cortana, or Google on a daily basis. While we often take these assistants for granted, they incorporate advanced AI techniques, including natural language processing and machine learning. Several new generative AI solutions offer voice synthesis and assistance as well.
Financial services companies and retailers often use highly advanced machine learning techniques to identify fraudulent transactions. They look for patterns in financial data, and when a transaction looks abnormal or fits a known pattern of fraud, they issue alerts that can stop or mitigate criminal activity.
Many of us use AI-based facial recognition to unlock our phones. This kind of AI also enables autonomous vehicles and automates processing for many health-related scans and tests.
Many industries like manufacturing, oil and gas, transportation, and energy rely heavily on machinery, and when that machinery experiences downtime, it can be extremely costly. Firms are now using a combination of object recognition and machine learning techniques to identify in advance when equipment is likely to break down so they can schedule maintenance at a time that minimizes disruptions.
Predictive and prescriptive analytics
Predictive algorithms can analyze just about any kind of business data and use that as the basis for forecasting likely future events. Prescriptive analytics, which is still in its infancy, goes a step further and not only makes a forecast but also offers recommendations as to what organizations should do to prepare for likely future events. These AI-powered approaches to analytics are used across a variety of industries but are particularly gaining steam in quote-based industries like insurance.
Most vehicles in production today have some autonomous features, such as parking assistance, lane centering, and adaptive cruise. And while they are still expensive and relatively rare, fully autonomous vehicles are already on the road, and the AI technology that powers them is getting better and less expensive every day.
Industrial robots were one of the earliest implementations of AI, and they continue to be an important part of the AI market. Consumer robots, such as robot vacuum cleaners, bartenders, and lawnmowers, are becoming increasingly commonplace.
Of course, these are just some of the more widely known use cases for AI. AI technology is seeping into daily life in so many ways that we often aren’t fully aware of.
AIOps — artificial intelligence for IT operations — is increasingly being used to simplify workflows and workloads for skilled tech workers. AI can be used to complete tasks related to service and performance management and data management and analysis.