Artificial intelligence and machine learning are very closely related and connected. Because of this relationship, when you look into AI vs. machine learning, you’re really looking into their interconnection.
Artificial intelligence (AI) is a single or collection of computer systems able to process information and perform tasks usually done by humans.
Artificial intelligence, or AI, is a simulation of intelligent human behavior. It’s a computer or system designed to perceive its environment, understand its behaviors, and take action. Consider self-driving cars: AI-driven systems like these integrate AI algorithms, such as machine learning and deep learning, into complex environments that enable automation.
AI can have simple forms of intelligence, such as recognizing speech or analyzing visual patterns in images. Or it can be more complex, such as learning from past mistakes and problem-solving.
Why Does AI Matter?
AI is estimated to create $13 trillion in economic value worldwide by 2030, according to a McKinsey forecast.
That’s because AI is transforming engineering in nearly every industry and application area.
Beyond automated driving, AI is also used in models that predict machine failure, indicating when they will require maintenance; health and sensor analytics such as patient monitoring systems; and robotic systems that learn and improve directly from experience.
What Is Artificial Intelligence?
Artificial intelligence is the capability of a computer system to mimic human cognitive functions such as learning and problem-solving. Through AI, a computer system uses math and logic to simulate the reasoning that people use to learn from new information and make decisions.
To understand what artificial intelligence means, think about what you observe in nature which makes you convinced something has intelligence. Something as simple as a lab rat learning the correct path through a maze represents a simple form of intelligence (there are four types of AI). It involves memory and learning, similar to human intelligence.
In 1950, Alan Turing described “thinking machines” as recognizable because they could use reason to solve puzzles. In the 1950s, John McCarthy said computers could “do things, which, when done by people, are said to involve intelligence.”
These ideas boil down to three characteristics used to identify a machine or computer as having “artificial intelligence.” They can:
- Use inputs, such as sensors or data, to analyze information.
- Process vast amounts of data to identify patterns, trends, or correlations.
- Adapt their decisions and actions based on learnings derived from inputs and data.
It’s precisely how human intelligence helps humans learn and adapt in our daily lives.
Components Making Up Artificial Intelligence
An “intelligent” machine is made up of many different components. These all work together to help a machine take input from the real world and make decisions.
If you think about how a human collects data from the real world, intelligent machines need sensors to collect the same information. These sensors can include:
- Cameras: Visual cues to do things like facial recognition, avoiding obstacles, or infrared cameras to detect when objects are hot.
- Microphones: Interact with humans via voice, detect activity in a room, or respond to music.
- Tactile sensors: Lets robots adjust their grip or game consoles’ strength to respond to how hard you’re moving a game controller.
- Position, temperature, or flow sensors: Provides information about gas or liquid flowing through pipes, temperatures of chemicals or metals, and even liquids’ chemical makeup.
In fact, with modern-day sensor technology, machines can detect things about the world that even humans can’t.
AI Data and Machine Learning
Machine learning is an application of AI. It’s the process of using mathematical models of data to help a computer learn without direct instruction. This enables a computer system to continue learning and improving on its own, based on experience.
An essential component of AI is machine learning. It’s the ability to collect vast amounts of information from multiple sources and analyze it for meaningful patterns and correlations.
For example, during vehicle crash tests, a computer can analyze pressures and temperatures. The computer can analyze the data and tell vehicle manufacturers where to place airbags to provide the highest safety level.
Machine learning also helps with troubleshooting problems. By collecting manufacturing data across hundreds of sensors, computers can identify anomalies that result in faulty products. Then, by correlating other sensor data, the computer can tell technicians which components in a process are flawed.
Since machine learning can do this in a fraction of the time a human can, companies can identify and fix problems faster, improve the quality of products, and boost overall production.
Benefits of AI and machine learning
The connection between artificial intelligence and machine learning offers powerful benefits for companies in almost every industry—with new possibilities emerging constantly. These are just a few of the top benefits that companies have already seen:
More sources of data input
AI and machine learning enable companies to discover valuable insights in a wider range of structured and unstructured data sources.
Better, faster decision-making
Companies use machine learning to improve data integrity and use AI to reduce human error—a combination that leads to better decisions based on better data.
Increased operational efficiency
With AI and machine learning, companies become more efficient through process automation, which reduces costs and frees up time and resources for other priorities.
Applications of AI and machine learning
Companies in several industries are building applications that take advantage of the connection between artificial intelligence and machine learning. These are just a few ways that AI and machine learning are helping companies transform their processes and products:
Retailers use AI and machine learning to optimize their inventories, build recommendation engines, and enhance the customer experience with visual search.
Health organizations put AI and machine learning to use in applications such as image processing for improved cancer detection and predictive analytics for genomics research.
Banking and finance
In financial contexts, AI and machine learning are valuable tools for purposes such as detecting fraud, predicting risk, and providing more proactive financial advice.
Sales and marketing
Sales and marketing teams use AI and machine learning for personalized offers, campaign optimization, sales forecasting, sentiment analysis, and prediction of customer churn.
AI and machine learning are powerful weapons for cybersecurity, helping organizations protect themselves and their customers by detecting anomalies.
Companies in a wide range of industries use chatbots and cognitive search to answer questions, gauge customer intent, and provide virtual assistance.
AI and machine learning are valuable in transportation applications, where they help companies improve the efficiency of their routes and use predictive analytics for purposes such as traffic forecasting.
Manufacturing companies use AI and machine learning for predictive maintenance and to make their operations more efficient than ever.
A more advanced form of machine learning is “deep learning,” when a machine identifies failures and learns the most efficient way to accomplish a task.
For example, a self-driving car will use machine learning to drive a car by watching road markings, looking for pedestrians, and identifying traffic lights. But a deep-learning, self-driving car would also learn how steering adjustments keep the car more in the center of the lanes. Over time, this car could teach itself how to become a better driver.
What’s the Purpose of Artificial Intelligence?
Scientists are developing artificial intelligence so we can use machines to improve the quality of life for humans. It lets machines do repetitive tasks which might injure or be dangerous for humans. Artificial intelligence can improve the safety of cars and airplanes.
Ultimately, their purpose is to supplement humans with insights from vast amounts of data only computers can process.
Dan Prince, CEO and Founder of Illumisoft, says that the starting point for understanding AI is to understand our own intelligence.
“Humans have the capacity to learn, to solve problems, to recognize patterns, and to explain and predict natural phenomena (which are) all attributes commonly associated with intelligence,” he says. “Perhaps most importantly, we’re able to act in ways that shape and transform our environment for our benefit. AI, understood most generally, refers to a system or group of systems that is able to simulate that kind of human intelligence. An intelligent system would be one that exhibits human-like capacities for reasoning, problem-solving, or even creativity.
“The ultimate goal for many researchers is to generate an artificial general intelligence (AGI), something analysts acknowledge has not yet been achieved. As technology currently stands, a particular AI might be good at simulating one aspect of human intelligence, but not others. There are AI systems, for example, that are proficient at understanding language, while others are good at fine motor control. There are very few that can do both.”
Philosophers often question whether we can take AI too far. What if artificial intelligence surpasses human intelligence to the point where robots become superior? Then there’s the question of whether machines will ever be able to understand emotion. Currently, there’s no sensor capable of emotion.
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However, most machines with AI are only capable of focused areas of learning. We can’t apply it to the multitude of decisions an average human makes daily. Because of that, the idea of machines overtaking humans any time soon is not something anyone needs to worry about now.
What are the four types of AI?
The four types of AI are reactive machines, limited memory, theory of mind, and self-awareness.
How do you make an artificial intelligence?
Generally, creating an AI involves identifying the problem you want the AI to solve, collecting data, then training algorithms using the data you organized. Some platforms such as Microsoft Azure Machine Learning and Google Cloud Prediction API can help you build and deploy your AI.
Who invented artificial intelligence?
British computer pioneer Alan Turing was responsible for the earliest work in artificial intelligence in the 1930s. John McCarthy, a professor emeritus of computer science at Stanford, first coined the term “artificial intelligence” in a written proposal in 1955.