Artificial intelligenceArtificial intelligence is a branch of computer science that deals with the simulation of human intelligence in machines. These machines are programmed to think and act like human beings. In other words, instead of writing software code with specific instructions on how to perform a certain task, machines are trained using complex, large amounts of algorithms and data that give them the capability to learn how to accomplish the task.
Artificial intelligence works by combining large volumes of datasets with intelligent algorithms and fast, iterative processing. It falls under two broad categories:
- Narrow artificial intelligence: Also known as weak artificial intelligence, this type of intelligence is a mockup of human intelligence and operates within a restricted context. Narrow artificial intelligence systems are designed to perform only a single task. And even though these systems may seem intelligent from the surface, but they operate under many more limitations and constraints than even the most simple or basic human intelligence.
- Artificial general intelligence: This is the kind of intelligence we see in movies. It is also referred to as strong artificial intelligence perhaps because its systems are much more intelligent than the narrow artificial intelligence. Artificial general intelligence is much more like human beings and can easily mimic humans to solve any problem.
Applications of artificial intelligenceWith technology being applied in almost every sector and industry today, the uses of artificial intelligence are endless. Here are the most common applications:
- Smart assistants like Alexa and Siri
- Disease prediction and mapping tools
- Manufacturing robots
- Optimization of personalized healthcare treatment
- Conversational robots for customer service and marketing
- Robot advisors for stock trading
- Spam filtering on emails
- Social media monitoring for false news or dangerous content
- TV shows or song recommendations from Netflix and Spotify
Deep learningDeep learning is a method of learning that utilizes artificial neural networks and algorithms inspired by the human brain. Just like how humans learn from experience, deep learning algorithms are also designed to learn from experience. They do this by performing a given task repeatedly and every time they modify the task a little to enhance the outcome. Deep learning is referred to as “deep” because the neural networks involved in the learning consist of numerous deep layers of nodes that enhance learning.
Deep learning modelsThere are two main types of deep learning models:
- Convolutional neural networks: The convolutional neural networks are some of the most commonly applied deep learning cases. They are excellent for computer vision applications or video/image processing. These models consist of deep artificial neural networks that classify images, cluster them by similarity, and perform object recognition. Their algorithms are designed to easily recognize faces, street signs, individuals, flowers, tumors, and many other elements of visual data.
- Recursive neural networks: Also, known as recurrent neural networks, recursive neural networks use previous input data to carry out a task. For instance, when analyzing handwriting, you can guess future letters and words better if you remember previous words and letters. The same case applies to recursive neural networks; they have some sort of a “memory” that stores information on what has been learned so far. Recursive neural networks can remember previous data inputs, which makes them more powerful than other artificial neural networks in regards to performing context-sensitive and sequential tasks like speech recognition.
Applications of deep learningDeep learning is utilized in a number of disciplines including:
- Virtual assistants like Alexa, Siri, and Cortana
- Language translations
- Vision for autonomous cars, driverless delivery trucks, and drones
- Chatbots and customer service bots
- Image colorization
- Facial recognition