Cognitive computing and AI
The terms AI and cognitive computing are sometimes used interchangeably, but, generally speaking, the label AI is used in reference to products and services that automate tasks, while the label cognitive computing is used in reference to products and services that augment human thought processes.
Examples of AI technology
It’s important to understand the key differences with web development:
- Automation. This makes a system or process function automatically. For example, robotic process automation (RPA) can be programmed to perform high-volume, repeatable tasks that humans normally performed. RPA is different from IT automation in that it can adapt to changing circumstances.
- Machine learning. This is the science of getting a computer to act without programming. Deep learning is a subset of machine learning that, in very simple terms, can be thought of as the automation of predictive analytics. There are three types of machine learning algorithms:
- Supervised Learning. Data sets are labeled so that patterns can be detected and used to label new data sets.
- Unsupervised Learning. Data sets aren't labeled and are sorted according to similarities or differences.
- Reinforcement Learning. Data sets aren't labeled but, after performing an action or several actions, the AI system is given feedback.
- Machine vision. This is the science of allowing computers to see. This technology captures and analyzes visual information using a camera, analog-to-digital conversion and digital signal processing. It is often compared to human eyesight, but machine vision isn't bound by biology and can be programmed to see through walls, for example. It is used in a range of applications from signature identification to medical image analysis. Computer vision, which is focused on machine-based image processing, is often conflated with machine vision.
- Natural language processing. This is processing of human -- and not computer -- language by a computer program. One of the older and best-known examples of NLP is spam detection, which looks at the subject line and the text of an email and decides if it's junk. Current approaches to NLP are based on machine learning. NLP tasks include text translation, sentiment analysis and speech recognition.
- Robotics. This field of engineering focuses on the design and manufacturing of robots. Robots are often used to perform tasks that are difficult for humans to perform or perform consistently. They are used in assembly lines for car production or by NASA to move large objects in space. Researchers are also using machine learning to build robots that can interact in social settings.
- Self-driving cars. These use a combination of computer vision image reorganization deep learning to build automated skill at piloting a vehicle while staying in a given lane and avoiding unexpected obstructions, such as pedestrians.