While many definitions of artificial intelligence have popped up over the last several decades, we can consider John McCarthy’s. In his 2004 paper titled, “What is Artificial Intelligence” he said, “It is the science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable.” In their foundational textbook on AI, authors Russell and Norvig define intelligence as “a machine’s ability to make good decisions.” In this way, agents — the very base of the new science of artificial intelligence — are computational units that receive precepts from outside the machine. And then they do something with what they’ve received.
Nowadays, there is quite a bit of noise about Artificial Intelligence development. It’s hardly surprising though that many people believe Artificial Intelligence is the future of technology. Artificial Intelligence is a catch-all term for our science of machine intelligence. It is hard to imagine modern life without its many forms. Every industry and company from finance to farming make use of some form of AI. And the ubiquity of this technology has created an influx in recent investment. Artificial intelligence (AI) is also a technology that resembles human intelligence. It includes all sorts of tasks people do such as sensing, perceiving, learning, and reasoning which can be done by any machines if and when developers know how to solve them. It is difficult to define AI broadly due to the lack of a general theory giving an algorithmic solution for every conceivable problem. However, advancements in machine learning and deep learning can directly impact virtually every sector of the tech industry, including yours.
Types of Artificial Intelligence
Artificial intelligence has two different categories. One type is limited (narrow), for example, Siri or Spotify recommendations. While the other is more powerful and adaptable to all tasks: AGI. Let’s break it down:
- Narrow AI: Sometimes referred to as weak artificial intelligence. This is dependent on databases. For example, the iPhone X’s facial recognition software “learns” your face by scanning other users’ faces. Narrow AI operates within a limited context and is imitative of human intelligence. Many of these machines may seem intelligent, but they are working under far more constraint than even the simplest of all thought in humans. Narrow AI are tools that can learn over time. These include: Google image recognition Software Siri, Alexa and other personal assistants Self-driving cars IBM’s Watson
- Artificial General Intelligence: Artificial General Intelligence, often called “Strong AI,” is a machine with general intelligence and understanding of everything around it, much like humans. AGI is used commonly for robots that work for all purposes, such as search engines or robot nurses. AGI could abide by commands yet simultaneously navigate unexpected situations, thus catering to our every desire.
Machine Learning Versus Deep Learning
Machine learning and deep learning are different types of artificial intelligence (AI). Both machine learning and deep learning are subfields of AI, and deep learning is a subset of machine learning. In other words, anything not deep learning is considered machine learning.
Both machine learning and deep learning both rely on computers to process data and find patterns or correlations without being explicitly programmed how to do so by humans. They are especially useful in areas involving predictive analysis — in everything from research projects to finance to fraud prevention analyses. Even though they aren’t quite the same, they bear many similar hallmarks.
Artificial Intelligence Applications
There is more than one way an artificial intelligence application can be presented. Here are the most common examples:
- Speech Recognition: It is also known as Automatic Speech Recognition or Computer Speech Recognition. It is when a device uses Natural Language Processing to translate human speech into written text. Many cell phones use this capability.
- Customer Service: The customer’s journey is about to change forever. Chatbots are answering frequently asked questions (FAQs) around topics, like shipping, and providing cross-selling products or sizing, while fitting right into websites and social media platforms for a more seamless experience across channels for brands. Computer vision: Eye in the sky. Computer vision helps machines to “see.” The ability of AI systems and computer hardware to derive meaningful information from digital images. It aims to enhance recommendation based on this information. With the help of convolutional neural networks, computer vision can even identify human faces.
- Recommendation Engines: If you have shopped at your e-commerce store before, the algorithms can use past data to find hidden relationships between customers and their interests. Recommendation engines help businesses discover trends and make more informed decisions about customer cross-selling strategies at check out.
- Automated Stock-Trading: AI-driven platforms complete thousands of transactions per day on your behalf, without any human intervention.