Artificial intelligence is when machines can learn and make decisions similar to humans. The term artificial intelligence was coined in 1956, but AI has become more popular today thanks to increased data volumes, advanced algorithms, and improvements in computing power and storage.
Why is Artificial Intelligence important?
- AI automates repetitive learning and discovery through data.
- AI adds intelligence to existing products.
- AI adapts through a progressive learning algorithm to let the data do the programming.
- AI analyzes more and deeper data using neural networks that have many hidden layers.
- AI achieves incredible accuracy though deep neural networks – which was previously impossible.
- AI gets the most out of data
Artificial intelligence forms the basis for all computer learning and is the future of all complex decisions making.
Application of AI can be seen in everyday scenarios such as financial services fraud detection, retail purchase prediction, and online customer support interactions.
Stated simply, AI is trying to make computers think and act like humans.
Achieving this end requires three key components:
- Computational systems
- Data and data management
- Advanced AI algorithms (code)
The more human-like the desired outcomes, the more data and processing power required.
While Hollywood movies and science fiction novels depict AI as human-like robots that take over the world, the current evolution of AI technologies isn’t that scary – or quite that smart. Instead, AI has evolved to provide many specific benefits in every industry. Keep reading for modern examples of artificial intelligence in healthcare, retail and more.
What are the challenges of using artificial intelligence?
Artificial intelligence is going to change every industry, but we have to understand its limits.
The principle limitation of AI is that it learns from the data. There is no other way in which knowledge can be incorporated. That means any inaccuracies in the data will be reflected in the results. And any additional layers of prediction or analysis have to be added separately.
Today’s AI systems are trained to do a clearly defined task. The system that plays poker cannot play solitaire or chess. The system that detects fraud cannot drive a car or give you legal advice. In fact, an AI system that detects health care fraud cannot accurately detect tax fraud or warranty claims fraud.
In other words, these systems are very, very specialized. They are focused on a single task and are far from behaving like humans.
Likewise, self-learning systems are not autonomous systems. The imagined AI technologies that you see in movies and TV are still science fiction. But computers that can probe complex data to learn and perfect specific tasks are becoming quite common.
In summary, the goal of AI is to provide software that can reason on input and explain output. AI will provide human-like interactions with software and offer decision support for specific tasks, but it’s not a replacement for humans – and won’t be anytime soon.