From Mythical Robots to Self-Driving Cars: A Journey Through the History of AI

Artificial Intelligence (AI), also known as machine learning, has evolved a lot since its conception in the 1940s. AI has changed the way people live, work, and communicate. It began with logic and reasoning and evolved to include deep learning and neural network technology.

In ancient Greek mythology Hephaestus (the god of metalworking, craftsmanship and metallurgy) created mechanical beings that aided him in his task. It was not until the early 20th century, however, that AI started to take shape.

In 1956, computer scientists from Dartmouth College, New Hampshire met to discuss the possibilities of creating a machine capable of thinking and reasoning like a person. AI is considered to have been born at this meeting.

Early AI research was dominated by symbolic reasoning and logic. Researchers believed the key to developing intelligent machines was creating algorithms that could make decisions and reason based on logic rules. This led to the creation of expert systems that were created to solve complex problems within specific domains such as finance or medicine.

In the 1980s AI research began to shift towards a new method known as connectionism. Connectionism is built on the notion that intelligent behavior can be derived from simple processing units known as neurons. This led to the creation of artificial neural network, which is modeled on the structure of the brain.

Machine learning is a subfield within AI which focuses on algorithms that learn from data. The availability of large datasets, faster computers and the ability to train complex models made this approach possible.

IBM's Deep Blue beat Garry Kasparov in 1997, marking an important milestone in the history AI. Deep Blue could analyze millions of moves and then make decisions using that analysis. This demonstrated the power of expert systems and machine learning.

In the early 2000s, a new subfield of AI called natural language processing (NLP) emerged. NLP is a subfield of AI that focuses on algorithms to understand and generate language. This led to the creation of virtual assistants such as Apple's Siri or Amazon's Alexa that can understand and respond natural language commands.

Deep learning has become a dominant method of AI research in recent years. Deep learning relies on artificial neural network with multiple layers that can recognize patterns within data. This approach has been instrumental in achieving breakthroughs in speech recognition, computer vision and natural language processing.

In 2012, a team of University of Toronto researchers developed a deep-learning algorithm named AlexNet. AlexNet classified images in the ImageNet Database with unprecedented accuracy. This paved the way for more complex deep-learning models.

AI is used today in many applications, ranging from self-driving vehicles to medical diagnosis. Researchers are exploring new approaches to AI, including reinforcement learning and generative modeling, which could lead to more breakthroughs.

The history of AI has been a constant story of innovation and evolution. AI has evolved a lot since the days of logic and reason. It is now able to perform deep learning, neural networks and other modern breakthroughs. We can expect more breakthroughs and a bright future for AI.