AI: The Path To a New Future

The Basics of AI

To answer the first question, AI can be described through one question: “Can machines think?” Alan Turing, famed mathematician, and computer scientist, asked this question in his paper, “COMPUTING MACHINERY AND INTELLIGENCE,” (1950) in which Turing addresses the problem of artificial intelligence and proposes something called the Turing test, an attempt to define a standard which a machine can be called “intelligent.”

AI, in its most basic form, is intelligence demonstrated by machines.

It’s a wide-ranging branch of computer science in which the main goal is to build “smart” machines that are capable of doing tasks that usually require human intelligence to be completed. It’s based on the principle that human intelligence can be mimicked in a way machines can understand, and at its central core, it’s really about providing an answer to Turing’s question.

A Quick History of AI

In order to really understand the concept of AI and how it’s matured over the years, let’s travel to the first time AI was mentioned in documented history: 1308. In 1308, Ramon Llul, philosopher and mathematician, published, “Ars generalis ultima,” (The Ultimate General Art) in which he “perfects” his methods of using mechanical means to “create” new knowledge. This is very similar to the mainstream of AI today, which as previously mentioned, is to basically build machines that are intelligent.

Narrow AI

As mentioned before, AI is a wide-ranging branch of computer science, so it’s important to differentiate all the unique types of AI from each other. There are mainly two types of AI: narrow AI (mainly referred to as AI) and artificial general intelligence (AGI).

General AI (AGI)

AGI (artificial general intelligence) is what is portrayed in books, the media, movies, and it’s also probably the first thing that comes to mind when we think of artificial intelligence.

Machine Learning

Deep Learning

Deep learning is a subset of machine learning, based on artificial neural networks. Artificial neural networks are computer systems that are vaguely programmed in order to resemble the biological neural network that exist in an animal’s brain. Deep learning is essentially a class of machine learning algorithms that “uses multiple layers to progressively extract higher-level features from the raw input.” (Wikipedia) One example may be for image processing; lower layers could recognize edges, while higher levels could recognize human concepts such as digits and faces.

Conclusion

It’s clear to see the complex concepts about artificial intelligence, but it’s important to recognize the infinite ways of how artificial intelligence can create new paths for our future. If we can continue our current progression in the field of narrow AI and AGI, our future will be limitless as to what we as humans can do and achieve. While AI is no more than an emerging field in science right now, it is no doubt the key to unlock the future that our world holds.

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