Though artificial intelligence has evolved recently and appears to be a new phenomenon in modern society, it is much older than you would imagine. Being actively involved in the global AI community, I’ve noticed that many people still associate AI with sci-fi Hollywood movies displaying the distant future powered by intelligent robots and machines. However, this perception is waning as AI becomes more commonplace in our daily lives.
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The early instances of intelligent machines were found in ancient Greek mythology with conceptions of mechanical robots made to help the Greek god Hephaestus. Following were some milestones in the history of AI, which started as a field of research in the late 1950s with the development of the first algorithms to solve complex mathematical problems. The term was thought up by John McCarthy in 1955. Then came the famous Turing test, the so-called “imitation game” for assessing the intelligence level of artificial neural networks — which was first passed in 2014 by Eugene, a computer program simulating a 13-year-old boy. The field continued to make progress, experiencing a real breakthrough in the first decades of the 21st century when the biggest companies like IBM took rapid strides to integrate AI.
Some of the AI algorithms we currently use may be traced back to the early 1700s. At that time, it was challenging to collect and manage such large amounts of data manually. However, with the rapid technological growth, technical capabilities of data processing have also improved and enabled faster collection and storage of data all in one place. In today’s digital age, data collection has been automated to identify the most interesting and hidden patterns, which AI can extract and use.
An AI model is trained on large amounts of data and algorithms, learning from an experience like a child and growing into intelligence. We feed and nurture it with lots of data so it can find relationships, develop understanding and make decisions from the training data it is provided. In most cases, the dataset used to teach an AI model usually consists of an annotated text, image, audio and video, and it needs to be labeled to ensure a more accurate performance. Big data is like AI’s library: The more data it gets, the smarter it becomes.
With such enormous datasets and the ability to learn and perform calculations that most humans can’t, it might seem that AI is not far off from reaching human-level intelligence. Indeed, AI can often exceed human performance in many tasks, but unlike the experiences that humans live throughout their lifetime, AI learns from a single experience only. Usually, AI is trained to solve only one specific problem at a time without the ability to complete the task on its own. […]
Read more: www.forbes.com
Though artificial intelligence has evolved recently and appears to be a new phenomenon in modern society, it is much older than you would imagine. Being actively involved in the global AI community, I’ve noticed that many people still associate AI with sci-fi Hollywood movies displaying the distant future powered by intelligent robots and machines. However, this perception is waning as AI becomes more commonplace in our daily lives.
Copyright by www.forbes.com
Some of the AI algorithms we currently use may be traced back to the early 1700s. At that time, it was challenging to collect and manage such large amounts of data manually. However, with the rapid technological growth, technical capabilities of data processing have also improved and enabled faster collection and storage of data all in one place. In today’s digital age, data collection has been automated to identify the most interesting and hidden patterns, which AI can extract and use.
An AI model is trained on large amounts of data and algorithms, learning from an experience like a child and growing into intelligence. We feed and nurture it with lots of data so it can find relationships, develop understanding and make decisions from the training data it is provided. In most cases, the dataset used to teach an AI model usually consists of an annotated text, image, audio and video, and it needs to be labeled to ensure a more accurate performance. Big data is like AI’s library: The more data it gets, the smarter it becomes.
With such enormous datasets and the ability to learn and perform calculations that most humans can’t, it might seem that AI is not far off from reaching human-level intelligence. Indeed, AI can often exceed human performance in many tasks, but unlike the experiences that humans live throughout their lifetime, AI learns from a single experience only. Usually, AI is trained to solve only one specific problem at a time without the ability to complete the task on its own. […]
Read more: www.forbes.com
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