The journey starts with the science fiction movie “The Wizard of Oz” in the first half of the 20th century where the concept of artificially intelligent robots was born. By the 1950s, a couple of scientists, mathematicians, and philosophers addressed the concept of artificial intelligence (AI) the very first time in a scientific context. However, what stopped the scientists from getting to work on these possibilities? First, computers needed to fundamentally change. Before 1949, computers lacked a key prerequisite for intelligence: they couldn’t store commands, only execute them. In other words, computers could be told what to do but couldn’t remember what they did. Second, computing was extremely expensive. In the early 1950s, the cost of leasing a computer ran up to 200.000 dollars a month.
Between 1957 to 1974 computers could store more information and became faster, cheaper, and more accessible. Machine learning algorithms also improved and people got better at knowing which algorithm to apply to their problem. The optimism was high and expectations were even higher. However, there was still a long way to go before the goals of AI could be achieved.
In the 1980s, AI was boosted by an expansion of the algorithmic toolkit, and available funds. The Japanese government heavily funded such projects and invested 400 million dollars with the goals of improving e.g. AI. Unfortunately, most of the ambitious goals were not met with the result that the funding has stopped, and AI fell out of the public hype. Ironically, in the absence of funding and the hype, AI developed continuously. During the 1990s and 2000s, many of the landmark goals of artificial intelligence had been achieved. In 1997, the grand master of chess Gary Kasparov was defeated by IBM’s Deep Blue, a chess playing computer program. This was a huge step for artificially intelligent decision making programs.
Today, we live in the world of “big data”, which has the capacity to collect huge sums of information and the necessary technologies to process this huge amount of data. AI applications are used successfully in several industries and allow us to solve many different problems, but how will AI affect the content migration process in the future?