Artificial Intelligence is an extremely powerful technology. Functionalities like voice translation and image recognition have been proven to represent billion-dollar industries. However, the artificial intelligence of today is a far cry from the Terminator-esque monster people typically associate it with. Artificial intelligence is actually quite limited when you look at it broadly. For instance, one technology that utilizes AI is Deep Learning. Deep Learning is on the cutting edge of AI programs, but the technology still has significant limitations. Many deep-learning models only work well when fed vast amounts of data, and they often struggle to adapt to fast-changing real-world conditions. In short, this means Deep Learning only works for very limited tasks and has little real-world application.
The reason this is dangerous is not because of its capabilities, but instead, our general ignorance about it. There is undoubtedly a stigma around Artificial Intelligence as some sort of magical, all-powerful technology, but this is simply not the case. This is especially dangerous because of its possible applications. If people think that AI or deep learning is a panacea to all of our problems, they will try to apply it to all of our problems. Unfortunately, over-reliance on these technologies might prove to be a costly endeavor. Especially if it prevents us from developing new and improved technologies, or if it is forced into an application that does not suit it.
One of the main critiques of AI is that it does not work at the high level needed to keep up with modern business. In order to do this, AI would need to become Artificial General Intelligence, which is sometimes called Strong AI, Full AI, or True AI. However, it is important to note that for AI to do this, it will need to perform the full range of human cognitive abilities. But this represents a problem, because even the most cutting-edge research today does not understand the human mind. Our minds are extremely complex, and it is unlikely that we will ever fully comprehend them. This quandary could prove to be fatal to artificial intelligence projects. If AI is not able to learn by itself, it will need to rely on increasingly large amounts of data to work. Unfortunately, there is a limit to this. Only so much high quality, usable data exists, and it is very difficult for companies to generate this data, much less use it effectively.
In conclusion, the difficulties faced by today's artificial intelligence might prove to be intractable. Yet, despite the myriad challenges it faces, it is still perceived as a panacea to many problems faced today. The defining question will soon become; can artificial intelligence as we currently know it, adapt to the demands our increasingly hectic world places upon it? As it stands, I do not think that it can, the challenges are simply overwhelming, and artificial intelligence and its uses are not enough for it to remain viable.