AI projects go to basic- But why?
Although artificial intelligence technology is more advanced than ever, the majority of AI projects still fail. How could these projects be successful?
Widespread and conventional would be the correct terms in terms of AI. The reason is simple, everywhere in our lives on our smartphones, laptops or even our personal assistant no matter if “Siri”, “Cortana” or “Alexa”, the Artificial Intelligence is present.
In addition, in the areas of production, logistics and much more, AI plays big roles nowadays.
That being said, AI is integrated into pretty much all new cars. The auto sector holds the first place because automated driving has become very popular. It is now known that in the past four years, the use of AI has almost quadrupled.
In recent years, more and more data has emerged. Even the data on the Internet itself make machine learning algorithms the best way to train.
WITH THE INCREASING NUMBER OF DATA, THE APPLICATION ROOM IS ALSO INCREASED.
With the help of AI, data can be stored much cheaper. Including graphics processors which are also always cheaper and performance bidder.
It takes millions of learning cycles to develop AI-based models. These calculations can be most effectively performed by using GPU. Also in this room, noticeable progress was achieved in a few years.
Given all the facts, one should not be fooled, because most companies with their AI projects actually can not achieve the desired success. Everyone wants to use the AI somehow for their own benefit, which leads to a lot of AI projects being designed. Unfortunately, most of these projects do not come into productive use.
Suspicious reasons are the following:
It lacks the developers to farsightedness!
The tests of most projects are performed on cloud systems. However, care is taken not to ensure that much of the data remains on-site in the corporate data centers. In contrast to test phases, petabytes of data in the cloud can not be transferred during productive use. These disregards lead to tremendous performance losses, resulting in over-cost and latency of cloud projects.
Another reason is the lack of quality of the data:
To bring the data to the desired level, algorithms must be used to obtain the tags and metadata. Only when these are sufficiently qualified in the test stadium, reliable statements are made.
Neglect of privacy
Another very important clue is data protection. When creating AI projects, attention is paid to sound private data. Although such data must be protected with confidence. A business model with no privacy or insufficient data protection will certainly not succeed.
The mentioned deficiencies should be considered and avoided in order to use Machine Learning and Artificial Intelligence with all their potentials. The Enterprise use of IT Infrastructure, to data mass with enormous size protected save and classify to can.