For years, artificial intelligence has dominated forecasts and reports regarding the industry trends. Moreover, numerous enterprises, not only the largest ones, have experimented with the implementation of its elements, with some already displaying significant achievements within the scope of production implementations.
In the enormous aggregation of information regarding AI development, one can easily find himself lost. Therefore, thoughtful and conscious approach, based on a previously designed strategy, that would meet the actual needs is of the great importance here.
For starters – fixing the concepts
In order to develop and implement such a strategy, understanding the nature of artificial intelligence is necessary. Without a doubt, it is far away from imaginations straight out of Sci-fi films. Until now, we have anticipated, that artificial intelligence and artificial consciousness are firmly associated.
While in the first case, we can already show interesting achievements with more and more applications, the artificial consciousness still remains to be a terra incognita. Moreover, the issue is not particularly explored by scientists or enterprises. The current approach to artificial intelligence is primarily based on copying the structure of the human brain. Using the current state of knowledge regarding neurophysiology, we are creating neural networks – grounds for what is currently referred to as artificial intelligence.
What are we doing with artificial intelligence now?
In result, the development of artificial intelligence (deprived of even a partial artificial consciousness) proceeds primarily within the scope of the development of artificial cognitive processes. In the portfolios of every respectful IT corporation today, we shall find numerous cognitive class solutions, primarily offered through a service model. These may include advanced algorithms of image, sound recognition, or text-to-voice processing algorithms, all of them using large data sets, and based on deep learning. Therefore, currently, „intelligence” is proportional to the size of processed data sets – the larger the amount of data is fed to machine learning algorithms, with an artificial neural network structure, the better will they be able to accomplish the given tasks.
This way we arrive at one of the largest challenges, which the scientists currently have to face when developing artificial intelligence. The more efficient our artificial intelligence is, the more it is specialised in particular tasks. Therefore, it becomes more and more difficult to adjust it to new purposes. A good example may be the incredibly advanced algorithms that currently defeat human opponents in chess or real-time strategy games. After processing a sufficient amount of information on previous games (much more information that would be required to teach a human player) they are able to defeat even the best human opponent. The problem is, the same algorithms can do nothing else – attempts to implement them in even slightly different applications require a renewed conduction of machine learning procedures.
Artificial intelligence in business – the practical approach
Having even such partial information at disposal one may already create scenarios of implementing A.I. in business. Considering the aforementioned paradigm, in which the efficiency of algorithms increases proportionally to their specialisation, artificial intelligence will do great even in the most niche applications. The implementation of the aforementioned artificial cognitive processes will allow to transform enterprises in compliance with digital transformation premises by adjusting them to the current market dynamics.
Another belief is that only those companies who are in some way associated with the IT industry can afford A.I. implementation. This can be demonstrated, e.g. by numerous successful experiments with the automation of support or customer support departments, e.g. by means of increasingly popular chatbots. At first, they may seem like a primitive solution, and, truth be told, they have been primitive for years but today’s chatbots are much more advanced. They use cognitive algorithms that process natural language, and even voice-to-text processing. Moreover, they collect data and self-improve on basis of conversations with users.
The most advanced intelligent voice assistant at the time, the Amazon Alexa, is in fact, a very sophisticated voice-to-text processing engine, and a voice synthesizer. The plenitude of Alexa’s capabilities lies not in the system itself, but in the open API, due to which each enterprise concerned may supply its own services to the Amazon platform.
Open source to the aid
The implementation of artificial intelligence elements may yield reasonable advantages not only due to the automation of tasks, but also due to opening entirely new channels for providing services or product sales. Free and open source software comes to the aid here, being in full bloom. Amazon, Google, or Microsoft, are racing to release machine learning models and libraries that are better and better tailored to various needs, i.a. TensorFlow, Microsoft Cognitive Toolkit, or Torch.
Obviously, the most popular machine learning libraries, models or frameworks are still far away from no-code development premises, as it must be mentioned, that in more professional and advanced scenarios, they still remain in the plane of wishful thinking. Using TensorFlow alone, or cognitive tools supplied on the Azure platform, will be more efficient, if we take advantage of services of a highly qualified team that possesses abundant experience in previous implementations. Therefore, the implementation of artificial intelligence is exactly the aspect, where outsourcing is being applied.
Learn from the experienced
In order to successfully outsource software development an appropriate strategy is necessary as without it is easy to develop a solution that will not meet the needs of an organisation. Particularly, when we speak of a highly specialised bespoke software, it is advisable to take advantage of the experience and knowledge of experts.
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