As we know that, the term artificial intelligence implies the machine which can reason. It is the area of computer science which emphasizes the creation of intelligent machine that works and reacts like humans. It can actually be defined by its characteristics below:
- Reasoning- It is ability for solving the problems with the help of logical deduction.
- Knowledge- It is an ability which represents knowledge about the world i.e. understanding that there are certain activities, entities, events and situations in the world, by which those elements have properties and those elements can be categorized.
- Planning- It is an ability to set and achieve the goals and objectives.
- Communication- The ability to understand written and spoken language.
- Perception- The ability to deduce things about the world from visual images, sounds and other sensory inputs.
The holy grail of artificial intelligence allows machines to function independently in a normal human environment. As, AI is evolving rapidly what we see today is mostly narrow AI. With this background, let us discuss 10 major types of artificial intelligence problem that organizations generally face as below:
This includes the tasks which are generally based on gaining knowledge on subjects like legal, financial, etc and then it formulates the process where the machines can simulate the expert in a field.
Here, the machine learns the complex body of knowledge such as information about existing mediation and then helps to suggest new insights into the domain itself like new drugs to cure disease.
There are many logistics industry and scheduling tasks which can be done by current algorithms. But increasingly, as the optimization becomes complex, AI can help. One of the best examples is the use of AI techniques in IoT for sparse datasets.
AI and deep learning have great advantages and many communication modes such as automatic translation, intelligent agents, etc.
AI and deep learning enable the newer forms of perception that helps to enable new services like autonomous vehicles.
While autonomous vehicles get a lot of media attention, AI will be deployed in almost many sectors of the economy. In each case, the same principles apply that AI will be used for creating new insights from automatic features detection with the help of deep learning which helps to turn into optimization and improves or changes the process and standards of business.
The vast range of data which is available to the most of organizations is unstructured – call logs, email transcripts, video and audio data which is full of valuable insights cannot be easily universally formatted into rows and columns.
Second order consequences
The second-order consequences of machine learning will help to exceed its immediate impact. Deep learning has greatly improved computer vision. Today, 90% of people and 80% of freight are transported with the road in the UK.
Evolution of the Expert system
Expert systems have been around for a long time. There is much of the vision of expert systems that can be implemented in deep learning or artificial intelligence in near future.
The interplay between AI and the sentiments analysis is also a new area. There are already many synergies between both of them because there are many functions of AI apps which require sentiments analysis feature.
To conclude, AI is rapidly and increasingly evolving its space in today’s environment. Although AI is more than deep learning, it advances in deep learning. Automatic features learning is the key feature of AI and needs many details and pragmatic strategies which will have its own impact on business.
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