With artificial intelligence, AI for short, problems can be solved where it is necessary to recognize and understand relationships. A combination of robotics and artificial intelligence is very powerful, as it can also be used to automate processes that are not based exclusively on clear rules or for which data must be interpreted. The robot is the executing element, which executes certain actions based on clear rules, while the AI analyzes data and provides the input to the robot in the form it needs.
In the following paragraph you will find a Use Case in which the use of artificial intelligence in connection with Robotics makes sense:
A company wants to automatically process received invoices from its suppliers. There are clear rules as to how to deal with which suppliers, how to check the amounts and who has to confirm the respective invoice. As soon as the supplier and the invoice amount are known, the invoice can be automatically processed with the help of Robotics. The necessary information can be collected and processed with the help of Robotics.
The problem lies in the recognition of the invoice itself. Since each supplier presents his invoice in a slightly different way, a robot cannot simply read out the invoices because the relevant information is listed at different locations. This is where artificial intelligence comes in.
Buy or Build?
There are two approaches how artificial intelligence can be used in your company:
Purchasing an AI Software
There are already many tools on the market which use AI to solve a specific problem. The more the same challenge is faced, the more likely it is that a dedicated AI software has already been trained. These tools can be easily integrated into the processes to be automated. Purchasing AI software can save substantial development costs. The disadvantage of a commercial solution is that these tools are usually very general, so that they can cover more use cases. This may lead to an incomplete solution of your problem.
Train artificial intelligence yourself
If no existing software exists for the problem, or the software cannot be used for other reasons, such as data protection, an own neural network can be built and trained with the help of AI frameworks such as Tensorflow. In order to develop a neural network or other forms of artificial intelligence, either a large amount of already processed and marked data (Input -> Output) or a direct feedback loop is required:
Supervised learning is the training of artificial intelligence on the basis of example data. These example data contain the input, in the case of the use case described above the scanned invoices, as well as the desired output, in our case the relevant data from the respective invoice. After sufficient training, the artificial intelligence can extract the relevant information from invoices it has never seen before.
In contrast to supervised learning, reinforced learning does not require sample data. However, the neural network must receive direct feedback on its actions. Based on the feedback, artificial intelligence can improve in order to solve the tasks assigned to it correctly in the end. An example of a direct feedback is the stock exchange. The stock prices with which the profit or loss can be calculated provide the feedback for the buying and selling actions of the AI. Company data, social media or previous share prices could serve as input.