Currently, the artificial intelligence industry is in its golden age. Various researches are conducted to develop artificial intelligence and seek opportunities in its application to other industries. Big companies are flocking to start early in research so that later when artificial intelligence is needed they expect to be implemented.
Interestingly large companies like Amazon have even begun to come up with services related to artificial intelligence itself. As reported by Venture Beat, Amazon Web Service has launched a service that provides services for AI training, translation, and transcription. This is arguably a major breakthrough, given that the AI industry is generally still in the stage of research and development. But Amazon is brave enough to provide services that have not been in the implementation phase at the industry stage.
AI industry will still leave a gap that has not been answered. Questions like will AI replace the performance of the full human power? or How can AI validate data exactly as validated by the human brain?
One of the questions that can be debated is where the source datasets will be used as a reference for the intelligence of the plant. As we know, AI is basically created from a collection of data from a particular topic that is collected into a dataset.
Datasets are data that has been labeled or grouped according to the needs and objectives for what artificial intelligence will be. The change from data to a dataset still requires a lot of effort and can only be done by human labor.
For example, if a company wants to create an artificial intelligence program in the health field, then the first thing to do is the company must collect what data is needed. Obviously, the data that is grouped is data relating to health.
Furthermore, the data will be grouped or labeled according to the type of data group. Such as disease type data, symptom data, medication data, contraindications data, side effect data, handling data, prevention data, and so on.
When categorizing the data, the company needs competent human resources. This is related in terms of validation whether the data is true there is in certain data groups. Do not let the data that should be contained in the disease data lies in the data contraindications, will certainly cause chaos in the program later.
You can imagine how much manpower is needed to create a decent AI program. That does not include other teams such as a team of programmers, designers, or other marketing teams.
The good news is that a startup will solve this problem. This startup is named Dbrain (https://dbrain.io). Dbrain itself aims to bridge the crowd worker, data owner, data scientist, and the company that will later develop a program for commercial needs.
For how Dbrain works please read the article here.
Dbrain connects the raw data owner who already has the required data for the next to do the labeling where this data should be grouped. Labeling is done by crowd worker. After the labeling, then the data is processed by data scientist who uses the data as a basis for making technology Artificial Intelligence. And further forwarded to the company as an AI user for their commercial or operational needs.
This innovation certainly allows companies to work on an AI project without having to hire workers in full just for the purposes of labeling data only. For more info on Dbrain please check the link below:
Official Site: https://dbrain.io/