At the beginning of the year 2018, many media outlets believed that AI would become very popular. Most analysts predicted that the worldwide revenues for cognitive and Artificial Intelligence (AI) systems had reached the sum of $12.5 billion in 2017— which was a 60% increase over the past year 2016.
Then again, all investments in AI would continue in that path in the year 2018 and beyond, resulting in a total annual compound growth rate of 54.4% down to 2020. By this time, time revenues would go over $46 billion. Although the jury is still trying to determine if these numbers have been realized, it is no secret that the year 2018 has been one successful year for AI.
Why is AI training data so important?
AI training data enables us to build and teach algorithms to carry out tasks. It is designed to have a pair of input information and labeled answers that correspond to the input information. In other fields, the input information may also have some relevant tags which enable the algorithm to make precise predictions. For instance, the AI training dataset used in sentiment analysis usually adds in an input text with a set of output attribute labels that are positive, negative, or neutral.
Nowadays, most researchers use the training data in a repeated manner, to fine-tune the prediction of the algorithm and to also enhance the success rate. As we may already know, for practical training of the algorithms, researchers would need a considerable sum of data. Nevertheless, the quality of data is also very essential. That is why researchers have to ensure that the data used must be clean and well-organized before it is used in training the algorithm. Any data that is irrelevant, duplicated, or flawed can affect the ability of the algorithm to identify patterns or develop unbiased results. It is so critical that a small error like giving a word a tag of a verb instead of a noun can have a significant impact on it.
Ways to boost the success
1. Carefully plan your data-gathering road map.
One of the main problems that most companies face is the inability to utilize Machine Learning for their businesses effectively. It is the reason why they are reluctant in collecting AI training data. As such, they see Machine Learning as nothing but an experiment or a side project. Then again, most companies that are aware of the importance of Machine Learning to their businesses are not familiar with it and so, may not know the best algorithm to use. If any company wants to achieve a successful AI project, then they require a training-data requirements document.
2. Don’t underestimate the time it takes to gather data.
The mistake most companies do is to implement machine learning at the last minute because they see that their competitors are now using a brand new AI product. What they do not know is that that single act leads to a stressful attempt to collect data, and it ultimately results in a scattered data set assembled from different sources. However, in a real sense, you should collect data over weeks or months to develop a machine learning model that will perform excellently. If the process is not done carefully or if you don’t build the algorithm with clean data, you risk creating a reduced model that you can’t use.
3. Tagging, labeling and classifying — Essential tasks that you can outsource
Machine Learning algorithms are created on ground truth training data, and to get useful training data which will provide an excellent base for a model; you would require human annotation. Irrespective of the fact that many crowd sourcing companies such as Gengo can readily create and annotate data sets inexpensively, many people often think that it requires a large and expensive workforce.