Artificial Intelligence and machine-learning technologies have been around for a while. However, one thing that piques the curiosity of many is why most firms and organizations are not tapping into this field. Ideally, artificial intelligence systems use the analyzed data to process and deliver near accurate predictions, aside from determining trends and highlighting any glitches.
One reason why most companies are yet to implement machine -earning tech is due to the high costs involved. However, this is about to change thanks to the five vectors of progress.
Transfer learning is a new technique aimed at sizing down the demand for training data.
More sophisticated processors focused on speeding up the training of machine-learning protocols are in production. These microchips will help cut down on the time spent developing AI machines.
Analyzing Big Data is mostly done by data scientists/engineers, which takes up a lot of time. Nonetheless, there are new tools designed to automate such activities which will not only speed up the process but also cut down on the human resources required.
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Challenges on Use of Machine Learning
As discussed earlier, some companies are yet to adopt machine-learning technologies despite their massive potential. A few reasons have made implementing machine learning an expensive affair.
Below are some of the top reasons.
The number one reason is that there is a shortage of qualified and experienced data scientists. Secondly, the tools used to automate services such as data analytics are still under development. Another issue is that acquiring the massive amounts of data necessary for machine learning and training is still quite expensive.
The issue of automated services has meant most firms put adopting machine learning on hold. The reason being the logical process used aren’t easy to understand which may become a challenge when explaining the theories to their clients.
Conquering the Challenges on Machine Learning Adoption
There are ways through which the challenges facing AI and machine-learning adoption and implementation can be solved to make the process quicker, cheaper, and simple.
Speeding up Training
Machine-learning and AI automation can take a long time to train for – sometimes even months. This is primarily due to the amount of data being worked on and the complex computational algorithms and processes involved.
However, manufacturers and other interested parties are working towards developing a robust process that’ll help accelerate training.
Automating the Processes
Coming up with solutions or developing algorithms for training often relies a lot on data scientists. The demand for data scientists is high, but there aren’t many of them. This challenge can be circumvented by automating services such as data normalization.
Automating such tasks can reduce the burden of data scientists, making them more effective.
Cutting Down on the Need for Training Data
Training an AI machine requires massive amounts of data. Getting the data and labeling it can be expensive and time-consuming, especially if you’re dealing with complex data such as images.
Synthetic data can make it easier for firms to get training at a cheaper cost.