3 challenges of AI that Vietnam IT Companies have to face

09.24,2021

 

AI is a technology that can revolutionize manufacturing industries, healthcare, space exploration, and more. As a result, artificial intelligence is growing and gaining popularity at an excellent rate. This growing popularity of AI has urged several businesses to invest in developing and researching different AI applications like robots and automated cars.

However, it is important to note that AI is still facing some challenges. Here are some of the common challenges that most Vietnam IT companies face when implementing Artificial Intelligence.

As most of you are probably aware, AI systems are driven and developed by leveraging quality data. Therefore, the difficulty of accurate data collecting and quality control and the expense of labeling data are the most challenging aspects for every AI business wherever in the globe. Vinfast, for example, is a major artificial intelligence application company in Vietnam. Vinfast is Vietnam’s sole autonomous vehicle research and development center. Vinfast has invested a lot of time and money into putting together a fleet of cars that can go around the streets and snap photographs in a range of time and weather situations.

According to Nguyen Minh Tan – Vice Managing Director of Rikkei AI

“An AI product is a synthesis of many different materials, and data is one of the essential components. According to statistics, up to 80% of AI product development time is spent on data-related processing.” 

In addition, training AI is similar to educating a child. AI can learn successfully if the correct data is used to train it. In contrast, if we teach AI with incorrect data, it will learn incorrectly. Furthermore, when heterogeneous data is used at an inaccurate moment, the AI becomes confused. Therefore, the privacy and security of data collecting and training for AI should be paid attention to; the more data appropriately utilized for training, the smarter the AI becomes and the more precisely it can recognize.

 

Here is one of the most critical challenges in AI, one that has kept researchers on edge for AI services in companies and start-ups. These companies might be boasting of above 90% accuracy, but humans can do better in all of these scenarios. For example, let our model predict whether the image is of a dog or a cat. The human can predict the correct output nearly every time, mopping up a stunning accuracy of above 99%.

For a deep learning model to perform a similar performance would require unprecedented finetuning, hyperparameter optimization, a large dataset, and a well-defined and accurate algorithm, along with robust computing power, uninterrupted training on train data, and testing on test data. That sounds like a lot of work, and it’s actually a hundred times more difficult than it sounds.

AI companies can avoid doing all the hard work by using a service provider to train specific deep learning models using pre-trained models. They are trained on millions of images and are fine-tuned for maximum accuracy, but the real problem is that they continue to show errors and would really struggle to reach human-level performance.

 

The amount of power these power-hungry algorithms use is a factor keeping most developers away. Machine Learning and Deep Learning are the stepping stones of this Artificial Intelligence, and they demand an ever-increasing number of cores and GPUs to work efficiently. There are various domains where we have ideas and knowledge to implement deep learning frameworks such as asteroid tracking, healthcare deployment, tracing of cosmic bodies, and much more.

They require a supercomputer’s computing power, and yes, supercomputers aren’t cheap. Although developers work on AI systems more effectively due to the availability of Cloud Computing and parallel processing systems, they come at a price. Not everyone can afford that with an increase in the inflow of unprecedented amounts of data and rapidly increasing complex algorithms.

Conclusion

Businesses will have to familiarize themselves with AI, which will help them understand how AI works. There is no denying that implementing AI in businesses can have several challenges, and you will start noticing these challenges when creating an AI strategy for your business. However, adopting a step-by-step and strategic approach will simplify the process of AI implementation to a certain level.

If you’re interested in updating more about technical knowledge, please stay tuned for the new Rikkeisoft blog! 

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