Bringing Scientific SOTA Approaches to Industry

Let’s consider all PROs and CONs of being a pioneer in the machine learning industry and bringing SOTA approaches into your business.

At AI Superior, we strongly believe that it is essential to follow the most recent academic findings in ML, to know freshly proposed machine learning methods, training datasets, and everything that has importance for keeping your ML projects successful revenue-generating assets.

Why We Need Scientific SOTA Machine Learning

Uniqueness is what makes companies strong players. In the machine learning industry, the level of competition used to not be so high a decade ago, when only a few startups dared to work with ML. But nowadays, when ML itself is turning SOTA for data-heavy businesses, it becomes more and more important to be one step ahead of your rivals.

The scientific community constantly generates a lot of promising findings. Not all of them will get into the industry quite soon. You should see it as your chance to pick up a new finding none else in the industry discovered yet.

Remember that these findings are often born at the heart of a prominent university that gathered the best scientific minds and a lot of relevant experience. You can tap on the results that emerged in a unique environment that you’d never be able to recreate. And, most of the time, academic papers are available for free or for a small fee.

When you manage to adopt SOTA ML in your business, you can expect a serious boost in your company’s performance. New approaches allow you to expand the range of your services, attracting new customers and turning existing ones into long-term loyal clients. Your customers will know that you deliver something that stands out and helps them to perform better.

Moreover, with SOTA ML, you can start solving problems that could not be handled efficiently with existing methods. This is especially important for service companies – like AI Superior – as it makes us more robust to the tense competition on the market. At AI Superior, we work on various projects with ambitious tasks that often can only be addressed properly by SOTA ML approaches.

Last but not least, scientific findings often have difficulties in reaching society since academic communities may be a bit disconnected from the broader audiences. Most of the findings are too high-level to have a direct impact on the end-users.

By adopting SOTA machine learning, you help to communicate these findings. You play the role of a moderator.

But let’s stay enthusiastic, but not get too euphoric. What are the drawbacks of being an early SOTA ML adopter?

State-Of-The-Art Machine Learning: Things to Be Aware Of

We said before that you can get the advantage of the knowledge none else in the industry yet has and uses. But the drawback is that no adoption of academic knowledge is per se a happy-end story. Some findings may turn out not to be directly applicable to real life or to be costly in their implementation.

Just think about how much hope we all had towards self-driving cars, and how less of it we have now knowing all the legal issues associated with it.

Being a pioneer is always a risk. None tried it yet, and the outcome is not secure. You may have the best SOTA ML technology but lack an understanding of how to monetize it.

Quite often, one of the reasons for delayed or failed implementation is missing frameworks that should help to transform your business model into an ML-based one. Sometimes, you do not have a bridging technology that can connect SOTA ML approaches with what you actually do for end-consumers, e.g. microcontrollers with ML abilities require C-programming and won’t work with other frameworks.

You also need people with certain skills. To adopt a real-time simulation from an academic paper, you’d have to hire someone with a PhD degree. This does not only require a money investment but may turn challenging for your corporate culture to integrate people with academic background.

Therefore, the main risk lies in trying to adopt SOTA ML approaches that have not been approbated and have not yet proven to be commercially attractive.

The Safe Way Of Bringing SOTA ML to Your Business

To become a moderator, you may need another moderator. The AI Superior team can help you in finding brand new ML approaches. As a part of our research and development service, we go through the recent academic papers and identify those that our customers can profit from. Moreover, we can help you in implementing SOTA ML approaches that we find. For instance, we helped Boehringer Ingelheim, a pharmaceutical company to successfully adopt new image recognition technologies. That does not only impact the company but also the society as it received a powerful tool for cancer diagnosis.

Conclusion

To what extent should you follow the newest developments in science as a machine learning company?

It is a difficult decision as a greater extent may mean more effort for your existing team or a need to hire a new one.

The result is not guaranteed but may have a highly positive impact on your business and your community.