When it comes to digital transformation, a term that has almost become synonymous with it is Artificial Intelligence. The technology has made an impact on almost every aspect of every business. The scale of importance artificial intelligence holds in the current space can be gauged by the fact that even the Government of India announced special initiatives for it in the Union Budget 2019.
A National Programme on ‘Artificial Intelligence’ has been envisioned by the government, which would be catalysed by the establishment of the National Centre on Artificial Intelligence as a hub along with Centres of Excellence. Also, nine priority areas have been identified for the same and a National Artificial Intelligence portal will be developed soon. However, along with all the potential that AI holds, there are certain pain points that need to be addressed in order to make the technology truly efficient. In an interview with DataQuest, Rahul Vishwakarma, CEO and Founder, Mate Labs, speaks about the enormous prospective that Artificial Intelligence holds, how the industry perception has changed and some pain points that need to be addressed.
Change in perception of the industry towards Artificial Intelligence
It is possible to say that a lot has changed quite positively, over the years. Back when the AI winter happened, right after the intense hype around it and subsequent unmet expectations, it was termed a hoax and research in the area was abruptly cut off. Much later, with advanced computing abilities of high-powered microprocessors, and numerous efforts from a small bunch of researchers and AI enthusiasts, AI became mainstream, once again. Now, businesses all over the world are tapping into the wide opportunities that it has offered.
As we can see, during the last 5 years, AI start-up’s have caught international attention, and multiple of these companies were acquired by trillion dollar companies, or have flourished by themselves. As a result of these innovations, they are now able to bring solutions that were previously unheard of, and even thought to be impossible. Now, we have solutions like automated Machine Learning, and automated data pre-processing, that takes away 50 to 90 percent of the strain from data scientists’ shoulders.
Implementations of Artificial Intelligence and future roadmap
AI has captured several industries across the world, mainly in automating routine tasks and processes, and also in making accurate predictions about oncoming issues beforehand, and solving them for their clients/customers. We see a major explosion in its adoption in the finance, retail, transport, food and healthcare sectors.
Process automations with Predictive Modelling is the hottest trend out there, right now. Going forward, predictive analytics will be used even more widely in managing production, financial assessments, warehouse stocking, system security checks, online purchase recommendations, virtual assistants, and other wide variety of applications.
The future would entail the use of AI, by almost everyone, without having to learn the intricacies of coding or its internal processes. Truly democratizing machine learning for everyone would pave the way towards a more inclusive, unbiased, and beneficial AI. When AI doomsayers predict the worst, enabling everyone with new age skills is bound to mitigate the ill-effects of any powerful tech.
How companies can harness true potential of Artificial Intelligence in order to use data
The true potential of data can be realized only when businesses exercise their power over it through data science and analytics. Moving away from primitive methods of analysis, there should be an open mind towards adopting modern and proven technologies.
It makes no sense for any organization to waste precious time, by burdening their analysts with using brute force on raw data, to extract insights from them. Simpler solutions exist with the use of AI, even those that don’t require writing a single line of code. They have inbuilt error detection techniques and algorithm selections, and provide comprehensive and detailed insights with robust visualizations.
Pain points and challenges involved in integration of Artificial Intelligence
Major pain points in implementing AI is the time it takes in pre-processing the data in order to build a Machine Learning model. Data Scientists spend at least 80 percentage of their time in cleaning the data, making it ready to build models on it. And, with the dearth of talent in the industry, most data experts are stuck with the data pre-processing, when they should be utilizing their skills in strategizing new business opportunities. Furthermore, data security and options to deploy AI models privately, is much sought after in the industry.
Hence, there is be a lot of stress inside the industry for automated Machine Learning solutions that can solve the existing problems in cleaning the data, and also build models that have the highest accuracy, security, as well as least bias.