Artificial Intelligence is increasingly driving significant developments in business today. The revolutionary technology is enabling enterprises to develop new solutions and improvising the way of doing things.
A few years ago, nobody would have considered AI to become one of the most important business and technology drivers. But today, more and more companies choose AI and ML to eliminate the inefficiencies in workflow and accelerate business growth. In an interview with Analytics, Oleksandr Odukha, Delivery Director at Intellias tells how the company helps companies optimize processes and increase operational efficiency by harnessing AI and ML solutions.
Oleksandr is a highly motivated team lead, driving machine learning and artificial intelligence expertise in the company. With over ten years of commercial software development experience, Oleksandr has a strong application-level technical background. His primary responsibilities are project, program and track management, delivering solutions to different clients — from “two-men army” startups to Fortune 500 enterprises.
Below are the highlights from the interview between Oleksandr and Analytics Insight:
Tell us how your company is contributing to the ML and AI industry of the nation and how the company is benefiting the clients.
Intellias develops AI solutions in the FinTech, automotive, retail and other domains, using computer vision and ML algorithms for object recognition, data-driven decisions, predictive analytics, and chatbot services. We believe that with the right data analysis, we can help companies optimize their time-consuming processes and increase operational efficiency, which is so important in today’s business realm.
How is AI evolving today in the industry as a whole? What are the most important trends that you see emerging across the globe?
There are two key features of AI evolving:
1) The rapid development of new AI/ML approaches
2) The continuous enhancement and improvement of the existing results
These two factors push all other industries to evolve faster than they were before the ML era, the fast development of novel solutions induce changes in the corresponding domains.
A lot of companies across the globe should pay more attention and efforts to be on the way of cutting-edge AI/ML technics. Of course, it is an investment, but it has great revenue for your effort in your outcome product.
What are the key trends driving the growth in AI and Machine Learning?
Key trends for any industry are strictly defined by end-user needs. Currently, the most common domains of AI and ML are the automotive industry, robotics, and finance. These industry areas are interested in computer vision, natural language processing, data analysis approaches, prediction of time sequences, etc. Other areas of artificial intelligence are also developing (mainly in the research format) and will be required soon. These areas are: Generative Adversary Networks, Capsule networks, etc.
How do you see the company and the industry in the future ahead?
Over the last few years, a lot of open-source, shared knowledge is available on the market. So, it’s not difficult to take a ready-to-use solution and transform up to your case. Only companies that have their own accelerators, conduct research, and develop state-of-art-algorithms will succeed. Intellias also pays a lot of attention to new cutting-edge approaches and develops own competitive AI/ML solutions in automotive, agriculture, FinTech domains.
How is Machine Learning (ML) shaping IT/Big Data/Robotics industry today? How is it changing the role of CIOs and Leaders?
The fast growth of AI and ML has a strong impact on other industries, creating new approaches and opportunities. This impact is bi-directional: ML pushes other industries, at the same time the industries set new goals for ML domain. AI solutions help reduce spending: starting with RPA solutions, companies save on operational activities and optimize processes.
With predictive analytics in automotive, for example, fleet owners can forecast which car parts will be required soon and tune inventory accordingly. It helps invest in what’s really important. Nowadays, ML in IT industry became the main component to achieve success on a new level. Hence, leaders should obtain a basic knowledge of AI and ML, its capabilities and restrictions. Only such experience can guarantee proper utilization of ML/AI power.
What is the reason that organizations are using AI/ML?
Just a few years ago we had not enough computational resources to run AI in production, but now we can run it even on our smartphones and smart-watches. This fact makes AI and ML widespread.
At the same time obtained results in different domains already prove that AI and ML can enhance many results in classic IT solutions, add new valuable results and outcomes. Solutions based on AI/ML can transform the power of information into real business values.
AI and ML allow adding new non-trivial functionality into final products making them more attractive for end-users. And, at the same time, open new opportunities for business discovering new knowledge in existing information.
Data analysis that companies like Walmart or Amazon make is a typical example of how ML and AI augment today’s customer services. Based on customers’ behavior, companies may create personalized offers and customized discounts. Traditionally, it was a manual process, but today, it’s ML that makes it work.
What are some of the challenges faced by your company today?
One of the main challenges in AI/ML domain is lack of training data: all machine learning algorithms are as strong as the training data is well-labeled. Labeling activities requires a lot of efforts and diligent and that is why Intellias pays a lot of attention to the continuous improvement of the labeling process. We are focused on the automation of data extraction and transformation, continuous improvement of annotation tools, data augmentation, training of annotation team, etc. (By the way, well-skilled labeling experts do 20x times more work than non-skilled colleagues.)
Intellias takes the whole responsibility on data preparation for AI and ML projects: we organize the ETL process, data cleaning and labeling task, and data validation.