We have had a blockchain winter as the hype around the technology moves towards a reality – and the same will happen with artificial intelligence (AI).
That’s according to Dr Karol Przystalski, CTO at IT consulting and software development provider Codete. Przystalski founded Codete having had a significant research background in AI, with previous employers including Sabre and IBM and a PhD exploring skin cancer pattern recognition using neural networks.
Yet what effect will the Covid-19 pandemic have on this change? Speaking with AI News, Przystalski argues – much like Dorian Selz, CEO of Squirro, in a piece published earlier this week – that while AI isn’t quite there to predict or solve the current pandemic, the future can look bright.
AI News: Hi Karol. Tell us about your career to date and your current role and responsibilities as the CTO of Codete?
Dr Karol Przystalski: The experience from the previous companies I worked at and the AI background that I had from my PhD work allowed me to get Codete off the ground. At the beginning, not every potential client could see the advantages of machine learning, but it has changed in the last couple of years. We’ve started to implement more and more machine learning-based solutions.
Currently, my responsibilities as the CTO are not focused solely on development, as we have already grown to 160 engineers. Even though I still devote some of my attention to research and development, most of my work right now is centred on mentoring and training in the areas of artificial intelligence and big data.
AI: Tell us about the big data and data science services Codete provides and how your company aims to differ from the competitors?
KP: We offer a number of services related to big data and data science: consulting, auditing, training, and software development support. Based on our extensive experience in machine learning solutions, we provide advice to our clients. We audit already implemented solutions, as well as whole processes of product development. We also have a workshop for managers on how not to fail with a machine learning project.
All the materials are based on our own case studies. As a technological partner, we focus on the quality of the applications that we deliver, and we always aim at full transparency in relationships with our clients.
AI: How difficult is it, in your opinion, for companies to gather data science expertise? Is there a shortage of skills and a gap in this area?
KP: In the past, to become a data scientist you had to have a mathematical background or, even better, a PhD in this field. We now know it’s not that hard to implement machine learning solutions, and almost every software developer can become a data scientist.
There are plenty of workshops, lectures, and many other materials dedicated to software developers who want to understand machine learning methods. Usually, the journey starts with a few proof of concepts and, in the next build, production solutions. It usually takes a couple of months at the very minimum to become a solid junior level data scientist, even for experienced software engineers. Codete is well-known in the machine learning communities at several universities, and that’s why we can easily extend our team with experienced ML engineers.
AI: What example can you provide of a client Codete has worked with throughout their journey, from research and development to choosing a solution for implementation?
KP: We don’t implement all of the projects that clients bring to us. In the first stage, we distinguish between projects that are buzzword-driven and the real-world ones.
One time, a client came to us with an idea for an NLP project for their business. After some research, it turned out that ML was not the best choice for the project – we recommended a simpler, cheaper solution that was more suitable in their case.
We are transparent with our clients, even if it takes providing them with constructive criticism on the solution they want to build. Most AI projects start with a PoC, and if it works well, the project goes through the next stages to a full production solution. In our AI projects, we follow the ‘fail fast’ approach to prevent our clients from potential over-investing.
AI: Which industries do you think will have the most potential for machine learning and AI and why?
KP: In the Covid-19 times, for sure the health, med, and pharma industries will grow and use AI more often. We will see more use cases applied in telemedicine and medical diagnosis. For sure, the pharma industry and the development of drugs might be supported by AI. We can see how fast the vaccine for Covid-19 is being developed. In the future, the process of finding a valid vaccine can be supported by AI.
But it is not only health-related industries which will use AI more often. I think that almost every industry will invest more in digitalisation, like process automation where ML can be applied. First, we will see an increasing interest in AI in the industries that were not affected by the virus so much, but in the long run even the hospitality and travel industry, as well as many governments, will introduce AI-based solutions to prevent future lockdown.
AI: What is the greatest benefit of AI in business in your opinion – and what is the biggest fear?
KP: There are plenty of ways machine learning can be applied in many industries. There is a machine learning and artificial intelligence hype going on now, and many managers become aware of the benefits that machine learning can bring to their companies. On the other hand, many can take AI for a solution for almost everything – but that’s how buzzword-driven projects are born, not real-world use cases.
This hype may end similarly to other tech hypes that we have witnessed before, when a buzzword was popular, but eventually only a limited number of companies applied the technology. Blockchain is a good example – many companies have tried using it, for almost everything, and in many cases the technology didn’t really prove useful, sometimes even causing new problems.
Blockchain is now being used with success in several industries. Just the same, we can have an ‘AI winter’ again, if we don’t distinguish between the hype and the true value behind an AI solution.