Karol Przystalski is the CTO and founder of Codete, a company specializing in IT consulting and implementation of innovative solutions for businesses. Karol obtained his Ph.D. in Computer Science from the Institute of Fundamental Technological Research, Polish Academy of Sciences, and has worked as a research assistant at Jagiellonian University in Krakow. Karol currently leads and mentors teams across various departments of Codete, and he has built a company research lab focused on machine learning methods and big data solutions.
What area of expertise do you see becoming more important for Codete in the coming years?
Definitely AI. We see that some of our customers are transforming from traditional businesses to businesses that are actually driven by AI. This is good for us, because we specialize in this area. We can help them with research and a lot of really cool stuff they may not think of on their own. From a data science perspective, it’s more ambitious to go deep into the details than to go through the standard e-commerce motions.
How as your role evolved as CTO of Codete?
In the past, I was just advising when it comes to technical points of view on a given project. Now I’m more involved in the research lab. My Ph.D. in computer science also includes an emphasis on AI (specifically HealthTech and diagnosing skin cancer), so I’m comfortable giving advice in this kind of setting. We are very often building AI-driven prototypes and concepts for companies that know the problem, but not necessarily the solution. I give advice on how to build the product and am happy about this direction.
How do you approach making data work more effectively for companies?
Each case is different, depending on what kind of direction you would like to go in the first place. There are obviously some emerging trends that are very often working well, such as AI, but that’s not your only option. If you are a new company, you also have to think about how you are going to obtain data.
What are some of the greatest digitilization challenges that your customers face?
Many times our customers know what they want to solve, but don’t have a technical understanding of how to solve it. They don’t have a core team with this knowledge. Additionally, the mindset is often the hardest thing to change for companies. I hear that big companies who already have access to data struggle to understand how they should best utilize it – basically a lack of knowledge or experience with these topics. That’s where we come in.
How do you see the AI market changing for devices?
When it comes to AI and mobile, we see companies like Apple releasing products that aim to show developers that AI is also applicable natively on mobile. We are currently building a platform like this that enables you to use any kind of framework for machine learning on any kind of platform or device. This is tricky, but we are hoping it will be a game-changer. Your product, for example, doesn’t need to be written in Python or Java; you can select your platform if you have a more flexible AI – the kind that we are working on.
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