My name is Barbora Polaskova, and I navigate two seemingly distinct careers. By day, I am the Head of Finance at DNAi, and in my spare time, I am a certified esports coach. I hope my story inspires you to realize that it is never too late to start something new. Here’s a glimpse into my unique journey and the unexpected connections between these two roles.

From Mother to Gamer: A Surprising Start

My venture into esports began five years ago, driven by a simple desire to connect with my son. He wanted a gaming partner, and I willingly joined in. Little did I know, the roles would soon reverse — my son moved on, but I found myself completely hooked. Specializing in Fortnite, a relatively new game at the time, I dived deep into its mechanics and community. The turning point came when I started writing articles about Fortnite for Playzone, a popular gaming platform. This involvement exposed me to the professional esports scene, where I observed and wrote about competitive matches. My passion and knowledge grew, leading me to join the Czech esports Association as a secretary and later pursue a certification course to become an esports coach.

The Path to Professional Coaching

Becoming a certified esports coach wasn’t just about mastering the game. The training, conducted at the Faculty of Physical Education and Sport at Charles University, emphasized a holistic approach. We learned to prepare players mentally and physically, incorporating aspects like ergonomics, nutrition, and sleep hygiene. This comprehensive training aims to ensure that aspiring gamers maintain a healthy balance between gaming and physical activity. We aim to achieve the same balance at youth camps, which I also help organize.

Finance and Esports: Unexpected Parallels

Parallel to my esports journey, my professional career has always been rooted in finance. With a degree in economics and years of experience in various financial roles, joining DNAi as Head of Finance was a natural progression. Managing financial operations for a company focused on artificial intelligence requires precision, strategic thinking, and a calm demeanor under pressure — skills that surprisingly overlap with esports coaching.

A core lesson from my coaching experience is the crucial importance of mentality. Success in both finance and esports hinges on clear focus and resilience. In esports, players must learn to leave past mistakes behind and approach each new game with a fresh mindset. Similarly, in finance, it is essential to analyze past decisions, learn from them, and move forward with a strategic plan.

Final Thoughts

Combining a career in finance with a passion for esports might seem daunting, but it is incredibly rewarding. The skills and lessons from each field complement and enhance my performance in the other. To anyone considering a similar path, I say embrace it. Whether you’re driven by professional ambition or a passion for gaming, the journey is enriching and offers valuable insights applicable to all areas of life.

Thank you for reading my story. I hope it inspires you to explore the intersections of your own interests and career paths.

Written by:

Barbora Polaskova

CFO | DNAi and certified eSports coach

My Personal Blueprint in Computer Vision

What if you could turn your operational challenges into major breakthroughs with AI?

As a computer vision research engineer at DNAi, I am deeply involved in transforming complex problems into effective, innovative AI-driven solutions. Each project I undertake is a unique journey — from understanding intricate client needs to deploying sophisticated AI systems that address these challenges directly. This blueprint of mine focuses on turning potential setbacks into significant technological and operational victories.

Understanding the Client’s Vision

The journey begins with a deep-dive discussion to fully grasp the client’s specific challenges and objectives. This foundational understanding is crucial, as it guides the entire project. By immersing ourselves in the client’s world, we can personalize the AI solution to address not just the symptoms of their problems but the root causes. Moreover at DNAi, we don’t just solve existing issues; we also excel at expanding and enhancing our clients’ vision for their products, often introducing innovative applications of AI that transform their operational capabilities. This proactive approach enables us to deliver solutions that not only address immediate needs but also provide scalable opportunities for future growth and innovation.

Data Collection: The Backbone of AI Solutions

High-quality data collection is pivotal. At DNAi, we follow a principle I like to call “garbage in — garbage out,” which focuses on gathering data that directly impacts the solution’s effectiveness. Whether navigating complex factory setups without disrupting ongoing operations or adapting to unique environmental conditions, I ensure that we collect the best possible data to feed into our AI models. Projects have ranged from detecting defects in manufacturing lines to automating the inspection of solar panels and road surfaces, significantly reducing human risk and improving operational efficiency.

Prototyping: From POC to MVP

Starting with a Proof of Concept (POC), I develop initial prototypes that demonstrate the core capabilities of the proposed solution. This phase is about proving the feasibility and refining the approach based on real-world data and feedback. The MVP stage is where we scale this concept into a deployable product, making necessary adjustments to enhance its functionality and reliability.

Scaling and Deployment

After MVP approval, we scale the solution. My role involves not only technical enhancement of the system but also ensuring it integrates seamlessly with the client’s operations. This phase often requires expanding detection capabilities and fine-tuning the system to adapt to diverse operational environments.

Continual Improvement and Client Training

Deploying the AI system marks the beginning of a new phase — ongoing optimization. We continually refine the model, enhancing its accuracy and efficiency as it encounters new data. Moreover, at DNAi, our role extends beyond troubleshooting; we actively work with our clients to enhance their understanding of what AI can achieve. This collaboration often leads to refined product visions and expanded operational capabilities, setting the stage for continued innovation and growth.

Conclusion: The Impact of Tailored AI Solutions

By customizing each solution to the specific needs of the client, we not only solve their immediate problems but also enhance operational safety and efficiency significantly. The AI systems we develop are designed to be robust and adaptable, ready to evolve with the client’s needs and ensure sustained benefits.

Through this personalized approach, we transform client challenges into triumphs in the realm of computer vision, solidifying our role not just as a solution provider but as a pivotal partner in their ongoing innovation journey.

Written by:

Adam Ukleh, AI computer vision research engineer

Unleashing Efficiency

AI-driven inventory optimization allows businesses to automate and refine their inventory processes. By integrating AI, companies can predict demand more accurately, optimize stock levels, and reduce holding costs, thereby freeing up capital for other strategic investments. This technology isn’t just about maintaining adequate stock levels; it’s about creating a dynamic system that adapts to market changes swiftly and efficiently.

For instance, large pharmaceutical companies have made AI in IO a priority project, driven directly by the need to free up cash tied up in slow-moving inventory. Similarly, small and medium enterprises (SMEs) are embracing AI to maintain high service levels and innovate without being bogged down by financial constraints due to tied-up inventory.

Transforming Workplaces

The impact of AI on the workforce within the realm of IO is profound. AI does not replace human workers; instead, it augments their capabilities. By automating routine and monotonous tasks, AI allows employees to focus on more strategic and creative activities. This shift not only enhances job satisfaction but also drives innovation within the organization.

For example, a logistics company that used AI to predict daily package demand found that its AI solution could perform complex calculations much faster and more accurately than human operators using traditional methods. This not only sped up the process but also reduced errors, leading to more efficient operations and better customer service.

Strategic Implementation

Implementing AI in inventory optimization requires a strategic approach tailored to the specific needs of a business. Companies need to assess their current infrastructure, data quality, and the skills of their workforce to identify the most impactful areas for AI application. Starting with pilot projects in critical areas can demonstrate the benefits of AI, encouraging wider adoption throughout the organization.

Moreover, for AI implementation to be successful, it should not be viewed in isolation. A holistic view that considers interconnected business processes will yield better results. For instance, improving demand forecasting can directly enhance inventory management, which in turn affects procurement and sales strategies.

The Road Ahead

As we look to the future, the integration of AI in inventory optimization is not just a trend but a necessity. The businesses that embrace this technology will likely lead their industries, while those that hesitate may find themselves at a competitive disadvantage.

In conclusion, the application of AI in inventory optimization is revolutionizing how businesses manage inventories and interact with their workforce. By embracing AI, companies can not only improve operational efficiencies but also foster a more engaged and innovative workforce, prepared for the challenges of the future.

Written by: Pavel Wimmer, CEO of DNAi

https://www.linkedin.com/in/pavelwimmer/

For one time only hosted by Scarlett Johansson 🎧.

Or is it?

Deepfakes, videos or images generated by AI using deep learning, are more and more successful in replacing a person’s face with another or creating realistic voice synthesis. On the one hand, a powerful tool for spreading misinformation, on the other hand, an amazing opportunity for applications in education, medicine and other areas. A new episode of the WIRED Beyond AI podcast, powered by DNAi will give you a deeper insight into the topic. Listen to a deep talk between Elnaz Babayeva, AI and machine learning researcher in computer vision and security, and Henry Ajder , whose work at MIT and for organizations such as Adobe, Meta, and the BBC, has changed the way we view deepfake and generative AI

Our client Dormer Pramet, the global manufacturer and supplier of tools for the metal cutting industry, faced a quality control problem. Their complex manufacturing process relied heavily on manual inspections, leaving the risk of undetected defects until a final inspection or even worse — by the customer. This approach not only affected the quality of the product but also limited the efficiency of production. Compounding the problem was their extensive portfolio. Tens of thousands of inserts with various features made thorough inspection a challenging task. To make things even more complicated, tiny defects, like microscopic cracks, invisible to the naked eye, required specialized equipment. Finally, the lack of detailed defect data hindered analysis and improvement. Our client was therefore looking for an AI-based visual inspection solution that would increase efficiency, reduce costs and deliver flawless products.

Moreover, the client’s extensive product portfolio, encompassing tens of thousands of insert variants with diverse shapes, sizes, materials, and surface finishes, complicated the quality control process. The sheer volume and variety of products made it challenging to implement a thorough inspection strategy, contributing to the need for a more sophisticated solution.

Manual inspection presented an additional hurdle due to the minute size of defects, often requiring the use of specialized equipment. For instance, cracks, a common type of defect, could be as small as 10 micrometers wide, making them impossible to identify without a microscope.

The absence of detailed defect statistics in the client’s information system further hindered the company’s ability to analyze and address recurring issues systematically. As a result, the client recognized the pressing need for a solution to enhance overall efficiency, reduce costs, and improve product quality. This realization marked the initiation of their journey towards implementing AI visual inspection in their manufacturing process.

Our Solution

To address the challenges faced in the manufacturing process, our company implemented a tailored end-to-end solution that integrates cutting-edge technologies, including high-resolution cameras, macro lenses, artificial intelligence (AI), and robotics. The strategic decision was made to initiate automated control in the press room, the starting point of production, where early defect detection proves most economically advantageous.

Given the microscopic nature of defects, some as small as 10 micrometers, a high-resolution camera and macro lens were deemed essential. However, the challenge arose due to the limited angle of view of this assembly (around 20x20mm), while inspected products could be up to approximately 70 mm in size. To overcome this limitation, the solution incorporates motorized stages that move the product under the camera, capturing several overlapping images. Software then combines these images into a single comprehensive view.

The core of the system involves AI deep-learning-based image analysis, which efficiently identifies defects in the product. This deep learning model offers unparalleled flexibility in conceptualizing and generalizing the appearance of various insert variants, accommodating the extensive product portfolio of our client. Unlike traditional machine vision, deep learning excels in handling natural variations in complex patterns, eliminating the need to define tolerance parameters individually for each variant.

Our solution features a clear and intuitive user interface, providing operators with tools for fast and accurate analysis. Operators are able to mark and classify the results of the visual inspection, if necessary, and provide feedback to the system, fostering a continuous learning process contributing to the training of the model and its ability to adapt to specific production characteristics.

The inspection station seamlessly communicates with a robotic arm responsible for product handling within the press. Upon completing the inspection, the station signals the robot for the removal of the piece and the insertion of another. Additionally, the system informs the robot whether the inspected piece is deemed good or defective.

A data management module allows the generation of detailed reports and statistics, providing valuable insight into the evolution of quality over time. Operators are able to easily access historical data, analyze visual inspection results and track the trend of defects on the production line.

One of the biggest questions about the development of AI is whether we need regulation.

On the one hand, there is the opinion that it would be better to slow down to keep up with the legal framework. On the other hand, artificially limiting the development of AI may unnecessarily hinder even the positive effects or give a head start to those who will develop despite the restrictions.

We consider the topic to be very important, which is why we’re already addressing it in the second episode of the WIRED Beyond AI podcast, powered by dnai.ai. 🎙️
Whatever your judgment is, a conversation with Nell Watson, President of the EURAIO — European Responsible Artificial Intelligence Office and leader and advisor to several other prominent organizations dedicated to engineering mechanisms protecting algorithmic trust, may help you reassess the situation.

Listen to the full episode now:

„Naturally, there are a lot of AI podcasts now. However, only a few of them go beyond the buzzwords that try to immediately attract our attention. When I saw the enormous potential of AI in the actual projects at DNAi, I wanted to demystify this technology and show its advantages and disadvantages to the widest audience possible,” says the author of the idea, CMO DNAi, Jitka Paterova.

Beyond AI fosters significant discussions among people around the world who are actively shaping the development and implementation of artificial intelligence in various fields. Topics covering the technical and ethical aspects of AI aim to help the widest possible audience understand why and how this technology will affect society.

The cooperation between WIRED and DNAi has enabled the creation of a podcast that crosses not only the boundaries of individual fields but also the borders of the Czech Republic. Internationally recognized WIRED has helped to attract big names and guarantees that the goal of the content is not to sell, but to inform. DNAi contributed its expertise and a wide network of contacts from the academic sphere.

„We knew that if we wanted to stand out among other podcasts, we had to aim high. We put together a perfect team and invited the biggest world experts in the field of artificial intelligence as guests,” says Michal Kučera, Chief editor of WIRED Czech Republic & Slovakia.

The opening episode, entitled Emotional Connections between People and Machines, explores the fascinating and controversial topic of human emotions and their role in distinguishing humans from AI. It questions whether and how large language models can understand and simulate emotions, examines the relationship between emotional intelligence, creativity and rational thinking, and discusses potential future developments. David Hanson, creator of the world-famous humanoid Sophia, and Oliver Tian, vice president of Global Robot Cluster and member of the University of Oxford’s Responsible Technology Institute, deeply involved themselves in the discussion moderated by Michal Kučera, CEO of Wired CZ & SK.

wired.cz

www.dnai.ai

Why I Cherish Initial Analysis

Before I even dream of coding, I immerse myself in the project’s world. It’s a period of intense curiosity — talking to people, poring over research, and exploring if our technology can breathe life into our vision. It’s a quest not just for the “what” but for the “why” behind every project. This stage is my guiding light, ensuring that each step forward is taken with purpose and understanding.

Sweating the Small Stuff

To me, every detail matters. I find myself questioning even the most minor assumptions, challenging my own thoughts, and planning for every conceivable hiccup. This level of scrutiny is my secret weapon, helping me unveil the optimal path forward and set precise goals for the POC. It’s like piecing together a puzzle, where every piece is crucial for the full picture.

The Art of Deconstruction

My toolkit often starts with a pencil and paper. I sketch, drawing out the client’s world — be it a warehouse layout or a system flow — using diagrams that bring clarity to complexity. These sketches do more than visualize; they dissect the grand vision into actionable insights, allowing us and our clients to see the interconnected steps necessary for success.

Venturing Deeper

These initial sketches are mere stepping stones. With each client conversation, the sketches evolve, adapting to the unique challenges each project brings. This phase is particularly exhilarating for AI projects, where innovation meets customization head-on.

Drawing is not just a task; it’s my way of thinking, planning, and dreaming. It’s how I navigate the intricate dance between project needs, technological possibilities, and unforeseen challenges.

Finalizing the Technical Blueprint

Before we kick start the coding phase, it’s crucial to nail down the technical design. This includes the entity model, where we define abstract entities like students and languages for a language learning app, and the database model, which translates these concepts into data structures for our database. These models guide our development, ensuring our solutions are both innovative and aligned with our clients’ needs.

Deciding When to Move Forward

Choosing the right time to shift to the development phase is a big deal. It’s a group effort.

Finding the balance between being thorough and getting started is tricky. Wait too long, and we’re behind schedule. Rush it, and we might miss something important.

It’s a moment of alignment between me, my team, and our clients, signifying we’re prepared to bring our vision to life.

Written by Jakub Szasz, AI Developer DNAi | AI solution company

Advanced Technology and Learning Capabilities

DNAi’s cognitive assistant is built upon sophisticated algorithms and advanced language processing models, enabling it to engage in conversations that closely resemble those with human agents. The programming of this assistant involves using detailed manuals, akin to those used for training call center staff, and it has the unique capability to learn from recorded dialogues. This feature is especially beneficial as it allows the assistant to adapt and personalize responses based on client-specific needs.

Beyond Traditional Chatbots

What sets this cognitive assistant apart from typical chatbots is its versatility and ingenuity in handling a wide array of customer inquiries. It can navigate through extensive knowledge databases, extracting and utilizing information to provide accurate and relevant responses. This functionality not only improves customer interactions but also offers a more efficient and cost-effective solution for businesses.

Improving Customer Support

The application of DNAi’s cognitive assistant in customer support is poised to reduce the dependence on human staff significantly. Its multilingual support makes it an invaluable asset for global companies, offering the potential to streamline operations and reduce operational costs without compromising the quality of customer service.

The Future of AI in Customer Service

The introduction of this assistant into the market reflects DNAi’s commitment to innovation in AI technology for customer support. With its ability to enhance customer interactions through accurate and empathetic communication, the cognitive assistant is a testament to the potential of AI in transforming business operations.

In summary, DNAi’s cognitive assistant represents a significant advancement in customer support technology. Its development is a step towards more efficient, cost-effective, and high-quality customer service, showcasing DNAi’s role as a leader in AI innovation. As the company continues to explore new frontiers in AI, this cognitive assistant is a clear indicator of the potential and direction of future developments in customer support technology.