The list features 100 Data, Advanced Analytics and AI practitioners who are “strongly dedicated to supporting the data community and accelerating the Data and AI innovation capabilities in the Nordic region.”
What is the Hyperight Nordic 100 list?
The Hyperight Nordic 100 list is an independent list curated by the Hyperight editorial team. The list is divided into nine categories:
- Data Management
- Business Analytics and Business Innovation
- Data Science
- Machine Learning
- Artificial Intelligence
- Data Engineering
- Applied Analytics
- Ethics, Diversity, and Regulation
The list is announced every year and recognizes 100 practitioners who are driving Data and AI innovation forward and forging the path for the future of technology and generations to come.
Recent Developments in Data and AI
When reflecting on his inclusion in the Hyperight Nordic 100 list, Hugo had some insights to share:
“As the Chief Data & AI Officer at DAIN Studios and with more than 20 years of experience in the AI industry, I have had the unique opportunity to witness the acceleration of AI technologies, especially in the recent years. With this rapid progress, it has become essential for companies to have experienced professionals who have a deep understanding of AI technologies and can drive the practical implementation of the projects to get business leverage.
AI is progressing at an unprecedented rate. This accelerated growth began when deep learning models began generating features automatically, achieving performance that was not possible with manually generated features. A great example is AlexNet, which dominated the field of image recognition in 2012. In the recent years, we’ve seen the language models take similar leaps forward, with some of their emergent behaviors or capabilities leaving us truly astonished. The rapid pace is also fuelled by the availability of large public datasets. Additionally, a focus on the generative capabilities of these models has opened new possibilities for creativity, enabling media creation capabilities that were previously unimaginable.
The significance of ethical AI and responsible data usage has grown, particularly as LLMs become increasingly integrated into all aspects of our lives. It is critical that companies respect privacy, transparency and fairness in their AI developments and deployments. As a result, we’re seeing stronger emphasis on data governance and robust policies for data collection and usage.
In the evolving AI landscape, it’s not enough to only understand the technologies but one must also understand how and where they are applied. With AI’s vast potential across industries, we must also be aware of the potential risks. Therefore, having an active dialogue and knowledge sharing is very important.”
Looking to the future (GenAI)
The new foundation models, such as the LLMs, are revolutionizing AI implementation. The systems are not only faster and cheaper to build, but they are also more user-friendly, enabling more companies to have the possibility to experiment with AI. While proof-of-concepts have become easier to create, AI systems in a business environment still require development, maintenance and oversight. The model is only a very small part of the whole infrastructure. A strong AI strategy is essential to staying on track and maintaining a competitive edge.