Interview Questions for Peter Sarlin, Chief Executive Officer of Silo.AI

The European economy, with its robust industrial sector, is poised for transformation through the integration of Artificial Intelligence (AI) technology. This transformation is particularly promising in sectors like predictive maintenance and fully automated production processes.

One company at the forefront of this AI revolution is Silo.AI, a private AI laboratory based in Helsinki, Finland. With offices in Finland, London, UK, and Palo Alto, US, Silo.AI is expanding its presence in the Nordics and wider Europe.

Silo.AI's AI solutions have been instrumental in improving operational efficiency in various industries. A notable example is Finnair, the Finnish airline, which uses Silo.AI's machine learning model to predict flight delays and enhance overall adaptability to the flight situation at Helsinki-Vantaa airport.

Founded with the goal of democratizing access to world-class AI expertise and tooling, Silo.AI boasts a team of over 70 AI experts, most with PhDs or extensive experience in AI-related R&D. Peter Sarlin, co-founder and CEO of Silo.AI, brings a background in applied machine learning from academia to the table.

AI, at its core, is about executing narrow, repetitive tasks that aid humans in their jobs. Most real-world AI applications rely on supervised learning, a process of teaching a machine with large volumes of labeled data. AI is particularly impactful in data-driven decision-making, especially in industries like automotive, maritime, mining, and others.

Assisted driving is a significant step towards autonomous vehicles, while visual quality control and predictive maintenance are steps towards fully automated production processes. Europe, with its focus on data privacy, provides a unique environment for AI-driven solutions, transformation, and businesses. AI is expected to significantly accelerate value creation by leveraging the business models of software and collecting closed-loop data.

However, AI advancements in unsupervised and reinforcement learning are often research-focused or applied to problems that are unrealistic in most real-world settings. Google DeepMind's AlphaGo, which demonstrated superhuman performance in the game Go, is a notable exception.

AI is also making strides in areas such as smart vehicles, ports, cities, and devices. For instance, the traditional explosive manufacturing company Orica transitioned into a scalable service by building a digital service for explosion optimization, collecting feedback from every explosion.

Peter Sarlin emphasizes the importance of data-driven decision-making in AI, highlighting the need for machine learning to become part of a workflow that combines repetitive tasks with human input. An example of a use case where AI is particularly suited for data-driven decision-making is building intelligent applications to personify, recommend, and filter information in optimal ways for the end user.

AI-driven solutions are often built close to sensors and devices in these environments for precise measurement of the world as it happens. As Europe continues to pave the way for data privacy, AI is set to play a crucial role in shaping the future of various industries.