Expertise and excellence are words that are often circulated here at Silo.AI. However, it is important to highlight that these are not considered as achievements, nor some kind of pre-requisites. We believe that expertise and excellence are a result of exploring, sharing and continuously learning with the people you work with. Active learning and learning strategies are also mentioned in the World Economic Forum’s Skills Outlook for 2022. Let’s see how we have taken that as part of our core values at Silo.AI.
Active learning matters and it needs to be fostered
Imagine a machine that was afraid of making a mistake. If the machine reacted like us humans, it would sweat and experience anxiety for not getting to the right answer straight away. Sounds funny doesn’t it. Lack of emotional blockers enables machines to treat learning process as what it essentially is – learning from mistakes. Humans learn the same way. It’s a continuous process of trial, validation and repeat. As a company, it is crucial to recognise this and facilitate an environment where both trial and validation are plentiful. As a default, trial comes with mistakes. The support and acceptance for failure needs to be built in the DNA of the company so that emotional blockers don’t get in the way of this natural process. Psychological safety is the key. The field of artificial intelligence and machine learning evolves so rapidly, that constant learning is something our experts have become used to it already during their research, their PhD studies or their career so far. Exploring new technologies, tools, industries and ways of working is a part of every Silo.AI employee’s work. We host internal research clubs, where together we explore a new research area, read scientific articles and industry reports, are not afraid to try new things – and above all: accept each others mistakes.
Trial, error and feedback
However, building an environment where trial is encouraged is not enough. For machines to learn from data, a systematic process of validation is needed. When the machine comes up with a suggestion, it is either rejected or approved – this way it learns from the input it gets. Again, this is the case for humans too. Without reflection and feedback learning does not happen. And without sharing the learnings, the company is not learning.Last year was our first full year of operations, as the company was started in the fall of 2017. At the end, we executed 50 AI projects on the course of the year in Finland, Sweden, UK, Central Europe, and US. All of these projects taught us but also our clients about what needed to get done, how we were to collaborate and how we could make the biggest impact. Sometimes we needed to start from the very beginning: where could we get the right data for the project. After a year of mostly experimentation with lot of proof-of-concept and pilot projects, 2019 shows itself as both promising and exciting in terms of deepening our knowledge and continuing to learn together with our clients, both existing and new. Towards the end of the year we have put a lot of effort into thinking about our vision of building AI for People. Our value base, where Keep Learning is at core, will help us learn both as individuals but also as a company. If you would like to join us on this journey, check out our open positions at https://silo.ai/careers.
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