How did Montel AI begin?
Back in 2016, the business was born out of a very simple question: with all the data that Montel has access to, why were we not doing any data analysis?
Thanks for that particular insight must go to Olav Edland, an external consultant that asked the right questions at the right time. As an open question to all departments of Montel, it became obvious that there was clearly some potential for something to happen.
In what has now become the AI team, we already had the know-how, the drive to do it and access to data, all that was needed was that initial spark.
The first year was mostly used to learn the technology and to create our own tools for working with it. After some time spent learning, we decided that price would be the first challenge we would try to seriously tackle. We got some excellent help from Norsk Regnesentral (Norwegian computing centre) to understand how to build classical forecasting models and began carving our own path into model building and forecasting.
AI is often seen as a futuristic technology, who can make use of it right now?
Everyone! There are plenty of ways to use the technology, but the important part is having access to the right knowledge and data to use it properly.
I have to say here that AI cannot solve everything; so it’s important to identify what kinds of problems you can solve with it. For us, the technology makes a lot of sense in scenarios where we can see room for steady improvements – in fact the harder the problem is, the better AI is likely to fit the task. On the other hand, if you have a problem that must be solved exactly, AI is probably not for you.
A defining trait in intelligence is the ability to fail and use it to learn and improve. This feature of failure also applies to AI. What matters is really the economy of the problem, or ‘is the problem solved better than any human can’ for example in AI image recognition; are there less accidents or deaths when using an AI driver; is the forecast more precise on average or on specific events than existing models; or can it play chess better than the current best human-created computer algorithms. These kinds of scenarios are never 100% solved, but improvement can come continually over time.
For ANI (Artificial Narrow Intelligence) where you try to learn and improve on one specific task, this is likely to be a slowly evolving process, where we will see some new types of products and gradual improvements year over year. For us in Montel AI however, AGI (Artificial General Intelligence) is the true future technology, one in which you strive for the ability to understand more than one specific task and maybe even achieve self-awareness. AGI is a totally different beast and therefore not likely to be a slow change when and if it arrives.
What makes Montel AI so useful?
I think it’s the ability to create nearly any model for any hard or inexact task. We are not all energy experts within the problems we solve, and this is actually a considerable advantage. We are technology and model experts and will use external industry expertise to guide us in what way our models should behave.
Much like with modern AI engines for chess, the creators do not need to be good or even average players to create the strongest chess computer algorithm in the world, they just need evaluation and guidance.