Training is difficult
Actually, training a Machine Learning model is a task not to be underestimated, Lønstad explains.
– You can buy pre-trained models like the ones provided by Nvidia. They give you the benefit that you’re quickly up to speed with what you want to do. But there is a downside: Precision is low. We are talking maybe 80 per cent correctness on pre-trained models for camera vision. That may be good enough for many applications, but in other use cases it’s unacceptable.
– If you want higher precision and you have items with specific features that you need to put in the system, then you need to get your fingers dirty and train the model yourself. You need to qualify your data and your algorithm, and this is where it gets complicated. That is a lot of work, and you need vast amounts of data.
Garbage in, garbage out
According to Hans Christian Lønstad, the performance of a Machine Learning system depends on the data that is fed into it. The old saying “Garbage in equals garbage out” applies very much to Machine Learning.
The quality of output is determined by the quality of the input. – Machine Learning is statistics. It is a statistical approach, as opposed to a conventional algorithm with some kind of direct connection between input and output. But you need a lot of high-quality data to train you Machine Learning system. And data is easily biased, so we will have systematic errors which is not a good thing. It’s a kind of paradox with all statistical data. If you want to reduce the variance in the result, you need to accept more bias and vice versa. So it will never get perfect.
– In my opinion, there is only a very, very exclusive group of companies that has access to enough high-quality data to build good Machine Learning systems. If you look at who has succeeded with Machine Learning, it’s basically the big Internet companies like Google and Facebook, which are collecting data from their users in any way they can. They have an abundance of data, and their users are giving it to them for free. In an industrial setting you won’t have the same possibilities.
Don’t get overambitious
Hans Christian Lønstad issues a warning to companies attracted to the high-flying concept of Artificial Intelligence: – Don’t think, that because companies like Facebook and other big league players are succeeding with this, you will as well. That’s a wrong assumption, so you should be careful not to get overambitious. Without access to similar amounts of data it’s impossible to build Machine Learning systems on that level of sophistication. But you can build something that’s good enough for some specific purposes, you just need to be careful to make the right choices.
– As mentioned before, there is potential in Machine Learning in an industrial setting in regards to computer vision for quality control, for instance. But it’s not for free. You need to put a lot of effort into training the systems, qualifying the data, and evaluate and develop over time.