by iglouboy | Jan 12, 2022 | White Papers
Algorithmic bias is not an error, but a systemic fault that’s rooted in the way algorithms work. In general, the more data an algorithm is exposed to, the better. But it can also have too much of the wrong data – or be lacking the right data in certain areas. It can...
by iglouboy | Oct 29, 2021 | White Papers
This machine learning method is based on rewards and/or punishments. Optimal behaviour or action by a machine is reinforced by a positive reward. Undesired ones are punished – not just by lack of rewards but by terminating a procedure (for example, in game playing...
by iglouboy | Oct 29, 2021 | White Papers
Unsupervised learning is where a machine learns for itself. It has to identify naturally occurring patterns within data sets on its own – there are no pre-assigned labels or scores to help it. The conclusions it draws are based on statistical methods such as...
by iglouboy | Oct 28, 2021 | White Papers
Supervised learning is example-based. A computer algorithm is trained on input data labelled for a particular output. Similar totest automation, the test cases have expected results. This is straightforward enough, but comes with challenges
by iglouboy | Oct 8, 2021 | White Papers
In this paper, we look at recent developments in AI and ML, together with the long-standing challenges involved and the different types of testing that result from these challenges.
