Artificial intelligence refers to a variety of software and hardware technologies that can be applied in numerous ways for different applications.
The terms ‘machine learning’ and ‘deep learning’ are often used interchangeably in the media, but they are not the same thing. In machine learning, the machine builds up the knowledge to complete specific actions based on training data covering multiple datasets.
There are many examples of machine learning in our daily lives.
The performance of machine learning algorithms is directly related to the available information, which is referred to as ‘representation’. A representation consists of all the features that are available to a given machine and selecting the right representation is highly complex.
Representation learning is a field of machine learning that exploits raw data to automate the task of selecting the right representation. This is dependent on various environmental factors and far from simple.
For example, it can be difficult to distinguish colours in low light. Snowflakes, seagulls and shadows from trees are just a few of the things that can baffle self-driving vehicles.
Deep learning is a subcategory of representation learning that can transform features and elaborates dependencies based on inputs received. When a deep learning machine sees a picture, for example, it will map the pixels to the edges, to the corners and finally to the contours in order to identify an object.
Find out about the joint committee set up by IEC and ISO to tackle the challenges of standardizing AI.