Artificial intelligence (AI) has been an influential concept not only in the scientific community but also in popular culture. Depending on the respective attitude towards AI, one might associate characters such as inhibited but likeable android Data from Star Trek or neurotic and vicious HAL 9000 from the Space Odysee movies with it. The form of AI those two represent is called General AI, i.e. the intelligence of a machine that enables it to perform intellectual tasks as good as or better than a human. General AI is – at least for the moment – still a topic for science fiction. What is interesting for various industries is the notion of Narrow AI (also termed Weak AI). These are technologies that enable humans to fulfil specific tasks in an automated manner just like or even better than they could.
In the context of data, machine learning can be seen as an approach to achieve artifical intelligence. Machine learning is about analyzing data, learning from it and using the insights gained for decision making or predicitions about something.
The learning in machine learning can be attempted in two generic forms:
What can fundraisers do? I have to say that I did not come across many practice sharing posts or articles when I conducted research for this post. I doubt that either the availability of data or the competence portfolio of analysts and data scientists limit the possibilities of fundraising organizations in the context of machine learning techniques. However, there might be a certain level of insecurity regarding where and how to start. I found an inspiring blog post by Stephen W. Lambert in which he explains that all you basically need is a computer, a database with relevant data and your brain to start diving into machine learning techniques. I think Lambert’s text invites fundraising organizations to do their theoretical and conceptual homework, process and prepare their data accordingly and start experimenting with maching learning techniques. So - go ahead and try.
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