What are the deductive and inductive component?
Fundamentally the deductive is what we learn at school. It's sort of an issue of rules and people that we've learned through scientific rigour, you know, laws of physics, things of that nature. The inductive is I saw 10,000 black crows, so therefore the 10,001th is going to be black. It's an issue of a sort of experiential data. And so what we're looking at is that the two should sort of jive together. It's an issue of, you know, I flip a coin a number of times, if I do it 50 times, I think approximately half will be heads, half will be tails, and then actually that's what we have from a mathematics perspective. So really what we're looking at is a number of examples, and looking and analysing what the examples tell us, and with the Web and the way we are today, and as we're collecting the data and capturing the data, we can get hundreds and thousands and millions of examples, and that really allows us to inform us, and in some ways what we need to really do is let the data speak, right? But at the same time, what we also have to do is we have to abstract it to a certain level, we have to look at it with respect to theories and hypotheses and come through that, and that's sort of what our basis for our scientific research has been, and that's really the deductive model. So it's really now how we can bring these two models together, and it's a fascinating area of data science right now, and it definitely departs from an old-school perspective, or there are definitely conflicts. But I really think it's not one or the other. Again, it's the synthesis that's really going to give us brand-new insights, and especially it's the places where they jive and where they don't that really now allows us to learn and move forward.
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