Transcript

So let’s flip it. How about one method that has influenced many disciplines? So I actually have to call on any computer scientists in the audience to help me answer this one, because I doubt the general public would know the answer. But I know there are some computer scientists in the audience so I challenge you. What method do you think has influenced so many disciplines today? – conceptional modelling – Of course I have the answer I want you to say. The answer is ‘machine learning’. How many people have heard of ‘machine learning’? More people have heard about ‘machine learning’ than ‘model checking’ so that’s good. So machine learning has completely transformed the whole field of statistics. In fact there is a whole department of machine learning at Carnegie Mellon, if you can believe that within the school of Computer Science and it’s made of faculties from Computer Science and Statistics and what they really do is Statistical Machine Learning. Another example is that there are departments of statistics now at universities for instance Purdue which has an excellent Statistics department who are hiring computer scientists because they see their future as in this combination of statistics and computing particularly in Statisitical Machine Learning. So we are seeing this in the next generation if you will. So let me give you some examples of where machine learning has influenced many, many fields. So in the sciences it is a technique that has been used to discover new brown dwarfs and fossil galaxies. This is really new scientific discoveries because of this particular technique. In medicine it has been used for discovering and inventing these kinds of drugs and uses in these applications. In meteorology: for tornado formation. In the neurosciences for understanding the brain, so I should at least give you a one sentence definition of Machine Learning given that many people may not know it. What it is, it’s a technique that allows you to analyse huge data sets, large amounts of data and find patterns and clusters in large amounts of data. So in this particular case what you feed this algorithm is lots and lots of fMRI scans, (scans of your brain). What they are able to do through using Machine Learning is to find out what part of your brain lights up when a subject sees a noun versus a verb or this kind of adjective versus that kind of adjective and it’s looking at lots and lots of fMRI scans that allow you to see those clusters, those patterns. Machine learning has been used beyond science and engineering, so for instance it is used in detecting credit card fraud. It’s used on Wall Street, (the answer from the back of the room). When you go to the supermarket and you hand the clerk your Safeway card, your affinity cards, they are tracking your purchases and the coupon you get out after your receipt, is using that kind of analysis of large amounts of data. Recommendation Systems and Reputation Systems like Netflix when you go to Travelocity to find out what customers use and so on. Machine learning is even used in sports, so I don’t know what basketball player this, is but maybe you do, but what they did was videotape lots of professional basketball players and then the coaches would use Machine Learning to find out what are the skills of these professionals so they can teach those skills to their own students. This is Lance Armstrong who used Machine Learning to analyse the kind of data he kept of himself. As you know, he is a machine – and he was quite mathematical and analytical in his training so that he could really hone his skills.