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We’re generating data at an exponential rate, everything we do produces more and more it. This isn’t a new phenomenon, what is new is our ability to store vast amounts of it. As it becomes impossible to reason about such volumes of data, so we turn to smart tools and intelligent machines for assistance.

This raises many interesting questions about what we can achieve from the intelligent processing of large amounts of data. It has given prominence to the ideas of artificial intelligence and machine learning. Also, it leads us to revisit age-old philosophical questions about the nature of knowledge and what it means to learn. Do you need an organic brain to ever be classified as intelligent? Can intelligence exist without sentience?

My interest in this subject centres around the acquisition of knowledge by humans and whether, in an age where knowledge workers spend their working lives typing at computers keyboards or talking on digital telephones, can machine determine that learning is taking place. My hope is that by observing knowledge acquisition in humans, the machines can understand what is being learned, and how (possibly why) this learning took place.

 

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Protein site classification using two machine learning techniques.

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Key Limitations of Knowledge Base Systems (in 200 words of less)

Knowledge Based Systems (KBS) denotes a field of artificial intelligence research for the encoding of expert knowledge in computer logic as repository of “if-then” rules. Though successful instances of such systems are worthy of note (e.g. MYCIN, DENDRAL and PROSPECTOR) KBS have key limitations. Namely, an expert may establish semantic narrative to relate the rules …

About

My name is Guillaume Nerzic. After 10 years as an IT trainer (teaching object oriented software design and development), I then spent the next 10 years working as a data analyst for an international financial institution.

I’m now wondering what I will focus on for the next 10 years of my life. I’m very interested in the subject of casual or passive learning and whether machines can identify and capture where/how such learning takes place. I’m not only interested in how individuals learns, but how teams and organisations acquire know-how. In many ways the knowledge gains by groups of individuals (where the whole expertise does not lie in a single individual) is much more ethereal and is only ever captured indirectly in process diagrams and organisation charts.