Ethics in Machine Learning - Special Post

Can't help but to mention that as I presented on the topic on Monday 25 April, a question of ethics was one that seriously kept me thinking.  So as I was reading I came across this link: http://www.kdnuggets.com/2016/03/ethics-machine-learning-tay-chatbot-fiasco.html. 

I think its a great initiative, one that I would like to follow closely.

New Era of Machine Learning

Machine has definitely shifted from the times it was based on theory, a thing people were talking about in the corridors before yet another conference of machine learning.  More and more applications of machine learning are now reported.

We have seen technology leaders like Google, Facebook, Microsoft; even banking industries implement these powerful technologies. 

We are now leaving in the world that is driven by technology; everyone talks about big data, cloud computing, adaptive security to mention few and Machine Learning seems to be the heart of them all. Without Machine learning it would be difficult to handle these terabytes of data and put defensive mechanisms against the ever improving attackers.

 

So in this era Machine Learning is revolutionizing the world we live in. Of great importance though is to mention that machine learning is still based on the very algorithms that were founded in the 80’s, which makes it a subject that is still very much dominated by academic specialist and researchers. We see Google hiring the likes of Sebastian Thrun, Fernando Pereira, Ray Kurzweil, all academics from different Universities. Facebook hiring Professor Yann LeCun of NYU, and Baidu which is considered to be China’s google hiring professor Andrew Ng from Stanford who previously worked at Google.  The completion gets tighter in this space.

 “If you want to beat the crowd now, you have to try and buy the people that really know this stuff—otherwise you’ll be a few years behind,” by Michael Mozer, from Colorado University.

Let’s look forward to discussing some of the applications of Machine Learning.

References:

Tom Simonite,2016, https://www.technologyreview.com/s/527301/chinese-search-giant-baidu-hires-man-behind-the-google-brain/

Josh Constine, 2013, http://techcrunch.com/2013/12/09/facebook-artificial-intelligence-lab-lecun

   

Methods, Challenges and Successes of Machine learning prior to the 20th Century

As promised on my previous blog, let’s see what methods, challenges and successes happened in machine learning prior to 20th century.

Prior to the 20th century the idea of machine learning was mostly knowledge driven, with a vision to automate learning by these machines so that the knowledge could be passed to others, in a way that a human being is unable to.  This was a great idea, as we all know that people get old and retire or they leave companies, although they can do a handover, fact remains their knowledge and expertise always leaves with them.

Techniques like decision trees, neural networks, multi-layered networks were used in training machines. As with any subject of exploration machine learning was characterised by some challenges; those were

Difficulty to get a sufficient degree of randomness built into the structure. The expense of creating a device large enough to exhibit behaviour not significantly influenced by the operation of any one of its components. Slow response, theoretical limitations and not enough data to learn from.

Despite all these there were some instances of success that were reported like the use of chaostron by the U.S. Navy for controlling their inventory, application of decision trees to industrial process controls and the integration of explanation-based learning into general knowledge-intensive reasoning systems.

After all was there still potential for advancement in machine learning.  Let’s find out in our next episode.

References:

CADWALLADER-COHEN, J., ZYSICZK , W., & DONNELLY, R. (1984). THE CHAOSTRON: AN IMPORTANT ADVANCE IN LEARNING MACHINES. Communications of the ACM, 356-357.
Carbonell, J. G. (1989). Introduction: Paradigms for Machine Learning. Elsevier Science Publisher, 1-9.

Jones, R. M., & Taube, M. (1961). Notes on distinction between character recognition machines and percieving machines. American Documentations, 292.