Wednesday, 27 April 2016

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.


  1. Yes Machine Learning is at the heart of adaptive security as most adaptive security tools need to be threat intelligent. This can be achieved when learning algorithms can constantly perform well under adversarial conditions.
    The concern though is that the adversarial opponents have the ability to design training data that can cause the learning system to produce rules that misidentify data. Do you think that the learning systems will be forever ahead of the attackers? Sehloho Moletsane(

  2. Yes indeed machine learning can also be attacked. Barreno et al, 2010, argues that adaptability is also vulnerability, as they assume attackers have knowledge of training algorithm, and their paper identify and analyse attacks against machine learning and suggest a new line of defence. Article: “The Security of Machine Learning”