Top 13 challenges AI is facing in 2017

AI and ML feed on data, and companies that center their business around the technology are growing a penchant for collecting user data, with or without the latter’s consent, in order to make their services more targeted and efficient. Already implementations of AI/ML are making it possible to impersonate people by imitating their handwriting, voice and conversation style, an unprecedented power … Continue reading Top 13 challenges AI is facing in 2017

Bayes craze, neural networks and uncertainty

Story, context and hype Named after its inventor, the 18th-century Presbyterian minister Thomas Bayes, Bayes’ theorem is a method for calculating the validity of beliefs (hypotheses, claims, propositions) based on the best available evidence (observations, data, information). Here’s the most dumbed-down description: Initial/prior belief + new evidence/information = new/improved belief. P(B|E) = P(B) X P(E|B) … Continue reading Bayes craze, neural networks and uncertainty

101 and failures of Machine Learning

Nowadays, 'artificial intelligence' (AI) and 'machine learning' (ML) are cliches that people use to signal awareness about technological trends. Companies tout AI/ML as panaceas to their ills and competitive advantage over their peers. From flower recognition to an algorithm that won against Go champion to big financial institutions, including ETFs of the biggest hedge fund in … Continue reading 101 and failures of Machine Learning

Musings on imitation, induction and how we perceive success

An idea has been afloat in my mind. What success means for us and how we go about becoming successful. There are numerous systems, frameworks and theories of how to become successful. Your nearest bookstores, to be sure, contains – if it is a bookstore at all – at least few books about success theories … Continue reading Musings on imitation, induction and how we perceive success