What's that productivity related quote by Charles Dickens? "My advice is never do tomorrow what you can do today." For years, machine learning has been written about and discussed widely with a focus on the benefits it will bring in the near future. But guess what? The future for machine learning
Double negatives seem to be everywhere, I have noticed them a lot in music recently. Since Pink Floyd sang "We don't need no education", to Rihanna's "I wasn’t looking for nobody when you looked my way". My own favourite song with a double negative is "I can't get no sleep" - Faithless. This
I am noticing a trend. At the ASSA meetings in January (where economics, sociology and finance academics and practitioners gather to discuss their research) I was surprised to see how much “machine learning” was trending with economists. The session “Machine Learning Methods in Economics and Econometrics,” with papers by Susan
Today’s natural language processing (NLP) systems can do some amazing things, including enabling the transformation of unstructured data into structured numerical and/or categorical data. Why is this important? Because once the key information has been identified or a key pattern modeled, the newly created, structured data can be used in
I press a button, a miracle machine churns through all the calculations in the world and the answer to the Ultimate Question of Life, the Universe, and Everything[1] is produced as a single number. Oh hang on, that’s 42. Alright, for our microcosm, let’s stick to the answer to my
“When it comes to the Internet of Things, the future clearly belongs to the Things”. I made this brash statement in a previous post (“Cloud encounters of the Fifth Kind”) referring to machine-to-machine (M2M) being the fastest growing component of non-human traffic on the Web. I say “brash” because that
Every time I pick up a new article about analytics, I am always disappointed by the fact that I cannot find any specifics mentioned about back-end processing. It is no secret that every vendor wishes they had the latest and greatest parallel processing capabilities, but the truth is that many
My Mum could have been a doctor – most can’t read her handwriting. It’s only because I’ve been trained to read it, I can. The analysis of unstructured data is similar. Text analysts can be quickly overwhelmed to learn that you have to manually develop a training corpus. Reading a
At the KDD conference this week I heard a great invited presentation called How to Create a $1 billion Model in 20 days: Predictive Modeling in the Real World – A Sprint Case Study. It was presented by Tracey de Poalo from Sprint and former Kaggle President and well known
Looking forward, ten of my SAS colleagues and I are heading to New York City this weekend for KDD 2014: Data Science for the Social Good, which runs August 24-27. This event’s full name is the 20th Association for Computing Machinery Special Interest Group on Knowledge Discovery and Data Mining,
In my region of North Carolina (Raleigh, Durham, and Chapel Hill) one of the most anticipated times of the year has arrived— the NCAA basketball tournament. This is a great time of year for me, because I get to combine several of my passions. For those who don’t live among crazed college
