McKinsey explains machine learning to execs
McKinsey explains machine learning to execs: Machine Learning is part of a broader conversation around Artificial Intelligence and related themes, which has recently begun to (re-)emerge from the labs and enter mainstream technology conversations. The terms are horribly abused, and mostly badly misunderstood, but there is clearly something afoot. And with good reason. These tools […]
Harvard medical professor: Big data and analytics help cure cancer
In almost every industry, aggregating data on a large scale and running predictive analytics have the power to improve our lives. With healthcare, this power is magnified because conclusions drawn from analytics can directly affect patient health and well-being. Unfortunately, discussions of so-called big data applications often are filled will vendor hype and sales hyperbole. […]
IBM doubles down on health with Watson
IBM doubles down on health with Watson: Hot off its Jeopardy win, IBM’s clever Watson quickly started looking for problems that might help Big Blue recoup some of the project’s development costs. Healthcare was one obvious area, with relatively constrained sets of problems, copious data to ingest and understand, and oodles (a technical term) of […]
A semantic journey
Gigaom announced their latest event; Structure Intelligence. “In the past year we’ve seen massive growth in Artificial Intelligence (AI) and deep learning. Our own Derrick Harris has been covering this area for years and we have decided it’s time to give this rapidly growing area a platform (and conference) of its own.” Personally, it’s great […]
Optimizing Data Centers Through Machine Learning
Google has published a paper outlining their approach on using machine learning, a neural network to be specific, to reduce energy consumption in their data centers. Joe Kava, VP, Data Centers at Google also has a blog post explaining the backfround and their approach. Google has one of the best data center designs in the industry […]
Unsupervised Machine Learning, Most Promising Ingredient Of Big Data
Orange (France Telecom), one of the largest mobile operators in the world, issued a challenge “Data for Development” by releasing a dataset of their subscribers in Ivory Coast. The dataset contained 2.5 billion records, calls and text messages exchanged between 5 million anonymous users in Ivory Coast, Africa. Various researchers got access to this dataset […]
Commoditizing Data Science
My ongoing conversations with several people continue to reaffirm my belief that Data Science is still perceived to be a sacred discipline and data scientists are perceived to be highly skilled statisticians who walk around wearing white lab coats. The best data scientists are not the ones who know the most about data but they […]
The Future of Organizations, The Future of Technology
While attending HP’s Discover event (disclosure – HP covered some of my expenses for attending the event) in Frankfurt in December I was invited to take part in an interview alongside fellow Antipodean Paul Muller. The interview started off with Paul Muller explaining what he does in terms of looking at the macro trends impacting […]
Hubris and the Data Scientist
ReadWriteWeb‘s Joe Brockmeier captures a recurring issue from last week’s O’Reilly Strata conference, asking “Can Big Data replace domain expertise?” According to Brockmeier, the audience (of data scientists) apparently narrowly agreed that their arsenal of tools and algorithms trumped the knowledge and experience of the meteorologists, financiers, and retailers to whose domains data scientists are increasingly […]
Executive Summit kicks of O’Reilly Strata Conference 2010
I’m in Santa Clara this week, attending O’Reilly‘s inaugural Strata Conference. Today, I’m spending the day in the event’s Executive Summit, where I hope to hear some of the ways in which ‘normal’ businesses are approaching the opportunity of making their data work harder. The notes that follow are a rather raw summary of some […]
