- What Machines Can’t Do (NY Times) — In the 1950s, the bureaucracy was the computer. People were organized into technocratic systems in order to perform routinized information processing. But now the computer is the computer. The role of the human is not to be dispassionate, depersonalized or neutral. It is precisely the emotive traits that are rewarded: the voracious lust for understanding, the enthusiasm for work, the ability to grasp the gist, the empathetic sensitivity to what will attract attention and linger in the mind. Cf the fantastic The Most Human Human. (via Jim Stogdill)
- The Technium: A Conversation with Kevin Kelly (Edge) — If we were sent back with a time machine, even 20 years, and reported to people what we have right now and describe what we were going to get in this device in our pocket—we’d have this free encyclopedia, and we’d have street maps to most of the cities of the world, and we’d have box scores in real time and stock quotes and weather reports, PDFs for every manual in the world—we’d make this very, very, very long list of things that we would say we would have and we get on this device in our pocket, and then we would tell them that most of this content was free. You would simply be declared insane. They would say there is no economic model to make this. What is the economics of this? It doesn’t make any sense, and it seems far-fetched and nearly impossible. But the next twenty years are going to make this last twenty years just pale. (via Sara Winge)
- Applying Machine Learning to Network Security Monitoring (Slideshare) — interesting deck on big data + machine learning as applied to netsec. See also their ML Sec Project. (via Anton Chuvakin)
- Medieval Unicode Font Initiative — code points for medieval markup. I would have put money on Ogonek being a fantasy warrior race. Go figure.
"machine learning" entries
Business users are starting to tackle problems that require machine-learning and statistics
I talk with many new companies who build tools for business analysts and other non-technical users. These new tools streamline and simplify important data tasks including interactive analysis (e.g., pivot tables and cohort analysis), interactive visual analysis (as popularized by Tableau and Qlikview), and more recently data preparation. Some of the newer tools scale to large data sets, while others explicitly target small to medium-sized data.
As I noted in a recent post, companies are beginning to build data analysis tools1 that target non-experts. Companies are betting that as business users start interacting with data, they will want to tackle some problems that require advanced analytics. With business analysts far outnumbering data scientists, it makes sense to offload some problems to non-experts2.
Moreover data seems to support the notion that business users are interested in more complex problems. I recently looked at data3 from 11 large Meetups (in NYC and the SF Bay Area) that target business analysts and business intelligence users. Altogether these Meetups had close to 5,000 active4 members. As you can see in the chart below, business users are interested in topics like machine learning (1 in 5), predictive analytics (1 in 4), and data mining (1 in 4):
An interview with Ash Damle of Lumiata on the role of data in healthcare.
Vinod Khosla has stirred up some controversy in the healthcare community over the last several years by suggesting that computers might be able to provide better care than doctors. This includes remarks he made at Strata Rx in 2012, including that, “We need to move from the practice of medicine to the science of medicine. And the science of medicine is way too complex for human beings to do.”
So when I saw the news that Khosla Ventures has just invested $4M in Series A funding into Lumiata (formerly MEDgle), a company that specializes in healthcare data analytics, I was very curious to hear more about that company’s vision. Ash Damle is the CEO at Lumiata. We recently spoke by phone to discuss how data can improve access to care and help level the playing field of care quality.
Tell me a little about Lumiata: what it is and what it does.
Ash Damle: We’re bringing together the best of medical science and graph analytics to provide the best prescriptive analysis to those providing care. We data-mine all the publicly available data sources, such as journals, de-identified records, etc. We analyze the data to make sure we’re learning the right things and, most importantly, what the relationships are among the data. We have fundamentally delved into looking at that whole graph, the way Google does to provide you with relevant search results. We curate those relationships to make sure they’re sensible, and take into account behavioral and social factors.
It's an extensive, well-documented, and accessible, curated library of machine-learning models
I use a variety of tools for advanced analytics, most recently I’ve been using Spark (and MLlib), R, scikit-learn, and GraphLab. When I need to get something done quickly, I’ve been turning to scikit-learn for my first pass analysis. For access to high-quality, easy-to-use, implementations1 of popular algorithms, scikit-learn is a great place to start. So much so that I often encourage new and seasoned data scientists to try it whenever they’re faced with analytics projects that have short deadlines.
I recently spent a few hours with one of scikit-learn’s core contributors Olivier Grisel. We had a free flowing discussion were we talked about machine-learning, data science, programming languages, big data, Paris, and … scikit-learn! Along the way, I was reminded by why I’ve come to use (and admire) the scikit-learn project.
Commitment to documentation and usability
One of the reasons I started2 using scikit-learn was because of its nice documentation (which I hold up as an example for other communities and projects to emulate). Contributions to scikit-learn are required to include narrative examples along with sample scripts that run on small data sets. Besides good documentation there are other core tenets that guide the community’s overall commitment to quality and usability: the global API is safeguarded, all public API’s are well documented, and when appropriate contributors are encouraged to expand the coverage of unit tests.
Models are chosen and implemented by a dedicated team of experts
scikit-learn’s stable of contributors includes experts in machine-learning and software development. A few of them (including Olivier) are able to devote a portion of their professional working hours to the project.
Covers most machine-learning tasks
Scan the list of things available in scikit-learn and you quickly realize that it includes tools for many of the standard machine-learning tasks (such as clustering, classification, regression, etc.). And since scikit-learn is developed by a large community of developers and machine-learning experts, promising new techniques tend to be included in fairly short order.
As a curated library, users don’t have to choose from multiple competing implementations of the same algorithm (a problem that R users often face). In order to assist users who struggle to choose between different models, Andreas Muller created a simple flowchart for users: