- 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.
ENTRIES TAGGED "machine learning"
Hardcore Data Science speakers provided many practical suggestions and tips
One of the most popular offerings at Strata Santa Clara was Hardcore Data Science day. Over the next few weeks we hope to profile some of the speakers who presented, and make the video of the talks available as a bundle. In the meantime here are some notes and highlights from a day packed with great talks.
We’ve come to think of analytics as being comprised primarily of data and algorithms. Once data has been collected, “wrangled”, and stored, algorithms are unleashed to unlock its value. Longtime machine-learning researcher Alice Zheng of GraphLab, reminded attendees that data structures are critical to scaling machine-learning algorithms. Unfortunately there is a disconnect between machine-learning research and implementation (so much so, that some recent advances in large-scale ML are “rediscoveries” of known data structures):
While there are many data structures that arise in computer science, Alice devoted her talk to two data structures1 that are widely used in machine-learning:
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.