- Global Forecast System — National Weather Service open sources its weather forecasting software. Hope you have a supercomputer and all the data to make use of it …
- High-reproducibility and high-accuracy method for automated topic classification — Latent Dirichlet allocation (LDA) is the state of the art in topic modeling. Here, we perform a systematic theoretical and numerical analysis that demonstrates that current optimization techniques for LDA often yield results that are not accurate in inferring the most suitable model parameters. Adapting approaches from community detection in networks, we propose a new algorithm that displays high reproducibility and high accuracy and also has high computational efficiency. We apply it to a large set of documents in the English Wikipedia and reveal its hierarchical structure.
- Army Open Sources Cyberdefense Code — git push is the new “for immediate release”.
- British Army Creates Team of Facebook Warriors (The Guardian) — no matter how much I know the arguments for it, it still feels vile.
"machine learning" entries
Practical machine-learning applications and strategies from experts in active learning.
What do you call a practice that most data scientists have heard of, few have tried, and even fewer know how to do well? It turns out, no one is quite certain what to call it. In our latest free report Real-World Active Learning: Applications and Strategies for Human-in-the-Loop Machine Learning, we examine the relatively new field of “active learning” — also referred to as “human computation,” “human-machine hybrid systems,” and “human-in-the-loop machine learning.” Whatever you call it, the field is exploding with practical applications that are proving the efficiency of combining human and machine intelligence.
Learn from the expertsThrough in-depth interviews with experts in the field of active learning and crowdsource management, industry analyst Ted Cuzzillo reveals top tips and strategies for using short-term human intervention to actively improve machine models. As you’ll discover, the point at which a machine model fails is precisely where there’s an opportunity to insert — and benefit from — human judgment.
- When active learning works best
- How to manage crowdsource contributors (including expert-level contributors)
- Basic principles of labeling data
- Best practice methods for assessing labels
- When to skip the crowd and mine your own data
Explore real-world examples
This report gives you a behind-the-scenes look at how human-in-the-loop machine learning has helped improve the accuracy of Google Maps, match business listings at GoDaddy, rank top search results at Yahoo!, refer relevant job postings to people on LinkedIn, identify expert-level contributors using the Quizz recruitment method, and recommend women’s clothing based on customer and product data at Stitch Fix. Read more…
The history of computing has been a constant pendulum — that pendulum is now swinging back toward distribution.
The trifecta of cheap sensors, fast networks, and distributing computing are changing how we work with data. But making sense of all that data takes help, which is arriving in the form of machine learning. Here’s one view of how that might play out.
Clouds, edges, fog, and the pendulum of distributed computingThe history of computing has been a constant pendulum, swinging between centralization and distribution.
The first computers filled rooms, and operators were physically within them, switching toggles and turning wheels. Then came mainframes, which were centralized, with dumb terminals.
As the cost of computing dropped and the applications became more democratized, user interfaces mattered more. The smarter clients at the edge became the first personal computers; many broke free of the network entirely. The client got the glory; the server merely handled queries.
Once the web arrived, we centralized again. LAMP (Linux, Apache, MySQL, PHP) buried deep inside data centers, with the computer at the other end of the connection relegated to little more than a smart terminal rendering HTML. Load-balancers sprayed traffic across thousands of cheap machines. Eventually, the web turned from static sites to complex software as a service (SaaS) applications.
Then the pendulum swung back to the edge, and the clients got smart again. First with AJAX, Java, and Flash; then in the form of mobile apps, where the smartphone or tablet did most of the hard work and the back end was a communications channel for reporting the results of local action. Read more…