Machine Learning Startup Skytree Lands $18 Million

In venture capital circles, machine learning startups are about to catch fire. This makes sense as the size of data sets that companies and organizations need to utilize spirals beyond what the human brain can fathom.

As Derrick Harris at Gigaom reports, Skytree landed $18 million in Series A funding from US Venture Partners, United Parcel Service and Scott McNealy, the Sun Microsystems co-founder and former CEO. The company began just over a year earlier with $1.5 million in seed funding.

Skytree co-founder Alexander Gray (second from left) at Structure: Data 2012. (c) Pinar Ozger
Skytree co-founder Alexander Gray (second from left) at Structure: Data 2012. (c) Pinar Ozger

As big data gets bigger ever more quickly, machine learning makes it possible to identify meaningful patterns in real time that would elude sharp humans even with the best of query tools.

Still, there’s often a place for human judgment to flesh out the findings of machine learning algorithms.

For example: Netflix recommendations, the ZestFinance credit risk analysis platform and ProPublica’s Message Machine project that combs through vast volumes of crowd-sourced emails to find important news stories on a given topic.

The flagship Skytree product, Skytree Server, lets users run advanced machine learning algorithms against their own data sources at speeds much faster than current alternatives. The company claims such rapid and complete processing of large datasets yields extraordinary boosts in accuracy.

Skytree’s new beta product, Adviser, allows novice users to perform machine learning analysis of their data on a laptop and receive guidance about methods and findings.

As the machine learning space becomes more accessible to a wider audience, expect to see more startups get venture funding.

And with DARPA striving to make it easier for machine learning developers to focus more on application design and less on the complexities of statistical inference, this trend could have momentum for some time to come.

Advertisements

DARPA Sets Stage for Giant Leap Forward in Machine Learning

Probabilistic Programming for Advanced Machine Learning
Courtesy of DARPA.mil

As the new frontier in computing. machine learning brings us software that can make sense of big data, act on its findings and draw insights from ambiguous information.

Spam filters, recommendation systems and driver assistance technology are some of today’s more mainstream uses of machine learning.

Like life on any frontier, creating new machine learning applications, even with the most talented of teams, can be difficult and slow for a lack of tools and infrastructure.

DARPA (The Defense Advanced Research Projects Agency) is tackling this problem head on by launching the Probabilistic Programming for Advanced Machine Learning Program (PPAML).

Probabilistic programming is a programming paradigm for dealing with uncertain information.

In much the same way that high level programming languages spared developers the need to deal with machine level issues, DARPA’s focus on probabilistic programming sets the stage for a quantum leap forward in machine learning.

More specifically, machine learning developers using new programming languages geared for probabilistic inference will be freed up to deliver applications faster that are more innovative, effective and efficient while relying less on big data, as is common today.

For details, see the DARPA Special Notice document describing the specific capabilities sought at http://go.usa.gov/2PhW.