Google, NASA Launch Quantum Artificial Intelligence Lab

Robin Wauters at TNW reports on Google’s move to establish a Quantum Artificial Intelligence Lab inside of NASA Ames Research Center.

Quantum computing holds out the promise of actual parallel processing.

Google Logo Sign (c)
Google Logo Sign (c)

While your smart device of today may appear to be multi-tasking with GPS, text messaging and music streaming all running at once, in reality, it’s cycling between these tasks, serially.

Computers have been operating this way since the computer age began.

Quantum computers, on the other hand, would address simultaneity from the ground up. They would perform many operations in parallel and be well-suited to machine learning where there’s a need to search instantly through a myriad of possibilities and choose the best solution.

One of the more controversial aspects of quantum computing’s massive potential is to render today’s data encryption technologies, obsolete.

(For a surprisingly easy-to-follow explanation of the difference between classical computing versus quantum computing, see  this 1999 article by Lov K. Grover, inventor of what may be the fastest possible search algorithm that could run on a quantum computer.)

One focus of the lab will be to advance machine learning. Google Director of Engineering, Hartmut Neven blogs:

Machine learning is all about building better models of the world to make more accurate predictions.

And if we want to build a more useful search engine, we need to better understand spoken questions and what’s on the web so you get the best answer.

The new lab will be outfitted with a D-Wave Systems quantum computer. NASA, Google, and Universities Space Research Association (USRA) plan to invite researchers worldwide to share time on  the quantum computer starting in Q3 2013.

The lab will serve as an incubator of practical solutions that require quantum computing. Neven goes so far as to write:

We actually think quantum machine learning may provide the most creative problem-solving process under the known laws of physics.


Machine Learning Predicts Students’ Final Grades As the Course Unfolds

“Can we predict a student’s final grade based on his or her behavior in the course so far?”

Writing for the Wall Street Journal, Don Clark showcases Canadian company Desire2Learn, a provider of cloud-based learning systems to enterprises and academia that recently announced this very capability.

With 10 million learners over 14 years, the company has collected detailed records on student engagement with instructional materials and their subsequent performance on tests.

Desire2Learn has developed machine learning algorithms it applies to its historical data that make predictions of how students will fare as the course unfolds.

Such predictive analysis serves as an early warning signal so instructors can give at-risk learners the additional, personalized attention they need, when they need it most.

The company’s CEO, John Baker, claims Desire2Learn’s algorithms yield greater than 90% accuracy at predicting letter grades.

John Baker, CEO Desire2Learn (c) Desire2Learn
John Baker, CEO Desire2Learn (c) Desire2Learn

Just the same, privacy issues do crop up. For example, instructors having student’s individual engagement statistics can expose a student’s general level of effort.

As a safeguard, Desire2Learn anonymizes personally identifiable information about student activities by stripping off such data when the student finishes the course.

The company makes predictive findings available to instructors through its Students Success System and they plan to do likewise for students through Degree Compass, a product currently in beta.

So, how would your business change if you had time predictions about future outcomes?

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.