Lifeboat Foundation

Almost two months to the day after Uber loaded its fleet of self-driving SUVs into the trailer of a self-driving truck and stormed off to Arizona in a self-driving huff, the company is preparing to launch its second experiment (if you don’t count the aborted San Francisco pilot) in autonomous ride-hailing.

What’s different is that this time, Uber has the blessing from Arizona’s top politician, Governor Doug Ducey, a Republican, who is expected to be “Rider Zero” on an autonomous trip along with Anthony Levandowski, VP of Uber’s Advanced Technologies Group. The Arizona pilot comes after California’s Department of Motor Vehicles revoked the registration of Uber’s 16 self-driving cars because the company refused to apply for the appropriate permits for testing autonomous cars.

What’s the ground truth on artificial intelligence (AI)? In this video, John Launchbury, the Director of DARPA’s Information Innovation Office (I2O), attempts to demystify AI–what it can do, what it can’t do, and where it is headed. Through a discussion of the “three waves of AI” and the capabilities required for AI to reach its full potential, John provides analytical context to help understand the roles AI already has played, does play now, and could play in the future.

Download the slides at: http://www.darpa.mil/about-us/darpa-perspective-on-ai

Going straight to Level 5 may hurt Ford in the short-term, as competitors will be able to offer some self-driving functionality to customers that want it. However, the decision let’s Ford power on ahead with its driverless dream, which it aims to have on the road by 2021.

Ford plans to skip ‘Level 3’ autonomy and shoot right for Level 5, the highest level of car automation. The automaker decided to skip the midway point after it noticed a few of its engineers dozing while testing semi-autonomous vehicles.

Even with “bells, buzzers, warning lights, vibrating seats and steering wheels, and another engineer in the passenger seat” the engineers struggled to maintain situational awareness, according to Raj Nair, Ford’s chief product development officer.

Continue reading “Ford To Skip Level 3 Autonomy To Keep Sleepy Drivers Happy” »

I believe that this is a stretch for me. However, wouldn’t be nice if we could. Imagine Steve Jobs could still run Apple, we could hear Einstein and Bhor debate, etc. Again cool concept but at this stage hard to believe it will be real until we learn more about Quantum Biosystem in the mix; and even then unlikely. Nonetheless, good luck with it MIT.

Imagine debating the interpretation of a Shakespearean sonnet and being able to clarify its meaning with the bard himself. Or sitting in history class and being able to ask George Washington questions about the Constitution, no soul-conjuring witchcraft required.

In the next decade, advancing AI technology will allow us to learn from the dead first-hand. New chatbot programs are being developed to keep our knowledge active after our physical being passes away.

Continue reading “Want to chat with Shakespeare? AI bots will soon allow us to talk to the dead” »

Sharing in case folks would like to listen in.

Microsoft’s Station Q was founded in 2006. The focus of the team has always been topological quantum computing. By taking a full systems architecture approach, we have reached the point where we now able to start engineering a scalable quantum computer. The goal is to be able to solve major problems in areas of interest (e.g., Chemistry, Materials and Machine Learning). This talk will focus on the types of applications that we will be trying to solve as well as the unique approach to quantum computation that we’ve developed. For reference, see:

Current Approach: https://arxiv.org/abs/1610.05289 Chemistry Application: https://arxiv.org/abs/1605.03590 Other papers: https://arxiv.org/find/all/1/all:+wecker_d/0/1/0/all/0/1

In the past, traditional methods to understand the behavior of quantum interacting systems have worked well, but there are still many unsolved problems. To solve them, Giuseppe Carleo of ETH Zurich, Switzerland, used machine learning to form a variational approach to the quantum many-body problem.

Before digging deeper, let me tell you a little about the many-body problem. It deals with the difficulty of analyzing “multiple nontrivial relationships encoded in the exponential complexity of the many-body wave function.” In simpler language, it’s the study of interactions between many quantum particles.

If we take a look at our current computing power, modeling a wave function will need lot more powerful supercomputers. But, according to Carleo, the neural networks are pretty good at generalizing. Hence, they need only limited information to infer something. So, fiddling with this idea, Carleo and Matthias Troyer created a simple neural network to reconstruct such multi-body wave function.

UPS has successfully tested a drone that launches from the top of a UPS truck and autonomously delivers packages. http://abcn.ws/2lsyjjM

“The automation of factories has already decimated jobs in traditional manufacturing, and the rise of artificial intelligence is likely to extend this job destruction deep into the middle classes, with only the most caring, creative or supervisory roles remaining.” — Stephen Hawking.

Automation is inevitable. But we still have time to take action and help displaced workers.

Automation is accelerating. The software powering these robots becomes more powerful every day. We can’t stop it. But we can adapt to it.

Continue reading “A warning from Bill Gates, Elon Musk, and Stephen Hawking” »

It wasn’t that long ago that building and training neural networks was strictly for seasoned computer scientists and grad students. That began to change with the release of a number of open-source machine learning frameworks like Theano, Spark ML, Microsoft’s CNTK, and Google’s TensorFlow. Among them, TensorFlow stands out for its powerful, yet accessible, functionality, coupled with the stunning growth of its user base. With this week’s release of TensorFlow 1.0, Google has pushed the frontiers of machine learning further in a number of directions.

TensorFlow isn’t just for neural networks anymore

In an effort to make TensorFlow a more-general machine learning framework, Google has added both built-in Estimator functionality, and support for a number of more traditional machine learning algorithms including K-means, SVM (Support Vector Machines), and Random Forest. While there are certainly other frameworks like SparkML that support those tools, having a solution that can combine them with neural networks makes TensorFlow a great option for hybrid problems.

Continue reading “Google gives everyone machine learning superpowers with TensorFlow 1.0” »