Lifeboat Foundation

Off-grid housing that actually works for families is hard to come by, but that’s what ReGen Villages is striving towards with their concept for new self-sustaining communities.

The startup real estate company has a dream to create regenerative communities that not only produce their own food but also generate their own power, meaning what’s usually only possible for rural areas with renewable energy sources would be a reality for people that want these luxuries while having close neighbors.

This idea is more than just a dream, however, as the development company has its sights on their first site in Almere, Netherlands with the goal of opening it in 2018.

Continue reading “Living Off The Grid: This Utopian Village starts to produce its own food and energy in 2018” »

Holding, B.V. R-Gen, Incorporated — Delaware C-Corp ReGen Labs — non-profit research in resiliency and regenerative system design.

I will admit that I have been distracted from both popular discussion and the academic work on the risks of emergent superintelligence. However, in the spirit of an essay, let me offer some uninformed thoughts on a question involving such superintelligence based on my experience thinking about a different area. Hopefully, despite my ignorance, this experience will offer something new or at least explain one approach in a new way.

The question about superintelligence I wish to address is the “paperclip universe” problem. Suppose that an industrial program, aimed with the goal of maximizing the number of paperclips, is otherwise equipped with a general intelligence program as to tackle with this objective in the most creative ways, as well as internet connectivity and text information processing facilities so that it can discover other mechanisms. There is then the possibility that the program does not take its current resources as appropriate constraints, but becomes interested in manipulating people and directing devices to cause paperclips to be manufactured without consequence for any other objective, leading in the worse case to widespread destruction but a large number of surviving paperclips.

This would clearly be a disaster. The common response is to take as a consequence that when we specify goals to programs, we should be much more careful about specifying what those goals are. However, we might find it difficult to formulate a set of goals that don’t admit some kind of loophole or paradox that, if pursued with mechanical single-mindedness, are either similarly narrowly destructive or self-defeating.

Suppose that, instead of trying to formulate a set of foolproof goals, we should find a way to admit to the program that the set of goals we’ve described is not comprehensive. We should aim for the capacity to add new goals with a procedural understanding that the list may never be complete. If done well, we would have a system that would couple this initial set of goals to the set of resources, operations, consequences, and stakeholders initially provided to it, with an understanding that those goals are only appropriate to the initial list and finding new potential means requires developing a richer understanding of potential ends.

Continue reading “The Nonparametric Intuition: Superintelligence and Design Methodology” »

This new tool uses “semantic segmentation” to bring a whole new level of power to image editing.

• In 2016 alone, 58,320 people in the U.S. died from leukemia. This new development could increase a patients’ chance of survival
• Current treatment methods can increase a patient’s risk of secondary cancer after remission. This Engineered T-Cell approach has not shown to be carcinogenic

Wouldn’t it be wonderful if the need for chemotherapy no longer existed? In some cases, these treatments aren’t even effective enough to send patients into remission, but for many people, there are few other options.

What if there was an easier and more effective way to tackle cancer? Thanks to one recent case, there is.

Arranging employees in an office is like creating a 13-dimensional matrix that triangulates human wants, corporate needs, and the cold hard laws of physics: Joe needs to be near Jane but Jane needs natural light, and Jim is sensitive to smells and can’t be near the kitchen but also needs to work with the product ideation and customer happiness team—oh, and Jane hates fans. Enter Autodesk’s Project Discover. Not only does the software apply the principles of generative design to a workspace, using algorithms to determine all possible paths to your #officegoals, but it was also the architect (so to speak) behind the firm’s newly opened space in Toronto.

That project, overseen by design firm The Living, first surveyed the 300 employees who would be moving in. What departments would you like to sit near? Are you a head-down worker or an interactive one? Project Discover generated 10,000 designs, exploring different combinations of high- and low-traffic areas, communal and private zones, and natural-light levels. Then it matched as many of the 300 workers as possible with their specific preferences, all while taking into account the constraints of the space itself. “Typically this kind of fine-resolution evaluation doesn’t make it into the design of an office space,” says Living founder David Benjamin. OK, humans—you got what you wanted. Now don’t screw it up.

COLORADO SPRINGS, Colo. – The seats in Blue Origin’s suborbital spaceship are like a dentist’s chair that’s fully extended, with a big difference. You can float out of this one when weightlessness sets in.

Of course, we couldn’t get the zero-G experience when we tried out the seats in a mock-up of the New Shepard crew capsule, on display here at the 33rd Space Symposium. But we did get a condensed version of the 11-minute flight scenario, from launch to landing.

Four years ago, Google started to see the real potential for deploying neural networks to support a large number of new services. During that time it was also clear that, given the existing hardware, if people did voice searches for three minutes per day or dictated to their phone for short periods, Google would have to double the number of datacenters just to run machine learning models.

The need for a new architectural approach was clear, Google distinguished hardware engineer, Norman Jouppi, tells The Next Platform, but it required some radical thinking. As it turns out, that’s exactly what he is known for. One of the chief architects of the MIPS processor, Jouppi has pioneered new technologies in memory systems and is one of the most recognized names in microprocessor design. When he joined Google over three years ago, there were several options on the table for an inference chip to churn services out from models trained on Google’s CPU and GPU hybrid machines for deep learning but ultimately Jouppi says he never excepted to return back to what is essentially a CISC device.

We are, of course, talking about Google’s Tensor Processing Unit (TPU), which has not been described in much detail or benchmarked thoroughly until this week. Today, Google released an exhaustive comparison of the TPU’s performance and efficiencies compared with Haswell CPUs and Nvidia Tesla K80 GPUs. We will cover that in more detail in a separate article so we can devote time to an in-depth exploration of just what’s inside the Google TPU to give it such a leg up on other hardware for deep learning inference. You can take a look at the full paper, which was just released, and read on for what we were able to glean from Jouppi that the paper doesn’t reveal.

Continue reading “First In-Depth Look at Google’s TPU Architecture” »

Those in the life sciences industry have mixed feelings about Alphabet playing in their backyard.