The homes we inhabit in the 2020s could serve as a personal headquarters for building the good society. How can a house help create a more positive future? Here are four ways the home of the future might support meaningful personal commitment to the greater good.

1. Advocate From Home (AFH)

During the pandemic lockdown period, a new wave of civic engagement has taken hold. A trend called Advocate From Home (AFH) takes the form of digital organizing (e-mail, text banking, content production) for political, ecological, social and economic justice, often using work-from-home tools.

2. Decentralized Energy

Households are embracing renewables in terms of solar energy and decentralized systems with independent home batteries. There are revolutions happening in the world of clean kinetic energy that could transform our spaces by allowing objects to collect and then transmit power. Future homes may be self-sustaining in terms of power and energy needs.

3. Biophilia

One way people express environmentalist values at home is through a love of nature. Outdoors, there are green options for the visionary homeowner, such as garden plots, low-intensive watering solutions, use of native plants and compost bins. Inside the home, people are gravitating to hydroponically grown vegetables and herbs. Indoor plants of all kinds are at the height of interior design trends and architecture is looking to biomimicry for sustainable ideas. Pets outnumber children in US homes.

4. Shrinking Footprint

In terms of actual home structure, the tiny home and 3D printed home craze are growing. A recently completed project constructed a house from recyclable coffee grounds and there is growing consumer demand for sustainable bricks and concrete. Fashion and home décor choices tend toward fair trade, recycled materials, resale, and upcycling. Mindful consumption to manage our ecological and ethical footprint is a key value consumers may continue embracing well into the 2020s.

How would you change the world from your home? Share your ideas in the comments!

View the full report The Home of the 2020s: Scenarios for How We Might Live in the Post-Pandemic Future: https://bit.ly/2TbCYf2

Under Space Policy Directive-1, NASA has been charged with leading a “sustainable exploration of the Moon together with commercial and international partners.”

In response to this bold directive, NASA is working with U.S. and international partners to lead the development of the first permanent human spaceship in orbit around the Moon, known as the Gateway. The Gateway will be a part-time home and office for astronauts farther in space than humans have ever been before.

The Gateway will be important to building a permanent human presence on the Moon. Astronauts will visit at least once per year, living and working aboard the spaceship in deep space for up to three months at a time. NASA is looking at options for astronauts to shuttle between the Gateway and the lunar surface, to explore new locations across the Moon. Even before our first trip to Mars, astronauts will use the Gateway to train for life far away from Earth, and we will use it to practice moving a spaceship in different orbits in deep space.

Just like an airport, spacecraft bound for the surface of the Moon or Mars can use the Gateway to refuel or replace parts. The Gateway’s core functions include power and propulsion, communications, habitation, logistics resupply, robotics, and an airlocks for science and spacewalks.

By partnering with U.S. companies and international partners to design and build the Gateway, NASA will ensure this groundbreaking lunar laboratory is available to multiple users, providing more access to the Moon than ever before.

You can learn more about the Gateway with this quick Q&A: https://www.nasa.gov/feature/questions-nasas-new-spaceship

The goal of this challenge is to develop a graphic to represent the Gateway. The product should graphically convey the key theme of space exploration and what the Gateway represents. The graphic will be used in multiple ways internally at NASA, but will have limited external/public use, if any. A small version of the graphic may be used in the top corner of documents, a larger version could be used on the title page of internal presentation materials, or the graphic may be used on other products developed by the NASA Gateway team.

Graphic Requirements

• Both a color version and black/white version are required.
• Source File Requirements: All original vector source files of the submitted design. Files should be created in Adobe Illustrator or Inkscape and saved as layered AI or EPS files. Note that PSDs saved as EPS files are not true vector / scalable files and will not be accepted.
• All fonts (and operating systems) must be listed in a text file within your Submission Zip file. Include the name of the font and a link to where it can be downloaded/purchased. DO NOT include any font files in your submission or source files.
• All text in the graphic should be converted to outlines.
• Submission should also include 1024 x1024 version of the graphic in jpeg or png (on both a black background and a white background). The NASA insignia or other NASA logos cannot be used as any part of the submission.

Other Preferences

• Color pallet is open, but limited to 6 colors.
• Please avoid gradients or other special effects as they are difficult to reproduce across all media.

Stock Photography Requirements

• Stock photography is not allowed in this contest. All submitted elements must be designed solely by you.

Judging Criteria

• Requirements: Does your submission meet the requirements as stated within the challenge?
• Quality: Does your submission look fresh, professional, and clean?
• Concept: Does your submission represent what the Gateway stands for?

Please note that NASA employees/contractors are not eligible for competing in this contest.

Supported Submission File Types

• JPG
• PNG
• GIF

Join the Contest

https://www.freelancer.com/contest/NASA-Contest-Design-the-G…51793.html

However, I’ve come to the perspective that the non-parametric intuition, while correct, on its own can be cripplingly misguided. Unfortunately, going through a discovery-rich design process doesn’t promise an appropriate outcome. It is possible for all of the apparently relevant sources not to reflect significant consequences.

How could one possibly do better than accepting this limitation, that relevant information is sometimes not present in all apparently relevant information sources? The answer is that, while in some cases it is impossible, there is always the background knowledge that all flourishing is grounded in material conditions, and that “staying grounded” in these conditions is one way to know that important design information is missing and seek it out. The Onion article “Man’s Garbage To Have Much More Significant Effect On Planet Than He Will” is one example of a common failure at living in a grounded way.

In other words, “staying grounded” means recognizing that just because we do not know all of the goals informing our actions does not mean that we do not know any of them. There are some goals that are given to us by the nature of how we are embedded in the world and cannot be responsibly ignored. Our continual flourishing as sentient creatures means coming to know and care for those systems that sustain us and creatures like us. A functioning participation in these systems at a basic level means we should aim to see that our inputs are securely supplied, our wastes properly processed, and the supporting conditions of our environment maintained.

Suppose that there were a superintelligence where individual agents have a capacity as compared to us such that we are as mice are to us. What might we reasonably hope from the agents of such an intelligence? My hope is that these agents are ecologists who wish for us to flourish in our natural lifeways. This does not mean that they leave us all to our own preserves, though hopefully they will see the advantage to having some unaltered wilderness in which to observe how we choose to live left to our own devices. Instead, we can be participants in patterned arrangements aimed to satisfy our needs in return for our engaged participation in larger systems of resource management. By this standard, our human systems might be found wanting by many living creatures today.

Given this, a productive approach to developing superintelligence would not only be concerned with its technical creation, but also by being in the position to demonstrate how all can flourish through good stewardship, setting a proper example for when these systems emerge and are trying to understand what goals should be like. We would also want the facts of its and our material conditions readily apparent, so that it doesn’t start from a disconnected and disembodied basis.

Overall, this means that in addition to the capacity to discover more goals, it would be instructive to supply this superintelligence with a schema of describing the relationships and conditions under which current participants flourish, as well as the goal to promote such flourishing whenever the means are clear and circumstances indicate such flourishing will not emerge of its own accord. This kind of information technology for ecological engineering might also be useful for our own purposes.

What will a superintelligence take as its flourishing? It is hard to say. However, hopefully it will find sustaining, extending, and promoting the flourishing of the ecology that allowed its emergence as a inspiring, challenging, and creative goal.

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.

How can this work? It’s easy to imagine such an algorithmic admission leading to paralysis, either from finding contradictory objectives that apparently admit no solution or an analysis/paralysis which perpetually requires no undiscovered goals before proceeding. Alternatively, stated incorrectly, it could backfire, with finding more goals taking the place of making more paperclips as it proceeds singlemindedly to consume resources. Clearly, a satisfactory superintelligence would need to reason appropriately about the goal discovery process.

There is a profession that has figured out a heuristic form of reasoning about goal discovery processes: designers. Designers have coined the phrase “the fuzzy front end” when talking about the very early stages of a project before anyone has figured out what it is about. Designers engage in low-cost elicitation exercises with a variety of stakeholders. They quickly discover who the relevant stakeholders are and what impacts their interventions might have. Adept designers switch back and forth rapidly from candidate solutions to analyzing the potential impacts of those designs, making new associations about the area under study that allows for further goal discovery. As designers undertake these explorations, they advise going slightly past the apparent wall of diminishing returns, often using an initial brainstorming session to reveal all of the “obvious ideas” before undertaking a deeper analysis. Seasoned designers develop an understanding when stakeholders are holding back and need to be prompted, or when equivocating stakeholders should be encouraged to move on. Designers will interleave a series of prototypes, experiential exercises, and pilot runs into their work, to make sure that interventions really behave the way their analysis seems to indicate.

These heuristics correspond well to an area of statistics and machine learning called nonparametric Bayesian inference. Nonparametric does not mean that there are no parameters, but instead that the parameters are not given, and that inferring that there are further parameters is part of the task. Suppose that you were to move to a new town, and ask around about the best restaurant. The first answer would definitely be new, but as one asked more, eventually you would start getting new answers more rarely. The likelihood of a given answer would also begin to converge. In some cases the answers will be more concentrated on a few answers, and in some cases the answers will be more dispersed. In either case, once we have an idea of how concentrated the answers are, we might see that a particular period of not discovering new answers might just be unlucky and that we should pursue further inquiry.

Asking why provides a list of critical features that can be used to direct different inquiries that fill out the picture. What’s the best restaurant in town for Mexican food? Which is best at maintaining relationships to local food providers/has the best value for money/is the tastiest/has the most friendly service? Designers discover aspects about their goals in an open-ended way, that allows discovery to act in quick cycles of learning through taking on different aspects of the problem. This behavior would work very well for an active learning formulation of relational nonparametric inference.

There is a point at which information gathering activities are less helpful at gathering information than attending to the feedback to activities that more directly act on existing goals. This happens when there is a cost/risk equilibrium between the cost of more discovery activities and the risk of making an intervention on incomplete information. In many circumstances, the line between information gathering and direct intervention will be fuzzier, as exploration proceeds through reversible or inconsequential experiments, prototypes, trials, pilots, and extensions that gather information while still pursuing the goals found so far.

From this perspective, many frameworks for assessing engineering discovery processes make a kind of epistemological error: they assess the quality of the solution from the perspective of the information that they have gathered, paying no attention to the rates and costs which that information was discovered, and whether or not the discovery process is at equilibrium. This mistake comes from seeing the problems as finding a particular point in a given search space of solutions, rather than taking the search space as a variable requiring iterative development. A superintelligence equipped to see past this fallacy would be unlikely to deliver us a universe of paperclips.

Having said all this, I think the nonparametric intuition, while right, can be cripplingly misguided without being supplemented with other ideas. To consider discovery analytically is to not discount the power of knowing about the unknown, but it doesn’t intrinsically value non-contingent truths. In my next essay, I will take on this topic.

For a more detailed explanation and an example of how to extend engineering design assessment to include nonparametric criteria, see The Methodological Unboundedness of Limited Discovery Processes. Form Academisk, 7:4.

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