Category: Future

When people on Earth don’t understand something, they create new disciplines. A lot of what we don’t understand comes from emergent behaviors, that is, the whole is drastically different than the building blocks. We may know the blocks reasonably well, but not the complex things built with them. For example, economics is a field to help understand the collective outcomes of a lot of interacting people. With ChatGPT and other large language models started showing abilities that we don’t quite know how to explain, other than they have emerged from the tools we (thought we) know, maybe it’s the time to have new fields? What about splitting the AI research field to “AI Neuroscience” and “AI Psychology”?

Lunar Helium-3 (3He) mining has the potential of addressing the energy and environmental challenges on Earth. As an essential fuel of future nuclear fusion reactors, 3He is extremely rare on Earth, with currently less than 2 kg/year production in the US, but is much more abundant on the lunar surface, with an estimated reserve of over one million tons. Just 40 tons of 3He, combined with deuterium, a resource available on Earth, could meet the current annual energy need of the U.S.

Mining 3He on the Moon would be quite different than traditional mining process on Earth. Over the time, 3He, along with other volatiles, were implanted into the lunar regolith by the solar wind. This means that surface mining operations would need to cover vast areas at shallow depths. Agitation to the regolith will lead to a significant release of the volatiles, which presents both a challenge and an opportunity. Other challenges associated with large-scale space industrialization, such as the limited human presence and the high reliability/autonomy requirements, meaning radically different mining concepts need to be developed.

Inspiration: Vascular plants use roots to anchor themselves, to explore and extract resources from earth, to store nutrients, and to compete with others. The roots grow by producing new cells, which are regulated by both extrinsic and intrinsic stimuli. Sensing of extrinsic factors, such as gravity, barriers, light, air, water, and nutrients, directs roots to grow toward favorable directions. Hormones (i.e., intrinsic stimuli) such as Cytokinins and Auxin (with often opposite effects), as a part of both local and long-distance signaling, also help coordinate the root development. The plant roots are time and real-world tested designs for functionality, intelligence, and resilience.

Vision: Entering rootbots, sprawling networks of robot organisms, for performing planetary resource extraction tasks at an unprecedented scale in the second half of the 21st century. A rootbot will be made of modular and interlocking smart components called the cells. Each cell interacts with the environment and makes its own decisions, influenced by local stimuli and decisions of nearby cells. One important decision to be made is whether for a cell to stay in place or relocate. If a cell decides to move, it will wait for a mobile agent, called a transporter, to come by and pick it up. Traveling only on top of the existing “root” network of cells, the transporters carry cells and deploy them in places where they desire. With two decentralized and interacting robot swarms (i.e., the cells and the transporters), a rootbot can grow like a plant root for exploring and exploiting favorable conditions.

Ok, the discussions above were modified from an unsuccessful NASA NIAC proposal. Feedback from NASA? “Overall, while there were many interesting aspects suggested, the proposal overreached without enough justification for any one concept, especially given the scale proposed.”

Are you currently stuck on a hard problem? I am. I’m actually stuck on several problems, which I think is a good thing. Let me explain why.

Let’s first look at the relationship between people and problems. I can think of four possibilities:

1. One person vs. one problem. Assuming this problem is solvable but is very hard (i.e., there are only a few possible ways of solving it), what would be the chance that this one person is able to find a solution? It’s possible but would take a lot of (relevant) skill, luck, and persistence. If a person is stuck on a problem, does this mean he or she is not a good problem solver? No; maybe just not a good fit, or lucky enough, or spent enough time on it (e.g., time was consumed by self-doubting instead). Also, some problem may not be a good fit to any one person, it would take a team of diverse background to solve.
2. Multiple people vs. one problem. Things are a bit more promising here. Maybe someone would come up with something, and another person can build on it, and so on. This is partially collective intelligence and partially just the tried-and-true brute force method of increasing the number of trials. For example, if you invite enough people to a party, someone will bring a gift you actually wanted.
3. One person vs. multiple problems. From the perspective of increasing trails, this is similar to the one above, without having to bother other people! If you keep several problems in (the back of your) mind, and you are maybe not able to solve most of them most of the time, but you may get lucky occasionally on one of them. I often work in this mode, but I am aware of my limitations.
4. Multiple people vs. multiple problems. I don’t fully understand this one yet, but to a limited degree this is how IRL operates.

Let’s now think about the problem of robot problem solving. Robots today are generally not good problem solvers, but we are trying to improve that.

1. One robot vs. one problem. If the problem is what the robot programed for, it would work extremely well. Arguably this is not robot problem solving though, because the problem has already been solved. If the problem is not what the robot was prepared for, most likely it would get stuck. Yes, robots do get stuck quite often.
2. Multiple robots vs. one problem. If each robot here makes its own decisions, this may become a robot swarm. A swarm is known to be able to solve problems not explicitly planned for, but we don’t fully understand it yet.
3. One robot vs. multiple problems. If we stop optimizing around one particular cost/reward function, but instead celebrating every meaningful thing a robot did, we may find novel ways for a robot to solve many different problems. Those problems and solutions are mostly irrelevant, but could become useful at some point. There are much to be done to harvest these randomly gathered experiences.
4. Multiple robots vs. multiple problems: If many robots are operating like #3 above, I really have no clue what would be the implications. It would be fun to try though.

Now back to us. You and I are particles participants in a giant Monte Carlo experiment called the human society, that’s how I see it at least. From a global perspective, it’s more important that someone solves the society’s big problems than who solves these problems. If the system is well designed (that’s a big if, and itself one of the hardest problems), creative ideas would emerge one after another. This is the hope for the multiple people vs. multiple problems argument. For us, individually, we don’t have control of when a good idea may come. But we can choose to be not totally stuck, and there may be ways to do that.

Tired of being stuck at home working alone remotely? You are not alone in that sense! Since COVID-19 turned the world upside down, many of us were forced to work from home. After a while, once we get used to it, working from home, or working remotely, is actually not all that bad. We can spend less time in the traffic and enjoy more flexibility sometimes. But there are important things missing, like face-to-face discussions and the ability to modify the environment (many jobs depend on these!). Or in short, we are missing out on the social and physical interactions.

Will remote work be the same as what we are doing today (e.g., meeting on Zoom), say, 20 years from now? Hopefully not…, OF COURSE NOT! So what may change?

Let’s envision a future hybrid workplace, for example, an office with local workers and a group of telepresence humanoid robots as “avatars” of remote workers. Whenever a remote worker needs to do something beyond a computer task, for example helping a customer or turning a knob, she/he may do so through one of the robots. The humanoids have articulated arms and bodies to support human-like interactions, e.g., during a “face-to-face” conversation. When an office worker or a customer puts on a pair of Augmented Reality glasses, the live image of the remote person would be overlaid over the robot. At homes, the workers also feel they are physically experiencing the remote work environment, instead of feeling isolated.

My student Trevor Smith created this illustration in Gazebo using images of a VR treadmill the robot Pepper.

Of course, a lot of research needs to be done for this dream to become a reality, but that’s what we roboticists are here for. Communication technology has allowed us to hear from a distance, then to see each other, maybe this time we would finally get to “travel, touch, feel, and experience” through internet and robots? Sounds farfetched but not impossible.

A 2019 MIT report on the Work of the Future pointed out that “Ironically, digitalization has had the smallest impact on the tasks of workers in low-paid manual and service jobs. Those positions demand physical dexterity, visual recognition, face-to-face communications, and situational adaptability. Such abilities remain largely out of reach of current hardware and software but are readily accomplished by adults with moderate levels of education.” By focusing on labor-complementing instead of labor-substituting technology development, improving remote work may be a way of using robotics and AI to support middle-class workers (e.g., teachers, social workers, farmers, and factory workers) of the future.