Probably – but it’s complicated.

That is often the unfortunate answer when we ask big questions about how best to manage the world. We want to feel good about ourselves for being good citizens, or at least champion clear policies that are objectively better and effective at achieving our goals. Reality rarely accommodates these desires.

Part of the problem is that there are over 7 billion people on the planet and growing, and our industrial civilization uses a lot of resources. So anything that millions or billions of people do is likely to have an impact. Because of this there may simply be no perfect option.

But further, the available options tend to have trade-offs. Therefore if you ask, which option is better, the answer often is – it depends. It depends on how you ask and how you answer the question. Defining the problem is often easier – the solution, not so much.

The problem here is clear – single use plastic products do not biodegrade. They survive in the environment for about 400 years. They will break down into microplastics, but these stay in the environment and can still cause problems. A recent study found microplastics in remote regions of the Pyrenees. They are basically everywhere. Plastics clog our oceans and increasingly animals are turning up dead bloated with plastic waste.

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I love the conversation stemming from my last post on flying cars, and also our discussion on the SGU. Some great points were raised, and I want to further the conversation by addressing them.

I will begin by more explicitly laying out the various ways to frame the question of what impact flying cars may have. Whenever we ask a question about how something will affect a complex system, the answer always seems to be – it depends. It depends on exactly what question you ask and how you try to measure the effect. Are air dryers better than paper towels? It depends on what factors you think are important, and on several variables.

Thinking about the flying car question more deeply, it seems to me there are three levels to consider when it comes to efficiency. The first level is trip efficiency – what is the energy cost of making one trip for one passenger from point A to point B, comparing an electric flying car to a gasoline ground car and electric ground car? This was the level of the analysis of the recent paper I was discussing.

What they found is that flying cars can be more efficient than gasoline ground cars if the travel distance is long enough. For a 100 km trip they found that flying cars are 35% more efficient than gasoline, but 28% less efficient than electric vehicles. So that’ right in the middle of the pack.

Further, for longer trips the relative efficiency improves, but also if we consider geography and traffic the flying car, in some situations, may become the most efficient option. The authors point out that the average commute is 17 miles (which is close to other sources I found, some say 16 miles) but this also translates into 1 hour round trip travel time. But actually this is not enough information. We also need to know the average trip efficiency of these commutes. In other words – how much does the commute path deviate from a straight line? Also, how long does the commute take compared to no-traffic travel times? And further, how much of that commute is city driving vs highway?

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Over the years I have waxed and waned in terms of my optimism that one day flying cars will be a reality – and not just prototypes, but in general use for transportation. I was at an ebb in my enthusiasm, but a recent article has nudged me toward more optimism.

By “flying car” I mean a vehicle used for personal transportation that can fly but also be driven by someone with about the level of training required to drive a regular car. If you need a pilot’s license, have to land an an airport, and the vehicle primarily uses wings for lift, then it’s not a “car”, it’s a plane. Occasionally developers put forward what is essentially a driving plane and claim it is a flying car, but I would not put it in that category.

The flying car has been a science-fiction promise for over half a century. Just about every vision of even the near future contains flying cars. It is a fixture in our mental image of the “future.” I think part of the reason it has so captured our imagination is the obvious utility. Who does not fantasize about flying over frustrating traffic and taking a straight line unobstructed path to your destination?

There have essentially been two hurdles to the development of the flying car. The first is safety – designing something that the average person can operate with moderate training and with minimal accidents, at the level of driving a regular car or lower. This nut has essentially been cracked. Computer algorithms can handle the difficult aspects of maintaining stability, and can even mostly fly themselves. Fifty years ago this was an issue, it no longer is.

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This is a big-picture story I have been following closely, partly because it feels like we are still on the steep part of the curve. Every now and then in the development of technology we hit upon something that may seem small at first, but in retrospect changed the world completely. Electricity is an obvious example. At first scientists didn’t think it would be useful for much, but once we realized we could power devices and do other things with electricity, we remade our world with technology that uses it. The digital revolution was another such transformative breakthrough. Such technologies don’t just win the game – they change the rules of the game.

It’s easy to see such disruptive technologies in retrospect, and much harder to anticipate them. We are somewhere between these two extremes with current technologies that are in the process of transforming our world. It is becoming increasingly clear, for example, that artificial intelligence (AI) is rapidly turning into such a transformative technology.

Specifically we are starting to see an explosion of deep learning algorithms. These are AI programs that can learn general rules or patterns from examples. They can be fed examples, or take in examples by observing the world, or even generate examples through trial and error. This technology also dovetails with the age of big data – we have massive data sets and now we also have the AI to make use of those data sets in novel ways.

As is often the case, the early use of a technology is for frivolous purposes, if only as a proof of concept. Deep learning algorithms have been demonstrated to the world by winning at Jeopardy, chess, and now the game, Go. The news of these milestones were a flash in the social media pan, commanding our attention for five minutes before we went onto the next thing. But I think in retrospect we will see them as the harbingers of a new age of technology. The age of AI.

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I warn frequently about the folly of trying to predict the future. Obviously we need to do this to some extent, but we always need to be aware of how difficult it is. It is especially hard to predict how people will use technology, even if the technology itself is inevitable. Until devices are in the hands of actual people out there in the world, who try to incorporate the tech into their daily lives, we just can’t know how it is going to shake out.

So, having said that, I am going to make a prediction about how people are going to incorporate future technology. I think robotic pets are going to be increasingly popular as the technology advances. At least I am going to build what I think is a strong case for this prediction. The risk is that there is something I cannot anticipate that will be a deal-killer. Feel free to try to shoot this down and bring up points, but hear me out first.

From a neurological point of view, I do not see any obstacle to people bonding fully with robotic pets. Neuroscience has clearly established that the human brain has certain algorithms that it uses to assign emotional significance to things, to form emotional attachments, and to respond to emotional signaling. In order for the full suite of emotional responses to be in play, being alive is simply not required. That is not how our brains work.

Our visual systems, for example, sort the world into two categories – things that have agency, and things that do not have agency. Having agency means that our brains infer that they are able to act with their own will and purpose. They infer this from how objects move. If they move in a way that cannot be explained simply as passive movement within an inertial frame of reference, then they must be moving on their own. Therefore they have agency.

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Researchers at the USC Viterbi School of Engineering have developed a robotic limb with artificially intelligent control that learns how to walk by trying to walk. This may seem like a small thing, but it represents a fascinating trend in AI and robotics – shifting more and more to a bottom up rather than top down approach to programming.

This recent advance is very incremental, but worth pointing out. The researchers tried to designs a limb based on biological principles. Rather than programming the limb with the processes necessary to walk, including dealing with difficult terrain and recovering from a trip, they developed an algorithm that will learn how to walk and adapt by trying to do it. This type of learning algorithm from scratch is nothing new, but the researchers claim this is the first time it was applied to this particular task.

The results were impressive – the robot was able to learn how to walk within minutes. Because the learning is mostly trial and error, different iterations of this algorithm will hit upon different solutions, so different robots might have distinctive gaits.

The first thing I thought of when I read this news item is – what about Boston Dynamic’s Big Dog? This is a four-legged robot about the size of a large dog developed as a pack mule for the military, and capable of handling rough terrain. Watch the video – it’s impressive. I tried to find out how much of the Big Dog walking algorithm is learned vs programmed, but what I found is that “it’s proprietary.” But the consensus of opinion seems to be that it is partly both, a lot of developed walking algorithms but maybe incorporating some learning AI. If true the USC robotic limb would be the first fully self-learning walking robot algorithm, as they claim.

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Michael Shellenberger has a provocative editorial in which he makes the case against renewables and for nuclear energy. At first you might think you are reading global-warming denial propaganda, but that’s not what it is. Shellenberger is a self-described ecomodernist who simply thinks that, if you look at the numbers, there is a strong case to be made for nuclear energy as the most practical solution to global warming.

I half-buy his editorial. I agree with everything he says about nuclear power, and have made the same points myself. Nuclear is the safest form of energy by far, and has the lowest environmental impact. It is also the only solution that will enable us to replace our existing fossil fuel infrastructure anytime soon. The alleged problems with nuclear are also overblown.

The typical points raised against nuclear are topped by – how to deal with the nuclear waste. There are two answers to this concern, however. The first is to simply deal with it. Approve waste disposal sites like Yucca Mountain and safely store the waste. The second solution, however, is even better – modern reactors can burn much of what is now considered waste, including waste from older reactors.

In fact, the definition of nuclear “waste” is flexible. It is simply nuclear material that we currently do not use as fuel in reactors. But it can be used as fuel – nuclear “waste” is just another form of nuclear fuel. We already have designs for nuclear reactors that can minimize waste, and even reduce existing waste, and what remains can easily be dealt with.

Another concern is that nuclear reactors are used to feed the production of weaponized fissible material. But this also does not have to be the case. In fact, this point and the previous one are related. It is true that current nuclear power plants were designed to produce “waste” that could then be used by the military to ultimately produce material for nuclear weapons. However, we can design plants as purely civilian, with a nuclear cycle that burns more of the nuclear material and does not create any weaponized material.

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If all goes well, the 2020s may be the decade that we return to the Moon. There is certainly renewed interest in the Moon, and we may, in fact, be entering a new space race with China.

In December 2018 NASA announced their updated Gateway mission plans – they seek to put a platform in permanent orbit around the moon, and then use the platform to shuttle astronauts to and from the Moon’s surface. They also hope to do all this within 10 years.

They will do this with the new Space Launch System (SLS) and the Orion capsule. This is a powerful rocket capable of deep space missions (beyond low-earth orbit). They also plan to partner with private industry for many of the components. NASA administrator, Jim Bridenstine, recently said:

“It’s important that we get back to the moon as fast as possible,” said Bridenstine in a meeting at NASA’s Washington headquarters, adding he hoped to have astronauts back there by 2028.

“This time, when we go to the Moon, we’re actually going to stay. We’re not going to leave flags and footprints and then come home to not go back for another 50 years” he said.

“We’re doing it entirely different than every other country in the world. What we’re doing is, we’re making it sustainable so you can go back and forth regularly with humans.”

I think this is exactly the right course. Mars can wait until after we flesh out an Earth-Moon infrastructure. Having a permanent presence on the Moon will be much easier than Mars, mainly because it’s a lot closer, so we should do that first.

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Climate change has altered the debate over the ethics of meat consumption, adding a new dimension that certainly changes the equation. There are also new options on the horizon, however, such as lab grown or cultured meat. How this will all shake out is still unknown, but it seems likely our meat consumption habits are going to be different in 50 years.

There are several issues to consider regarding meat consumption. One is simply the ethics of animal consumption. This one is a bit personal, and I don’t think there is any one answer. I personally advocate for humane animal treatment, but I do not think it is unethical to breed animals for consumption or to use their body parts for whatever purpose we like after they are dead. As long as their life is reasonably comfortable and their death painless and stress free, I don’t see an ethical issue.

This is essentially the position of Temple Grandin, who is a staunch advocate for animals rights but is not against meat consumption, she simply advocates for a system of animal handling from beginning to end which is humane.

A second issue is land use and the overall impact on the environment. Here the issues are perhaps ethically a bit clearer, but technically still complex. There is no question that growing meat requires more energy and more land and resources overall than plant consumption. However, there are many variables involved and depending on the details plants are not always more efficient.

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Would you sacrifice a human to save a robot? Psychologists have set out to answer that question using the classic trolley problem.

Most people by now have probably heard about the trolley dilemma, as it has seeped into popular culture. This is a paradigm of psychology research in which subjects are presented with a dilemma – a trolley is racing down the tracks and the breaks have failed. It is heading toward 5 people who are unaware they are about to be killed. You happen to be right next to the lever that can switch the trolley to a different track, where there is only one person at risk. Would you switch the tracks to save the 5 people, but condemning the 1 person to death? Most people say yes. What if in the same situation you were on the trolley at the front of the car, and in front of you was a particularly large person – large enough that if you pushed them off the front of the trolley their bulk would stop the car and save the 5 people, but surely kill the person you pushed over (I know, this is contrived, but just go with it). Would you do it? Far fewer people indicate that they would.

The basic setup is meant to test the difference between being a passive vs active cause of harm to others in the context of human moral reasoning. We tend not to be strictly utilitarian in our moral reasoning, thinking only of outcomes (1 death vs 5), but are emotionally concerned with whether we are the direct active cause of harm to others vs allowing harm to come through inaction or as a side consequence of our actions. The more directly involved we are, the more it bothers us, not just the ultimate outcome.

The trolley problem has become so famous because you can use it as a basic framework and then change all sorts of variables to see how it affects typical human moral reasoning. You can play with the numbers, to see if there is a threshold (how many lives must be saved in order to make a sacrifice worth it?), or you can vary the age of those saved vs those sacrificed, or perhaps the person you might sacrifice is a coworker. Does that make their life more valuable? What if they are kind of a jerk?

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