How can we use our resources to help others the most?

This is the fundamental question of the Effective Altruism movement, and it should be the fundamental question of all charitable giving. I think the first fundamental insight of effective altruism (which really took it from Peter Singer) is that your donation can change someone’s life, and the wrong donation can accomplish nothing. People do not imagine charity in terms of “investments” and “payoffs”, yet GiveWell estimates that you can save a human life for somewhere in the magnitude of $2500.

Many American households donate that much to charity every year, and simply put, if the charities we donate to don’t try to maximize their impact, our donations may not help many people, when they could be saving a life.

This post is a short reminder that we have researched empirical evidence that you can make a difference in the world! The EA movement has already done very impressive work on how we might evaluate charitable giving, why the long term future matters, and what the most important and tractable issues might be.

Apart from the baseline incredible giving opportunities in global poverty (see GiveWell’s top charities), the long term future is an important and underfocused area of research. If humanity lives for a long time, then the vast majority of conscious humans who will exist will exist in the far future. Taking steps to ensure their existence could have massive payoffs, and concrete research in this area to avoid things like existential risk seems very important and underfunded.

I write this blog post not to shame people into donating their entire incomes (see Slate Star Codex on avoiding being eaten by consequentialist charitable impacts), but rather to ask donors to evaluate where you are sending your money within your budget and to see if perhaps the risk of paying such a high opportunity cost is worth it. Alma maters and church groups are the most common form of charity Americans give to, but the impacts from these areas seem much lower than donating to global poverty programs or the long term future.

Finally, part of this blog post is simply to publicly discuss what I donate to and to encourage others to create a charitable budget and allocate it to address problems that are large in the number of people they impact, highly neglected, and highly solvable. I thus donate about a third of my budget to GiveWell as a baseline based on evidence backed research to save lives today. I then donate another third of my budget to long term causes where I think the impact is the highest, but the tractability is perhaps the lowest. Top charities I’ve donated to here include the Machine Intelligence Research Institute for AI alignment research, as well as the Long Term Future Fund from EA Funds.

The last third of my budget is reserved to focusing on policy, which is where I believe the EA movement is currently weakest. I donate money to the Institute for Justice, as they work on fairly neglected problems in a tractable way, winning court cases to improve civil liberties for U.S. citizens. I also like the Center for Election Science as they work to improve the democratic processes in the US. It would be great to be able to move good policies to polities with bad institutions (i.e. many developing nations), but that problem seems highly intractable. It may be that the best we can do is create good institutions here and hope they are copied. I’m open to different ideas, but I am a relatively small donor and so I believe that taking risks with a portion of my donations in ways that differ from the main EA thrust is warranted. This is by far my most uncertain category, and thus usually I will not entirely fulfill my budget for policy charities. I plan on giving anything remaining to GiveWell.

There are many resources from the Effective Altruism community, and I’ll include several links of similar recommendations from around the EA community. If you haven’t heard of EA charities, consider giving some of your charity budget to GiveWell, or other EA organization you find convincing. If you don’t have a charity budget, consider making one for next year. Even small amounts a year can potentially save dozens of cumulative lives!

Links 2019-03-07

First links post in a while because I have some housekeeping. After trying to have comments just on reddit, I’ve realized it makes way more sense to just have comments right below the articles again. I really don’t like the WordPress default comment system so I’ve opted instead for Disqus. These have been implemented for a while, but I wanted to bring your attention to them.

I’ve also finally updated the site to default to https. Kind of an embarrassment for a site promoting encryption to not have https defaulted, but this blog is a volunteer project done for personal interest (and personal expense!).

I’ve removed Greg Mankiw’s blog from the sidebar because I realized I wasn’t reading it much anymore and it doesn’t talk about too much interesting econ stuff very often. I also removed Jeffrey Tucker’s blog beautiful anarchy, because I don’t think he posts there anymore now that he’s running aier.org.

I’ve added gwern.net because this past year I’ve realized how much more I’ve been going to his site even though I’ve known about it for a long time. Gwern is a rationalist independent researcher. He doesn’t really write blogs so much as essays on a topic. I recommend his site wholeheartedly. Seriously, his site is the first link on this post for a reason. If you are overwhelmed by the amount of content, see if anything in his “Most Popular” or “Notable” categories jump out at you and start there. I personally found “Embryo Selection For Intelligence” to be quite engrossing.

Slate Star Codex has had some good posts about the importance of OpenAI’s GPT-2. First some background on GPT-2. Next, GPT-2 seems to have learned things haphazardly, in almost a human-like way, to attain its goals of creating good responses to prompts. It connects things in a stream of consciousness reminiscent of a child’s thoughts. As Scott says, simply pattern matching at a high level is literally what humans do.

Also on AI, I found an amazing 2018 AI Alignment Literature Review and Charity Comparison by LessWrong user Larks. It’s a very impressive in depth look at groups concerned about the AI alignment problem.

From Vox: “The case that AI threatens humanity, explained in 500 words”.

Noah Smith writes A Proposal for an Alternative Green New Deal. It makes vastly more sense than the vague, progressive wishlist discussed by current Democratic members of Congress. However, even Smith’s suggestions seem pretty poorly thought out to me; he endorses massive subsidies to green technology, on the order of $30 billion a year, without addressing how the state will know where to invest the money. As I recall, the government isn’t a great central planner. He also just kind of tosses in there universal health insurance, apparently paid for by the government, which sounds like Medicare for all. That seems to both massively politically complicate anything actually trying to fix climate change, and also destroy the entire federal budget, which I think is a national security problem.

Related, on a more nuanced note, John Cochrane discusses a letter signed by many economists endorsing a carbon tax, which seems much more precise and useful to people concerned about climate change. To make it politically palatable, they suggest making a carbon dividend paid to all taxpayers out of this tax. Noah Smith also endorsed this approach as just one piece of his Green New Deal. On brand, The Economist endorses carbon taxes as well.

Bitcoin Hivemind developer Paul Sztorc writes about Bitcoin’s future security budget. It’s a really good technical discussion of how Bitcoin can be funded in the future, and why we need sidechains to help pay for the cost of keeping Bitcoin secure.

Bruce Schneier writes about the need for Public Interest Cybersecurity, envisioning it as a parallel to public interest legal work. It’s an interesting take, and I’m not sure how I feel about it. On the one hand, he’s right that lawmakers know little about the technologies they are supposed to regulated, but that’s also true of literally every industry. Sure it would be great if we had more things like the EFF, but I’m have to ask 80,000 Hours if they thought people going into charity work should work for the EFF or AI Alignment research or other existential risk. I’m also not sure I agree that there aren’t enough incentives to invent new security protocols. Google is taking security very seriously on their own, but so are tons of Bitcoin and cryptocurrency developers who are constantly seeking ways to make their projects more secure and do more creative things with crypto.

The U.S. trade deficit hit a 10 year high. Here is the actual Bloomberg article. This is silly political bickering, so I won’t spend much time on it, but it reflects just how the president fails to grasp very simple economics. The trade deficit doesn’t mean anything by itself, it’s just a measure of the goods traded, and it’s not even very good at that (goods designed here but manufactured in another country see their whole value “subtracted” in the trade deficit despite American labor inputs). The drivers of the trade deficit are things like relative values of currencies and national savings rates, not the levels of tariffs. Meanwhile, Trump’s tax cuts have spurred U.S. growth while the rest of the world has been sluggish, leading to higher trade deficits because Americans are relatively more wealthy. This flurry of economic activity prompted the Fed to raise rates to stave off inflation, which also drives up the trade deficit, and so Trump has taken the horrible tact of trying to publicly attack the Fed to lower rates, which is terrible for any sort of responsible Fed policy. The whole thing is a ridiculous mess which could have been avoided if Trump had any semblance of economic knowledge.

The Fifth Column podcast is a highly entertaining libertarian politics podcast. Episode 132 is a little different as Michael Moynihan takes the opportunity to interview Mark Weisbrot, Co-Director of the Center for Economic and Policy Research, a left-wing think tank, on the Maduro government in Venezuela. I have a lot of thoughts on this interview, but my foremost is whether Weisbrot counts as an actual representative voice of the Left. I think one of the worst things social media does is to hold up the most controversial person on one side because they generate the most clicks and buzz and force both sides to jump in and flame each other. In an hour long interview, Weisbrot takes, as far as I can tell, no opportunity to criticize the Maduro regime, nor offers any way in which they could have improved their policies. He accepts and touts statistics that support his view, and dismisses, minimizes, or ignores stats that counter him–even if they are all from the same source! Even though he’s a big deal at a left-wing think tank, I have to point out that most left-leaning academics don’t need to be in think tanks because most university politics skew left. This might explain how someone with this level of willful ignoranceg could hold such a key position. I think the interview is worth listening to if you would like to see the extent of what humans can do to put up mental barriers to seeing their own logical inconsistencies and motivated reasoning. Nonetheless, I feel bad about linking to this interview as I think it unfairly represents actual socialists who would like to nationalize all industries and seize the means of production.

Artificial General Intelligence and Existential Risk

The purpose of this post is to discuss existential risk, and why artificial intelligence is a relatively important aspect of existential risk to consider. There are other essays about the dangers of artificial intelligence that I will link to throughout and at the end of this post. This essay is a different approach that perhaps will appeal to someone who has not seriously considered artificial general intelligence as an issue requiring civilization’s attention. At the very least, I’d like to signal that it should be more socially acceptable to discuss this problem.

First is the section on how I approached thinking about existential risk. My train of thought is a follow up to Efficient Advocacy. Also worth reading: Electoral Reform Fantasies.

Background

Political fights, especially culture war battles that President Trump seems so fond of, are loud, obnoxious, and tend to overshadow more impactful policy debates. For example, abortion debates are pretty common, highly discussed political issues, but there have been almost no major policy changes since the Supreme Court’s decision 40 years ago.  The number of abortions in the US has declined since the 1980s, but it seems uncorrelated with any political movements or electoral victories. If there aren’t meaningful differences from different political outcomes, and if political effort, labor, and capital is limited, these debates seem to distract from other areas that could impact more people. Trump seems especially good at finding meaningless conflicts to divide people, like NFL players’ actions during the national anthem or tweeting about Lavar Ball’s son being arrested in China.

Theorizing about how to combat this problem, I started making a list of what might be impactful-but-popular (or at least not unpopular) policies that would make up an idealized congressional agenda: nominal GDP futures markets, ending federal prohibition of marijuana, upgrading Social Security Numbers to be more secure, reforming bail. However, there is a big difference between “not unpopular”, “popular”, and “prioritized”. I’m pretty sure nominal GDP futures markets would have a pretty positive effect on Federal Reserve policy, and I can’t think of any political opposition to it, but almost no one is talking about it. Marijuana legalization is pretty popular across most voters, but it’s not a priority, especially for this congress. So what do you focus on? Educating more people about nominal GDP futures markets so they know such a solution exists? Convincing more people to prioritize marijuana legalization?

The nagging problem is that effective altruist groups like GiveWell have taken a research based approach to identify at what the best ways are to use our money and time to improve the world. For example, the cost of distributing anti-mosquito bed nets is extremely low, resulting in an average life saved from malaria at a cost in the thousands of dollars. The result is that we now know our actions have a significant opportunity cost; if a few thousand dollars worth of work or donations doesn’t obviously have as good an impact as literally saving someone’s life, we need a really good argument as to why we should do that activity as opposed to contributing to GiveWell’s top charities.

One way to make a case as to why there are other things worth spending money on besides GiveWell’s top charities, is to take a long term outlook, trying to effect a large change that would impact a large amount of people in the future.  For example, improving institutions in various developing countries would help those populations become richer. Another approach would be to improve the global economy, which would both allow for more investment in technology as well as push investment into developing countries looking for returns. Certainly long term approaches are more risky compared to direct impact charities that improve outcomes as soon as possible, but long term approaches can’t be abandoned either.

Existential Risk

So what about the extreme long term? What about existential risk? This blog’s philosophy takes consequentialism as a founding principle, and if you’re interested in the preceding questions of what policies are the most helpful, and where we should focus our efforts, you’ve already accepted that we should be concerned about the effects of our actions. The worst possible event, from a utilitarian perspective would be the extinction of the human race, as it would not just kill all the humans alive today (making it worse than a catastrophe that only kills half the humans), but also ends the potential descendants of all of humanity, possibly trillions of beings. If we have any concern for the the outcomes of our civilization, we must investigate sources of existential risk. Another way to state this is: assume it’s the year 2300, and humans no longer exist in the universe. What is the most likely cause of our destruction?

Wikipedia actually has a very good article on Global Catastrophic Risk, which is a broad category encompassing things that could seriously harm humanity on a global scale. Existential risks are a strict subset of those events, which could end humanity’s existence permanently. Wikipedia splits them up into natural and anthropogenic. First, let’s review the non-anthropogenic risks (natural climate change, megatsunamis, asteroid impacts, cosmic events, volcanism, extraterrestrial invasion, global pandemic) and see whether they qualify as existential.

Natural climate change and megatsunamis do not appear to be existential in nature. A megatsunami would be terrible for everyone living around the affected ocean, but humans on the other side of the earth would appear to be fine. Humans can also live in a variety of climates, so natural climate change would likely be slow enough for some humans to adapt, even if such an event causes increased geopolitical tensions.

Previous asteroid impacts have had very devastating impacts on Earth, notably the Cretaceous-Paleocene extinction event some 66 million years ago. This is a clear existential risk, but you need a pretty large asteroid to hit Earth, which is unusual. Larger asteroids can also be more easily identified from further away, giving humanity more time to do something (push it off path, blow it up, etc). The chances here are thus pretty low.

Other cosmic events are also low probability. Gamma-ray bursts are pretty devastating, but they’d have to be close-by (with a few hundred light-years at least) as well as aimed directly at Earth. Neither of these is likely within the next million years.

Volcanism is also something that has the potential to be pretty bad, perhaps existential level (see Toba Catastrophe Theory), but it is also pretty rare.

An alien invasion could easily destroy all of humanity. Any species with the capability to travel across interstellar space with military ambitions would mean they are extremely technologically superior. However, we don’t see any evidence of a galactic alien civilization (see Fermi Paradox 1 & 2 and The Great Filter). Additionally, solving this problem seems somewhat intractable; on a cosmic timescale, an alien civilization that arose before our own would likely have preceded us by millennia, meaning the technology gap between us and them would be hopelessly and permanently large.

A global pandemic seems pretty bad, certainly much more likely than anything else we’ve covered in the short term. This is also exacerbated by human actions creating a more interconnected globe. However, it is counterbalanced by the fact that no previous pandemic has ever been 100% lethal, and that modern medicine is much better than it was during the Black Plague. This is a big risk, but it may not be existential. Definitely on our shortlist of things-to-worry-about though.

Let’s talk about anthropogenic risks next: nuclear war, conventional war, anthropogenic climate change, agricultural crises, mineral exhaustion, artificial intelligence, nanotechnology, biotechnology.

A common worry is nuclear war. A massive nuclear exchange seems somewhat unlikely today, even if a regional disagreement in the Korean peninsula goes poorly in the worst possible way. It’s not common knowledge, but the “nuclear winter” scenario is still somewhat controversial, and I remain unconvinced that it poses a serious existential threat, although clearly a nuclear exchange would kill millions. Conventional war is also out as it seems strictly less dangerous than a nuclear war.

For similar reasons to nuclear winter, I’m not quite worried about global warming on purely existential terms. Global warming may be very expensive, it may cause widespread weather, climate, and ecological problems, but I don’t believe humanity will be entirely wiped out. I am open to corrections on this.

Agricultural crises and mineral exhaustion seem pretty catastrophic-but-not-existential as well. These would result in economic crises, but by definition, economic crises need humans to exist; if there are fewer humans, it seems that an agricultural crisis would no longer be an issue.

The remaining issues are largely technological in nature: artificial intelligence, biotechnology, nanotechnology, or technical experiments going wrong (like if the first nuclear test set the atmosphere on fire). These all seem fairly concerning.

Technological Existential Risk

Concern arises because technological progress means the likelihood that we will have these technologies grows over time, and, once they exist, we would expect their cost to decrease. Additionally, unlike other topics listed here, these could wipe out humanity permanently. For example, a bioengineered virus could be far more deadly than what would naturally occur, possibly resulting in a zero survival rate. The cost of DNA technology has steadily dropped, and so over time, we might expect the number of organizations or people who have the knowledge and funding to engineer deadly pathogens to increase. The more people who have this ability, the more likely that someone makes a mistake and releases a deadly virus that kills everyone. An additional issue is that it is quite likely that military research teams are right now researching bioweapons like an engineered pathogen. Incentives leading to the research of dangerous weapons like this are unlikely to change, even as DNA engineering improves, meaning the risk of this threat should grow over time.

Nanotechnology also has the potential to end all life on the planet, especially under a so-called “grey goo” scenario, where nanobots transform all the matter on Earth. This has a lot of similarities to a engineered pathogen, except the odds of any human developing some immunity no longer matter, and additionally all non-human life, indeed, all matter on Earth is also forfeit, not just the humans. Like biotechnology threats, we don’t have this technology yet, but it is an active area of research. We would also expect this risk to grow over time.

Artificial General Intelligence

Finally, artificial general intelligence contains some similar issues to the others: as technology advances, we have a higher chance of creating it; the more people who can create it, the more dangerous it is; once it is created, it could be deadly.

This post isn’t a thesis on why AI is or isn’t going to kill all humans. We made an assumption that we were looking exclusively at existential risk in the near future of humanity. Given that assumption, our question is why will AI be more likely to end humanity than anything else? Nonetheless, there are lingering questions as to whether AI is an actual “real” threat to humanity, or just an unrealistic sci-fi trope. I will outline three basic objections to AI being dangerous with three basic counterarguments.

The first objection is that AI itself will not be dangerous because it will be too stupid. Related points are that AI is too hard to create, or we can just unplug it if it has differing values from us. Counterarguments are that experts disagree on exactly when we can create human-level AI, but most agree that it’s plausible in the next hundred or couple hundred years (AI Timelines). It’s also true that we’ve seen improvements in AI ability to solve more general and more complex problems over time; AlphaZero learned how to play both Go and Chess better than any human without changes in its base code, YouTube uses algorithms to determine what content to recommend and what content to remove ads from, scanning through thousands of hours of video content every minute, Google’s Pixel phone can create software based portrait photos via machine learning rather than needing multiple lenses. We should expect this trend to continue, just like with other technologies.

However, the difference between other technological global risks and AI is that the machine learning optimization algorithms could eventually be applied to machine learning itself. This is the concept of an “intelligence explosion”, where an AI uses its intelligence to design and create successively better versions of itself. Thus, it’s not just that an organization might make a dangerous technological breakthrough, like an engineered virus, but that once the breakthrough occurs, the AI would rapidly become uncontrollable and vastly more intelligent than us. The intelligence analogy being that a mouse isn’t just less smart than a human, it literally doesn’t comprehend that its environment can be so manipulated by humans that entire species depend on the actions of humans (i.e. conservation, rules about overhunting) for their own survival.

Another objection is that if an AI is actually as intelligent as we fear it could be, it wouldn’t make “stupid” mistakes like destroying all of humanity or consuming the planet’s resources, because that wouldn’t count as “intelligent”. The counterpoint is the Orthogonality Thesis. This simply states that an AI can have any goal. Intelligence and goals are orthogonal and independent. Moreover, an AI’s goal does not have to explicitly target humans as bad (e.g. “kill all the humans”) to cause us harm. For example, a goal to calculate all the digits of pi or solve the Riemann Hypothesis might require as much computing power as possible. As part of achieving this goal, a superintelligence would determine that it must manufacture computing equipment and maximize energy to its computation equipment. Humans use energy and are made of matter, so as a way to achieve its goal, it would likely exterminate humanity, and convert all matter it could into computation equipment. Due to its superintelligence, it would accomplish this.

A final objection is that despite experts believing human level AI will happen in the next 100 years, if not sooner, there is nothing to be done about it today or that it is a waste of time to work on this problem now. This is also known as the “worrying about overpopulation on Mars” objection, comparing the worry about AI to something that is several scientific advancements away.  Scott Alexander has an entire blog post on this subject, which I recommend checking out. The basic summary is that AI advancement and AI alignment research are somewhat independent. And we really need to learn how to properly align AI values before we get human level AI.

We have a lot of theoretical philosophy that we need to figure out how to impart to a computer. Things like how humans actually make decisions, or how to value different moral tradeoffs. This could be extraordinarily complicated, as an extremely smart optimization algorithm could misinterpret almost everything we say if it did not already share our values for human life, health, and general brain state. Computer scientists set out to teach computers how to understand natural human language some 60 years ago, and we still haven’t quite nailed it. If imparting philosophical truths is similarly difficult, there is plenty of work to be done today.

Artificial intelligence could advance rapidly from human level to greater than human very quickly; the best human Go player lost to an AI (AlphaGo) in 2016, and a year later, AlphaGo lost to a new version, AlphaGo Zero, 100 games to none. It would thus not be surprising if a general intelligence achieved superhuman status a year after achieving human-comparable status, or sooner. There’s no fire alarm for artificial general intelligence. We need to be working on these problems as soon as possible.

I’d argue then, that of all scenarios listed here, a misaligned AI is the most likely to actually destroy all of humanity as a result of the Orthogonality Thesis. I also think that unlike many of the other scenarios listed here, human level AI will exist sometime soon, compared to the timescale of asteroids and vulcanism (see AI Timelines, estimates are highly variable, anywhere from 10 to 200 years). There is also a wealth of work to be done surrounding AI value alignment. Correctly aligning future AI with goals compatible with human values is thus one of the most important challenges facing our civilization within the next hundred years or so, and probably the most important existential threat we face.

The good news is that there are some places doing this work, notably the Machine Intelligence Research Institute, OpenAI, and the Future of Humanity Institute. The bad news is that despite the importance of this issue, there is very little in the way of conversations, money, or advocacy. AI Safety research is hard to calculate in total, as some research is likely done by private software companies, but is optimistically on the order of tens of millions of dollars a year. By comparison, the U.S. Transportation Security Administration, which failed to find 95% of test weapons in a recent audit, costs $7.5 billion a year.

Further Reading

I have focused this essay on trying to convey the mindset of thinking about existential risk generally and why AI is specifically worrying in this context. I’ve also tried to keep it short. The following are further resources on the specifics of why Artificial General Intelligence is worth worrying about in a broader context, arranged by length. If you felt my piece did not go in depth enough on whether AI itself is worth being concerned about, I would urge you to read one of the more in depth essays here which focus on that question directly.

 


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