In my 2013 article on strong AI forecasting, I made several suggestions for how to do better at forecasting strong AI, including this suggestion quoted from Phil Tetlock, arguably the leading forecasting researcher in the world:

Signposting the future: Thinking through specific scenarios can be useful if those scenarios “come with clear diagnostic signposts that policymakers can use to gauge whether they are moving toward or away from one scenario or another… Falsifiable hypotheses bring high-flying scenario abstractions back to Earth.”

Tetlock hadn’t mentioned strong AI at the time, but now it turns out he wants suggestions for strong AI signposts that could be forecast on GJOpen, the forecasting tournament platform.

@PTetlock Any thoughts on the AlphaGo victory?

— Brandon Wilson (@brandonwilson) March 20, 2016

important: it was one of our signpost indicators. It nudges probability we are on a strong AI scenario trajectory https://t.co/XuAcpjlWtm

— Philip E. Tetlock (@PTetlock) March 20, 2016

@PTetlock What other signpost indicators are you watching?

— Alexander Berger (@albrgr) March 21, 2016

evolving list will be on https://t.co/wsHsvAiV9h by July 1 (driverless ubers; robotics spending,…). Ideas welcome https://t.co/5jdSE8IzFO

— Philip E. Tetlock (@PTetlock) March 21, 2016

Specifying crisply formulated signpost questions is not easy. If you come up with some candidates, consider posting them in the comments below. After a while, I will collect them all together and send them to Tetlock. (I figure that’s probably better than a bunch of different people sending Tetlock individual emails with overlapping suggestions.)

Tetlock’s framework for thinking about such signposts, which he calls “Bayesian question clustering,” is described in Superforecasting:

In the spring of 2013 I met with Paul Saffo, a Silicon Valley futurist and scenario consultant. Another unnerving crisis was brewing on the Korean peninsula, so when I sketched the forecasting tournament for Saffo, I mentioned a question IARPA had asked: Will North Korea “attempt to launch a multistage rocket between 7 January 2013 and 1 September 2013?” Saffo thought it was trivial. A few colonels in the Pentagon might be interested, he said, but it’s not the question most people would ask. “The more fundamental question is ‘How does this all turn out?’ ” he said. “That’s a much more challenging question.”

So we confront a dilemma. What matters is the big question, but the big question can’t be scored. The little question doesn’t matter but it can be scored, so the IARPA tournament went with it. You could say we were so hell-bent on looking scientific that we counted what doesn’t count.

That is unfair. The questions in the tournament had been screened by experts to be both difficult and relevant to active problems on the desks of intelligence analysts. But it is fair to say these questions are more narrowly focused than the big questions we would all love to answer, like “How does this all turn out?” Do we really have to choose between posing big and important questions that can’t be scored or small and less important questions that can be? That’s unsatisfying. But there is a way out of the box.

Implicit within Paul Saffo’s “How does this all turn out?” question were the recent events that had worsened the conflict on the Korean peninsula. North Korea launched a rocket, in violation of a UN Security Council resolution. It conducted a new nuclear test. It renounced the 1953 armistice with South Korea. It launched a cyber attack on South Korea, severed the hotline between the two governments, and threatened a nuclear attack on the United States. Seen that way, it’s obvious that the big question is composed of many small questions. One is “Will North Korea test a rocket?” If it does, it will escalate the conflict a little. If it doesn’t, it could cool things down a little. That one tiny question doesn’t nail down the big question, but it does contribute a little insight. And if we ask many tiny-but-pertinent questions, we can close in on an answer for the big question. Will North Korea conduct another nuclear test? Will it rebuff diplomatic talks on its nuclear program? Will it fire artillery at South Korea? Will a North Korean ship fire on a South Korean ship? The answers are cumulative. The more yeses, the likelier the answer to the big question is “This is going to end badly.”

I call this Bayesian question clustering because of its family resemblance to the Bayesian updating discussed in chapter 7. Another way to think of it is to imagine a painter using the technique called pointillism. It consists of dabbing tiny dots on the canvas, nothing more. Each dot alone adds little. But as the dots collect, patterns emerge. With enough dots, an artist can produce anything from a vivid portrait to a sweeping landscape.

There were question clusters in the IARPA tournament, but they arose more as a consequence of events than a diagnostic strategy. In future research, I want to develop the concept and see how effectively we can answer unscorable “big questions” with clusters of little ones.

(Note that although I work as a GiveWell research analyst, my focus at GiveWell is not AI risks, and my views on this topic are not necessarily GiveWell’s views.)

How much time usually elapses between when a technical problem is posed and when it is solved? How much effort is usually required? Which variables most predict how much time and effort will be required to solve a technical problem?

The main paper I’ve seen on this is Hisano & Sornette (2013).¹ Their method was to start with Wikipedia’s List of conjectures and then track down the year each conjecture was first stated and the year it was solved (or, whether it remains unsolved). They were unable to determine exact-year values for 16 conjectures, leaving them with a dataset of 144 conjectures, of which 60 were solved as of January 2012, with 84 still unsolved. The time between first conjecture statement and first solution is called “time to proof.”

For the purposes of finding possible data-generating models that fit the data described above, they assume the average productivity per mathematician is constant throughout their career (they didn’t try to collect more specific data), and they assume the number of active mathematicians tracks with total human population — i.e., roughly exponential growth over the time period covered by these conjectures and proofs (because again, they didn’t try to collect more specific data).

I didn’t try to understand in detail how their model works or how reasonable it is, but as far as I understand it, here’s what they found:

• Since 1850, the number of new conjectures (that ended up being listed on Wikipedia) has tripled every 55 years. This is close to the average growth rate of total human population over the same time period.
• Given the incompleteness of the data and the (assumed) approximate exponential growth of the mathematician population, they can’t say anything confident about the data-generating model, and therefore basically fall back on Occam: “we could not reject the simplest model of an exponential rate of conjecture proof with a rate of 0.01/year for the dataset (translating into an average waiting time to proof of 100 years).”
• They expect the Wikipedia dataset severely undersamples “the many conjectures whose time-to-proof is in the range of years to a few decades.”
• They use their model to answer the question that prompted the paper, which was about the probability that “P vs. NP” will be solved by 2024. Their model says there’s a 41.3% chance of that, which intuitively seems high to me.
• They make some obvious caveats to all this: (1) the content of the conjecture matters for how many mathematician-hours are devoted to solving it, and how quickly they are devoted; (2) to at least a small degree, the notion of “proof” has shifted over time, e.g. the first proof of the four-color theorem still has not been checked from start to finish by humans, and is mostly just assumed to be correct; (3) some famous conjectures might be undecidable, leaving some probability mass for time-to-proof at infinity.

What can we conclude from this?

Not much. Sometimes crisply-posed technical problems are solved quickly, sometimes they take many years or decades to solve, sometimes they take more than a century to solve, and sometimes they are never solved, even with substantial effort being targeted at the problem.²

And unfortunately, it looks like we can’t say much more than that from this study alone. As they say, their observed distribution of time to proof must be considered with major caveats. Personally, I would emphasize the likely severe undersampling of conjectures with short times-to-proof, the fact that they didn’t try to weight data points by how important the conjectures were perceived to be or how many resources went into solving them (because doing so would be very hard!), and the fact that they didn’t have enough data points (especially given the non-stationary number of mathematicians) to confirm or reject ~any of the intuitively / a priori plausible data-generating models.

Are there other good articles³ on “time to proof” or “time to solution” for relatively well-specified research problems, in mathematics or other fields? If you know of any, please let me know!

1. Slightly different arxiv version here. [↩]
2. This “substantial effort” claim isn’t in the paper, but I’m pretty sure it’s true for many of the conjectures, including many of those with time to proof of >10 years). [↩]
3. Besides the few that Hisano & Sornette cite, which I think are basically superceded by Hisano & Sornette. [↩]

On Facebook, AI scientist Yann LeCun recently posted the following:

<not_being_really_serious>
I have said publicly on several occasions that the purported AI Apocalypse that some people seem to be worried about is extremely unlikely to happen, and if there were any risk of it happening, it wouldn’t be for another few decades in the future. Making robots that “take over the world”, Terminator style, even if we had the technology. would require a conjunction of many stupid engineering mistakes and ridiculously bad design, combined with zero regards for safety. Sort of like building a car, not just without safety belts, but also a 1000 HP engine that you can’t turn off and no brakes.

But since some people seem to be worried about it, here is an idea to reassure them: We are, even today, pretty good at building machines that have super-human intelligence for very narrow domains. You can buy a $30 toy that will beat you at chess. We have systems that can recognize obscure species of plants or breeds of dogs, systems that can answer Joepardy questions and play Go better than most humans, we can build systems that can recognize a face among millions, and your car will soon drive itself better than you can drive it. What we don’t know how to build is an artificial general intelligence (AGI). To take over the world, you would need an AGI that was specifically designed to be malevolent and unstoppable. In the unlikely event that someone builds such a malevolent AGI, what we merely need to do is build a “Narrow” AI (a specialized AI) whose only expertise and purpose is to destroy the nasty AGI. It will be much better at this than the AGI will be at defending itself against it, assuming they both have access to the same computational resources. The narrow AI will devote all its power to this one goal, while the evil AGI will have to spend some of its resources on taking over the world, or whatever it is that evil AGIs are supposed to do. Checkmate.
</not_being_really_serious>

Since LeCun has stated his skepticism about potential risks from advanced artificial intelligence in the past, I assume his “not being really serious” is meant to refer to his proposed narrow AI vs. AGI “solution,” not to his comments about risks from AGI. So, I’ll reply to his comments on risks from AGI and ignore his “not being really serious” comments about narrow AI vs. AGI.

First, LeCun says:

if there were any risk of [an “AI apocalypse”], it wouldn’t be for another few decades in the future

Yes, that’s probably right, and that’s what people like myself (former Executive Director of MIRI) and Nick Bostrom (author of Superintelligence, director of FHI) have been saying all along, as I explained here. But LeCun phrases this as though he’s disagreeing with someone.

Second, LeCun writes as though the thing people are concerned about is a malevolent AGI, even though I don’t know anyone is concerned about malevolent AI. The concern expressed in Superintelligence and elsewhere isn’t about AI malevolence, it’s about convergent instrumental goals that are incidentally harmful to human society. Or as AI scientist Stuart Russell put it:

A system that is optimizing a function of n variables, where the objective depends on a subset of size k<n, will often set the remaining unconstrained variables to extreme values; if one of those unconstrained variables is actually something we care about, the solution found may be highly undesirable.  This is essentially the old story of the genie in the lamp, or the sorcerer’s apprentice, or King Midas: you get exactly what you ask for, not what you want. A highly capable decision maker – especially one connected through the Internet to all the world’s information and billions of screens and most of our infrastructure – can have an irreversible impact on humanity.

(Note that although I work as a GiveWell research analyst, my focus at GiveWell is not AI risks, and my views on this topic are not necessarily GiveWell’s views.)

I listen to music >10 hrs per day, and I love the convenience of wireless earbuds. They are tiny and portable, and I can do all kinds of stuff — work on something with my hands, take on/off my jacket or my messenger bag, etc. — without getting tangled up in a cord.

So which wireless earbuds are the best? For this kind of thing I always turn first to The Wirecutter, which publishes detailed investigations of consumer products, like Consumer Reports but free and often more up-to-date.

I bought their recommended wireless earbuds a while back, when their recommendation was the Jaybird Bluebuds X. After several months I lost that pair and bought the new Wirecutter recommendation, the JLab Epic Bluetooth. Those were terrible so I returned them and bought the now-available Jaybird X2, which has been awesome so far.

So long as a pair of wireless earbuds have decent sound quality and >6 hrs battery life, the most important thing to me is low frequency of audio cutting.

See, Bluetooth is a very weak kind of signal. It can’t really pass through your body, for example. That’s why it uses so little battery power, which is important for tiny things like wireless earbuds. As a result, I got fairly frequent audio cutting when trying to play music from my phone in my pants pocket to my Jaybird Bluebuds X. After some experimentation, I learned that audio cutting was less frequent if my phone was in my rear pocket, on the same side of my body as the earbuds’ Bluetooth receiver. But it still cut out maybe an average of 200 times an hour (mostly concentrated in particularly frustrating 10-minute periods with lots of cutting).

When I lost that pair and got the JLab Epic Bluetooth, I hoped that with the newer pair they’d have figured out some extra tricks to reduce audio cutting. Instead, the audio cutting was terrible. Even with my phone in the optimal pants pocket, there was usually near-constant audio cutting, maybe about 2000 times an hour on average. Moreover, when I used them while reclining in bed, I would get lots of audio cutting whenever my neck was pressed up against my pillow! So, pretty useless. I returned them to Amazon for a refund.

I replaced this pair with The Wirecutter’s 2nd choice, the Jaybird X2. So far these have been fantastic. In my first ~15 hours of using them I’ve gotten exactly two split-second audio cuts.

So if you want to make the leap to wireless earbuds, I recommend the Jaybird X2. Though if you don’t mind waiting, the Jaybird X3 and Jaybird Freedom are both coming out this spring, and they might be even better.

One final note: I got my last two pairs of wireless earbuds in white so that others can see I’m wearing them. With my original black Bluebuds X, people would sometimes talk at me for >30 seconds without realizing I couldn’t hear them because I had music in my ears.

As far as I can tell, MarginNote is the only iPhone app that lets you annotate & highlight both PDFs and epub files, and sync those annotations to your computer. And by “PDFs and epub files” I basically mean “all text files,” since Calibre and other apps can convert any text file into an epub, except for PDFs with tables and images. (The Kindle iPhone app can annotate text files, but can’t sync those annotations anywhere unless you bought the text directly from Amazon.)

This is important for people who like to read nonfiction “on the go,” like me — and plausibly some of my readers, so I figured I’d share my discovery.

The best plays and films have had great writing for a long time. The best TV shows have had great writing for about a decade now. But the writing in the best videogames is still cringe-inducingly awful. This is despite the fact that videogame blockbusters regularly have production budgets of $50M or more. When will videogames hit their “golden age” (at least, for writing)?

I think I’ve finally realized that I have a favorite kind of music, though unfortunately it doesn’t have a genre name, and it cuts across many major musical traditions — Western classical, jazz, rock, electronica, and possibly others.¹

I tend to love music that:²

1. Is primarily tonal but uses dissonance for effective contrast. (The Beatles are too tonal; Arnold Schoenberg and Cecil Taylor are too atonal; Igor Stravinsky and Charles Mingus are just right.)
2. Obsessively composed, though potentially with substantial improvisation within the obsessively composed structure. (Coleman’s Free Jazz is too free. Amogh Symphony’s Vectorscan is innovative and complex but doesn’t sound like they tried very hard to get the compositional details right. The Rite of Spring and Chiastic Slide and even Karma are great.)
3. Tries to be as emotionally affecting as possible, though this may include passages of contrastingly less-emotional music. (Anthony Braxton and Brian Ferneyhough are too cold and anti-emotional. Rich Woodson shifts around too quickly to ever build up much emotional “momentum.” Master of Puppets and Escalator Over the Hill and Tabula Rasa are great.)
4. Is boredom-resistant by being fairly complex or by being long and subtly-evolving enough that I don’t get bored of it quickly. (The Beatles are too short and simple — yes, including their later work. The Soft Machine is satisfyingly complex and varied. The minimalists and Godspeed! You Black Emperor are often simple and repetitive, but their pieces are long enough and subtly-evolving enough that I don’t get bored of them.)

Property #2, I should mention, is pretty similar to Holden Karnofsky’s notion of “awe-inspiring” music. Via email, he explained:

One of the emotions I would like to experience is awe … A piece of music might be great because the artists got lucky and captured a moment, or because it’s just so insane that I can’t find anything else like it, or because I have an understanding that it was the first thing ever to do X, or because it just has that one weird sound that is so cool, but none of those make me go “Wow, this artist is awesome. I am in awe of them. I feel like the best parts of this are things they did on purpose, by thinking of them, by a combination of intelligence and sweat that makes me want to give them a high five. I really respect them for their achievement. I feel like if I had done this I would feel true pride that I had used the full extent of my abilities to do something that really required them.”

It’s no accident that most of the things that do this for me are “epic” in some way and usually took at least a solid year of someone’s life, if not 20 years, to create.

To illustrate further what I mean by each property, here’s how I would rate several musical works on each property:

	Tonal w/ dissonance?	Obsessively composed?	Highly emotional?	Boredom-resistant?
Mingus, The Black Saint and the Sinner Lady	Yes	Yes	Yes	Yes, complex
Stravinsky, The Rite of Spring	Yes	Yes	Yes	Yes, complex
The Soft Machine, Third	Yes	Yes	Yes	Yes, complex
Schulze, Irrlicht	Yes	I think so?	Yes	Yes, slowly-evolving
Adams, Harmonielehre	Yes	Yes	Yes	Yes, complex
The Beatles, Sgt. Pepper	Not enough dissonance	Yes	Yes	No
Coleman, Free Jazz	Yes	Not really	Sometimes	Yes, complex
Amogh Symphony, Vectorscan	Yes	Not really	Yes	Yes, complex
Stockhausen, Licht cycle	Too dissonant	Yes	Not often	Yes, complex
Autechre, Chiastic Slide	Yes	Yes	Yes	Yes, complex
Anthony Braxton, For Four Orchestras	Too dissonant	Yes	No	Yes, complex

 

1. I haven’t listened to enough non-Western classical or folk musics to know whether this theory of my favorite kind of music holds up across those styles. [↩]
2. Note that I like and sometimes love lots of music that doesn’t fit one or more of these criteria (including e.g. Sgt. Pepper), but I think my absolute favorite pieces of music tend to have all these properties. [↩]

I realized recently that when I want to learn about a subject, I mentally group the available books into three categories.

I’ll call the first category “convincing.” This is the most useful kind of book for me to read on a topic, but for most topics, no such book exists. Many basic textbooks on the “hard” sciences (e.g. “settled” physics and chemistry) and the “formal” sciences (e.g. “settled” math, statistics, and computer science) count. In the softer sciences (including e.g. history), I know of very few books with the intellectual honesty and epistemic rigor to be convincing (to me) on their own. David Roodman’s book on microfinance, Due Diligence, is the only example that comes to mind as I write this.

Don’t get me wrong: I think we can learn a lot from studying softer sciences, but rarely is a single book on the softer sciences written in such a way as to be convincing to me, unless I know the topic well already.¹

I think of my 2nd category as “raw data.” These books make a good case that the data they present were collected and presented in a fairly reasonable way, and I find it useful to know what the raw data are, but if and when the book attempts to persuade me of non-obvious causal hypotheses, I find the book illuminating but unconvincing (on its own). Some examples:

• The CIA World Factbook 2016
• The Better Angels of Our Nature (excluding the parts on hunter-gatherers)
• The Measure of Civilization
• Human Accomplishment

Finally, my 3rd category for nonfiction is “food for thought.” Besides being unconvincing about non-obvious causal inferences, these books also fail to make a good case that the data supporting their arguments were collected and presented in a reasonable way. So what I get from them is just some basic terminology, and some hypotheses and arguments and stories I didn’t know about before. This category includes the vast majority of all non-fiction, e.g.:

• Psychology Applied to Modern Life
• How the World Works
• Bowling Alone
• Understanding Power
• Guns, Germs, and Steel
• The Shadow World

My guess is that I’m more skeptical than most heavy readers of non-fiction, including most scientists. I’m sure I’ll blog more in the future about why.

1. This didn’t use to be the case. I’ve become a lot more skeptical of scientific literature over time. E.g. nearly all of my Less Wrong posts were written with a less skeptical mindset. [↩]

There are about a dozen melodies that I find myself humming and whistling without realizing it. One is “Yellow Submarine.” Another begins at about 2:20 in Adams’ “On the Dominant Divide.”

Another is one that I’ve been humming for years but couldn’t remember where I had heard it.

Well, today, I finally stumbled into that melody once again! It turns out it’s the melody that begins at about 1:09 into the 3rd movement of Sibelius’ 5th symphony.

Ahhhhhhhh. So good.

Recently, the study of potential risks from advanced artificial intelligence has attracted substantial new funding, prompting new job openings at e.g. Oxford University and (in the near future) at Cambridge University, Imperial College London, and UC Berkeley.

This is the dawn of a new field. It’s important to fill these roles with strong candidates. The trouble is, it’s hard to find strong candidates at the dawn of a new field, because universities haven’t yet begun to train a steady flow of new experts on the topic. There is no “long-term AI safety” program for graduate students anywhere in the world.

Right now the field is pretty small, and the people I’ve spoken to (including e.g. at Oxford) seem to agree that it will be a challenge to fill these roles with candidates they already know about. Oxford has already re-posted one position, because no suitable candidates were found via the original posting.

So if you’ve developed some informal expertise on the topic — e.g. by reading books, papers, and online discussions — but you are not already known to the folks at Oxford, Cambridge, FLI, or MIRI, now would be an especially good time to de-lurk and say “I don’t know whether I’m qualified to help, and I’m not sure there’s a package of salary, benefits, and reasons that would tempt me away from what I’m doing now, but I want to at least let you know that I exist, I care about this issue, and I have at least some relevant skills and knowledge.”

Maybe you’ll turn out not to be a good candidate for any of these roles. Maybe you’ll learn the details and decide you’re not interested. But if you don’t let us know you exist, neither of those things can even begin to happen, and these important roles at the dawn of a new field will be less likely to be filled with strong candidates.

I’m especially passionate about de-lurking of this sort because when I first learned about MIRI, I just assumed I wasn’t qualified to help out, and wouldn’t want to, anyway. But after speaking to some folks at MIRI, it turned out I really could help out, and I’m glad I did. (I was MIRI’s Executive Director for ~3.5 years.)

So if you’ve been reading and thinking about long-term AI safety issues for a while now, and you have some expertise in computer science, AI, analytic/formal philosophy, mathematics, statistics, policy, risk analysis, forecasting, or economics, and you’re not already in contact with the people at the organizations I named above, please step forward and tell us you exist.

UPDATE Jan. 2, 2016: At this point in the original post, I recommended that people de-lurk by emailing me or by commenting below. However, I was contacted by far more people than I expected (100+), so I had to reply to everyone (on Dec. 19th) with a form email instead. In that email I thanked everyone for contacting me as I had requested, apologized for not being able to respond individually, and made the following request:

If you think you might be interested in a job related to long-term AI safety either now or in the next couple years, please fill out this 3-question Google form, which is a lot easier than filling out any of the posted job applications. This will make it much easier for the groups that are hiring to skim through your information and decide which people they want to contact and learn more about.

Everyone who contacted/contacts me after Dec. 19th will instead receive a link to this section of this blog post. If I’ve linked you here, please consider filling out the 3-question Google form above.

(Note that although I work as a GiveWell research analyst, my focus at GiveWell is not AI risks, and my views on this topic are not necessarily GiveWell’s views.)

I use last.fm to track what music I listen to. Unfortunately, it’s not very accurate.

The first problem is that it doesn’t track music listened to on most online services (e.g. Youtube, Bandcamp). But I can’t really complain about that, since I just discovered there’s an app for that. Though, its Youtube support is shaky, I assume because it’s hard to tell what’s a non-musical video and what is a music track.

A bigger problem for me is that last.fm counts up what I listen to by counting tracks played rather than by counting time played. So if I listen to a punk band for one hour, and then I listen to Miles Davis for one hour, last.fm will make it look as though I like the punk band 10x more than I like Miles Davis, because the punk band writes 3 minute tracks and Miles Davis records 30 minute tracks.

I like reading things via the Kindle app on my phone, because then I can read from anywhere. Unfortunately, most of what I want to read is in PDF format, so the text can’t “reflow” on my phone’s small screen like a normal ebook does. PDF text extraction programs aim to solve this problem by extracting the text (and in some cases, other elements) from a PDF and exporting it to a format that allows text reflow, for example .docx or .epub.

Which PDF text extraction program is best? I couldn’t find any credible comparisons, so I decided to do my own.

My criteria for were:

1. The program must run on Mac OS X or run in the cloud.
2. It must be free or have a free trial available, so I can run this test without spending hundreds of dollars.
3. It must be easy to use. If I have to install special packages or tweak environment variables to run the program, it doesn’t qualify.
4. It must preserve images, tables, and equations amidst the text, since the documents I want to read often include important charts, tables, and equations. (It’s fine if equations and tables are simply handled as images.)
5. It must be able to handle multi-column pages.
6. It must work with English, but I don’t care about other languages because I can’t read them anyway.
7. I don’t care that much about final file size or how long the conversion takes, so long as the program doesn’t crash on 1 out of every 10 attempts and doesn’t create crazy 200mb files or something like that.

To run my test, I assembled a gauntlet of 16 PDFs of the sort I often read, including several PDFs from journal websites, a paper from arXiv, and multiple scanned-and-OCRed academic book chapters.

A quick search turned up way too many Mac or cloud-based programs to test, so I decided to focus on a few that were from major companies or were particularly easy to use.¹

[Read more…]

1. Microsoft Word 2016 for Mac doesn’t allow PDF to Word conversion like its Windows counterpart does; that’s why I tried Word Online instead. Nuance‘s PDF programs seem to be either not available for Mac, or not have a free trial. Nitro PDF to Word Converter is only for Windows, and the online version doesn’t support files larger than 5MB, so I didn’t include it. Lighten PDF to Word Converter has a free trial, but it only converts 3 pages per PDF, so I excluded it. Same problem for FreePDFconvert.com and iSkySoft PDF Converter, which have 2-page and 5-page limits for their trial versions, respectively. As far as I can tell, Qoppa PDF Studio Pro can extract plain text but not rich text from PDFs. I used Adobe Acrobat XI (instead of a newer version) because I already owned it. [↩]

I think there is a ton of modern classical music (MCM) that listeners would enjoy if they had a way to more cheaply discover it.

Why can’t people cheaply discover MCM they like, and what can be done about it? Below are some guesses.

 

Obstacle 1: There are almost no full-time critics of classical/MCM music.

According to this post, there may be about a dozen full-time critics of classical/MCM music in the USA.¹ This is probably more a symptom of the difficulty of exploring MCM than a cause, but it helps perpetuate the problem. The best solution to this is probably to increase demand for MCM critics by fixing the problems below (and perhaps others I’ve missed).

Obstacle 2: MCM critics do not rate works/albums or give them genre labels.

This one really bugs me. Honestly, how is someone with limited time supposed to navigate the world of MCM if nobody will tell them which works and albums are the best ones, and roughly what they sound like? Sure, this information is sometimes (but not always!) buried somewhere in reviews of new works or albums, but it needs to be at the top of the review, right under the artist and work/album title. Yeah, yeah, musical quality can’t be reduced to a single number and a list of genre tags, blah blah blah GET OVER IT and BE MORE HELPFUL.

Obstacle 3: Because of obstacle #2, there’s no way to aggregate expert opinion on MCM works.

Rock/pop fans have Metacritic, AOTY, etc. This is impossible for MCM because MCM critics do not rate albums.

Obstacle 4: MCM critics don’t even make lists.

Even if critics didn’t rate every album they reviewed, they could at least make year-end “best of” lists and genre-specific “best of” lists. But MCM critics almost never do this. Seriously, how is anyone with limited time supposed to navigate MCM without listicles?

Obstacle 5: Many MCM works aren’t recorded for years after their debut.

Suppose you read a review of a new rock/pop album, and you want to hear it. What do you do? You stream it on Spotify or buy it on iTunes.

But suppose you read a review of a new MCM work, and you want to hear it. What do you do? The answer is probably “buy a plane ticket to another city on a specific date and pay $80 to hear it once in a concert hall, otherwise wait 1-15 years for the work to be recorded and released and hope you remember to listen to it then.” To someone used to the rock/pop world, this is utter madness.

To become more consumable by a mass-ish market, MCM works need to be recorded and released first, and performed for the public later, after people have had a chance to stream/buy it and decide whether they want to endure the cost and inconvenience of seeing it live.

Unfortunately, I don’t know enough about the MCM business to know how to fix this.

 

Not on this list

Conspicuously missing from my list of obstacles is “most MCM composers write unlistenable random noise.” They do, of course, but I don’t see that as a problem.

“Unlistenable random noise” is hyperbole, I know, except for some pieces like John Cage’s HPSCHD. What I mean is that most MCM composers tend to write music that sounds to most people like “unlistenable random noise.” As Joe Queenan put it,

During a radio interview between acts at the Metropolitan Opera in New York, a famous singer recently said she could not understand why audiences were so reluctant to listen to new music, given that they were more than ready to attend sporting events whose outcome was uncertain. It was a daft analogy… when Spain plays Germany, everyone knows that the game will be played with one ball, not eight; and that the final score will be 1-0 or 3-2 or even 8-1 – but definitely not 1,600,758 to Arf-Arf the Chalet Ate My Banana. The public may not know in advance what the score will be, but it at least understands the rules of the game.

… It is not the composers’ fault that they wrote uncompromising music… but it is not my fault that I would rather listen to Bach.

If the obstacles listed above were fixed (plus some I probably missed), then MCM would be in the same place rock/pop music is: composers can write whatever they want, including unlistenable random noise, and some of it will find a big audience, most of it won’t, and that’s fine.²

1. If you click the red markers on the map, you’ll see that many of them point to people who are not remotely full-time critics of classical/MCM. Probably there are more full-time critics in Europe, but still not that many. [↩]
2. Though I’m not sure governments should be subsidizing the work, any more than they should be subsidizing avant-garde death metal or free jazz, some of which is also really great and also “unlistenable random noise.” [↩]

If you’re exploring musical styles, e.g. via my guides to contemporary classical and modern art jazz, remember to get some good headphones. This is obvious in retrospect but often neglected. When I switched from cheap to good headphones years ago, I realized there were entire instruments in my favorite songs that I hadn’t been hearing. When my boss Holden finally got good headphones, he started to like minimalism much more than he had previously. Seriously, get some decent headphones.

Which headphones, exactly? Probably just get whatever The Wirecutter recommends for your style: on-ear vs. over-ear vs. in-ear, wireless vs. wired, exercise vs. normal, etc.

You’ll hear a big difference between e.g. the default iPhone headphones and something that costs $70, and you’ll probably hear another difference between a $70 set and a $300 set, but I’m skeptical that most people can hear a difference beyond $300 (for headphones).

If you’re very cost conscious, go with the currently-$23 in-ear headphones here or the currently-$85 over-ear headphones here.

If you’re less cost conscious, go with either the wired or Bluetooth noise-canceling over-ear options here. (I suspect everyone can tell the difference between active noise-canceling and no noise-canceling, but few people can tell the difference between the very good sound of the best noise-canceling headphones and the absolute best sound quality available from non-noise-canceling headphones.)

Tom Dietterich and Eric Horvitz have a new opinion piece in Communications of the ACM: Rise of Concerns about AI. Below, I comment on a few passages from the article.

 

Several of these speculations envision an “intelligence chain reaction,” in which an AI system is charged with the task of recursively designing progressively more intelligent versions of itself and this produces an “intelligence explosion.”

I suppose you could “charge” an advanced AI with the task of undergoing an intelligence explosion, but that seems like an incredibly reckless thing for someone to do. More often, the concern is about intelligence explosion as a logical byproduct of the convergent instrumental goal for self-improvement. Nearly all possible goals are more likely to be achieved if the AI can first improve its capabilities, whether the goal is calculating digits of Pi or optimizing a manufacturing process. This is the argument given in the book Dietterich and Horvitz cite for these concerns: Nick Bostrom’s Superintelligence.

 

[Intelligence explosion] runs counter to our current understandings of the limitations that computational complexity places on algorithms for learning and reasoning…

I follow this literature pretty closely, and I haven’t heard of this result. No citation is provided, so I don’t know what they’re talking about. I doubt this is the kind of thing you can show using computational complexity theory, given how under-specified the concept of intelligence explosion is.

Fortunately, Dietterich and Horvitz do advocate several lines of research to make AI systems more safe and secure, and they also say:

we believe scholarly work is needed on the longer-term concerns about AI. Working with colleagues in economics, political science, and other disciplines, we must address the potential of automation to disrupt the economic sphere. Deeper study is also needed to understand the potential of superintelligence or other pathways to result in even temporary losses of control of AI systems. If we find there is significant risk, then we must work to develop and adopt safety practices that neutralize or minimize that risk.

(Note that although I work as a GiveWell research analyst, my focus at GiveWell is not AI risks, and my views on this topic are not necessarily GiveWell’s views.)

Pedro Domingos, an AI researcher at the University of Washington and the author of The Master Algorithm, on the podcast Talking Machines:

There are these fears that computers are going to get very smart and then suddenly they’ll become conscious and they’ll take over the world and enslave us or destroy us like the Terminator. This is completely absurd. But even though it’s completely absurd, you see a lot of it in the media these days…

Domingos doesn’t identify which articles he’s talking about, but nearly all the articles like this that I’ve seen lately are inspired by comments on AI risk from Stephen Hawking, Elon Musk, and Bill Gates, which in turn are (as far as I know) informed by Nick Bostrom’s Superintelligence.

None of these people, as far as I know, have expressed a concern that machines will suddenly become conscious and then take over the world. Rather, these people are concerned with the risks posed by extreme AI competence, as AI scientist Stuart Russell explains.

I don’t know what source Domingos read that talked about machines suddenly becoming conscious and taking over the world. I don’t think I’ve seen such scenarios described outside of fiction.

Anyway, in his book, Domingos does seem to be familiar with the competence concern:

The point where we could turn off all our computers without causing the collapse of modern civilization has long passed. Machine learning is the last straw: if computers can start programming themselves, all hope of controlling them is surely lost. Distinguished scientists like Stephen Hawking have called for urgent research on this issue before it’s too late.

Relax. The chances that an AI equipped with the [ultimate machine learning algorithm] will take over the world are zero. The reason is simple: unlike humans, computers don’t have a will of their own. They’re products of engineering, not evolution. Even an infinitely powerful computer would still be only an extension of our will and nothing to fear…

[AI systems] can vary what they do, even come up with surprising plans, but only in service of the goals we set them. A robot whose programmed goal is “make a good dinner” may decide to cook a steak, a bouillabaisse, or even a delicious new dish of its own creation, but it can’t decide to murder its owner any more than a car can decide to fly away.

…[The] biggest worry is that, like the proverbial genie, the machines will give us what we ask for instead of what we want. This is not a hypothetical scenario; learning algorithms do it all the time. We train a neural network to recognize horses, but it learns instead to recognize brown patches, because all the horses in its training set happened to be brown.

I was curious to see what his rebuttal to the competence concern (“machines will give us what we ask for instead of what we want”) was, but this section just ends with:

People worry that computers will get too smart and take over the world, but the real problem is that they’re too stupid and they’ve already taken over the world.

Which isn’t very clarifying, especially since elsewhere he writes that “any sufficiently advanced AI is indistinguishable from God.”

The next section is about Kurzweil’s law of accelerating returns, and doesn’t seem to address the competence concern.

So… I guess I can’t tell why Domingos thinks the chances of a global AI catastrophe are “zero,” and I can’t tell what he thinks of the basic competence concern expressed by Hawking, Musk, Gates, Russell, Bostrom, etc.

Update 05-09-2016: For additional Domingos comments on risks from advanced AI, see this episode of EconTalk, starting around minute 50.

(Note that although I work as a GiveWell research analyst, my focus at GiveWell is not AI risks, and my views on this topic are not necessarily GiveWell’s views.)

Why do some pieces of music, or film, or visual art strike me as “great works of art,” independent of how much I enjoy them? When I introspect on this, and when I test my brain’s mostly subconsious “artistic greatness function” against a variety of test cases, it seems to me that the following features play a major role in my artistic greatness judgments:

1. Innovation
2. Cohesion
3. Execution
4. Emotionality

Below is a brief sketch of what I mean by each of these. In the future, I’ll elaborate on various points, and run additional thought experiments as I try to work toward reflective equilibrium for my aesthetic judgments.

[Read more…]

I recently listened to 70 albums by Anthony Braxton, several of them very long, between 2 and 12 CDs. I thought a few of his albums were pretty great as works of art, but I enjoyed exactly one of them (Eugene), and I thought zero of them were accessible enough to include in my guide.

I should also mention that I basically only listened to the albums with original compositions on them. And if you know Braxton, you know these compositions are complicated — e.g. Composition 82, scored for simultaneous performance by four different orchestras. The dude is prolific.

To see which Braxton albums I listened to, Ctrl+F on my (in-progress) jazz guide for “Anthony Braxton’s” (it’s in a footnote).

 

On The Late Show, Stephen Colbert asked Elon Musk if he was trying to save the world. The obvious, transparent answer is “yes.” But Elon’s reply was “Well, I’m trying to do useful things.” Perhaps Elon’s PR person told him that trying to save the world comes off as arrogant, even if you’re a billionaire.