"Pride is not the opposite of shame, but its source. True humility is the only antidote to shame."

Uncle Iroh, "Avatar: The Last Airbender"

Shame is one of the trickiest emotions to deal with. It is difficult to think about, not to mention discuss with others, and gives rise to insidious ugh fields and negative spirals. Shame often underlies other negative emotions without making itself apparent - anxiety or anger at yourself can be caused by unacknowledged shame about the possibility of failure. It can stack on top of other emotions - e.g. you start out feeling upset with someone, and end up being ashamed of yourself for feeling upset, and maybe even ashamed of feeling ashamed if meta-shame is your cup of tea. The most useful approach I have found against shame is invoking humility.

What is humility, anyway? It is often defined as a low view of your own importance, and tends to be conflated with modesty. Another common definition that I find more useful is acceptance of your own flaws and shortcomings. This is more compatible with confidence, and helpful irrespective of your level of importance or comparison to other people. What humility feels like to me on a system 1 level is a sense of compassion and warmth towards yourself while fully aware of your imperfections (while focusing on imperfections without compassion can lead to beating yourself up). According to LessWrong, "to be humble is to take specific actions in anticipation of your own errors", which seems more like a possible consequence of being humble than a definition.

Humility is a powerful tool for psychological well-being and instrumental rationality that is more broadly applicable than just the ability to anticipate errors by seeing your limitations more clearly. I can summon humility when I feel anxious about too many upcoming deadlines, or angry at myself for being stuck on a rock climbing route, or embarrassed about forgetting some basic fact in my field that I am surely expected to know by the 5th year of grad school. While humility comes naturally to some people, others might find it useful to explicitly build an identity as a humble person. How can you invoke this mindset?

One way is through negative visualization or pre-hindsight, considering how your plans could fail, which can be time-consuming and usually requires system 2. A faster and less effortful way is to is to imagine a person, real or fictional, who you consider to be humble. I often bring to mind my grandfather, or Uncle Iroh from the Avatar series, sometimes literally repeating the above quote in my head, sort of like an affirmation. I don't actually agree that humility is the only antidote to shame, but it does seem to be one of the most effective.

(Cross-posted from my blog. Thanks to Janos Kramar for his feedback on this post.)

Among those concerned about risks from advanced AI, I've encountered people who would be interested in a career in AI research, but are worried that doing so would speed up AI capability relative to safety. I think it is a mistake for AI safety proponents to avoid going into the field for this reason (better reasons include being well-positioned to do AI safety work, e.g. at MIRI or FHI). This mistake contributed to me choosing statistics rather than computer science for my PhD, which I have some regrets about, though luckily there is enough overlap between the two fields that I can work on machine learning anyway. I think the value of having more AI experts who are worried about AI safety is far higher than the downside of adding a few drops to the ocean of people trying to advance AI. Here are several reasons for this:

1. Concerned researchers can inform and influence their colleagues, especially if they are outspoken about their views.
2. Studying and working on AI brings understanding of the current challenges and breakthroughs in the field, which can usefully inform AI safety work (e.g. wireheading in reinforcement learning agents).
3. Opportunities to work on AI safety are beginning to spring up within academia and industry, e.g. through FLI grants. In the next few years, it will be possible to do an AI-safety-focused PhD or postdoc in computer science, which would hit two birds with one stone.

To elaborate on #1, one of the prevailing arguments against taking long-term AI safety seriously is that not enough experts in the AI field are worried. Several prominent researchers have commented on the potential risks (Stuart Russell, Bart Selman, Murray Shanahan, Shane Legg, and others), and more are concerned but keep quiet for reputational reasons. An accomplished, strategically outspoken and/or well-connected expert can make a big difference in the attitude distribution in the AI field and the level of familiarity with the actual concerns (which are not about malevolence, sentience, or marching robot armies). Having more informed skeptics who have maybe even read Superintelligence, and fewer uninformed skeptics who think AI safety proponents are afraid of Terminators, would produce much needed direct and productive discussion on these issues. As the proportion of informed and concerned researchers in the field approaches critical mass, the reputational consequences for speaking up will decrease.

A year after FLI's Puerto Rico conference, the subject of long-term AI safety is no longer taboo among AI researchers, but remains rather controversial. Addressing AI risk on the long term will require safety work to be a significant part of the field, and close collaboration between those working on safety and capability of advanced AI. Stuart Russell makes the apt analogy that "just as nuclear fusion researchers consider the problem of containment of fusion reactions as one of the primary problems of their field, issues of control and safety will become central to AI as the field matures". If more people who are already concerned about AI safety join the field, we can make this happen faster, and help wisdom win the race with capability.

(Cross-posted from my blog. Thanks to Janos Kramar for his help with editing this post.)

The OpenAI research team is doing a Reddit AMA today! A good opportunity to ask them questions about AI safety and machine learning. 

A great overview article on AI breakthroughs by Richard Mallah from FLI, linking to many excellent recent papers worth reading. 

Progress in artificial intelligence and machine learning has been impressive this year. Those in the field acknowledge progress is accelerating year by year, though it is still a manageable pace for us. The vast majority of work in the field these days actually builds on previous work done by other teams earlier the same year, in contrast to most other fields where references span decades.

Creating a summary of a wide range of developments in this field will almost invariably lead to descriptions that sound heavily anthropomorphic, and this summary does indeed. Such metaphors, however, are only convenient shorthands for talking about these functionalities. It's important to remember that even though many of these capabilities sound very thought-like, they're usually not very similar to how human cognition works. The systems are all of course functional and mechanistic, and, though increasingly less so, each are still quite narrow in what they do. Be warned though: in reading this article, these functionalities may seem to go from fanciful to prosaic.

The biggest developments of 2015 fall into five categories of intelligence: abstracting across environments, intuitive concept understanding, creative abstract thought, dreaming up visions, and dexterous fine motor skills. I'll highlight a small number of important threads within each that have brought the field forward this year.