Highlights and impressions from NIPS conference on machine learning

This year's NIPS was an epicenter of the current enthusiasm about AI and deep learning – there was a visceral sense of how quickly the field of machine learning is progressing, and two new AI startups were announced. Attendance has almost doubled compared to the 2014 conference (I hope they make it multi-track next year), and several popular workshops were standing room only. Given that there were only 400 accepted papers and almost 4000 people attending, most people were there to learn and socialize. The conference was a socially intense experience that reminded me a bit of Burning Man – the overall sense of excitement, the high density of spontaneous interesting conversations, the number of parallel events at any given time, and of course the accumulating exhaustion.
Some interesting talks and posters
Sergey Levine's robotics demo at the crowded Deep Reinforcement Learning workshop (we showed up half an hour early to claim spots on the floor). This was one of the talks that gave me a sense of fast progress in the field. The presentation started with videos from this summer's DARPA robotics challenge, where the robots kept falling down while trying to walk or open a door. Levine proceeded to outline his recent work on guided policy search, alternating between trajectory optimization and supervised training of the neural network, and granularizing complex tasks. He showed demos of robots successfully performing various high-dexterity tasks, like opening a door, screwing on a bottle cap, or putting a coat hanger on a rack. Impressive!

Generative image models using a pyramid of adversarial networks by Denton & Chintala. Generating realistic-looking images using one neural net as a generator and another as an evaluator – the generator tries to fool the evaluator by making the image indistinguishable from a real one, while the evaluator tries to tell real and generated images apart. Starting from a coarse image, successively finer images are generated using the adversarial networks from the coarser images at the previous level of the pyramid. The resulting images were mistaken for real images 40% of the time in the experiment, and around 80% of them looked realistic to me when staring at the poster.
Path-SGD by Salakhutdinov et al, a scale-invariant version of the stochastic gradient descent algorithm. Standard SGD uses the L2 norm in as the measure of distance in the parameter space, and rescaling the weights can have large effects on optimization speed. Path-SGD instead regularizes the maximum norm of incoming weights into any unit, minimizing the max-norm over all rescalings of the weights. The resulting norm (called a "path regularizer") is shown to be invariant to weight rescaling. Overall a principled approach with good empirical results.
End-to-end memory networks by Sukhbaatar et al (video), an extension of memory networks – neural networks that learn to read and write to a memory component. Unlike traditional memory networks, the end-to-end version eliminates the need for supervision at each layer. This makes the method applicable to a wider variety of domains – it is competitive both with memory networks for question answering and with LSTMs for language modeling. It was fun to see the model perform basic inductive reasoning about locations, colors and sizes of objects.
Neural GPUs (video), Deep visual analogy-making (video), On-the-job learning, and many others.
Algorithms Among Us symposium (videos)
A highlight of the conference was the Algorithms Among Us symposium on the societal impacts of machine learning, which I helped organize along with others from FLI. The symposium consisted of 3 panels and accompanying talks – on near-term AI impacts, timelines to general AI, and research priorities for beneficial AI. The symposium organizers (Adrian Weller, Michael Osborne and Murray Shanahan) gathered an impressive array of AI luminaries with a variety of views on the subject, including Cynthia Dwork from Microsoft, Yann LeCun from Facebook, Andrew Ng from Baidu, and Shane Legg from DeepMind. All three panel topics generated lively debate among the participants.

3 panels at #AlgorithmsAmongUs symposium #NIPS2015 pic.twitter.com/X1KNAwwkWW
— Victoria Krakovna (@vkrakovna) December 15, 2015

Andrew Ng took his famous statement that "worrying about general AI is like worrying about overpopulation on Mars" to the next level, namely "overpopulation on Alpha Centauri" (is Mars too realistic these days?). His main argument was that even superforecasters can't predict anything 5 years into the future, so any predictions on longer time horizons are useless. This seemed like an instance of the all-too-common belief that "we don't know, therefore we are safe". As Murray pointed out, having complete uncertainty past a 5-year horizon means that you can't rule out reaching general AI in 20 years either. Encouragingly, Ng endorsed long-term AI safety research, saying that it's not his cup of tea but someone should be working on it.
With regards to roadmapping the remaining milestones to general AI, Yann LeCun gave an apt analogy of traveling through mountains in the fog – there are some you can see, and an unknown number hiding in the fog. He also argued that advanced AI is unlikely to be human-like, and cautioned against anthropomorphizing it.
In the research priorities panel, Shane Legg gave some specific recommendations – goal system stability, interruptibility, sandboxing / containment, and formalization of various thought experiments (e.g. in Superintelligence). He pointed out that AI safety is both overblown and underemphasized – while the risks from advanced AI are not imminent the way they are usually portrayed in the media, more thought and resources need to be devoted to the challenging research problems involved.
One question that came up during the symposium is the importance of interpretability for AI systems, which is actually the topic of my current research project. There was some disagreement about the tradeoff between effectiveness and interpretability. LeCun thought that the main advantage of interpretability is increased robustness, and improvements to transfer learning should produce that anyway, without decreases in effectiveness. Percy Liang argued that transparency is needed to explain to the rest of the world what machine learning systems are doing, which is increasingly important in many applications. LeCun also pointed out that machine learning systems that are usually considered transparent, such as decision trees, aren't necessarily so. There was also disagreement about what interpretability means in the first place – as Cynthia Dwork said, we need a clearer definition before making any conclusions. It seems that more work is needed both on defining interpretability and on figuring out how to achieve it without sacrificing effectiveness.
Overall, the symposium was super interesting and gave a lot of food for thought (here's a more detailed summary by Ariel from FLI). Thanks to Adrian, Michael and Murray for their hard work in putting it together.
AI startups
It was exciting to see two new AI startups announced at NIPS – OpenAI, led by Ilya Sutskever and backed by Musk, Altman and others, and Geometric Intelligence, led by Zoubin Ghahramani and Gary Marcus.
OpenAI is a non-profit with a mission to democratize AI research and keep it beneficial for humanity, and a whopping $1Bn in funding pledged. They believe that it's safer to have AI breakthroughs happening in a non-profit, unaffected by financial interests, rather than monopolized by for-profit corporations. The intent to open-source the research seems clearly good in the short and medium term, but raises some concerns in the long run when getting closer to general AI. As an OpenAI researcher emphasized in an interview, "we are not obligated to share everything – in that sense the name of the company is a misnomer", and decisions to open-source the research would in fact be made on a case-by-case basis.
While OpenAI plans to focus on deep learning in their first few years, Geometric Intelligence is developing an alternative approach to deep learning that can learn more effectively from less data. Gary Marcus argues that we need to learn more from how human minds acquire knowledge in order to build advanced AI (an inspiration for the venture was observing his toddler learn about the world). I'm looking forward to what comes out of the variety of approaches taken by these new companies and other research teams.
(Cross-posted on the FLI blog. Thanks to Janos Kramar for his help with editing this post.)


Risks from general artificial intelligence without an intelligence explosion

"An ultraintelligent machine could design even better machines; there would then unquestionably be an 'intelligence explosion,' and the intelligence of man would be left far behind."
– Computer scientist I. J. Good, 1965
Artificial intelligence systems we have today can be referred to as narrow AI – they perform well at specific tasks, like playing chess or Jeopardy, and some classes of problems like Atari games. Many experts predict that general AI, which would be able to perform most tasks humans can, will be developed later this century, with median estimates around 2050. When people talk about long term existential risk from the development of general AI, they commonly refer to the intelligence explosion (IE) scenario. AI risk skeptics often argue against AI safety concerns along the lines of "Intelligence explosion sounds like science-fiction and seems really unlikely, therefore there's not much to worry about". It's unfortunate when AI safety concerns are rounded down to worries about IE. Unlike I. J. Good, I do not consider this scenario inevitable (though relatively likely), and I would expect general AI to present an existential risk even if I knew for sure that intelligence explosion were impossible.

Here are some dangerous aspects of developing general AI, besides the IE scenario:

Human incentives. Researchers, companies and governments have professional and economic incentives to build AI that is as powerful as possible, as quickly as possible. There is no particular reason to think that humans are the pinnacle of intelligence – if we create a system without our biological constraints, with more computing power, memory, and speed, it could become more intelligent than us in important ways. The incentives are to continue improving AI systems until they hit physical limits on intelligence, and those limitations (if they exist at all) are likely to be above human intelligence in many respects.
Convergent instrumental goals. Sufficiently advanced AI systems would by default develop drives like self-preservation, resource acquisition, and preservation of their objective functions, independent of their objective function or design. This was outlined in Omohundro's paper and more concretely formalized in a recent MIRI paper. Humans routinely destroy animal habitats to acquire natural resources, and an AI system with any goal could always use more data centers or computing clusters.
Unintended consequences. As in the stories of Sorcerer's Apprentice and King Midas, you get what you asked for, but not what you wanted. This already happens with narrow AI, like in the frequently cited example from the Bird & Layzell paper: a genetic algorithm was supposed to design an oscillator using a configurable circuit board, and instead designed a makeshift radio that used signal from neighboring computers to produce the requisite oscillating pattern. Unintended consequences produced by a general AI, more opaque and more powerful than a narrow AI, would likely be far worse.
Value learning is hard. Specifying common sense and ethics in computer code is no easy feat. As argued by Stuart Russell, given a misspecified value function that omits variables that turn out to be important to humans, an optimization process is likely to set these unconstrained variables to extreme values. Think of what would happen if you asked a self-driving car to get you to the airport as fast as possible, without assigning value to obeying speed limits or avoiding pedestrians. While researchers would have incentives to build in some level of common sense and understanding of human concepts that is needed for commercial applications like household robots, that might not be enough for general AI.
Value learning is insufficient. Even an AI system with perfect understanding of human values and goals would not necessarily adopt them. Humans understand the "goals" of the evolutionary process that generated us, but don't internalize them – in fact, we often "wirehead" our evolutionary reward signals, e.g. by eating sugar.
Containment is hard. A general AI system with access to the internet would be able to hack thousands of computers and copy itself onto them, thus becoming difficult or impossible to shut down – this is a serious problem even with present-day computer viruses. When developing an AI system in the vicinity of general intelligence, it would be important to keep it cut off from the internet. Large scale AI systems are likely to be run on a computing cluster or on the cloud, rather than on a single machine, which makes isolation from the internet more difficult. Containment measures would likely pose sufficient inconvenience that many researchers would be tempted to skip them.

Some believe that if intelligence explosion does not occur, AI progress will occur slowly enough that humans can stay in control. Given that human institutions like academia or governments are fairly slow to respond to change, they may not be able to keep up with an AI that attains human-level or superhuman intelligence over months or even years. Humans are not famous for their ability to solve coordination problems. Even if we retain control over AI's rate of improvement, it would be easy for bad actors or zealous researchers to let it go too far – as Geoff Hinton recently put it, "the prospect of discovery is too sweet".
As a machine learning researcher, I care about whether my field will have a positive impact on humanity in the long term. The challenges of AI safety are numerous and complex (for a more technical and thorough exposition, see Jacob Steinhardt's essay), and cannot be rounded off to a single scenario. I look forward to a time when disagreements about AI safety no longer derail into debates about IE, and instead focus on other relevant issues we need to figure out.
(Cross-posted on the FLI blog. Thanks to Janos Kramar for his help with editing this post.)


Nomadism and Burning Man

The past few months had been quite nomadic even by my standards. Visiting the MIRI fellows program, EA Global, Alaska camping (in 4 different parks), CFAR alumni reunion, and finally Burning Man. It was an exciting social time – I had many great conversations and coordinated with various people. It was also tiring to keep up with all the schedule changes, packing and unpacking, and my habits fell through the cracks (meditation, exercise and the like). It is a relief to be back at Citadel with all my stuff in one place, in a stable work and social environment. It almost feels like I'd never left.
It seemed appropriate to conclude the wanderings with Burning Man, which was the most like an actual vacation for me. This year was a good combination of spontaneity and scheduling, adventures and conversations.

Janos and I practiced the beginner mindset at various circus arts. In the long wait on the road into BRC, it turned out that the car in front of us had some hula hoops, buugengs and fans, and people who knew how to use them and were willing to teach. A buugeng is an S-shaped wooden object that can be twisted and thrown around – learning to throw it over my shoulder and catch it involved a lot of running around and hitting my fingers, but I got it eventually. We discovered that our camp was right next to the Hellfire Society, which ran fire spinning classes. We were late for the actual instruction, but got to do a 30-second demo with a real fire staff (turns out, even the most basic clockwise spin looks cool with real fire), and watch Michael Vassar spin fire poi like a pro. We also went to an aerial silks workshop, which inspired at least one friend to continue learning this in the default world.
We got to fight in the Thunderdome! This consists of whacking each other with foam swords while hanging from the ceiling in harnesses. The fighter who is judged to be the most aggressive wins, though they make a bigger deal out of the show than the outcome. Part of the challenge is retaining contact with the other person (or their sword) while whacking them with your sword as much as possible. We stood in line for an hour watching other people fight, despite our attempts to bribe the leather-clad staff with a bottle of alcohol to skip the line (they refused to accept anything other than their customary bribe of good whiskey). The deafening metal music and the shouts of the numerous spectators built up a lot of adrenaline. I promptly forgot all the advice the staff gave me about positions and strategies, and just had fun getting my ass kicked by Sam. Janos fought a guy who was a head taller than him and skilled in martial arts, who he managed to disarm twice. We left the place excitedly tired, and spent the rest of the evening in a relaxing cuddle puddle.
Our playa adventures were interspersed with various conversations and reflections:

A (surprisingly analytical) talk about shame at Mystic camp. The main insight was that a lot of what sustains addictions is the shame around them, and the shame itself is addictive. One prediction made by this theory is that signaling endorsement for addictive activities (e.g. eating junk food on a nice plate at a candlelit table) reduces their addictiveness a lot. It might be useful to install a trigger-action plan like "notice I am ashamed of something I do regularly -> try self-signaling endorsement for the activity".
A discussion about common sources of motivation for doing things. I'm often motivated by importance or indignation about the status quo, while puzzle-solving motivation is something I used to engage more in my math contest days, and could use more of now. There is also a kind of motivation to do procedural things with my hands, like helping to build camp, which could be called tactile motivation, though that doesn't sound quite right.
Learning about an underappreciated physics phenomenon called counterflow heat exchange, which allows two containers of fluid to go almost all the way towards swapping temperatures. (h/t Danielle Fong)

Burning Man is a good place to practice spontaneity, going along with whatever comes up. This year I tried to retain this frame of mind even while going to scheduled events. The go-to phrase that I kept repeating in my head was "roll with it". This was actually quite effective at eliminating fear of missing out, which is usually pervasive in a place where a hundred events are going on at any given time.
This is also the only time when I actually manage to "unplug" for a whole week. It doesn't seem to work during other vacations – for example, in Alaska I kept looking for wifi during rest stops in towns. Burning Man, on the other hand, was both sufficiently stimulating and sufficiently remote that I experienced no desire to check email or other internet things, even though there is actually a wifi camp on the playa. Occasionally reminding my system 1 that the sky does not fall if I ignore my email for a week is pretty valuable. I am now trying to sustain the spontaneity and email-ignoring mindsets in the default world to some extent.

Nomadism and Burning Man


Systems I have tried: an overview

I have used various organization and productivity systems in the past few years – this is an overview of what worked and what didn't.
Main systems I currently use:

Follow Up Then: Sends an email to a future self, with the date and time specified in the email address, e.g. <EMAIL>. I use it for delaying tasks, recurring reminders, and following up on email threads. This reduces clutter in my todo list, calendar and inbox, and frees my working memory. Lately, I noticed myself remembering a thing shortly before receiving a follow up about it – probably due to the same mechanism that sometimes wakes me up a few minutes before the morning alarm.
Complice: Daily to-do list organized according to goals, with archives and regular reviews. Helpful for specifying the next action to take at a given time, and for tracking progress on individual goals. Downside: I sometimes hesitate to enter tasks into the list, because entered tasks cannot be erased, and leaving a task unfinished is aversive, so often end up entering tasks after they are done instead.
Workflowy: Nested list structure – searchable, with collapsible and sharable sublists. I keep my ongoing todo list (in GTD form) and most of my notes here. Downside: doesn't work for goal factoring, since it only supports tree structures.
Google Calendar: Self-explanatory. I have recently started adding tentative meeting slots, indicated by a question mark, e.g. "dinner with Janos?". This has been helpful for keeping track of which time slots I've offered to someone. I also added a calendar that shows Facebook events that I've been invited to, which is handy.
42 Goals: Goal tracking with summary graphs and cute symbols. I use this for tracking habits (like exercise and meditation) and other random things (like insomnia occurrences). The graphs are useful – this is how I know that I have the most insomnia on Mondays! Downsides: doesn't allow non-binary categories, and the phone app is so unreliable that I never use it – if you know good alternative tracking systems, let me know!

Systems I no longer use:

Beeminder: Goal tracking with nice graphs, and goal setting with reminders and financial penalties in case of failure. I liked the graphs and reminders, but the penalties made me feel even more overwhelmed than usual, and sometimes induced suboptimal short-term priorities. I decided to obtain the different benefits separately, setting recurring reminders for habits on Follow Up Then, and using 42 Goals for tracking.
Toggl: Time tracking for activities and tasks, organized by project or goal, with an option for retroactive time entries. I started out using it to track all my time, and though I stopped after about a month due to the excessive overhead of tracking and categorizing short activities, I learned a lot about where my time was going. I used it for about a year after that to track work hours, and eventually stopped because of overhead and redundancy with Complice.
Paper checklist: Checklist for daily habits. Worked well in terms of catching my eye in the morning, but was often forgotten when traveling. It was redundant with 42 Goals, and required double data entry, so I eventually gave up on the paper version.
Coach.me: Habit tracking with reminders, with a pretty good phone app. I found it particularly useful for several-times-a-week habits. It also has built-in habit programs like building up to a certain number of chinups. I mostly stopped using it because I had too many other systems that were redundant with it.
Pomodoros: Setting a timer to focus on a specific task for 25-40 minutes, followed by a break of 5 minutes. I found it unpleasant to be forced to take breaks, developed a habit of ignoring the break signal, and gave up on using pomodoros altogether.

Over the past couple of years, I have become less willing to force myself to do things or overwhelm myself with instructions or data entry overhead, which has led me to reduce the number of systems I use, and to prefer gently guiding systems to strict ones.

Systems I have tried: an overview


Hamming questions and bottlenecks

The CFAR alumni workshop on the first weekend of May was focused on the Hamming question. Mathematician Richard Hamming was known to approach experts from other fields and ask "what are the important problems in your field, and why aren't you working on them?". The same question can be applied to personal life: "what are the important problems in your life, and what is stopping you from working on them?".
Over the course of the weekend, the twelve of us asked this question of ourselves and each other, in many forms and guises: "if Vika isn't making a major impact on the world in 5 years, what would have stopped her?", "what are your greatest bottlenecks?", "how can we actually try?", etc. The intense focus on mental pain points was interspersed with naps and silly games to let off steam. On the last day, we did a group brainstorm, where everyone who wanted to receive feedback took a turn in the center of the circle, and everyone else speculated on what they thought were the biggest bottlenecks of the person in the center. By this time, we had mostly gotten to know each other, and even the impressions from those who knew me less well were surprisingly accurate. I am very grateful to everyone at the workshop for being so insightful and supportive of each other (and actually caring).

Most of the issues that came up were things I was aware of on some level, but over the course of the workshop it became particularly salient to me how interconnected my problems are and how the gears in the system affect each other. Working memory overload leads to confusion, which reduces confidence. Sleep deprivation reduces working memory and increases anxiety. Anxiety reduces the affordance for exploration and creativity, and increases the frequency of insomnia. Ignoring or neglecting signals from system 1 takes up working memory slots with looping messages from system 1, and increases anxiety. After a few of these circular explanations, I gave up on writing them all down, and made a diagram instead.

A few things jump out at me about this diagram. The highest degree nodes are anxiety and working memory, both of which are difficult to affect directly. The two nodes I have the most influence over are the amount of sleep I get and the degree to which I listen to system 1 signals. I have started experimenting with sleep interventions that I haven't yet tried, like taking melatonin 4 hours before bedtime, using a weighted blanket, etc. Attunement to system 1 can be improved through meditation, Focusing, belief reporting and such. While I have sporadically meditated for years, I could use more practice at the other techniques, which involve more explicit internal querying than meditation.
Curiously, the graph also appears to have a source and a sink. The source node is my overdeveloped sense of duty and a tendency to assume I should do things or be able to do things causes a lot of downstream issues. It would be impactful to directly hack this and become more selfish, but it appears to be a bit trickier than doing a find-and-replace on my source code, replacing "I have to do X" with "my goals require X". The sink node has to do with my capacity to allow myself time and mental space for exploration and creativity, which would among other things enable me to do my high-level goals better (e.g. research and organization strategy).
A week after the workshop, I moved to California for my summer internship at Google. The context shift and my new location a few blocks down from the CFAR office will allow me to work on my bottlenecks more systematically. I have wrestled with these for a long time, but now I feel that I have better tools and resources than ever before.

Hamming questions and bottlenecks


Negative visualization, radical acceptance and stoicism

In anxious, frustrating or aversive situations, I find it helpful to visualize the worst case that I fear might happen, and try to accept it. I call this "radical acceptance", since the imagined worst case is usually an unrealistic scenario that would be extremely unlikely to happen, e.g. "suppose I get absolutely nothing done in the next month". This is essentially the negative visualization component of stoicism.
There are many benefits to visualizing the worst case:

Feeling better about the present situation by contrast.
Turning attention to the good things that would still be in my life even if everything went wrong in one particular domain.
Weakening anxiety using humor (by imagining an exaggerated "doomsday" scenario).
Being more prepared for failure, and making contingency plans (pre-hindsight).
Helping make more accurate predictions about the future by reducing the "X isn't allowed to happen" effect (or, as Anna Salamon once put it, "putting X into the realm of the thinkable").
Reducing the effect of ugh fields / aversions, which thrive on the "X isn't allowed to happen" flinch.
Weakening unhelpful identities like "person who is always productive" or "person who doesn't make stupid mistakes".

Let's say I have an aversion around meetings with my advisor, because I expect him to be disappointed with my research progress. When I notice myself worrying about the next meeting or finding excuses to postpone it so that I have more time to make progress, I can imagine the worst imaginable outcome a meeting with my advisor could have – perhaps he might yell at me or even decide to expel me from grad school (neither of these have actually happened so far). If the scenario is starting to sound silly, that's a good sign. I can then imagine how this plays out in great detail, from the disappointed faces and words of the rest of the department to the official letter of dismissal in my hands, and consider what I might do in that case, like applying for industry jobs. While building up these layers of detail in my mind, I breathe deeply, which I associate with meditative acceptance of reality. (I use the word "acceptance" to mean "acknowledgement" rather than "resignation".)
I am trying to use this technique more often, both in the regular and situational sense. A good default time is my daily meditation practice. I might also set up a trigger-action habit of the form "if I notice myself repeatedly worrying about something, visualize that thing (or an exaggerated version of it) happening, and try to accept it". Some issues have more natural triggers than others – while worrying tends to call attention to itself, aversions often manifest as a quick flinch away from a thought, so it's better to find a trigger among the actions that are often caused by an aversion, e.g. procrastination. A trigger for a potentially unhelpful identity could be a thought like "I'm not good at X, but I should be". A particular issue can simultaneously have associated worries (e.g. "will I be productive enough?"), aversions (e.g. towards working on the project) and identities ("productive person"), so there is likely to be something there that makes a good trigger. Visualizing myself getting nothing done for a month can help with all of these to some degree.
System 1 is good at imagining scary things – why not use this as a tool?
Cross-posted


Future of Life Institute's recent milestones in AI safety

In January, many months of effort by FLI's founders and volunteers finally came to fruition – the Puerto Rico conference, open letter and grants program announcement took place in rapid succession. The conference was a resounding success according to many of the people there, who were impressed with the quality of the ideas presented and the way it was organized. There were opportunities for the attendees to engage with each other at different levels of structure, from talks to panels to beach breakout groups. The relaxed Caribbean atmosphere seemed to put everyone at ease, and many candid and cooperative conversations happened between attendees with rather different backgrounds and views.
It was fascinating to observe many of the AI researchers get exposed to various AI safety ideas for the first time. Stuart Russell's argument that the variables that are not accounted for by the objective function tend to be pushed to extreme values, Nick Bostrom's presentation on takeoff speeds and singleton/multipolar scenarios, and other key ideas were received quite well. One attending researcher summed it up along these lines: "It is so easy to obsess about the next building block towards general AI, that we often forget to ask ourselves a key question – what happens when we succeed?".

A week after the conference, the open letter outlining the research priorities went public. The letter and research document were the product of many months of hard work and careful thought by Daniel Dewey, Max Tegmark, Stuart Russell, and others. It was worded in optimistic and positive terms – the most negative word in the whole thing was "pitfalls". Nevertheless, the media's sensationalist lens twisted the message into things like "experts pledge to rein in AI research" to "warn of a robot uprising" and "protect mankind from machines", invariably accompanied by a Terminator image or a Skynet reference. When the grants program was announced soon afterwards, the headlines became "Elon Musk donates…" to "keep killer robots at bay", "keep AI from turning evil", you name it. Those media portrayals shared a key misconception of the underlying concerns, that AI has to be "malevolent" to be dangerous, while the most likely problematic scenario in our minds is a misspecified general AI system with beneficial or neutral objectives. While a few reasonable journalists actually bothered to get in touch with FLI and discuss the ideas behind our efforts, most of the media herd stampeded ahead under the alarmist Terminator banner.
The open letter expresses a joint effort by the AI research community to step up to the challenge of advancing AI safety as responsible scientists. My main worry about this publicity angle is that this might be the first major exposure to AI safety concerns for many people, including AI researchers who would understandably feel attacked and misunderstood by the media's framing of their work. It is really unfortunate to have some researchers turned away from the cause of keeping AI beneficial and safe without even engaging with the actual concerns and arguments.
I am sometimes asked by reporters whether there has been too much emphasis on the superintelligence concerns that is "distracting" from the more immediate AI impacts like the automation of jobs and autonomous weapons. While the media hype is certainly not helpful towards making progress on either the near-term or long-term concerns, there is a pervasive false dichotomy here, as both of these domains are in dire need of more extensive research. The near-term economic and legal issues are already on the horizon, while the design and forecasting of general AI is a complex interdisciplinary research challenge that will likely take decades, so it is of utmost importance to begin the work as soon as possible.
The grants program on AI safety, fueled by Elon Musk's generous donation, is now well under way, with the initial proposals due March 1. The authors of the best initial proposals will be invited to submit a more detailed full proposal by May 17. I hope that our program will help kickstart the emerging subfield of AI safety, stimulate open discussion of the ideas among the AI experts, and broaden the community of researchers working on these important and difficult questions. Stuart Russell put it well in his talk at the Puerto Rico conference: "Solving this problem should be an intrinsic part of the field, just as containment is a part of fusion research. It isn't 'Ethics of AI', it's common sense!".

Future of Life Institute’s recent milestones in AI safety


2014-15 New Year review

2014 progress
If someone told me at the beginning of 2014 that I would co-found an organization to mitigate technological risks to humanity, I might not have believed them. Thanks Max, Meia, Anthony and Jaan for the great initiative!
I am almost done with my first research project on variable selection and classification using a Bayesian forest model – I simplified the variable partition in the model, came up with better tree updates, added a hyperprior, sped up the algorithm by an order of magnitude, and started testing on real data. Among the other ambitious projects of the past year are two MIRIx workshops plus writing up the results, and starting this blog.
Improvements in personal effectiveness:

started using a daily checklist of morning habits
started taking melatonin every night
started tagging new thoughts
started using FollowUpThen to schedule future tasks without overloading my todo list
started using Toggl to track work hours only
stopped using Beeminder (too stressful), and replaced it with a combination of 42goals and FollowUpThen (works well)
quit as President of the Toastmasters club
made a volunteer application form for FLI, so that instead of being inundated with 7 freeform emails per month from interested folks, I get the relevant information in an organized spreadsheet and I'm not required to respond

Random cool things I did:

climbing a 5.11a
climbing outdoors
indoor surfing
polar bear swim

2014 resolutions
A year ago, I made a number of New Year resolutions (and assigned a probability of completion for each goal). Here is how they worked out:
Succeeded:

continue meditation practice (>10 minutes daily, > 120 times) (70%) – did ~200 times
start a new research project (70%) – started working with a new advisor, did some background reading, narrowed down a topic, wrote a grant proposal
do at least 5 pullups in a row (85%)
reading stats blogs
go to 3 conferences, including a MIRI workshop (80%) – went to NESS, JSM, NIPS, and two MIRIx workshops
give 5 speeches at Toastmasters (75%)
give at least 5 LessWrong meetup talks (70%)
run comfort zone expansion outings at least twice (80%)
start a group project at Citadel (40%) – FLI work groups and MIRIx workshops sort of count for this
introduce at least 3 friends to LW meetups (50%)
help people achieve their goals – helped run a weekly habit training session at Citadel

Essentially succeeded:

publish paper about current research project (90%) – almost done, hope to submit by March 1
write at least 5 LW posts (80%) – wrote 4 posts
more writing – reflections (did some), stories (sort of), poems (nope), also journals and blog posts

Failed and no longer endorsed:

continue to avoid Beeminder debt – didn't work, then stopped using Beeminder, now use 42goals for goal tracking and FollowUpThen for reminders
do consulting for Metamed (60%)
read 90% of the LW Sequences (70%) – made progress, but no longer want to read such a high fraction (waiting for the ebook to come out)
finish Pearl's Causality (50%) – read the LW review of the book instead
learn more economics and biology

Calibration:

low predictions, 40-60%: 2/4 = 50% (perfectly calibrated)
high predictions, 70-90%: 7/10 = 70% (overconfident)

Conclusions:

Reading goals mostly don't work for me – if I do set them, flexible goals of the form "read some of X" (like stats blogs) do better than more fixed and time-consuming goals like "read most of X" (like the Sequences)
The rate of goal disendorsement is 5/19 = 26%.
I don't tend to completely fail on goals I continue to endorse – yay!

2015 goals and habits
Broad categories of goals for the coming year:

Research:

wrap up and submit BFC project,
start and make a significant progress on 1-2 new projects

FLI:

help streamline operations and communication,
continue work on AI safety outreach to AI researchers,
encourage AI safety research,
start projects in risk areas other than AI safety

Self-improvement:

reduce weekend/free-time anxiety,
increase acceptance of suboptimal situations,
improve introspection ability and my model of myself,
improve retention of information (using Anki),
eliminate cluttery speech pattern

Habits to maintain:

daily meditation
daily pushups
daily melatonin
tagging and writing down new thoughts
tracking goals in 42goals
tracking work hours in Toggl
journaling 1-2 times / week

2015 predictions

I will submit the Bayesian Forest Classifier paper for publication (95%)
I will submit another paper besides BFC (40%)
I will present BFC results at a conference (JSM, ICML or NIPS) (40%)
I will get a new external fellowship to replace my expiring NSERC fellowship (50%)
I will skim at least 20 research papers in machine learning (70%)
I will write at least 12 blog posts (70%)
I will climb a 5.12 without rope cheating (50%)
I will lead climb a 5.11a (50%)
I will be able to do 10 pullups in a row (60%)
I will meditate at least 150 times (80%)
I will record at least 150 new thoughts (70%)
I will make at least 100 Anki cards by the end of the year (70%)
I will read at least 10 books (60%)
I will attend Burning Man (90%)
Boston will have a second rationalist house by the end of the year (30%)
FLI will hire a full-time project manager or administrator (80%)
FLI will start a project on biotech safety (70%)

I will update this post if other goals and predictions for the year come to mind before the end of January.

2014-15 New Year review


Open and closed mental states

I learned a game at Burning Man this year that was about connecting to people and reading their nonverbal signals, called the "open-closed" game (h/t Minda Myers). There are two people in the game, and one is trying to approach the other and place a hand on their shoulder. No words can be exchanged, except that person who is being approached can announce their emotional state as "open" or "closed". When they say "closed", the approacher may not get any closer until they say "open" again. The approachee monitors themselves for any internal discomfort associated with the other person, and says "closed" if that is the case. The approacher tries to keep the other person comfortable through their body language and eye contact, to get them to remain "open".
I have recently started playing this game with myself, with "open" representing openness to experience or being in the moment, and "closed" representing tunnel vision or discomfort with the way things are going. In a way, I imagine being "approached" by whatever situation I'm in, or whatever sequence of experiences is happening, instead of a person. I ask myself whether I am in the open or closed state, and try to shift to the open state whenever I notice being in the closed state.

There are a couple of reasons to try to do this. In the open state, I tend to be happier, more curious and observant and have more new thoughts. From a week of tracking my mental states and thought status using TagTime, I can make a preliminary conclusion that while old thoughts do occur in the open state, new thoughts never occur in the closed state. While the closed state makes me more efficient at doing straightforward tasks (e.g. by making me less distractable), it makes me less efficient at doing less straightforward tasks (e.g. by increasing my tendency to optimize locally rather than globally).
This is related to the concept of "againstness" taught by Valentine Smith at the Center for Applied Rationality, which is a sense of resisting something about the situation at hand. Learning to notice this sense more quickly is a valuable thing I learned at CFAR and through my meditation practice. Redirecting attention to body sensations is supposed to be helpful for dissipating againstness, but I have found it difficult to get myself to do this in the moment, and not particularly reliable. Following the driving principle of "focusing on the road and not the curb", I find it easier to shift to a mental state with a simple salient label like "open" instead of a clunky label like "non-againsty". It also feels less judgmental to ask myself "what am I closed to right now, what experience am I not letting in?" than "what am I against right now?".
The againstness approach seems to be about relaxing the mind by relaxing the body first, while for some people relaxing the mind first comes more naturally – I actually find myself automatically breathing deeper when shifting into the open state. For both approaches, the goal is the same – to let go of mental and physical tension before proceeding with what you are doing. The rule of thumb, like in the game, is to first get into the open state and then approach the situation at hand.
(Cross-posted to LessWrong.)

Open and closed mental states


Importance motivation: a double-edged sword

Working on something important is often stressful. A hypothesis came up in conversation (h/t Michael Vassar) that labeling your work as important actually decreases the quality of the output. I have seen this effect in action quite a bit, e.g. when someone can write an essay when commenting on a blogpost, but gets nailed by writer's block when writing an essay on purpose. Feeling responsible for something important can be an obstacle to productivity when the work requires creativity. How can we do important creative work without being held back by importance considerations?
This has come up in my thesis research project on variable selection. It's been "almost done" for a year now, and my advisor keeps reminding me that it's important to finish soon. This motivation results in making little tweaks to the algorithm at the expense of looking at the big picture. However, most of the improvements to the algorithm came about through exploring models that I thought were more interesting than the default one. When I came up with a more streamlined version of the model, for a while the algorithm was doing worse than the original one, and I started cursing myself for following my elegance heuristics instead of doing what needed to be done. Then I found a bug, and the new algorithm reached a similar performance level to the old one. (I do eventually want to stop playing with the model and actually publish the thing, though…)

I would like to distinguish between two types of importance: extrinsic and intrinsic. Extrinsic importance comes from external restrictions, like deadlines, funding availability, user demand or evaluation by high-status people. Intrinsic importance arises from the problem you're solving – in my research example, this includes criteria like model simplicity and algorithm correctness. This is somewhat of a continuum, since a particular criterion can combine intrinsic and extrinsic importance, for example if my advisor insists on the algorithm being correct. Intrinsic importance fuels interest and curiosity, while extrinsic importance can inhibit creativity.
There is a quote by Howard Thurman: "Don't ask yourself what the world needs; ask yourself what makes you come alive. And then go and do that. Because what the world needs is people who have come alive." This can be interpreted as a suggestion to focus on intrinsic rather than extrinsic importance of your work. However, when choosing projects, it's a good idea to be guided by extrinsic importance at least to some degree, lest you end up working on underwater basket weaving. Ideally, you'd want to find the intersection between the things that the world needs and the things that make you come alive. Throwing out the importance criterion once you start the project seems suboptimal, since it also applies to choosing subproblems within the project.
This can be helped by separating idea generation from idea evaluation, process focus from outcome focus. Write a sloppy draft of the essay, or imagine that you are writing to a close friend, and then step back and edit it based on the needs of your intended audience. Play with a new model for a while, occasionally asking yourself whether to prune this line of inquiry in favor of more promising ones. This process is analogous to a Kalman filter – an algorithm that alternates between an evolution step, where the next step is proposed, and an observation step, where that step is updated using data from the real world. In practice, much more time needs to be spent on developing the next step than on evaluating it, and more importantly, longer chunks of time. You want to correct your course once in a while, instead of stifling good ideas before fully developing them.
There is a saying that I find both annoying and insightful – "life is too important to be taken seriously". Like many sayings, it's annoyingly vague, but can be interpreted usefully. My interpretation of choice is that too much evaluation of what you're doing can make the outcome less good, which conflicts with the purpose of evaluating it in the first place. "Life is too important to be taken seriously too often", how about that?


Citadel house sessions – a year in review

Since we moved into Citadel House in Boston a year ago, we ran self-improvement and rationality sessions every week, for the housemates and some local LessWrongers. There were around 3 sessions running at a time, and some of them caught on much more than others. I will discuss the sessions in decreasing order of success.
Meta Mondays
Meta Mondays are self-improvement meetings with a theme – most meetings involved a particular activity announced (slightly) in advance, aimed at practicing a skill. Some example activities were:

Feedback-a-thon. A large group of us got on Admonymous, and sent each other feedback messages simultaneously. We had a list of feedback categories on the board (social skills, hygiene, speech patterns, etc), and some people requested feedback in specific categories. We were sufficiently persistent and prolific to overwhelm the website's email quota!
Table Topics Against Rationality. We combined the idea of Table Topics from Toastmasters (1-2 minute impromptu speeches) with Cards Against Rationality. Everyone took turns drawing two cards from the deck, and then giving a speech that connected the concepts on the two cards to each other. This devolved into silliness at the end, when we threw in the Cards Against Humanity deck.
WRAP decision framework. ("Widen your options, Reality test assumptions, Attain emotional distance, Prepare to be right/wrong.") I taught this decision procedure, and people played around with applying it, but most of the people present didn't have a major upcoming decision to practice on.

At times when we didn't have a theme, we would have a general conversation about what people were optimizing in their lives these days. The themed meetings were generally better attended and more focused, but also required more preparation. In future, we should invite guest hosts for Meta Mondays, instead of coming up with all the topics ourselves.

Order of the Sphex
This is a habit building session, named after the carpenter wasp of CFAR fame, known for stalwartly adhering to its routines. We would go around the circle proposing habits to try for a week, and then on the second round everyone would commit to 1-2 habits to work on. These could be the same habits as last week, sometimes with modifications, or new habits. At the end of the session, I sent everyone involved an email with their commitments.
Due to fluctuating attendance, in practice people followed up on their habits every few weeks instead of weekly. Some found it helpful, others said that talking about a difficult habit without immediately doing something about them made the action more aversive. Depending on why someone is not sticking to habits, this session can be useful if their main failure mode is forgetting or indecision, and counterproductive if the underlying problem is aversion.
Writing
Since many people procrastinate on writing things, we decided to get together and write every week, optionally sharing the writing with others. This session was successful for a while – people wrote blog posts, LessWrong articles, stories, journal entries, emails, etc. Gradually, some regular attendees dropped out, and we started skipping the session on most weeks. Some said that it was hard to write on demand or to finish a piece of writing in a two-hour time block. The usefulness of this session depended on the ability to pick up and continue a previously started piece of writing, which was challenging for many of us.
Goal Factoring
We used the goal factoring technique from CFAR to analyze the motivations behind our actions, and alternative ways to achieve the same goals more cheaply. We also did some aversion factoring – analysis of potentially useful but unpleasant actions. (Here, "action" has a broad meaning that includes things like "thinking about X" or "having emotion X".) We usually worked by ourselves for a while, and then (optionally) discussed our findings with each other.
One failure mode was people choosing more sharable actions to factor, which were usually less private or embarrassing, and thus likely less useful. We tried to remedy this by holding a "secret goal factoring" session, where people agreed not to share their results. In practice, most people  said they didn't factor anything particularly private or important in this session, so other barriers to analyzing important actions might be more significant than fear of sharing with a group.
After a few months, we ran out of low hanging fruit in terms of actions to factor, and this session was transformed into Meta Mondays.
Strategic review
For a month or so, I tried to run a weekly CFAR-style strategic review session. This didn't catch on at all – it was difficult to explain, and since the benefit of the technique depends on regularity, inconsistent attendance made it useless to everyone except me. These days, I do strategic reviews on my own once every few weeks.
All the sessions had the difficulty of staying focused on the activity without devolving into unstructured conversation. This could be helped by running at most two sessions at a time, since there would be more evenings free for random conversation. Another general takeaway is that sessions need a clear purpose that people buy in to.
I am running a survey to see which sessions people found useful last year and/or want to happen this coming year. Stay tuned!