trentmkelly/LessWrong-43k huggingface dataset:
Introduction to Existential Risks from Artificial Intelligence, for an EA audience
by JoshuaFox — 2020-06-02T08:30:51.940Z

Among the causes that Effective Altruists support, existential risk stands out as different from ordinary charitable concerns, and existential risk from Artificial Intelligence even more so.

In this talk, which assumes no prior knowledge in artificial intelligence, Vanessa Kosoy will explain the problem in question, and how researchers in the field are trying to solve it. Vanessa is a Research Associate with the Machine Intelligence Research Institute (MIRI) studying the mathematical formalization of general intelligence and value alignment.

The talk is brought to you by Effective Altruism Israel and LessWrong Israel.

We start on Tuesday June 23, at 19:00 Israel Daylight Time, 16:00 GMT.
See the time in your timezone here.

Please sign up here and we'll send you the URL for the Zoom meeting.

Keep up-to-date at the Facebook event.
Restricted Diet and Longevity, does eating pattern matter?
by jmh — 2020-06-01T21:28:19.010Z

From what I've been able to find there is fairly good evidence for reducing one's diet by about 25% from the recommended caloric intake.

What I cannot find (most of what I've tried to find is abstract only and the papers behind a paywall) is if the pattern of intake matters. In other words, does the approach required that one leave every meal a bit hungry?

I have no problems skipping meals, sometime two even, but find it more a challenge to put the fork down once I've decided it is time to eat.

So if I wanted to try following the approach clearly one way would be much easier for me to implement than the other.

Anyone having any insights or specific references that have looked at the claims from this particular perspective?

Answers that say it's not clearly the case disspite what people like Sinclair or the earlier experiments say also welcomed.
You See The Territory or Nothing at All
by calebo — 2020-06-01T15:24:18.725Z

Crossposted from here.

This post is about the philosophy of perception, what makes a philosophical debate substantive, and cognitive qualia.

Consider two views about the nature of perception and the world: on indirect realism we perceive a veil, we only indirectly get at the world through this veil. On direct realism, we perceive the world. The veil is lifted and we are in direct contact with the world.

On indirect realism what we perceive are mental objects like sense data. This is the veil that stands between us and the world. We must infer the existence of the world.

You can visualize the two views in the following ways:

indirect: perceiver => sense data => world
direct: perceiver => world
indirect: perceiver => map => territory
direct: perceiver => territory
indirect: perceiver => veil => world
direct: perceiver => world

Indirect realism gets its intuitive appeal from the idea of hallucination. If I have the experience of a red elephant in my room then I am perceiving my visual field, not a red elephant in the room. The object of my perception is the appearance of a red elephant, nothing more. We can visualize this as:

perceiver => experience of a red elephant =>

The last arrow points to nothing because there is no red elephant in the world. Here’s how the direct realist would visualize this case:

perceiver =>

The direct realist says that you don’t perceive anything when you hallucinate. You either see the territory or nothing at all.

Despite the intuitive appeal of indirect perception, the direct view is better. Using our visualization, you can get at this by stepping back and considering the question that a theory of perception should answer: what is the object of one’s perception? In the case where perception is accurate, the answer is the world. But for the indirect realist, the object of perception is a mental object, like sense data.

But how is the object of perception perceived? What is it perceived through?

On direct realism the object of perception is perceived through mental states, which we can call experience. Returning to the visualization, the `=>` is experience.

perceiver => world = perceiver experiences the world

The indirect realist cannot say this. This is because the object of perception is a mental object. Which is to say, it is experience. On this, picture one only experiences ...
Blog Post Day III
by Daniel Kokotajlo — 2020-06-01T13:56:10.037Z

You are invited to join us online on Saturday the 13th of June, to write a blog post, e.g. that one you've been thinking about writing but never got around to. This is the third event of this type; the first one went well, and so did the second.

We'll set an official goal of having the post put up by midnight. Also, we'll meet up in a special-purpose discord channel to chat, encourage each other, swap half-finished drafts, etc. If like me you are intending to write the thing one day eventually, well, here's a reason to make that day this day.
Sparsity and interpretability?
by Ada Böhm — 2020-06-01T13:25:46.557Z

A series of interactive visualisations of small-scale experiments to generate intuitions for the question: "Are sparse models more interpretable? If so, why?"

Assumptions and motivation

Understanding interpretability may help AI alignment directly (e.g. recursive approaches, short/mid-term value; see here) or indirectly by supporting other directions (mechanistic transparency, open-source game theory).

Recent deep learning sparsification techniques retain most of the network’s performance (even when dropping 99% of the weights).

At least small systems are easier to both analyze when sparse (e.g. information flow, case debugging, local behaviour interpretation) and explore & visualize.

Experiments with small networks can generate or challenge general intuitions (with some caveats) and inform further technical research.

In particular, we assume that moving towards early, simplified or naive definitions of interpretability concepts and other engagement are useful steps towards more general understanding (as one possible way).

We want more people to engage with these questions by playing with hands-on examples and challenging & debating concrete propositions. Reach out and comment!

Many of the visualisations here are interactive. Due to technical forum limitations, the interactive versions are on an external page here and are linked to from every visualisation snapshot in this post.

Introduction [Optional]

We want to understand under which circumstances (if any) a sparser model is more interpretable. A mathematical formulation of “transparent or interpretable” is currently not available: sparsity has been proposed as a possible candidate proxy for these properties, and so we want to investigate how well the links between sparsity and interpretability holds, and what kinds of sparsity aid in what kinds of interpretability.

(A note on the field of Interpretability: we’ve talked a bit previously about what interpretability is, as the topic is broad and the term is very underdefined. It’s important to note, when discussing whether a particular property (like sparsity) makes a model more or less interpretable, we need to specify what kind of interpretability method we’re using, as different methods will benefit from different properties of the model.)

We have several intuitions that sparsity in neural networks might imply greater interpretability, and some wh

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Feedback is central to agency
by Alex Flint — 2020-06-01T12:56:51.587Z

An agent seems to be a particular kind of device that creates a feedback loop between the world and itself. It seems to have a lot to do with devices whose behavior depends on the consequences of their own behavior: that is, devices that behave in ways that cause the overall system in which they are embedded to evolve towards a target configuration, for a wide variety of possible environments.

If we only need the device to work in a single embedding system, then we can use simple feed-forward control. For example, to build an autonomous car that navigates from one precisely known location to another, via a precisely known trajectory, we can hardcode all the necessary turns and just let the car go. We might call this a non-adaptive system.

But if we want the autonomous car to navigate successfully when there are other vehicles on the road whose positions are apriori unknown to us, then we need a car that adapts its behavior based on input from sensors. The particular kind of adaptation we need is that the car behaves such that it evolves towards a target configuration in which the car is at its desired destination.

One way we can build such a feedback loop is by designing a feed-forward algorithm that takes sensor inputs and produces actuator outputs. A thermostat is an example of this: we just design the thermostat to turn on the heater when the observed temperature is below the target temperature. We could similarly design an autonomous car that observes vehicles around it and moves a little to the left whenever there is a vehicle too close on the right, etc. We might call these systems adaptive systems. PID controllers are adaptive systems.

One very powerful special case of adaptive system is a system whose behavior is determined by optimizing over an explicit internal simulation of its environment. This is how contemporary autonomous cars are actually built: we observe other vehicles on the road, then we search for a plan for how to behave for the next few seconds or minutes. Search means we consider many possible plans, and for each one we run a simulation of what would happen if we executed that plan: Would we hit another vehicle? Would we arrive at our destination? This search process is itself a feedback loop: we use some gradient descent or another optimization algorithm to set up a computation with a tendency to evolve towards a working plan, then we us

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Overcorrecting As Deliberate Practice
by  — 2020-06-01T06:26:31.504Z

Something that I've gotten a lot of value out of when learning many different things is to deliberately overcorrect when I notice I'm doing something the wrong way. For example, if I'm trying to hit the ball straight on a golf club, and I'm always hitting to the left, I make a deliberate effort to hit it to the right, rather than just trying to hit straight. Or if I'm speaking my lines too fast, I will slow down and try to speak them at 0.75x speed instead of just 1.0x speed.

In these situations, what often ends up happening is that, instead of actually overcorrecting in the opposite direction, it turns out that the amount of effort I'm putting in to intentionally try and do the opposite thing is actually just about what's needed to make me hit the happy medium. If I had instead strove for just trying to reach the medium, I would likely still be biased in the same direction, but just less so.

The other benefit is getting a feel for the entire range of motion / the entire action space. By default, I think I feel cautions to try and overcorrect or overextend. There's some part of me that thinks, "Well, I'm already obviously biased towards X, why try and become biased towards not-X?" There's some sort of fear there of "Is trying to be extra wrong in the other direction even allowed?" and some internal disdain, like "I shouldn't try and intentionally do something wrong!"

But I think the right thing to do is to flip that gut feeling around–"If I'm already biased towards X, then I already have a propensity for it. In that case, it makes really good sense to try and understand the things I'm not good at, which in this case is not-X!" This is really good experiential knowledge from a purely exploratory standpoint, as well as from a skill gain perspective.
My prediction for Covid-19
by TheMajor — 2020-05-31T23:25:17.814Z

In writing a reply to Zvi’s recent post on Covid-19 predictions I realized I had been procrastinating on writing out my thoughts on Covid-19 in full. In this post I will explain in detail how I think about the Covid-19 countermeasures for the next few months, and explain why I think all this.

Disclaimer: my predictions are among the most pessimistic (and speculative) of the ones I've run into. Having expectations that COVID-19 will be worse than people are thinking so far can and does have negative effects on your mental health, so stop reading, now.

I’d love to be proven wrong on all of this.

What is our long term goal?

There has been a lot of discussion on the impact of countermeasures on the spread of Covid-19. However, most of this has been relatively unhelpful in trying to make predictions on longer timescales - say, until March 2021. For this reason I want to first figure out what plausible scenarios we have for the long term - i.e. what situations would be stable. I think there are three main contenders.

We establish group immunity, and the virus is no longer a great risk.
We reduce the incidence rate to such a low level that we can prevent the spread by actively intervening in every case (for example through contact tracing, isolating only people who have been exposed).
We (permanently) change our lifestyle to one with reduced social contact, and we make sufficiently many countermeasures against Covid-19 permanent to reduce R0 to 1 or below.

Some countries are currently aiming for option number 2 (for example South Korea, as far as I know). I think this places serious strain on globalization, but I must confess I haven't looked into this all that much. I think experts are saying it is a near certainty that Covid-19 will become endemic, so this approach would require constant vigilance to prevent new cases. If it is possible to turn this approach into a permanent state of affairs I would love to know more about it, but for now I'd like to ignore it. It also sounds very difficult in places like the US, with approximately 1,189,003 active cases today.

Option number 3 is the doom scenario economists are talking about. Entire industries will have to adapt (or, more likely, collapse). We would, as a species, be cutting into our growth to survive this virus. Most of the discussions I've had are about the (lack of) desirability of th...
Welcome to Ann Arbor SSC
by Sam Rossini — 2020-05-31T18:55:50.386Z

We typically meet once a month for un-programmed conversation. We also have a google doc for people that want to hang out more often, if you attend a meeting and join our email list you can add yourself to the doc too.

This group started out of the SSC Meetups Everywhere Tour, but a deep knowledge of SSC is not required to participate.

Please feel free to join even if you feel awkward about it, even if you’re not the typical SSC reader, even if you’re worried people won’t like you, etc.
Possible takeaways from the coronavirus pandemic for slow AI takeoff
by Vika — 2020-05-31T17:51:26.437Z

(Cross-posted from personal blog. Summarized in Alignment Newsletter #104. Thanks to Janos Kramar for his helpful feedback on this post.)

Epistemic status: fairly speculative, would appreciate feedback

As the covid-19 pandemic unfolds, we can draw lessons from it for managing future global risks, such as other pandemics, climate change, and risks from advanced AI. In this post, I will focus on possible implications for AI risk. For a broader treatment of this question, I recommend

FLI's covid-19 page

that includes expert interviews on the implications of the pandemic for other types of risks.

A key element in AI risk scenarios is the speed of takeoff - whether advanced AI is developed gradually or suddenly. Paul Christiano's post on

takeoff speeds

defines slow takeoff in terms of the economic impact of AI as follows: "There will be a complete 4 year interval in which world output doubles, before the first 1 year interval in which world output doubles." It argues that slow AI takeoff is more likely than fast takeoff, but is not necessarily easier to manage, since it poses different challenges, such as large-scale coordination. This post expands on this point by examining some parallels between the coronavirus pandemic and a slow takeoff scenario. The upsides of slow takeoff include the ability to learn from experience, act on warning signs, and reach a timely consensus that there is a serious problem. I would argue that the covid-19 pandemic had these properties, but most of the world's institutions did not take advantage of them. This suggests that, unless our institutions improve, we should not expect the slow AI takeoff scenario to have a good default outcome.

Learning from experience. In the slow takeoff scenario, general AI is expected to appear in a world that has already experienced transformative change from less advanced AI, and institutions will have a chance to learn from problems with these AI systems. An analogy could be made with learning from dealing with less "advanced" epidemics like SARS that were not as successful as covid-19 at spreading across the world. While some useful lessons were learned, they were not successfully generalized to covid-19, which had somewhat different properties than these previous pathogens (such as asymptomatic transmission and higher virulence). Similarly, general AI may

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Covid-19: My Current Model
by Zvi — 2020-05-31T17:40:00.945Z

The post will be a summary of my current key views on various aspects what is going on, especially in places where I see many or most responsible-looking people getting it importantly wrong.

This post is not making strong evidence-based arguments for these views. This is not that post. This is me getting all this out there, on the record, in a place one can reference.

Risks Follow Power Laws

It is impossible to actually understand Covid-19 if you think of some things as ‘risky’ and other things as ‘safe’ and group together all the things in each category. And yet, that’s exactly how most of our thinking is directed.

Instead, think of risks as following power laws.

The riskiest activities are indoors, involve close physical proximity with others, while those for extended periods of time others cough, sing, puff or otherwise powerfully exhale, or talk directly at us, or we are in actual physical contact that then reaches one’s eyes, nose or mouth.

Activities missing any of those components are much, much safer than activities that share all those components.

Then other actions, such as masks and hand washing and not-face-touching, can reduce that risk by further large percentages.

Slight reductions in the frequency and severity of your very risky actions is much more important than reducing the frequency of nominally risky actions.

The few times you end up talking directly with someone in the course of business, the one social gathering you attend, the one overly crowded store you had to walk through, will dominate your risk profile. Be paranoid about that, and think how to make it less risky, or ideally avoid it. Don’t sweat the small stuff.

And think about the physical world and what’s actually happening around you!

Sacrifices To The Gods Are Demanded Everywhere

A sacrifice to the Gods (post of this topic to be linked in when finally written) is an action with physical costs but with no interest in any meaningful physical benefits, taken in the hope that it will make one less blameworthy. Things are bad because we have sinned. The Gods demand sacrifice. If we do not act appropriately repentant and concerned, things will surely get worse.

Once we act appropriately, we are virtuous and will doubtless be saved. We can stop. There is no need to proceed in a way that would actually work, once the Gods have been placated. Everything

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The Presumptuous Philosopher, self-locating information, and Solomonoff induction
by Charlie Steiner — 2020-05-31T16:35:48.837Z

I need help working some ideas out.

Background:

If you're not familiar with the Presumptuous Philosopher, it's a thought experiment due to Bostrom about predicting the laws of the universe based on anthropic reasoning:

It is the year 2100 and physicists have narrowed down the search for a theory of everything to only two remaining plausible candidate theories: T1 and T2 (using considerations from super-duper symmetry). According to T1 the world is very, very big but finite and there are a total of a trillion trillion observers in the cosmos. According to T2, the world is very, very, very big but finite and there are a trillion trillion trillion observers. The super-duper symmetry considerations are indifferent between these two theories.

Physicists are preparing a simple experiment that will falsify one of the theories. Enter the presumptuous philosopher: “Hey guys, it is completely unnecessary for you to do the experiment, because I can already show you that T2 is about a trillion times more likely to be true than T1!”

If you're not familiar with Solomonoff induction, the relevant aspects are that it tries to predict your sequence of observations (not the laws of physics, at least not directly), and that it assigns probability to different sequences that decreases exponentially in how complicated those sequences of observations are. By "complicated" we don't merely mean how verbose the laws of physics are, we also have to include how complicated it is to go from the laws of physics to specifying your specific observations.

Main event:

Imagine that our Presumptuous Philosopher has just explained to the physicists that they already know that T2 is a trillion times more likely, because it has more observers in it.

But one of the engineers present is a Solomonoff inductor, and they ask "So does this mean that I should expect the experiment to show us T2, even though the theories are of similar complexity? How does the number of observers change the probability, if all I care about is how complicated it is to predict my sequence of observations?"

"Excellent question," says the Presumptuous Philosopher. "Think of T1 and T2 not as two different hypotheses, but as two different collections of hypothetical ways to predict your observations, with one actual hypothesis for each copy of you in the theory. If t...