Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: AGI and the EMH: markets are not expecting aligned or unaligned AI in the next 30 years, published by basil.halperin on January 10, 2023 on LessWrong.

by Trevor Chow, Basil Halperin, and J. Zachary Mazlish

In this post, we point out that short AI timelines would cause real interest rates to be high, and would do so under expectations of either unaligned or aligned AI. However, 30- to 50-year real interest rates are low. We argue that this suggests one of two possibilities:

Long(er) timelines. Financial markets are often highly effective information aggregators (the “efficient market hypothesis”), and therefore real interest rates accurately reflect that transformative AI is unlikely to be developed in the next 30-50 years.

Market inefficiency. Markets are radically underestimating how soon advanced AI technology will be developed, and real interest rates are therefore too low. There is thus an opportunity for philanthropists to borrow while real rates are low to cheaply do good today; and/or an opportunity for anyone to earn excess returns by betting that real rates will rise.

In the rest of this post we flesh out this argument.

Both intuitively and under every mainstream economic model, the “explosive growth” caused by aligned AI would cause high real interest rates.

Both intuitively and under every mainstream economic model, the existential risk caused by unaligned AI would cause high real interest rates.

We show that in the historical data, indeed, real interest rates have been correlated with future growth.

Plugging the Cotra probabilities for AI timelines into the baseline workhorse model of economic growth implies substantially higher real interest rates today.

In particular, we argue that markets are decisively rejecting the shortest possible timelines of 0-10 years.

We argue that the efficient market hypothesis (EMH) is a reasonable prior, and therefore one reasonable interpretation of low real rates is that since markets are simply not forecasting short timelines, neither should we be forecasting short timelines.

Alternatively, if you believe that financial markets are wrong, then you have the opportunity to (1) borrow cheaply today and use that money to e.g. fund AI safety work; and/or (2) earn alpha by betting that real rates will rise.

An order-of-magnitude estimate is that, if markets are getting this wrong, then there is easily $1 trillion lying on the table in the US treasury bond market alone – setting aside the enormous implications for every other asset class.

Interpretation. We view our argument as the best existing outside view evidence on AI timelines – but also as only one model among a mixture of models that you should consider when thinking about AI timelines. The logic here is a simple implication of a few basic concepts in orthodox economic theory and some supporting empirical evidence, which is important because the unprecedented nature of transformative AI makes “reference class”-based outside views difficult to construct. This outside view approach contrasts with, and complements, an inside view approach, which attempts to build a detailed structural model of the world to forecast timelines (e.g. Cotra 2020; see also Nostalgebraist 2022).

Outline. If you want a short version of the argument, sections I and II (700 words) are the heart of the post. Additionally, the section titles are themselves summaries, and we use text formatting to highlight key ideas.

I. Long-term real rates would be high if the market was pricing advanced AI

Real interest rates reflect, among other things:

Time discounting, which includes the probability of death

Expectations of future economic growth

This claim is compactly summarized in the “Ramsey rule” (and the only math that we will introduce in this post), a version of the “Euler equation” that in one form or a...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: 200 COP in MI: Interpreting Reinforcement Learning, published by Neel Nanda on January 10, 2023 on The AI Alignment Forum.

This is the ninth post in a sequence called 200 Concrete Open Problems in Mechanistic Interpretability. Start here, then read in any order. If you want to learn the basics before you think about open problems, check out my post on getting started. Look up jargon in my Mechanistic Interpretability Explainer

I’ll make another post every 1-2 days, giving a new category of open problems. If you want to read ahead, check out the draft sequence here!

Motivating papers: Acquisition of Chess Knowledge in AlphaZero, Understanding RL Vision

Disclaimer: My area of expertise is language model interpretability, not reinforcement learning. These are fairly uninformed takes and I’m sure I’m missing a lot of context and relevant prior work, but this is hopefully more useful than nothing!

Motivation

Reinforcement learning is the study of how to create agents - models that can act in an environment and form strategies to get high reward. I think there are a lot of deep confusions we have about how RL systems work and how they learn, and that trying to reverse engineer these systems could teach us a lot! Some high-level questions about RL that I’d love to get clarity on:

How much do RL agents learn to actually model their environment vs just being a big bundle of learned heuristics?

In particular, how strong a difference is there between model-free and model-based systems here?

Do models explicitly reason about their future actions? What would a circuit for this even look like?

What are the training dynamics of an RL system like? What does its path to an eventual solution look like? A core hard part about training RL systems is that the best strategies tend to require multi-step plans, but taking the first step only makes sense if you know that you’ll do the next few steps correctly.

What does it look like on a circuit level when a model successfully explores and finds a new and better solution?

How much do the lessons from mechanistic interpretability transfer? Can we even reverse engineer RL systems at all?

How well do our conceptual frameworks for thinking about networks transfer to RL? Are there any things that clearly break, or new confusions that need to be grappled with?

I expect training dynamics to be a particularly thorny issue - rather than learning a fixed data distribution, whether an action is a good idea depends on which actions the model will take in future.

Further, I think that it’s fairly likely that, whatever AGI looks like, it involves a significant component of RL. And that training human-level and beyond systems to find creative solutions to optimise some reward is particularly likely to result in dangerous behaviour. Eg, where models learn the instrumental goals to seek power or to deceive us, or just more generally learn to represent and pursue sophisticated long term goals and to successfully plan towards these.

But as is, much alignment work here is fairly speculative and high level, or focused on analysing model behaviour and forming inferences. I would love to see this grounded in a richer understanding of what is actually going on inside the system. Some more specific questions around alignment that I’m keen to get clarity on:

Do models actually learn internal representations of goals? Can we identify these?

I’m particularly interested in settings with robust capabilities but misgeneralised goals - what’s going on here? Can we find any evidence for or against inner optimisation concerns?

How much are models actually forming plans internally? Can we identify this?

Do models ever simulate other agents? (Especially in a multi-agent setting)

A work I find particularly inspiring here is Tom McGrath et al’s work, Acquisition of Chess Knowled...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Escape Velocity from Bullshit Jobs, published by Zvi on January 10, 2023 on LessWrong.

Without speculating here on how likely this is to happen, suppose that GPT-4 (or some other LLM or AI) speeds up, streamlines or improves quite a lot of things. What then?

The Dilemma

Samo and Ben’s dilemma: To the extent that the economy is dominated by make-work, automating it away won’t work because more make-work will be created, and any automated real work gets replaced by new make-work.

Consider homework assignments. ChatGPT lets students skip make-work. System responds by modifying conditions to force students to return to make-work. NYC schools banned ChatGPT.

Consider a bullshit office job. You send emails and make calls and take meetings and network to support inter-managerial struggles and fulfill paperwork requirements and perform class signaling to make clients and partners feel appreciated. You were hired in part to fill out someone’s budget. ChatGPT lets you compose your emails faster. They (who are they?) assign you to more in person meetings and have you make more phone calls and ramp up paperwork requirements.

The point of a bullshit job is to be a bullshit job.

There is a theory that states that if you automate away a bullshit job, it will be instantly replaced by something even more bizarre and inexplicable.

There is another theory that states this has already happened.

Automating a real job can even replace it with a bullshit job.

This argument applies beyond automation. It is a full Malthusian economic trap: Nothing can increase real productivity.

Bullshit eats all.

Eventually.

Two Models of the Growth of Bullshit

Samo’s Law of Bullshit: Bullshit rapidly expands to fill the slack available.

Law of Marginal Bullshit: There is consistent pressure in favor of marginally more bullshit. Resistance is inversely proportional to slack.

In both cases, the lack of slack eventually collapses the system.

In the second model, increased productivity buys time, and can do so indefinitely.

Notice how good economic growth feels to people. This is strong evidence for lags, and for the ability of growth and good times to outpace the problems.

A Theory of Escape Velocity

We escaped the original Malthusian trap with the Industrial Revolution, expanding capacity faster than the population could grow. A sufficient lead altered underlying conditions to the point where we should worry more about declining population than rising population in most places.

Consider the same scenario for a potential AI Revolution via GPT-4.

Presume GPT-4 allows partial or complete automation of a large percentage of existing bullshit jobs. What happens?

My model says this depends on the speed of adaptation.

Shoveling Bullshit

Can improvements outpace the bullshit growth rate?

A gradual change over decades likely gets eaten up by gradual ramping up of requirements and regulations. A change that happens overnight is more interesting.

How fast can bullshit requirements adapt?

The nightmare is ‘instantaneously.’ A famous disputed claim is that the NRC defined a ‘safe’ nuclear power plant as one no cheaper than alternative plants. Cheaper meant you could afford to Do More Safety. Advancements are useless.

Most regulatory rules are not like that. Suppose the IRS requires 100 pages of paperwork per employee. This used to take 10 hours. Now with GPT-4, as a thought experiment, let’s say it takes 1 hour.

The long run result might be 500 pages of more complicated paperwork that takes 10 hours even with GPT-4, while accomplishing nothing. That still will take time. It is not so easy or fast to come up with 400 more pages. I’d assume that would take at least a decade. It likely would need to wait until widespread adaptation of AI powered tools, or it would bury those without them.

Meanwhile, GPT-5 comes out. Gains c...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: AGI and the EMH: markets are not expecting aligned or unaligned AI in the next 30 years, published by basil.halperin on January 10, 2023 on The Effective Altruism Forum.

by Trevor Chow, Basil Halperin, and J. Zachary Mazlish

In this post, we point out that short AI timelines would cause real interest rates to be high, and would do so under expectations of either unaligned or aligned AI. However, 30- to 50-year real interest rates are low. We argue that this suggests one of two possibilities:

Long(er) timelines. Financial markets are often highly effective information aggregators (the “efficient market hypothesis”), and therefore real interest rates accurately reflect that transformative AI is unlikely to be developed in the next 30-50 years.

Market inefficiency. Markets are radically underestimating how soon advanced AI technology will be developed, and real interest rates are therefore too low. There is thus an opportunity for philanthropists to borrow while real rates are low to cheaply do good today; and/or an opportunity for anyone to earn excess returns by betting that real rates will rise.

In the rest of this post we flesh out this argument.

Both intuitively and under every mainstream economic model, the “explosive growth” caused by aligned AI would cause high real interest rates.

Both intuitively and under every mainstream economic model, the existential risk caused by unaligned AI would cause high real interest rates.

We show that in the historical data, indeed, real interest rates have been correlated with future growth.

Plugging the Cotra probabilities for AI timelines into the baseline workhorse model of economic growth implies substantially higher real interest rates today.

In particular, we argue that markets are decisively rejecting the shortest possible timelines of 0-10 years.

We argue that the efficient market hypothesis (EMH) is a reasonable prior, and therefore one reasonable interpretation of low real rates is that since markets are simply not forecasting short timelines, neither should we be forecasting short timelines.

Alternatively, if you believe that financial markets are wrong, then you have the opportunity to (1) borrow cheaply today and use that money to e.g. fund AI safety work; and/or (2) earn alpha by betting that real rates will rise.

An order-of-magnitude estimate is that, if markets are getting this wrong, then there is easily $1 trillion lying on the table in the US treasury bond market alone – setting aside the enormous implications for every other asset class.

Interpretation. We view our argument as the best existing outside view evidence on AI timelines – but also as only one model among a mixture of models that you should consider when thinking about AI timelines. The logic here is a simple implication of a few basic concepts in orthodox economic theory and some supporting empirical evidence, which is important because the unprecedented nature of transformative AI makes “reference class”-based outside views difficult to construct. This outside view approach contrasts with, and complements, an inside view approach, which attempts to build a detailed structural model of the world to forecast timelines (e.g. Cotra 2020; see also Nostalgebraist 2022).

Outline. If you want a short version of the argument, sections I and II (700 words) are the heart of the post. Additionally, the section titles are themselves summaries, and we use text formatting to highlight key ideas.

I. Long-term real rates would be high if the market was pricing advanced AI

Real interest rates reflect, among other things:

Time discounting, which includes the probability of death

Expectations of future economic growth

This claim is compactly summarized in the “Ramsey rule” (and the only math that we will introduce in this post), a version of the “Euler equation” th...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: What would it look like if it looked like AGI was far?, published by Tomás B. on January 9, 2023 on LessWrong.

This is a sequel to my 2021 post What would it look like if it looked like AGI was very near?, which was a scenario-based planning thought exercise about short timelines, and prompted an excellent comment from Aiyen.

As average timelines have decreased substantially in the last two years, if only for shear contrarianism it seems worthwhile to think a little bit about long timeline scenarios.

So the scenario-based planning thought exercise goes like this:

Imagine it is 30 years from now and AGI has NOT happened and you are writing a post on how in hindsight it was obvious that AGI was much harder than it appeared to many in 2023.

What symptoms of this do you see of this now now? Contingent on this scenario being true, what symptoms would you expect in the next couple decades.

Thanks for listening. To help us out with The Nonlinear Library or to learn more, please visit nonlinear.org.

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: The Alignment Problem from a Deep Learning Perspective (major rewrite), published by SoerenMind on January 10, 2023 on The AI Alignment Forum.

We've recently uploaded a major rewrite of Richard Ngo's:

The Alignment Problem from a Deep Learning Perspective

We hope it can reach ML researchers by being more grounded in the deep learning literature and empirical findings, and being more rigorous than typical introductions to the alignment problem.

There are many changes. Most obviously, the paper was previously structured into three phases of training: planning towards a mix of desirable and undesirable internally-represented goals, situational awareness, and goals that generalize OOD. Now it is structured around three emergent phenomena: deceptive reward hacking, planning towards internally represented goals, and power-seeking.

Feedback request

Since we're submitting this to ICML in two weeks, we're looking for feedback, including feedback on presentation and feedback you'd give as a critical ML reviewer. It can be nitpicky. If your feedback isn't that interesting to forum readers, you may want to email it to us (find our emails in the Arxiv PDF). It's most useful by 17 January. Many thanks in advance!

Full text copy

Richard Ngo - OpenAI, Lawrence Chan - UC Berkeley (EECS), Sören Mindermann - University of Oxford (CS)

Abstract

Within the coming decades, artificial general intelligence (AGI) may surpass human capabilities at a wide range of important tasks. We outline a case for expecting that, without substantial effort to prevent it, AGIs could learn to pursue goals which are very undesirable (in other words, misaligned) from a human perspective. We argue that AGIs trained in similar ways as today's most capable models could learn to act deceptively to receive higher reward; learn internally-represented goals which generalize beyond their training distributions; and pursue those goals using power-seeking strategies. We outline how the deployment of misaligned AGIs might irreversibly undermine human control over the world, and briefly review research directions aimed at preventing these problems.

Contents

(Page numbers from the PDF version)

1 Introduction 2

2 Deceptive reward hacking 3

2.1 Reward misspecification and reward hacking ........ 3

2.2 Defining situational awareness ....... . . . . . . . . . . 4

2.3 Situational awareness enables deceptive reward hacking ...... 4

3 Internally-represented goals 5

4 Power-seeking behavior 7

4.1 Many broadly-scoped goals incentivize power-seeking ....... 8

4.2 Power-seeking policies would choose high-reward behaviors for instrumental reasons ..8

4.3 Misaligned AGIs could gain control of key levers of power .... 9

5 Research directions in alignment 9

6 Conclusion 10

Introduction

Over the last decade, advances in deep learning have led to the development of large neural networks with impressive capabilities in a wide range of domains. In addition to reaching human-level performance on complex games like Starcraft [Vinyals et al., 2019] and Diplomacy [Bakhtin et al., 2022], large neural networks show evidence of increasing generality [Bommasani et al., 2021], including advances in sample efficiency [Brown et al., 2020, Dorner, 2021], cross-task generalization [Adam et al., 2021], and multi-step reasoning [Chowdhery et al., 2022]. The rapid pace of these advances highlights the possibility that, within the coming decades, we develop artificial general intelligence (AGI) - that is, AI which can apply domain-general cognitive skills (such as reasoning, memory, and planning) to perform at or above human level on a wide range of cognitive tasks relevant to the real world (such as writing software, formulating new scientific theories, or running a company).

1 This possibility is taken seriously by leading ML researchers, who in two recent...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Non-trivial Fellowship: start an impactful project with €500 and expert guidance, published by Peter McIntyre on January 10, 2023 on The Effective Altruism Forum.

We’ve just launched our new Fellowship for pre-university students (aged 14 - 20) in the EU and UK.

It's a 7-week online fellowship with a €500 scholarship to start an impactful research, policy, or entrepreneurial project.

The cohort will be full of ambitious, curious, and analytical teenagers in the EU/UK who want to work towards having the biggest impact they can, starting today.

There are also up to €5,000 in prizes for the best projects up for grabs. These will be awarded based on projects that have made the most progress, show the most promise, and are the best presented.

It's run by me, Peter McIntyre. Before Non-trivial, I spent 5 years getting new programs off the ground at 80,000 Hours.

No EA background necessary. It’s more important that someone could be interested in working on a project that could change the world for the better than have already learned a lot about EA.

We’d be very grateful if you could share the website with any talented teenagers based in the EU/UK you know.

The deadline to apply is Sunday, Jan. 29, 2023.

Thanks for listening. To help us out with The Nonlinear Library or to learn more, please visit nonlinear.org.

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Shallow Investigation: Arsenic Remediation, published by Francis on January 10, 2023 on The Effective Altruism Forum.

Arsenic: A toxicant worth thinking more about

This report summarizes a shallow investigation into the effects of arsenic on global health and wellbeing, interventions to reduce these effects, and existing programs in this space. I estimate that this report is the result of roughly 50 hours of research and writing. This report was produced as part of Cause Innovation Bootcamp’s fellowship program.

Summary

Arsenic is a toxicant that contaminates drinking water and other groundwater in some countries. This contamination typically results from groundwater flowing through soil and mineral deposits that contain arsenic in the right conditions to be soluble, but human activities can also lead to arsenic contamination in some cases. Approximately 300 million people live in areas where the groundwater is contaminated by arsenic to a degree that exceeds the World Health Organization Standard of 10 μg/L, and approximately 100 million of those people are exposed to arsenic levels in drinking water of more than 50 μg/L. It is associated with a variety of negative health outcomes and other effects, resulting in higher mortality rates, cognitive damage, and lower lifetime incomes. Although Bangladesh has the most well-known and severe rate of arsenic contamination, groundwater arsenic affects many countries, and arsenic interventions outside of Bangladesh are relatively neglected.

This report investigates various possible arsenic interventions. The lower bound for cost-per-death-averted was $630; limiting only to interventions that are backed by field studies, the lower bound for cost-per-death-averted was $774. Various factors, primarily uncertainty about the degree of harmful effects from arsenic, may reduce the overall cost-effectiveness, but arsenic interventions nonetheless have the potential to be promising, with the potential to compete with GiveWell top charities.

Introduction and Scope of the Problem

Human exposure to arsenic primarily results from groundwater contamination. Inorganic arsenic contamination in groundwater can lead to human exposure in three main ways: drinking contaminated water, using contaminated water for cooking, and using contaminated water to irrigate crops. The primary negative health effects of arsenic are increased risk of cancer and cardiovascular disease (including heart attacks), but other adverse effects include a higher risk of skin lesions, diabetes, pulmonary disease, stroke, cognitive deficits (in the case of prenatal and early childhood exposure), and (in some circumstances) Blackfoot disease.

As of 2021, an estimated 300 million people worldwide live in areas with groundwater contaminated by arsenic (more than 10 μg/L), with approximately 100 million of those people living in areas with groundwater arsenic levels of more than 50 μg/L, which has been linked to an especially high likelihood of severe health consequences. According to the World Health Organization, several countries have been found to have a high level of arsenic in groundwater, including Argentina, Bangladesh, Cambodia, Chile, China, India, Mexico, Pakistan, the United States of America, and Vietnam. Arsenic exposure is particularly common in Bangladesh, where the number of people exposed to arsenic in groundwater has been estimated at 35 million - 77 million. More recent estimates from 2012 indicate that remediation efforts have reduced this number to 19 million people exposed to arsenic levels greater than 50 μg/L, and it is likely that this number has continued to decline since 2012.

Arsenic contamination around the world.

Various studies have attempted to determine the effects of arsenic exposure on overall mortality. A ten-year cohort study in Banglades...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: How did our historical moral heroes deal with severe adversity and/or moral compromise?, published by Linch on January 9, 2023 on The Effective Altruism Forum.

In one way we think a great deal too much of the atomic bomb. “How are we to live in an atomic age?” I am tempted to reply: “Why, as you would have lived in the sixteenth century when the plague visited London almost every year, or as you would have lived in a Viking age when raiders from Scandinavia might land and cut your throat any night; or indeed, as you are already living in an age of cancer, an age of syphilis, an age of paralysis, an age of air raids, an age of railway accidents, an age of motor accidents.”

In other words, do not let us begin by exaggerating the novelty of our situation.

Julia Wise, quoting C.S. Lewis

That does not kill us makes us stronger

Hilary Clinton, quoting Kelly Clarkson, quoting Nietszche

In light of current events, I've personally found it difficult to reach equilibrium. In particular, I've found it hard to navigate a) the 2022 loss of ~3/4 of resources available to longtermist EA, b) the consequentially large harms in the world caused by someone who I thought was close to us, c) setbacks in the research prioritization of my own work, d) some vague feelings that our community is internally falling apart, e) the general impending sense of doom, f) some personal difficulties this year (not all of which is related to global events), and g) general feelings of responsibility and also inadequacy to address the above. I imagine many other people reading this are going through similar difficulties.

I'll find it personally helpful to understand how our (my) historical heroes dealt with problems akin to the ones we're currently facing. In particular, I'd be interested in hearing about similar situations faced by 1) the Chinese Mohists and 2) the English utilitarians.

I will be interested in hearing stories of how the Chinese Mohists and English utilitarians dealt with situations of i) large situational setbacks and ii) large-scale moral compromise.

In the past, I've found it helpful to draw connections between my current work/life and that of those I view as my spiritual or intellectual ancestors. Perhaps this will be true again. I confess to not knowing much of the relevant histories here, but presumably they've faced similar issues? I'm guessing the Mohists couldn't have been happy that states they defended ended up being conquered anyway, and Qin Shihuang unified China with fire and blood. As for the English utilitarians, I assume some of the policies they've advocated backfired severely in their lifetimes, whether obviously or more subtly.

I'd be interested in seeing and possibly learning from how they responded, both practically and on an emotional level.

So this is my question for the historians/amateur historians: In what ways have our historical moral heroes dealt with large-scale adversity and moral compromise?

For example, it was helpful for me to learn about what John Stuart Mill viewed as his personal largest emotional difficulties, as well as the Mohist approaches to asceticism in a corrupt world.

Thanks for listening. To help us out with The Nonlinear Library or to learn more, please visit nonlinear.org.

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Mental Health Providers and Resources Listed on the Mental Health Navigator Website, published by Emily on January 9, 2023 on The Effective Altruism Forum.

The Mental Health Navigator is here to help you find mental health support!

We heard that effective altruists were struggling to find therapists – it’s hard to find providers who are good or take your insurance or are accepting new clients.

List of providers

We recently overhauled a database of providers recommended by community members, so you can more easily find a therapist who works for you. You can sort by availability, average rating, location, cost, specialties, etc., plus read community reviews. We’ve even included links or emails for scheduling an intake so you don’t need to pick up a phone.

It’s a place for members of the Effective Altruism community to provide information about mental health providers they have found helpful and would recommend to other community members, including therapists, coaches, counselors, and psychiatrists. It's also now where the lists of community members who provide coaching or mental health services are stored, rather than these being on separate web pages.

The database is still limited (e.g. only a handful of providers accept insurance and the majority are based in the US or UK). We’re always working to grow the list of highly recommended providers, so we greatly appreciate you sharing new reviews here anonymously:

List of mental health resources

The Mental Health Navigator also provides a list of mental health resources. It has been set up to provide as comprehensive a list of mental health resources as possible, though it admittedly still has a lot of room to grow in terms of diversity and geographic coverage. We always welcome recommendations for the table via this form:

Advisory Service

Finally, the Mental Health Navigator aims to provide an Advisory Service where a volunteer will work with you to find a provider who meets your needs. However, our Advisory Service is currently not accepting new applications while we work through our wait list. We will post again on this forum and on our social media pages once we have reopened to everyone.

Thanks for listening. To help us out with The Nonlinear Library or to learn more, please visit nonlinear.org.