In a nutshell:

You can start building software and tech skills by trying out learning to code, and then doing some programming projects before applying for jobs. You can apply (as well as continue to develop) your software and tech skills by specialising in a related area, such as technical AI safety research, software engineering, or information security. You can also earn to give, and this in-demand skill set has great backup options.

Why are software and tech skills valuable?

By “software and tech” skills we basically mean what your grandma would call “being good at computers.”

When investigating the world’s most pressing problems, we’ve found that in many cases there are software-related bottlenecks.

For example, machine learning (ML) engineering is a core skill needed to contribute to AI safety technical research. Experts in information security are crucial to reducing the risks of engineered pandemics, as well as other risks. And software engineers are often needed by nonprofits, whether they’re working on reducing poverty or mitigating the risks of climate change.

Also, having skills in this area means you’ll likely be highly paid, offering excellent options to earn to give.

Moreover, basic programming skills can be extremely useful whatever you end up doing. You’ll find ways to automate tasks or analyse data throughout your career.

What does a career using software and tech skills involve?

A career using these skills typically involves three steps:

1. Learn to code with a university course or self-study and then find positions where you can get great mentorship. (Read more about how to get started.)
2. Optionally, specialise in a particular area, for example, by building skills in machine learning or information security.
3. Apply your skills to helping solve a pressing global problem. (Read more about how to have an impact with software and tech.)

There’s no general answer about when to switch from a focus on learning to a focus on impact. Once you have some basic programming skills, you should look for positions that both further improve your skills and have an impact, and then decide based on which specific opportunities seem best at the time.

Software and tech skills can also be helpful in other, less directly-related career paths, like being an expert in AI hardware (for which you’ll also need a specialist knowledge skill set) or founding a tech startup (for which you’ll also need an organisation-building skill set). Being good with computers is also often part of the skills required for quantitative trading.

Programming also tends to come in handy in a wide variety of situations and jobs; there will be other great career paths that will use these skills that we haven’t written about.

How to evaluate your fit

How to predict your fit in advance

Some indications you’ll be a great fit include:

• The ability to break down problems into logical parts and generate and test hypotheses
• Willingness to try out many different solutions
• High attention to detail
• Broadly good quantitative skills

The best way to gauge your fit is just to try out programming.

It seems likely that some software engineers are significantly better than average — and we’d guess this is also true for other technical roles using software. In particular, these very best software engineers are often people who spend huge amounts of time practicing. This means that if you enjoy coding enough to want to do it both as a job and in your spare time, you are likely to be a good fit.

How to tell if you’re on track

If you’re at university or in a bootcamp, it’s especially easy to tell if you’re on track. Good signs are that you’re succeeding at your assigned projects or getting good marks. An especially good sign is that you’re progressing faster than many of your peers.

In general, a great indicator of your success is that the people you work with most closely are enthusiastic about you and your work, especially if those people are themselves impressive!

If you’re building these skills at an organisation, signs you’re on track might include:

• You get job offers at organisations you’d like to work for.
• You’re promoted within your first two years.
• You receive excellent performance reviews.
• You’re asked to take on progressively more responsibility over time.
• After some time, you’re becoming someone in your team who people look to solve their problems, and people want you to teach them how to do things.
• You’re building things that others are able to use successfully without your input.
• Your manager / colleagues suggest you might take on more senior roles in the future.
• You ask your superiors for their honest assessment of your fit and they are positive (e.g. they tell you you’re in the top 10% of people they can imagine doing your role).

How to get started building software and tech skills

Independently learning to code

As a complete beginner, you can write a Python program in less than 20 minutes that reminds you to take a break every two hours.

A great way to learn the very basics is by working through a free beginner course like Automate the Boring Stuff with Python by Al Seigart.

Once you know the fundamentals, you could try taking an intro to computer science or intro to programming course. If you’re not at university, there are plenty of courses online, such as:

• Udacity’s Intro to Theoretical Computer Science
• MIT’s Introduction to Computer Science and Programming
• Stanford’s Programming Methodology

Don’t be discouraged if your code doesn’t work the first time — that’s what normally happens when people code!

A great next step is to try out doing a project with other people. This lets you test out writing programs in a team and working with larger codebases. It’s easy to come up with programming projects to do with friends — you can see some examples here.

Once you have some more experience, contributing to open-source projects in particular lets you work with very large existing codebases.

Attending a coding bootcamp

We’ve advised many people who managed to get junior software engineer jobs in less than a year by going to a bootcamp.

Coding bootcamps are focused on taking people with little knowledge of programming to as highly paid a job as possible within a couple of months. This is a great entry route if you don’t already have much background, though some claim the long-term prospects are not as good as if you studied at university or in a particularly thorough way independently because you lack a deep understanding of computer science. Course Report is a great guide to choosing a bootcamp. Be careful to avoid low-quality bootcamps. To find out more, read our interview with an App Academy instructor.

Studying at university

Studying computer science at university (or another subject involving lots of programming) is a great option because it allows you to learn to code in an especially structured way and while the opportunity cost of your time is lower.

It will also give you a better theoretical understanding of computing than a bootcamp (which can be useful for getting the most highly-paid and intellectually interesting jobs), a good network, some prestige, and a better understanding of lower-level languages like C. Having a computer science degree also makes it easier to get a US work visa if you’re not from the US.

Doing internships

If you can find internships, ideally at the sorts of organisations you might want to work for to build your skills (like big tech companies or startups), you’ll gain practical experience and the key skills you wouldn’t otherwise pick up from academic degrees (e.g. using version control systems and powerful text editors). Take a look at our our list of companies with software and machine learning internships.

AI-assisted coding

As you’re getting started, it’s probably worth thinking about how developments in AI are going to affect programming in the future — and getting used to AI-assisted coding.

We’d recommend trying out using GitHub CoPilot, which writes code for you based on your comments. Cursor is a popular AI-assisted code editor based on VSCode.

You can also just ask AI chat assistants for help. ChatGPT is particularly helpful (although only if you use the paid version).

We think it’s reasonably likely that many software and tech jobs in the future will be heavily based on using tools like these.

Building a specialty

Depending on how you’re going to use software and tech skills, it may be useful to build up your skills in a particular area. Here’s how to get started in a few relevant areas:

Depending on how you’re aiming to have an impact with these skills (see the next section), you may also need to develop other skills. We’ve written about some other relevant skill sets:

• How to get started building research skills
• How to get started building specialist knowledge relevant to a pressing problem
• How to get started building skills for communicating ideas

For more, see our full list of impactful skills.

Once you have these skills, how can you best apply them to have an impact?

The problem you work on is probably the biggest driver of your impact. The first step is to make an initial assessment of which problems you think are most pressing (even if you change your mind over time, you’ll need to decide where to start working).

Once you’ve done that, the next step is to identify the highest-potential ways to use software and tech skills to help solve your top problems.

There are five broad categories here:

• Use software and tech skills in research. Lots of technical research relevant to the world’s most pressing problems makes heavy use of software and tech skills — most notably, AI safety technical research. To be successful, you might also need a research skill set, which we’ve written about separately. For some paths, you’ll also need specialist knowledge in an area related to a pressing problem — e.g. hardware for becoming an expert in AI hardware.
• ML engineering for AI safety research. Most AI safety researchers work closely with engineers (and in many organisations, no clear distinction is made). This is a particularly high-impact way of using software and tech skills because we think risks from AI is one of the world’s most pressing problems.
• Build software for organisations working on pressing problems. Most organisations working on everything from global health to reducing the risk of nuclear war need software engineers to manage computer systems, apps, and websites. The key feature that draws this work together is that you’ll be building a product for others to use. Read more about software engineering careers and organisation-building skills.
• Protect sensitive information. Some organisations need help protecting information that could be hugely dangerous if it was known more widely, such as harmful genetic sequences or powerful AI technology. Breaches in areas like these could have disastrous consequences — which makes information security a great option for people who want to have a high-impact career. Read more about information security.
• Earn to give. Most jobs that use software and tech skills, whether software engineering, information security, data science, or something else entirely, command high salaries (particularly in the US) — and so they offer a great option for earning to give. Skilled software engineers can earn $300,000 a year or more at big tech companies. Probably the highest-paying routes are trading in quantitative hedge funds or founding a tech startup.

While some of these options (like protecting dangerous information) will require building up some more specialised skills, being a great programmer will let you move around most of these categories relatively easily, and the earning to give options means you’ll always have a pretty good backup plan.

Find jobs that use software and tech skills

See our curated list of job opportunities for this path.

View all opportunities

Career paths we’ve reviewed that use these skills

• AI safety technical research
• Information security in high-impact areas
• Expert in AI hardware
• Forecasting and related research and implementation
• Data collection for AI alignment
• Software engineering
• Found a tech startup
• Earning to give in quantitative trading
• Machine learning PhDs
• Early-stage startup employee
• Working at a leading AI lab
• Computer science PhD
• Data science
• Product manager in tech
• Web designer

Read next:  Explore other useful skills

Want to learn more about the most useful skills for solving global problems, according to our research? See our list.

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The post Software and tech skills appeared first on 80,000 Hours.

Note from the author: At its core, this problem profile tries to predict the future of technology. This is a notoriously difficult thing to do. In addition, there has been much less rigorous research into the risks from AI than into the other risks 80,000 Hours writes about (like pandemics or climate change).¹ That said, there is a growing field of research into the topic, which I’ve tried to reflect. For this article I’ve leaned especially on this draft report by Joseph Carlsmith at Open Philanthropy (also available as a narration), as it’s the most rigorous overview of the risk that I could find. I’ve also had the article reviewed by over 30 people with different expertise and opinions on the topic. (Almost all are concerned about advanced AI’s potential impact.)

If you have any feedback on this article — whether there’s something technical we’ve got wrong, some wording we could improve, or just that you did or didn’t like reading it — we’d really appreciate it if you could tell us what you think using this form.

Why do we think that reducing risks from AI is one of the most pressing issues of our time? In short, our reasons are:

1. Even before getting into the actual arguments, we can see some cause for concern — as many AI experts think there’s a small but non-negligible chance that AI will lead to outcomes as bad as human extinction.
2. We’re making advances in AI extremely quickly — which suggests that AI systems could have a significant influence on society, soon.
3. There are strong arguments that “power-seeking” AI could pose an existential threat to humanity² — which we’ll go through below.
4. Even if we find a way to avoid power-seeking, there are still other risks.
5. We think we can tackle these risks.
6. This work is neglected.

We’re going to cover each of these in turn, then consider some of the best counterarguments, explain concrete things you can do to help, and finally outline some of the best resources for learning more about this area.

1. Many AI experts think there’s a non-negligible chance AI will lead to outcomes as bad as extinction

In May 2023, hundreds of AI prominent scientists — and other notable figures — signed a statement saying that mitigating the risk of extinction from AI should be a global priority.

So it’s pretty clear that at least some experts are concerned.

But how concerned are they? And is this just a fringe view?

We looked at three surveys of AI researchers who published at NeurIPS and ICML (two of the most prestigious machine learning conferences) — one in 2016, one in 2019, and one in 2022.³

It’s important to note that there could be considerable selection bias on surveys like this. For example, you might think researchers who go to the top AI conferences are more likely to be optimistic about AI, because they have been selected to think that AI research is doing good. Alternatively, you might think that researchers who are already concerned about AI are more likely to respond to a survey asking about these concerns.⁴

All that said, here’s what we found:

In all three surveys, the median researcher thought that the chances that AI would be “extremely good” was reasonably high: 20% in the 2016 survey, 20% in 2019, and 10% in 2022.⁵

Indeed, AI systems are already having substantial positive effects — for example, in medical care or academic research.

But in all three surveys, the median researcher also estimated small — and certainly not negligible — chances that AI would be “extremely bad (e.g. human extinction)”: a 5% chance of extremely bad outcomes in the 2016 survey, 2% in 2019, and 5% in 2022.^{6 7}

In the 2022 survey, participants were specifically asked about the chances of existential catastrophe caused by future AI advances — and again, over half of researchers thought the chances of an existential catastrophe was greater than 5%.⁸

So experts disagree on the degree to which AI poses an existential risk — a kind of threat we’ve argued deserves serious moral weight.

This fits with our understanding of the state of the research field. Three of the leading labs developing AI — DeepMind, Anthropic and OpenAI — also have teams dedicated to figuring out how to solve technical safety issues that we believe could, for reasons we discuss at length below, lead to an existential threat to humanity.⁹

There are also several academic research groups (including at MIT, Oxford, Cambridge, Carnegie Mellon University, and UC Berkeley) focusing on these same technical AI safety problems.¹⁰

It’s hard to know exactly what to take from all this, but we’re confident that it’s not a fringe position in the field to think that there is a material risk of outcomes as bad as an existential catastrophe. Some experts in the field maintain, though, that the risks are overblown.

Still, why do we side with those who are more concerned? In short, it’s because there are arguments we’ve found persuasive that AI could pose such an existential threat — arguments we will go through step by step below.

It’s important to recognise that the fact that many experts recognise there’s a problem doesn’t mean that everything’s OK, the experts have got it covered. Overall, we think this problem remains highly neglected, with only around 400 people working directly on the issue worldwide (more on this below).

Meanwhile, there are billions of dollars a year going into making AI more advanced.¹¹

2. We’re making advances in AI extremely quickly

Before we try to figure out what the future of AI might look like, it’s helpful to take a look at what AI can already do.

Modern AI techniques involve machine learning (ML): models that improve automatically through data input. The most common form of this technique used today is known as deep learning.

ML systems today can only perform a very small portion of tasks that humans can do, and (with a few exceptions) only within narrow specialties (like playing one particular game or generating one particular kind of image).

That said, since the increasingly widespread use of deep learning in the mid-2010s, there has been huge progress in what can be achieved with ML. Here’s a brief timeline of only some of the advances we saw from 2019 to 2022:

• AlphaStar, which can beat top professional players at StarCraft II (January 2019)
• MuZero, a single system that learned to win games of chess, shogi, and Go — without ever being told the rules (November 2019)
• GPT-3, a natural language model capable of producing high-quality text (May 2020)
• GPT-f, which can solve some Maths Olympiad problems (September 2020)
• AlphaFold 2, a huge step forward in solving the long-perplexing protein-folding problem (July 2021)
• Codex, which can produce code for programs from natural language instructions (August 2021)
• PaLM, a language model which has shown impressive capabilities to reason about things like cause and effect or explaining jokes (April 2022)
• DALL-E 2 (April 2022) and Imagen (May 2022), which are both capable of generating high-quality images from written descriptions
• SayCan, which takes natural language instructions and uses them to operate a robot (April 2022)
• Gato, a single ML model capable of doing a huge number of different things (including playing Atari, captioning images, chatting, and stacking blocks with a real robot arm), deciding based on its context what it should output (May 2022)
• Minerva can solve complex maths problems — fairly well at college level, and even better at high school maths competition level. (Minerva is far more successful than forecasters predicted in 2021.)

If you’re anything like us, you found the complexity and breadth of the tasks these systems can carry out surprising.

And if the technology keeps advancing at this pace, it seems clear there will be major effects on society. At the very least, automating tasks makes carrying out those tasks cheaper. As a result, we may see rapid increases in economic growth (perhaps even to the level we saw during the Industrial Revolution).

If we’re able to partially or fully automate scientific advancement we may see more transformative changes to society and technology.¹³

That could be just the beginning. We may be able to get computers to eventually automate anything humans can do. This seems like it has to be possible — at least in principle. This is because it seems that, with enough power and complexity, a computer should be able to simulate the human brain. This would itself be a way of automating anything humans can do (if not the most efficient method of doing so).

And as we’ll see in the next section, there are some indications that extensive automation may well be possible through scaling up existing techniques.

Current trends show rapid progress in the capabilities of ML systems

There are three things that are crucial to building AI through machine learning:

1. Good algorithms (e.g. more efficient algorithms are better)
2. Data to train an algorithm
3. Enough computational power (known as compute) to do this training

We spoke to Danny Hernandez, who (at the time) was a research scientist on the Foresight team at OpenAI. Hernandez and his team looked at how two of these inputs (compute and algorithm efficiency) are changing over time.

They found that, since 2012, the amount of compute used for training the largest AI models has been rising exponentially — doubling every 3.4 months.

That is to say, since 2012, the amount of computational power used to train our largest machine learning models has grown by over 1 billion times.

Hernandez and his team also looked at how much compute has been needed to train a neural network to have the same performance as AlexNet (an early image classification algorithm).

They found that the amount of compute required for the same performance has been falling exponentially — halving every 16 months.

So since 2012, the amount of compute required for the same level of performance has fallen by over 100 times. Combined with the increased compute used, that’s a lot of growth.¹⁶

It’s hard to say whether these trends will continue, but they speak to incredible gains over the past decade in what it’s possible to do with machine learning.

Indeed, it looks like increasing the size of models (and the amount of compute used to train them) introduces ever more sophisticated behaviour. This is how things like GPT-3 are able to perform tasks they weren’t specifically trained for.

These observations have led to the scaling hypothesis: that we can simply build bigger and bigger neural networks, and as a result we will end up with more and more powerful artificial intelligence, and that this trend of increasing capabilities may increase to human-level AI and beyond.

If this is true, we can attempt to predict how the capabilities of AI technology will increase over time simply by looking at how quickly we are increasing the amount of compute available to train models.

But as we’ll see, it’s not just the scaling hypothesis that suggests we could end up with extremely powerful AI relatively soon — other methods of predicting AI progress come to similar conclusions.

When can we expect transformative AI?

It’s difficult to predict exactly when we will develop AI that we expect to be hugely transformative for society (for better or for worse) — for example, by automating all human work or drastically changing the structure of society.¹⁷ But here we’ll go through a few approaches.

One option is to survey experts. Data from the 2019 survey of 300 AI experts implies that there is 20% probability of human-level machine intelligence (which would plausibly be transformative in this sense) by 2036, 50% probability by 2060, and 85% by 2100.¹⁸ There are a lot of reasons to be suspicious of these estimates,⁴ but we take it as one data point.

Ajeya Cotra (a researcher at Open Philanthropy) attempted to forecast transformative AI by comparing modern deep learning to the human brain. Deep learning involves using a huge amount of compute to train a model, before that model is able to perform some task. There’s also a relationship between the amount of compute used to train a model and the amount used by the model when it’s run. And — if the scaling hypothesis is true — we should expect the performance of a model to predictably improve as the computational power used increases. So Cotra used a variety of approaches (including, for example, estimating how much compute the human brain uses on a variety of tasks) to estimate how much compute might be needed to train a model that, when run, could carry out the hardest tasks humans can do. She then estimated when using that much compute would be affordable.

Cotra’s 2022 update on her report’s conclusions estimates that there is a 35% probability of transformative AI by 2036, 50% by 2040, and 60% by 2050 — noting that these guesses are not stable.¹⁹

Tom Davidson (also a researcher at Open Philanthropy) wrote a report to complement Cotra’s work. He attempted to figure out when we might expect to see transformative AI based only on looking at various types of research that transformative AI might be like (e.g. developing technology that’s the ultimate goal of a STEM field, or proving difficult mathematical conjectures), and how long it’s taken for each of these kinds of research to be completed in the past, given some quantity of research funding and effort.

Davidson’s report estimates that, solely on this information, you’d think that there was an 8% chance of transformative AI by 2036, 13% by 2060, and 20% by 2100. However, Davidson doesn’t consider the actual ways in which AI has progressed since research started in the 1950s, and notes that it seems likely that the amount of effort we put into AI research will increase as AI becomes increasingly relevant to our economy. As a result, Davidson expects these numbers to be underestimates.

Holden Karnofsky, co-CEO of Open Philanthropy, attempted to sum up the findings of all of the approaches above. He guesses there is more than a 10% chance we’ll see transformative AI by 2036(!), 50% by 2060, and 66% by 2100. And these guesses might be conservative, since they didn’t incorporate what we see as faster-than-expected progress since the estimates were made.

All in all, AI seems to be advancing rapidly. More money and talent is going into the field every year, and models are getting bigger and more efficient.

Even if AI were advancing more slowly, we’d be concerned about it — most of the arguments about the risks from AI (that we’ll get to below) do not depend on this rapid progress.

However, the speed of these recent advances increases the urgency of the issue.

(It’s totally possible that these estimates are wrong – below, we discuss how the possibility that we might have a lot of time to work on this problem is one of the best arguments against this problem being pressing).

3. Power-seeking AI could pose an existential threat to humanity

We’ve argued so far that we expect AI to be an important — and potentially transformative — new technology.

We’ve also seen reason to think that such transformative AI systems could be built this century.

Now we’ll turn to the core question: why do we think this matters so much?

There could be a lot of reasons. If advanced AI is as transformative as it seems like it’ll be, there will be many important consequences. But here we are going to explain the issue that seems most concerning to us: AI systems could pose risks by seeking and gaining power.

We’ll argue that:

1. It’s likely that we’ll build AI systems that can make and execute plans to achieve goals
2. Advanced planning systems could easily be ‘misaligned’ — in a way that could lead them to make plans that involve disempowering humanity
3. Disempowerment by AI systems would be an existential catastrophe
4. People might deploy AI systems that are misaligned, despite this risk

Thinking through each step, I think there’s something like a 1% chance of an existential catastrophe resulting from power-seeking AI systems this century. This is my all things considered guess at the risk incorporating considerations of the argument in favour of the risk (which is itself probabilistic), as well as reasons why this argument might be wrong (some of which I discuss below). This puts me on the less worried end of 80,000 Hours staff, whose views on our last staff survey ranged from 1–55%, with a median of 15%.

It’s likely we’ll build advanced planning systems

We’re going to argue that future systems with the following three properties might pose a particularly important threat to humanity:²⁰

1. They have goals and are good at making plans.

   Not all AI systems have goals or make plans to achieve those goals. But some systems (like some chess-playing AI systems) can be thought of in this way. When discussing power-seeking AI, we’re considering planning systems that are relatively advanced, with plans that are in pursuit of some goal(s), and that are capable of carrying out those plans.

2. They have excellent strategic awareness.

   A particularly good planning system would have a good enough understanding of the world to notice obstacles and opportunities that may help or hinder its plans, and respond to these accordingly. Following Carlsmith, we’ll call this strategic awareness, since it allows systems to strategise in a more sophisticated way.

3. They have highly advanced capabilities relative to today’s systems.

   For these systems to actually affect the world, we need them to not just make plans, but also be good at all the specific tasks required to execute those plans.

   Since we’re worried about systems attempting to take power from humanity, we are particularly concerned about AI systems that might be better than humans on one or more tasks that grant people significant power when carried out well in today’s world.

   For example, people who are very good at persuasion and/or manipulation are often able to gain power — so an AI being good at these things might also be able to gain power. Other examples might include hacking into other systems, tasks within scientific and engineering research, as well as business, military, or political strategy.

These systems seem technically possible and we’ll have strong incentives to build them

As we saw above, we’ve already produced systems that are very good at carrying out specific tasks.

We’ve also already produced rudimentary planning systems, like AlphaStar, which skilfully plays the strategy game Starcraft, and MuZero, which plays chess, shogi, and Go.²¹

We’re not sure whether these systems are producing plans in pursuit of goals per se, because we’re not sure exactly what it means to “have goals.” However, since they consistently plan in ways that achieve goals, it seems like they have goals in some sense.

Moreover, some existing systems seem to actually represent goals as part of their neural networks.²²

That said, planning in the real world (instead of games) is much more complex, and to date we’re not aware of any unambiguous examples of goal-directed planning systems, or systems that exhibit high degrees of strategic awareness.

But as we’ve discussed, we expect to see further advances within this century. And we think these advances are likely to produce systems with all three of the above properties.

That’s because we think that there are particularly strong incentives (like profit) to develop these kinds of systems. In short: because being able to plan to achieve a goal, and execute that plan, seems like a particularly powerful and general way of affecting the world.

Getting things done — whether that’s a company selling products, a person buying a house, or a government developing policy — almost always seems to require these skills. One example would be assigning a powerful system a goal and expecting the system to achieve it — rather than having to guide it every step of the way. So planning systems seem likely to be (economically and politically) extremely useful.²³

And if systems are extremely useful, there are likely to be big incentives to build them. For example, an AI that could plan the actions of a company by being given the goal to increase its profits (that is, an AI CEO) would likely provide significant wealth for the people involved — a direct incentive to produce such an AI.

As a result, if we can build systems with these properties (and from what we know, it seems like we will be able to), it seems like we are likely to do so.²⁴

Advanced planning systems could easily be dangerously ‘misaligned’

There are reasons to think that these kinds of advanced planning AI systems will be misaligned. That is, they will aim to do things that we don’t want them to do.²⁵

There are many reasons why systems might not be aiming to do exactly what we want them to do. For one thing, we don’t know how, using modern ML techniques, to give systems the precise goals we want (more here).²⁶

We’re going to focus specifically on some reasons why systems might by default be misaligned in such a way that they develop plans that pose risks to humanity’s ability to influence the world — even when we don’t want that influence to be lost.²⁷

What do we mean by “by default”? Essentially, unless we actively find solutions to some (potentially quite difficult) problems, then it seems like we’ll create dangerously misaligned AI. (There are reasons this might be wrong — which we discuss later.)

Three examples of “misalignment” in a variety of systems

It’s worth noting that misalignment isn’t a purely theoretical possibility (or specific to AI) — we see misaligned goals in humans and institutions all the time, and have also seen examples of misalignment in AI systems.²⁸

Example 1: Winning elections

The democratic political framework is intended to ensure that politicians make decisions that benefit society. But what political systems actually reward is winning elections, so that’s what many politicians end up aiming for.

This is a decent proxy goal — if you have a plan to improve people’s lives, they’re probably more likely to vote for you — but it isn’t perfect. As a result, politicians do things that aren’t clearly the best way of running a country, like raising taxes at the start of their term and cutting them right before elections.

That is to say, the things the system does are at least a little different from what we would, in a perfect world, want it to do: the system is misaligned.

Example 2: The profit incentive

Companies have profit-making incentives. By producing more, and therefore helping people obtain goods and services at cheaper prices, companies make more money.

This is sometimes a decent proxy for making the world better, but profit isn’t actually the same as the good of all of humanity (bold claim, we know). As a result, there are negative externalities: for example, companies will pollute to make money despite this being worse for society overall.

Again, we have a misaligned system, where the things the system does are at least a little different from what we would want it to do.

Example 3: Specification gaming in existing AI systems

DeepMind has documented examples of specification gaming: an AI doing well according to its specified reward function (which encodes our intentions for the system), but not doing what researchers intended.

In one example, a robot arm was asked to grasp a ball. But the reward was specified in terms of whether humans thought the robot had been successful. As a result, the arm learned to hover between the ball and the camera, fooling the humans into thinking that it had grasped the ball.²⁹

So we know it’s possible to create a misaligned AI system.

Why these systems could (by default) be dangerously misaligned

Here’s the core argument of this article. We’ll use all three properties from earlier: planning ability, strategic awareness, and advanced capabilities.

To start, we should realise that a planning system that has a goal will also develop ‘instrumental goals’: things that, if they occur, will make it easier to achieve an overall goal.

We use instrumental goals in plans all the time. For example, a high schooler planning their career might think that getting into university will be helpful for their future job prospects. In this case, “getting into university” would be an instrumental goal.

A sufficiently advanced AI planning system would also include instrumental goals in its overall plans.

If a planning AI system also has enough strategic awareness, it will be able to identify facts about the real world (including potential things that would be obstacles to any plans), and plan in light of them. Crucially, these facts would include that access to resources (e.g. money, compute, influence) and greater capabilities — that is, forms of power — open up new, more effective ways of achieving goals.

This means that, by default, advanced planning AI systems would have some worrying instrumental goals:

• Self-preservation — because a system is more likely to achieve its goals if it is still around to pursue them (in Stuart Russell’s memorable phrase, “You can’t fetch the coffee if you’re dead”).
• Preventing any changes to the AI system’s goals — since changing its goals would lead to outcomes that are different from those it would achieve with its current goals.
• Gaining power — for example, by getting more resources and greater capabilities.

Crucially, one clear way in which the AI can ensure that it will continue to exist (and not be turned off), and that its objectives will never be changed, would be to gain power over the humans who might affect it (we talk here about how AI systems might actually be able to do that).

What’s more, the AI systems we’re considering have advanced capabilities — meaning they can do one or more tasks that grant people significant power when carried out well in today’s world. With such advanced capabilities, these instrumental goals will not be out of reach, and as a result, it seems like the AI system would use its advanced capabilities to get power as part of the plan’s execution. If we don’t want the AI systems we create to take power away from us this would be a particularly dangerous form of misalignment.

In the most extreme scenarios, a planning AI system with sufficiently advanced capabilities could successfully disempower us completely.

As a (very non-rigorous) intuitive check on this argument, let’s try to apply it to humans.

Humans have a variety of goals. For many of these goals, some form of power-seeking is advantageous: though not everyone seeks power, many people do (in the form of wealth or social or political status), because it’s useful for getting what they want. This is not catastrophic (usually!) because, as human beings:

• We generally feel bound by human norms and morality (even people who really want wealth usually aren’t willing to kill to get it).
• We aren’t that much more capable or intelligent than one another. So even in cases where people aren’t held back by morality, they’re not able to take over the world.

(We discuss whether humans are truly power-seeking later.)

A sufficiently advanced AI wouldn’t have those limitations.

It might be hard to find ways to prevent this sort of misalignment

The point of all this isn’t to say that any advanced planning AI system will necessarily attempt to seek power. Instead, it’s to point out that, unless we find a way to design systems that don’t have this flaw, we’ll face significant risk.

It seems more than plausible that we could create an AI system that isn’t misaligned in this way, and thereby prevent any disempowerment. Here are some strategies we might take (plus, unfortunately, some reasons why they might be difficult in practice):³⁰

• Control the objectives of the AI system. We may be able to design systems that simply don’t have objectives to which the above argument applies — and thus don’t incentivise power-seeking behaviour. For example, we could find ways to explicitly instruct AI systems not to harm humans, or find ways to reward AI systems (in training environments) for not engaging in specific kinds of power-seeking behaviour (and also find ways to ensure that this behaviour continues outside the training environment).

  Carlsmith gives two reasons why doing this seems particularly hard.

  First, for modern ML systems, we don’t get to explicitly state a system’s objectives — instead we reward (or punish) a system in a training environment so that it learns on its own. This raises a number of difficulties, one of which is goal misgeneralisation. Researchers have uncovered real examples of systems that appear to have learned to pursue a goal in the training environment, but then fail to generalise that goal when they operate in a new environment. This raises the possibility that we could think we’ve successfully trained an AI system not to seek power — but that the system would seek power anyway when deployed in the real world.³¹

  Second, when we specify a goal to an AI system (or, when we can’t explicitly do that, when we find ways to reward or punish a system during training), we usually do this by giving the system a proxy by which outcomes can be measured (e.g. positive human feedback on a system’s achievement). But often those proxies don’t quite work.³² In general, we might expect that even if a proxy appears to correlate well with successful outcomes, it might not do so when that proxy is optimised for. (The examples above of politicians, companies, and the robot arm failing to grasp a ball are illustrations of this.) We’ll look at a more specific example of how problems with proxies could lead to an existential catastrophe here.

  For more on the specific difficulty of controlling the objectives given to deep neural networks trained using self-supervised learning and reinforcement learning, we recommend OpenAI governance researcher Richard Ngo’s discussion of how realistic training processes lead to the development of misaligned goals.

• Control the inputs into the AI system. AI systems will only develop plans to seek power if they have enough information about the world to realise that seeking power is indeed a way to achieve its goals.

• Control the capabilities of the AI system. AI systems will likely only be able to carry out plans to seek power if they have sufficiently advanced capabilities in skills that grant people significant power in today’s world.

But to make any strategy work, it will need to both:

• Retain the usefulness of the AI systems — and so remain economically competitive with less safe systems. Controlling the inputs and capabilities of AI systems will clearly have costs, so it seems hard to ensure that these controls, even if they’re developed, are actually used. But this is also a problem for controlling a system’s objectives. For example, we may be able to prevent power-seeking behaviour by ensuring that AI systems stop to check in with humans about any decisions they make. But these systems might be significantly slower and less immediately useful to people than systems that don’t stop to carry out these checks. As a result, there might still be incentives to use a faster, more initially effective misaligned system (we’ll look at incentives more in the next section).

• Continue to work as the planning ability and strategic awareness of systems improve over time. Some seemingly simple solutions (for example, trying to give a system a long list of things it isn’t allowed to do, like stealing money or physically harming humans) break down as the planning abilities of the systems increase. This is because, the more capable a system is at developing plans, the more likely it is to identify loopholes or failures in the safety strategy — and as a result, the more likely the system is to develop a plan that involves power-seeking.

Ultimately, by looking at the state of the research on this topic, and speaking to experts in the field, we think that there are currently no known ways of building aligned AI systems that seem likely to fulfil both these criteria.

So: that’s the core argument. There are many variants of this argument. Some have argued that AI systems might gradually shape our future via subtler forms of influence that nonetheless could amount to an existential catastrophe; others argue that the most likely form of disempowerment is in fact just killing everyone. We’re not sure how a catastrophe would be most likely to play out, but have tried to articulate the heart of the argument, as we see it: that AI presents an existential risk.

There are definitely reasons this argument might not be right! We go through some of the reasons that seem strongest to us below. But overall it seems possible that, for at least some kinds of advanced planning AI systems, it will be harder to build systems that don’t seek power in this dangerous way than to build systems that do.

Disempowerment by AI systems would be an existential catastrophe

When we say we’re concerned about existential catastrophes, we’re not just concerned about risks of extinction. This is because the source of our concern is rooted in longtermism: the idea that the lives of all future generations matter, and so it’s extremely important to protect their interests.

This means that any event that could prevent all future generations from living lives full of whatever you think makes life valuable (whether that’s happiness, justice, beauty, or general flourishing) counts as an existential catastrophe.

It seems extremely unlikely that we’d be able to regain power over a system that successfully disempowers humanity. And as a result, the entirety of the future — everything that happens for Earth-originating life, for the rest of time — would be determined by the goals of systems that, although built by us, are not aligned with us. Perhaps those goals will create a long and flourishing future, but we see little reason for confidence.³³

This isn’t to say that we don’t think AI also poses a risk of human extinction. Indeed, we think making humans extinct is one highly plausible way in which an AI system could completely and permanently ensure that we are never able to regain power.

People might deploy misaligned AI systems despite the risk

Surely no one would actually build or use a misaligned AI if they knew it could have such terrible consequences, right?

Unfortunately, there are at least two reasons people might create and then deploy misaligned AI — which we’ll go through one at a time:³⁴

1. People might think it’s aligned when it’s not

Imagine there’s a group of researchers trying to tell, in a test environment, whether a system they’ve built is aligned. We’ve argued that an intelligent planning AI will want to improve its abilities to effect changes in pursuit of its objective, and it’s almost always easier to do that if it’s deployed in the real world, where a much wider range of actions are available. As a result, any misaligned AI that’s sophisticated enough will try to understand what the researchers want it to do and at least pretend to be doing that, deceiving the researchers into thinking it’s aligned. (For example, a reinforcement learning system might be rewarded for certain apparent behaviour during training, regardless of what it’s actually doing.)

Hopefully, we’ll be aware of this sort of behaviour and be able to detect it. But catching a sufficiently advanced AI in deception seems potentially harder than catching a human in a lie, which isn’t always easy. For example, a sufficiently intelligent deceptive AI system may be able to deceive us into thinking we’ve solved the problem of AI deception, even if we haven’t.

If AI systems are good at deception, and have sufficiently advanced capabilities, a reasonable strategy for such a system could be to deceive humans completely until the system has a way to guarantee it can overcome any resistance to its goals.

2. There are incentives to deploy systems sooner rather than later

We might also expect some people with the ability to deploy a misaligned AI to charge ahead despite any warning signs of misalignment that do come up, because of race dynamics — where people developing AI want to do so before anyone else.

For example, if you’re developing an AI to improve military or political strategy, it’s much more useful if none of your rivals have a similarly powerful AI.

These incentives apply even to people attempting to build an AI in the hopes of using it to make the world a better place.

For example, say you’ve spent years and years researching and developing a powerful AI system, and all you want is to use it to make the world a better place. Simplifying things a lot, say there are two possibilities:

1. This powerful AI will be aligned with your beneficent aims, and you’ll transform society in a potentially radically positive way.
2. The AI will be sufficiently misaligned that it’ll take power and permanently end humanity’s control over the future.

Let’s say you think there’s a 90% chance that you’ve succeeded in building an aligned AI. But technology often develops at similar speeds across society, so there’s a good chance that someone else will soon also develop a powerful AI. And you think they’re less cautious, or less altruistic, so you think their AI will only have an 80% chance of being aligned with good goals, and pose a 20% chance of existential catastrophe. And only if you get there first can your more beneficial AI be dominant. As a result, you might decide to go ahead with deploying your AI, accepting the 10% risk.

4. Even if we find a way to avoid power-seeking, there are still risks

So far we’ve described what a large proportion of researchers in the field² think is the major existential risk from potential advances in AI, which depends crucially on an AI seeking power to achieve its goals.

If we can prevent power-seeking behaviour, we will have reduced existential risk substantially.

But even if we succeed, there are still existential risks that AI could pose.

AI could worsen war

We’re concerned that great power conflict could also pose a substantial threat to our world, and advances in AI seem likely to change the nature of war — through lethal autonomous weapons³⁵ or through automated decision making.³⁶

In some cases, great power war could pose an existential threat — for example, if the conflict is nuclear. It’s possible that AI could exacerbate risks of nuclear escalation, although there are also reasons to think AI could decrease this risk.³⁷

Finally, if a single actor produces particularly powerful AI systems, this could be seen as giving them a decisive strategic advantage. For example, the US may produce a planning AI that’s intelligent enough to ensure that Russia or China could never successfully launch another nuclear weapon. This could incentivise a first strike from the actor’s rivals before these AI-developed plans can ever be put into action.

AI could be used to develop dangerous new technology

We expect that AI systems will help increase the rate of scientific progress.³⁸

While there would be clear benefits to this automation — the rapid development of new medicine, for example — some forms of technological development can pose threats, including existential threats, to humanity. This could be through biotechnology³⁹ (see our article on preventing catastrophic pandemics for more) or through some other form of currently unknown but dangerous technology.⁴⁰

AI could empower totalitarian governments

An AI-enabled authoritarian government could completely automate the monitoring and repression of its citizens, as well as significantly influence the information people see, perhaps making it impossible to coordinate action against such a regime.⁴¹

If this became a form of truly stable totalitarianism, this could make people’s lives far worse for extremely long periods of time, making it a particularly scary possible scenario resulting from AI.

Other risks from AI

We’re also concerned about the following issues, though we know less about them:

• Existential threats that result not from the power-seeking behaviour of AI systems, but as a result of the interaction between AI systems. (In order to pose a risk, these systems would still need to be, to some extent, misaligned.)
• Other ways we haven’t thought of in which AI systems could be misused — especially ones that might significantly affect future generations.
• Other moral mistakes made in the design and use of AI systems, particularly if future AI systems are themselves deserving of moral consideration. For example, perhaps we will (inadvertently) create conscious AI systems, which could then suffer in huge numbers. We think this could be extremely important, so we’ve written about it in a separate problem profile.

So, how likely is an AI-related catastrophe?

This is a really difficult question to answer.

There are no past examples we can use to determine the frequency of AI-related catastrophes.

All we have to go off are arguments (like the ones we’ve given above), and less relevant data like the history of technological advances. And we’re definitely not certain that the arguments we’ve presented are completely correct.

Consider the argument we gave earlier about the dangers of power-seeking AI in particular, based off Carlsmith’s report. At the end of his report, Carlsmith gives some rough guesses of the chances that each stage of his argument is correct (conditional on the previous stage being correct):

1. By 2070 it will be possible and financially feasible to build strategically aware systems that can outperform humans on many power-granting tasks, and that can successfully make and carry out plans: Carlsmith guesses there’s a 65% chance of this being true.
2. Given this feasibility, there will be strong incentives to build such systems: 80%.
3. Given both the feasibility and incentives to build such systems, it will be much harder to develop aligned systems that don’t seek power than to develop misaligned systems that do, but which are at least superficially attractive to deploy: 40%.
4. Given all of this, some deployed systems will seek power in a misaligned way that causes over $1 trillion (in 2021 dollars) of damage: 65%.
5. Given all the previous premises, misaligned power-seeking AI systems will end up disempowering basically all of humanity: 40%.
6. Given all the previous premises, this disempowerment will constitute an existential catastrophe: 95%.

Multiplying these numbers together, Carlsmith estimated that there’s a 5% chance that his argument is right and there will be an existential catastrophe from misaligned power-seeking AI by 2070. When we spoke to Carlsmith, he noted that in the year between the writing of his report and the publication of this article, his overall guess at the chance of an existential catastrophe from power-seeking AI by 2070 had increased to >10%.⁴²

The overall probability of existential catastrophe from AI would, in Carlsmith’s view, be higher than this, because there are other routes to possible catastrophe — like those discussed in the previous section — although our guess is that these other routes are probably a lot less likely to lead to existential catastrophe.

For another estimate, in The Precipice, philosopher and advisor to 80,000 Hours Toby Ord estimated a 1-in-6 risk of existential catastrophe by 2120 (from any cause), and that 60% of this risk comes from misaligned AI — giving a total of a 10% risk of existential catastrophe from misaligned AI by 2120.

A 2021 survey of 44 researchers working on reducing existential risks from AI found the median risk estimate was 32.5% — the highest answer given was 98%, and the lowest was 2%.⁴³ There’s obviously a lot of selection bias here: people choose to work on reducing risks from AI because they think this is unusually important, so we should expect estimates from this survey to be substantially higher than estimates from other sources. But there’s clearly significant uncertainty about how big this risk is, and huge variation in answers.

All these numbers are shockingly, disturbingly high. We’re far from certain that all the arguments are correct. But these are generally the highest guesses for the level of existential risk of any of the issues we’ve examined (like engineered pandemics, great power conflict, climate change, or nuclear war).

That said, I think there are reasons why it’s harder to make guesses about the risks from AI than other risks – and possibly reasons to think that the estimates we’ve quoted above are systematically too high.

If I was forced to put a number on it, I’d say something like 1%. This number includes considerations both in favour and against the argument. I’m less worried than other 80,000 Hours staff — our position as an organisation is that the risk is between 3% and 50%.

All this said, the arguments for such high estimates of the existential risk posed by AI are persuasive — making risks from AI a top contender for the most pressing problem facing humanity.

5. We can tackle these risks

We think one of the most important things you can do would be to help reduce the gravest risks that AI poses.

This isn’t just because we think these risks are high — it’s also because we think there are real things we can do to reduce these risks.

We know of two broad approaches:

1. Technical AI safety research
2. AI governance research and implementation

For both of these, there are lots of ways to contribute. We’ll go through them in more detail below, but in this section we want to illustrate the point that there are things we can do to address these risks.

Technical AI safety research

The benefits of transformative AI could be huge, and there are many different actors involved (operating in different countries), which means it will likely be really hard to prevent its development altogether.

(It’s also possible that it wouldn’t even be a good idea if we could — after all, that would mean forgoing the benefits as well as preventing the risks.)

As a result, we think it makes more sense to focus on making sure that this development is safe — meaning that it has a high probability of avoiding all the catastrophic failures listed above.

One way to do this is to try to develop technical solutions to prevent the kind of power-seeking behaviour we discussed earlier — this is generally known as working on technical AI safety, sometimes called just “AI safety” for short.

Read more about technical AI safety research below.

AI governance research and implementation

A second strategy for reducing risks from AI is to shape its development through policy, norms-building, and other governance mechanisms.

Good AI governance can help technical safety work, for example by producing safety agreements between corporations, or helping talented safety researchers from around the world move to where they can be most effective. AI governance could also help with other problems that lead to risks, like race dynamics.

But also, as we’ve discussed, even if we successfully manage to make AI do what we want (i.e. we ‘align’ it), we might still end up choosing something bad for it to do! So we need to worry about the incentives not just of the AI systems, but of the human actors using them.

Read more about AI governance research and implementation below.

6. This work is neglected

We estimate there are around 400 people around the world working directly on reducing the chances of an AI-related existential catastrophe (with a 90% confidence interval ranging between 200 and 1,000). Of these, about three quarters are working on technical AI safety research, with the rest split between strategy (and other governance) research and advocacy.⁴⁴ We think there are around 800 people working in complementary roles, but we’re highly uncertain about this estimate.⁴⁵

In The Precipice, Ord estimated that there was between $10 million and $50 million spent on reducing AI risk in 2020.

That might sound like a lot of money, but we’re spending something like 1,000 times that amount¹¹ on speeding up the development of transformative AI via commercial capabilities research and engineering at large AI labs.

To compare the $50 million spent on AI safety in 2020 to other well-known risks, we’re currently spending several hundreds of billions per year on tackling climate change.

Because this field is so neglected and has such high stakes, we think your impact working on risks from AI could be much higher than working on many other areas — which is why our top two recommended career paths for making a big positive difference in the world are technical AI safety and AI policy research and implementation.

What do we think are the best arguments against this problem being pressing?

As we said above, we’re not totally sure the arguments we’ve presented for AI representing an existential threat are right. Though we do still think that the chance of catastrophe from AI is high enough to warrant many more people pursuing careers to try to prevent such an outcome, we also want to be honest about the arguments against doing so, so you can more easily make your own call on the question.

Here we’ll cover the strongest reasons (in our opinion) to think this problem isn’t particularly pressing. In the next section we’ll cover some common objections that (in our opinion) hold up less well, and explain why.

Arguments against working on AI risk to which we think there are strong responses

We’ve just discussed the major objections to working on AI risk that we think are most persuasive. In this section, we’ll look at objections that we think are less persuasive, and give some reasons why.

What you can do concretely to help

As we mentioned above, we know of two main ways to help reduce existential risks from AI:

1. Technical AI safety research
2. AI strategy/policy research and implementation

The biggest way you could help would be to pursue a career in either one of these areas, or in a supporting area.

The first step is learning a lot more about the technologies, problems, and possible solutions. We’ve collated some lists of our favourite resources here, and our top recommendation is to take a look at the technical alignment curriculum from AGI Safety Fundamentals.

If you decide to pursue a career in this area, we’d generally recommend working at an organisation focused on specifically addressing this problem (though there are other ways to help besides working at existing organisations, as we discuss briefly below).

Technical AI safety

Approaches

There are lots of approaches to technical AI safety, including:

• Scalably learning from human feedback. Examples include iterated amplification, AI safety via debate, building AI assistants that are uncertain about our goals and learn them by interacting with us, and other ways to get AI systems trained with stochastic gradient descent to report truthfully what they know.
• Threat modelling. An example of this work would be demonstrating the possibility of (allowing us to study) dangerous capabilities, like deceptive or manipulative AI systems. You can read an overview in a recent Google DeepMind paper. This work splits into work that evaluates whether a model has dangerous capabilities (like the work of METR in evaluating GPT-4), and work that evaluates whether a model would cause harm in practice (like Anthropic’s research into the behaviour of large language models and this paper on goal misgeneralisation).
• Interpretability research. This work involves studying why AI systems do what they do and trying to put it into human-understandable terms. For example, this paper examined how AlphaZero learns chess, and this paper looked into finding latent knowledge in language models without supervision. This category also includes mechanistic interpretability — for example, Zoom In: An Introduction to Circuits by Olah et al.). For more, see this survey paper, as well as Hubinger’s a transparency and interpretability tech tree, and Nanda’s A Longlist of Theories of Impact for Interpretability for overviews of of how interpretability research could reduce existential risk from AI.
• Other anti-misuse research to reduce the risks of catastrophe caused by misuse of systems. (We’ve written more on this in our problem profile on AI risk. For example, this work includes training AIs so they’re hard to use for dangerous purposes. (Note there’s lots of overlap with the other work on this list).
• Research to increase the robustness of neural networks. This work involves ensuring that the sorts of behaviour neural networks display when exposed to one set of inputs continues when exposed to inputs they haven’t previously been exposed to, in order to prevent AI systems changing to unsafe behaviour. See section 2 of Unsolved Problems in AI safety for more.
• Work to build cooperative AI. Find ways to ensure that even if individual AI systems seem safe, they don’t produce bad outcomes through interacting with other sociotechnical systems. For more, see Open Problems in Cooperative AI by Dafoe et al. or the Cooperative AI Foundation. This seems particularly relevant for the reduction of ‘s-risks.’
• More generally, there are some unified safety plans. For more, see Hubinger’s 11 possible proposals for building safe advanced AI, or Karnofsky’s How might we align transformative AI if it’s developed very soon.⁴⁷

See Neel Nanda’s overview of the AI alignment landscape for more details.

Key organisations

AI labs in industry that have empirical technical safety teams, or are focused entirely on safety:

• Anthropic is an AI safety company working on building interpretable and safe AI systems. They focus on empirical AI safety research. Anthropic cofounders Daniela and Dario Amodei gave an interview about the lab on the Future of Life Institute podcast. On our podcast, we spoke to Chris Olah, who leads Anthropic’s research into interpretability, and Nova DasSarma, who works on systems infrastructure at Anthropic.
• Model Evaluation and Threat Research works on assessing whether cutting-edge AI systems could pose catastrophic risks to civilization, including early-stage, experimental work to develop techniques, and evaluating systems produced by Anthropic and OpenAI.
• The Center for AI Safety is a nonprofit that does technical research and promotion of safety in the wider machine learning community.
• FAR AI is a research nonprofit that incubates and accelerates research agendas that are too resource-intensive for academia but not yet ready for commercialisation by industry, including research in adversarial robustness, interpretability and preference learning.
• Google DeepMind is probably the largest and most well-known research group developing general artificial machine intelligence, and is famous for its work creating AlphaGo, AlphaZero, and AlphaFold. It is not principally focused on safety, but has two teams focused on AI safety, with the Scalable Alignment Team focusing on aligning existing state-of-the-art systems, and the Alignment Team focused on research bets for aligning future systems.
• OpenAI, founded in 2015, is a lab that is trying to build artificial general intelligence that is safe and benefits all of humanity. OpenAI is well known for its language models like GPT-4. Like DeepMind, it is not principally focused on safety, but has a safety team and a governance team. Jan Leike (head of the alignment team) has some blog posts on how he thinks about AI alignment.
• Ought is a machine learning lab building Elicit, an AI research assistant. Their aim is to align open-ended reasoning by learning human reasoning steps, and to direct AI progress towards helping with evaluating evidence and arguments.
• Redwood Research is an AI safety research organisation, whose first big project attempted to make sure language models (like GPT-3) produce output following certain rules with very high probability, in order to address failure modes too rare to show up in standard training.

Theoretical / conceptual AI safety labs:

• The Alignment Research Center (ARC) is attempting to produce alignment strategies that could be adopted in industry today while also being able to scale to future systems. They focus on conceptual work, developing strategies that could work for alignment and which may be promising directions for empirical work, rather than doing empirical AI work themselves. Their first project was releasing a report on Eliciting Latent Knowledge, the problem of getting advanced AI systems to honestly tell you what they believe (or ‘believe’) about the world. On our podcast, we interviewed ARC founder Paul Christiano about his research (before he founded ARC).
• The Center on Long-Term Risk works to address worst-case risks from advanced AI. They focus on conflict between AI systems.
• The Machine Intelligence Research Institute was one of the first groups to become concerned about the risks from machine intelligence in the early 2000s, and its team has published a number of papers on safety issues and how to resolve them.
• Some teams in commercial labs also do some more theoretical and conceptual work on alignment, such as Anthropic’s work on conditioning predictive models and the Causal Incentives Working Group at Google DeepMind.

AI safety in academia (a very non-comprehensive list; while the number of academics explicitly and publicly focused on AI safety is small, it’s possible to do relevant work at a much wider set of places):

• The Algorithmic Alignment Group in the Computer Science and Artificial Intelligence Laboratory at MIT, led by Dylan Hadfield-Menell
• The Center for Human-Compatible AI at UC Berkeley, led by Stuart Russell, focuses on academic research to ensure AI is safe and beneficial to humans. (Our podcast with Stuart Russell examines his approach to provably beneficial AI.)
• Jacob Steinhardt’s research group in the Department of Statistics at UC Berkeley
• The NYU Alignment research Group led by Sam Bowman
• David Krueger’s research group at the Computational and Biological Learning Laboratory at the University of Cambridge
• The Foundations of Cooperative AI Lab at Carnegie Mellon University
• The Future of Humanity Institute at the University of Oxford has an AI safety research group
• The Alignment of Complex Systems research group at Charles University, Prague

If you’re interested in learning more about technical AI safety as an area — e.g. the different techniques, schools of thought, and threat models — our top recommendation is to take a look at the technical alignment curriculum from AGI Safety Fundamentals.

We discuss this path in more detail here:

Career review of technical AI safety research

Alternatively, if you’re looking for something more concrete and step-by-step (with very little in the way of introduction), check out this detailed guide to pursuing a career in AI alignment.

It’s important to note that you don’t have to be an academic or an expert in AI or AI safety to contribute to AI safety research. For example, software engineers are needed at many places conducting technical safety research, and we also highlight more roles below.

AI governance and strategy

Approaches

Quite apart from the technical problems, we face a host of governance issues, which include:

• Coordination problems that are increasing the risks from AI (e.g. there could be incentives to use AI for personal gain in ways that can cause harm, or race dynamics that reduce incentives for careful and safe AI development).
• Risks from accidents or misuse of AI that would be dangerous even if we are able to prevent power-seeking behaviour (as discussed above).
• A lack of clarity on how and when exactly risks from AI (particularly power-seeking AI) might play out.
• A lack of clarity on which intermediate goals we could pursue that, if achieved, would reduce existential risk from AI.

To tackle these, we need a combination of research and policy.⁴⁸

We are in the early stages of figuring out the shape of this problem and the most effective ways to tackle it. So it’s crucial that we do more research. This includes forecasting research into what we should expect to happen, and strategy and policy research into the best ways of acting to reduce the risks.

But also, as AI begins to impact our society more and more, it’ll be crucial that governments and corporations have the best policies in place to shape its development. For example, governments might be able to enforce agreements not to cut corners on safety, further the work of researchers who are less likely to cause harm, or cause the benefits of AI to be distributed more evenly. So there eventually might be a key role to be played in advocacy and lobbying for appropriate AI policy — though we’re not yet at the point of knowing what policies would be useful to implement.

Key organisations

AI strategy and policy organisations:

• AI Impacts attempts to find answers to all sorts of relevant questions about the future of AI, like “How likely is a sudden jump in AI progress at around human-level performance?”
• The AI Security Initiative at UC Berkeley’s Center for Long-Term Cybersecurity.
• The Centre for the Governance of AI (GovAI) aims to build a global research community, dedicated to helping humanity navigate the transition to a world with advanced AI. On our podcast we’ve spoken to Ben Garfinkel, acting director of GovAI, about some weaknesses of classic AI risk arguments, as well as Allan Dafoe, president of GovAI and leader of DeepMind’s Long-Term Strategy and Governance team, about the destabilising effects of AI.
• The Centre for Long-Term Resilience is a UK think tank focused on existential threats, including those from AI.
• The Center for Security and Emerging Technology at Georgetown researches the foundations of AI (talent, data, and computational power). It focuses on how AI can be used in national security. Listen to our podcast with Helen Toner, their Director of Strategy, for more.
• The Centre for the Study of Existential Risk at the University of Cambridge has a group considering the governance of AI.
• DeepMind and OpenAI both have policy teams (listen to our podcast with members of the OpenAI policy team and our podcast with the head of DeepMind’s governance team, Allan Dafoe).
• The Future of Life Insitute advocates for awareness of AI risk within the academic community and gives out grants for work focused on AI safety.
• The Future of Humanity Institute at the University of Oxford has a macrostrategy research group that considers the future of AI and its contribution to existential risk.
• The Leverhulme Centre for the Future of Intelligence is an interdisciplinary research centre at the University of Cambridge focusing on the impacts of AI on humanity.
• Open Philanthropy provides grants to organisations working on altruistic issues. As a result they have research teams looking at the issues they focus on, including a team looking at potential risks from advanced AI. On our podcast, we spoke to Holden Karnofsky, co-CEO of Open Philanthropy, about his views on risks from AI. (Note: Open Philanthropy is 80,000 Hours’ biggest funder.)
• The Institute for AI Policy and Strategy is focused on AI governance and strategy.

If you’re interested in learning more about AI governance, our top recommendation is to take a look at the governance curriculum from AGI safety fundamentals.

We discuss this path in more detail here:

Career review of AI strategy and policy careers

Also note: it could be particularly important for people with the right personal fit to work on AI strategy and governance in China.

Complementary (yet crucial) roles

Even in a research organisation, around half of the staff will be doing other tasks essential for the organisation to perform at its best and have an impact. Having high-performing people in these roles is crucial.

We think the importance of these roles is often underrated because the work is less visible. So we’ve written several career reviews on these areas to help more people enter these careers and succeed, including:

• Operations management to help impactful organisations grow and function as effectively as possible.
• Research management at an AI safety research organisation.
• Being an executive assistant to someone who’s doing really important work on safety and governance.
• Other non-technical roles in leading AI labs.

Other ways to help

AI safety is a big problem and it needs help from people doing a lot of different kinds of work.

One major way to help is to work in a role that directs funding or people towards AI risk, rather than working on the problem directly. We’ve reviewed a few career paths along these lines, including:

• Founding new projects — in this case, starting new initiatives aimed at reducing risks from advanced AI.
• Being a grantmaker to fund promising projects focused on reducing catastrophic AI risk.
• Working in communication roles
• Helping to build communities of people working on this problem. The most relevant community is the AI safety community itself, but it could also be impactful to help build the community of people working on the world’s most pressing problems (including risks from AI).

There are ways all of these could go wrong, so the first step is to become well-informed about the issue.

There are also other technical roles besides safety research that could help contribute, like:

• Working in information security to protect AI (or the results of key experiments) from misuse, theft, or tampering.
• Becoming an expert in AI hardware as a way of steering AI progress in safer directions.

You can read about all these careers — why we think they’re helpful, how to enter them, and how you can predict whether they’re a good fit for you — on our career reviews page.

Find vacancies on our job board

Our job board features opportunities in AI technical safety and governance:

View all opportunities

Read next:  Explore other pressing world problems

Want to learn more about global issues we think are especially pressing? See our list of issues that are large in scale, solvable, and neglected, according to our research.

Continue →

Acknowledgements

Huge thanks to Joel Becker, Tamay Besiroglu, Jungwon Byun, Joseph Carlsmith, Jesse Clifton, Emery Cooper, Ajeya Cotra, Andrew Critch, Anthony DiGiovanni, Noemi Dreksler, Ben Edelman, Lukas Finnveden, Emily Frizell, Ben Garfinkel, Katja Grace, Lewis Hammond, Jacob Hilton, Samuel Hilton, Michelle Hutchinson, Caroline Jeanmaire, Kuhan Jeyapragasan, Arden Koehler, Daniel Kokotajlo, Victoria Krakovna, Alex Lawsen, Howie Lempel, Eli Lifland, Katy Moore, Luke Muehlhauser, Neel Nanda, Linh Chi Nguyen, Luisa Rodriguez, Caspar Oesterheld, Ethan Perez, Charlie Rogers-Smith, Jack Ryan, Rohin Shah, Buck Shlegeris, Marlene Staib, Andreas Stuhlmüller, Luke Stebbing, Nate Thomas, Benjamin Todd, Stefan Torges, Michael Townsend, Chris van Merwijk, Hjalmar Wijk, and Mark Xu for either reviewing this article or their extremely thoughtful and helpful comments and conversations. (This isn’t to say that they would all agree with everything we’ve said here — in fact, we’ve had many spirited disagreements in the comments on this article!)

The post Preventing an AI-related catastrophe appeared first on 80,000 Hours.

Not all of these questions are best tackled with abstract mathematics research; some can be approached with concrete coding experiments and machine learning (ML) prototypes. As a result, some AI safety research teams are looking to hire a growing number of Software Engineers and ML Research Engineers.

Additionally, some research teams that may not think of themselves as focussed on ‘AI Safety’ per se, nonetheless work on related problems like verification of neural nets or learning from human feedback, and are often hiring engineers.

Note that this guide was written in November 2018 to complement an in-depth conversation on the 80,000 Hours Podcast with Catherine Olsson and Daniel Ziegler on how to transition from computer science and software engineering in general into ML engineering, with a focus on alignment and safety. If you like this guide, we’d strongly encourage you to check out the podcast episode where we discuss some of the instructions here, and other relevant advice.

What are the necessary qualifications for these positions?

Software Engineering: Some engineering roles on AI safety teams do not require ML experience. You might already be prepared to apply to these positions if you have the following qualifications:

• BSc/BEng degree in computer science or another technical field (or comparable experience)
• Strong knowledge of software engineering (as a benchmark: could pass a Google software engineering interview)
• Interest in working on AI safety
• (usually) Willingness to move to London or the San Francisco Bay Area

If you’re a software engineer with an interest in these roles, you may not need any additional preparation, and may be ready to apply right away.

ML Engineering and/or Research Engineering: Some roles require experience implementing and debugging machine learning algorithms. If you don’t yet have ML implementation experience, you may be able to learn the necessary skills quickly, so long as you’re willing to spend a few months studying. Before deciding to do this, you should check that you meet all the following criteria:

• BSc/BEng degree in computer science or another technical field (or comparable experience)
• Strong knowledge of software engineering (as a benchmark: could pass a Google software engineering interview)
• Interest in working on AI safety
• (usually) Willingness to move to London or the San Francisco Bay Area

How can I best learn Machine Learning engineering skills if I don’t yet have the necessary experience?

Initial investigation

Implementing and debugging ML algorithms is different from traditional software engineering. The following can help you determine whether you’ll like the day-to-day work:

• Matthew Rahtz’s blog post Lessons Learned Reproducing a Deep Reinforcement Learning Paper
• S. Zayd Enam’s blog post Why is machine learning “hard”?

ML basics

If you don’t have any experience in machine learning, start by familiarizing yourself with the basics. If you have some experience, but haven’t done a hands-on machine learning project recently, it’s also probably a good idea to brush up on the latest tools (writing TensorFlow, starting a virtual machine with a GPU, etc).

Although it can be difficult to find time for self-study if you’re already employed full-time or have other responsibilities, it’s far from impossible. Here are some ideas of how you might get started:

• Consider spending a few hours a week on an online course. We recommend either of these two:
  • The fast.ai online course, “Practical Deep Learning For Coders, Part 1”
  • Google’s ML Crash Course
• If you’re employed full-time in a software engineering role, you might be able to learn ML basics without leaving your current job:
  • If you’re at a large tech company, take advantage of internal trainings, including full-time ML rotation programs.
  • Ask your manager if you can incorporate machine learning into your current role: for example, to spend 20% of your time learning ML, to see if it could improve one of the projects you work on.

For simple ML problems, you can get pretty far just on CPU on your laptop, but for larger problems it’s useful to buy a GPU and/or rent some cloud GPUs. You can often get some cloud computing credits through a free trial, educational credits for students, or asking a friend with a startup.

Learn ML implementation and debugging, and speak with the team you want to join

Once you know the 101-level basics of ML, the next thing to learn is how to implement and debug ML algorithms. (Based on the experiences of others in the community who have taken this path, we expect this to take at minimum 200 hours of focused work, and likely more if you are starting out with less experience).

Breadth of experience is not important here: you don’t need to read all the latest papers, or master an extensive reading list. You also don’t need to do novel research or come up with new algorithms. Nor do you need to focus on safety at this stage; in fact, focusing on well-known and established ML algorithms is probably better for your learning.

What you do need is to get your hands dirty implementing and debugging ML algorithms, and to build evidence for job interviews that you have some experience doing this.

You should strongly consider contacting the teams you’re interested in at this stage. Send them an email with the specifics of what you’re planning on spending your time on to get feedback on it. The manager of the team may suggest specific resources to use, and can help you avoid wasting time on extraneous skills you don’t need for the role.

The most straightforward way to gain this experience is to choose a subfield of ML relevant to a lab you’re interested in. Then read a few dozen of the subfield’s key papers, and reimplement a few of the foundational algorithms that the papers are based on or reference most frequently. Potential sub-fields include the following:

• Deep reinforcement learning
• Defenses against adversarial examples
• Verification and robustness proofs for neural nets
• Interpretability & visualization

If it isn’t clear how to get started – for example, if you don’t have access to a GPU, or don’t know how to write TensorFlow – many of the resources in the “basics” section above have useful tips.

If you need to quit your job to make time for learning in this phase, but don’t have enough runway to self-fund your studies, consider applying for an EA grant when it next opens – they are open to funding career transitions such as this one.

Case study: Daniel Ziegler’s ML self-study experience

In January 2018, Daniel had strong software engineering skills but only basic ML knowledge. He decided that he wanted to work on an AI safety team as a research engineer, so he talked to Dario Amodei (the OpenAI Safety team lead). Based on Dario’s advice, Daniel spent around six full-time weeks diving into deep reinforcement learning together with a housemate. He also spent a little time reviewing basic ML and doing supervised learning on images and text. Daniel then interviewed and became an ML engineer on the safety team.

Daniel and his housemate used Josh Achiam’s Key Papers in Deep RL list to guide their efforts. They got through about 20-30 of those papers, spending maybe 1.5 hours independently reading and half an hour discussing each paper.

More importantly, they implemented a handful of the key algorithms in TensorFlow:

• Q-learning: DQN and some of its extensions, including prioritized replay and double DQN
• Policy gradients: A2C, PPO, DDPG

They applied these algorithms to try to solve various OpenAI Gym environments, from the simple ‘Cartpole-v0’ to Atari games like ‘Breakout-v4’.

They spent 2-10 days on each algorithm (in parallel as experiments ran), depending on how in-depth they wanted to go. For some, they only got far enough to have a more-or-less-working implementation. For one (PPO), they tried to fix bugs and tune things for long enough to come close to the performance of the OpenAI Baselines implementation.

For each algorithm, they would first test on very easy environments, and then move to more difficult environments. Note that an easy environment for one algorithm may not be easy for another: for example, despite its simplicity, the Cartpole environment has a long time horizon, which can be challenging for some algorithms.

Once the algorithm was partially working, they would attain higher performance by looking for remaining bugs, both by reviewing the code carefully, and by collecting metrics such as average policy entropy to perform sanity-checks, rather than just tune hyperparameters. Finally, when they wanted to match the performance of Baselines, they scrutinized the Baselines implementations for small important details, such as exactly how to preprocess and normalize observations.

By the end of six weeks, Daniel was able to talk fluently about the key ideas in RL and the tradeoffs between different algorithms. Most importantly, he was able to implement and debug ML algorithms, going from math in a paper to running code. In retrospect, Daniel reports wishing he had spent a little more time on ML conceptual & mathematical fundamentals, but that overall this process prepared Daniel well for the interview and the role, and was particularly well-suited for OpenAI’s focus on reinforcement learning.

Now apply for jobs

These positions will eventually be filled, but you can find a constantly updated list of some of the most promising positions on the 80,000 Hours job board.

The following example job postings for software engineers on AI safety research teams specify that machine learning experience is not required:

• OpenAI’s safety team is currently hiring a software engineer for a range of projects, including interfaces for human-in-the-loop AI training and collecting data for larger language models. (Update: this job posting is now closed.)
• MIRI is hiring software engineers.
• Ought is hiring research engineers with a focus on candidates who are excited by functional programming, compilers, program analysis, and related topics.

The following example job postings do expect experience with machine learning implementation:

• DeepMind is hiring research engineers for their Technical AGI Safety team, Safe and Robust AI team – which works on neural net verification and robustness – and potentially others as well.
• Google AI is hiring research software engineers in locations worldwide. Although Google AI does not have an “AI Safety” team, there are research efforts focused on robustness, security, interpretability, and learning from human feedback.
• OpenAI’s safety team is hiring machine learning engineers to work on alignment and interpretability.
• The Center for Human Compatible AI at Berkeley is hiring machine learning research engineers for 1-2 year visiting scholar positions to test alignment ideas for deep reinforcement learning systems.

When you apply to a larger organization that has multiple areas of research, specify in your application which of them you are most interested in working on. Investigate the company’s research areas in advance, in order to make sure that the areas you list are in fact ones that the company works on. For example, don’t specify “value alignment” on an application to a company that does not have any researchers working on value alignment.

If you find that you cannot get a role contributing to safety research right now, you might look for a role in which you can gain relevant experience, and transition to a safety position later.

Non-safety-related research engineering positions are also available at other industry AI labs though these are likely to be more competitive than roles on AGI safety teams.

Finally, you could consider applying to a 1-year fellowship/residency program at Google, OpenAI, Facebook, Uber, or Microsoft.

Learn more

• Working at a leading AI lab might cause harm. Read more on whether it might still be a high-impact career step.
• This curriculum on AI safety (or, for something shorter, this sequence of posts by Richard Ngo)
• Our in-depth conversation on the 80,000 Hours Podcast with Catherine Olsson and Daniel Ziegler, on which this guide is based.
• Our guide to positively shaping the development of advanced artificial intelligence
• Our career review of an ML PhD
• Our podcasts with Chris Olah on what the hell is going on inside neural networks and working at top AI labs without an undergrad degree

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