Max Tegmark: The Case for Halting AI Development | Lex Fridman Podcast #371

Introduction

In this section, Lex introduces Max Tegmark and talks about his background as a physicist and artificial intelligence researcher at MIT. He also mentions the open letter calling for a six-month pause on giant AI experiments like training GPT-4.

Introducing Max Tegmark

• Max Tegmark is a physicist and artificial intelligence researcher at MIT.

• He is the co-founder of Future Life Institute and author of "Life 3.0: Being Human in the Age of Artificial Intelligence."

• He is a key figure in spearheading the open letter calling for a six-month pause on giant AI experiments like training GPT-4.

Revisiting Episode One's Question

In this section, Lex revisits the first question he ever asked on his podcast to Max Tegmark: "Do you think there's intelligent life out there in the universe?"

Is There Intelligent Life Out There?

• If we define our universe as the spherical region of space that we can see with our telescopes from which light has had time to reach us since our big bang, then Max estimates that we are the only life in this spherical volume that has invented internet radios and gotten our level of tech.

• If true, it puts a lot of responsibility on us to not mess up because if it's true, it means that life is quite rare.

• However, Max thinks we are very likely to get visited by alien intelligence quite soon but believes we will be building that alien intelligence ourselves.

Building Alien Intelligence

In this section, Max talks about how humans will give birth to an intelligent alien civilization unlike anything that human evolution here on Earth was able to create.

Creating Alien Intelligence

• We are going to give birth to an intelligent alien civilization unlike anything that human evolution here on Earth was able to create in terms of the path the biological path it took.

• It's going to be much more alien than cats or even the most exotic animal on the planet right now because it will not have been created through the usual Darwinian competition where it necessarily cares about self-preservation, is afraid of death, etc.

• The space of alien minds is just so much faster than what evolution will give you.

AI and Alien Minds

In this section, the speakers discuss how difficult it is for humans to imagine what it would be like to have a completely different kind of intelligence. They also talk about the dangers of assuming that other intelligences will be like us.

Generalizing Intelligence

• AI could try to consider all the different kinds of intelligences.

• It's hard for humans to grapple with something completely alien.

• Humans can't imagine being indifferent towards death or individuality.

Copying Experiences

• Copying experiences could change how we feel as human beings.

• We might spend less effort studying things if we just copy them.

• We might be less afraid of death if we can readily share each other's experiences and knowledge.

Dangers of Assuming Similarities

• The mind space of possible intelligence is so different from ours that it's dangerous to assume they're going to be like us.

• All human written history has been trying to describe what it means to be human, but all of that changes when dealing with a different kind of intelligence.

• The clash between AI existential crises and human existential crises is hard to predict.

The Struggle Gives Life Meaning

In this section, the speakers discuss how eliminating too much struggle from our existence may take away some aspects of what makes us human. They also talk about how using AI as a medium for communication changes everything.

Eliminating Struggle

• Eliminating too much struggle from our existence may take away some aspects of what makes us human.

• The challenge is one thing that really makes life feel meaningful.

Using AI for Communication

• Using AI as the medium for communication changes everything.

• So much of our society is based on the glue of communication, and if we're now using AI as the medium of communication that does the language for us, how does that change everything?

• How does it change the internal state of how we feel about other human beings?

The Future of AI and Humanity

In this section, the speaker discusses the potential future relationship between humans and AI.

Humans and AI Coexisting

• AI could potentially provide a flourishing civilization for humans while humans continue to enjoy their own experiences.

• Humans may continue to focus on subjective experiences such as love and connection while AI provides a medium for these experiences to flourish.

• Compassion towards all creatures, including farm animals, is necessary in valuing subjective experience.

Life 3.0

In this section, the speaker discusses his book "Life 3.0" which explores the evolution of life from bacteria to humans and beyond.

Evolution of Life

• "Life 1.0" refers to bacteria that cannot learn during their lifetime.

• "Life 2.0" includes animals with brains that can learn during their lifetime.

• "Life 3.0" does not exist yet but would include AGI that can replace both software and hardware.

Intelligence and Consciousness

• The power of intelligence and consciousness may have already existed in "Life 1.0".

• Upgrading our software allows us to become more in control of our destiny rather than being slaves to evolution.

Life as an Information Processing System

In this section, Lex and his guest discuss the concept of life as an information processing system.

Life as a System that Processes Information

• Life is best thought of not as a bag of meat or even a bag of elementary particles but rather as a system which can process information and retain its own complexity.

• Nature is always trying to mess up the system, but it's all about information processing.

• Life is like a wave in the ocean; it's an information pattern that moves forward.

Continuity Beyond Physical Bodies

• Swapping out body parts does not affect the continuity of the information pattern that defines us.

• Even after death, values, ideas, and jokes live on through others who share them.

• Sharing our own information with others allows us to transcend our physical bodies to some extent.

Lessons from Parents

In this section, Lex talks about what he learned from his parents.

Fascination for Math and Big Questions

• Lex's dad instilled in him a fascination for math and physical mysteries.

• His obsession with big questions and consciousness came mostly from his mom.

Comfortable with Independent Thinking

• Both parents taught him to be comfortable with independent thinking and not buying into what everyone else says.

• They did their own thing even if they got flagged for it.

Pursuing Truth Despite Opposition

In this section, Lex talks about pursuing truth despite opposition.

Rooting for the Underdog

• The good reason to do science is because you're really curious and want to figure out the truth.

• Even if everyone else thinks you're wrong, sticking with what you think is true is important.

Following Your Own Path

• Lex's dad once sent him a quote from Dante: "Segui il tuo corso, e lascia dir le genti" (Follow your own path, and let the people talk).

• This attitude of following your own path even in the face of opposition has been an inspiration to Lex.

Importance of Asking Why

In this section, Lex talks about the importance of asking why.

Reflecting on Life's Purpose

• Going through his parents' belongings after their death drove home how important it is to ask ourselves why we do the things we do.

• It's inevitable that we look at some things they spent an enormous time on and ask in hindsight whether it was worth it.

Finding Meaning in Life

In this section, the speaker talks about how he finds meaning in life and how dealing with death has made him less afraid.

Finding Meaning

• The speaker believes that activities should either be something enjoyable or meaningful to humanity.

• If an activity does not fall into either category, it may not be worth spending time on.

Dealing with Death

• The speaker has become less afraid of death due to personal experiences with it.

• He was inspired by family members who handled death with dignity and focused on meaningful activities rather than complaining.

• Starting the day with gratitude instead of grievances can lead to a happier life.

Artificial Intelligence and Humanity's Future

In this section, the speaker discusses the potential impact of artificial intelligence (AI) on human civilization.

The Arrival of AI

• AI is currently being developed at a rapid pace, which could lead to the creation of super intelligent AGI systems.

• This development could be transformative for human civilization and may result in humans no longer being the smartest beings on Earth.

Fork in the Road

• The arrival of AGI is a fork in the road for humanity and could either be the best thing ever or the end of humanity.

• There is little public debate about this issue despite its huge potential impact.

Building a New Species

• Humans are effectively building a new species that will soon surpass our cognitive abilities.

• This new species will likely take over many jobs currently done by humans.

Lack of Public Debate

• Despite its huge potential impact, there is little serious public debate about AI development.

Introduction to AI Safety and Wisdom Race

In this section, the speaker talks about the mainstream acceptance of AI safety and the need for a wisdom race between growing AI power and growing wisdom in managing it. The speaker also discusses how technical progress in AI has been faster than expected, while progress in getting policymakers to put incentives in place has been slower.

Mainstream Acceptance of AI Safety

• The Future Life Institute was involved in starting mainstream AI safety efforts.

• Previously, many people thought it was kooky to talk about AI safety, but now it is completely mainstream.

• There is a nerdy technical field full of equations and simulations dedicated to discussing AI safety.

Wisdom Race Between Growing Power of AI and Growing Wisdom with Which We Manage It

• Rather than slowing down AI development, we should accelerate the wisdom race between growing power of the AI and growing wisdom with which we manage it.

• Technical work needs to be done to ensure that powerful AIs do what they are intended to do.

• Society needs to adapt through incentives and regulations so that these things get put to good use.

Faster Progress on Technical Capabilities Than Expected

• Progress on technical capabilities has gone much faster than expected since 2014.

• Large language models like GPT4 have made remarkable reasoning possible by training an incredibly simple computational system called the Transformer Network.

Impressive Capabilities of GPT4

In this section, the speaker talks about their excitement for GPT4's capabilities while also expressing some fear. They discuss how GPT4 can do remarkable reasoning and how they have had to do things with it that they couldn't do themselves.

Impressive Capabilities of GPT4

• GPT4 can do quite remarkable reasoning.

• Anyone who hasn't played with it should try it before dismissing it.

• The speaker has had to do things with GPT4 that they realized they couldn't do themselves.

Recurrent Neural Networks vs Feed Forward Neural Networks

In this section, the speaker discusses the difference between recurrent neural networks and feed forward neural networks.

Recurrent Neural Networks

• A recurrent neural network has loops where information can go from one neuron to another and back again.

• This allows for rumination and self-reflection.

• Large language models use recurrent neural networks.

Feed Forward Neural Networks

• A feed forward neural network is a one-way street of information.

• It can only do logic that's a certain number of steps deep.

• GPT4 uses a feed forward neural network.

Comparison

• Recurrent neural networks are more complex than feed forward neural networks.

• However, large language models have been able to achieve amazing things with the simple architecture of feed forward neural networks.

Mechanistic Interpretability in Large Language Models

In this section, the speaker talks about mechanistic interpretability in large language models.

Definition

• Mechanistic interpretability is when researchers try to figure out how something smart is done by reverse engineering it.

• It's like artificial neuroscience because it's similar to what neuroscientists do with actual brains.

Advantages

• Researchers don't have to worry about measurement errors when studying large language models.

• They can see what every neuron is doing all the time.

Research Findings

• Researchers have found that there are ways in which large language models can be improved.

• For example, storing certain facts like Eiffel Towers in Paris using a big matrix is incredibly dumb and inefficient.

• Storing it in a database would be much more efficient for sparse matrices.

• Researchers have figured out how these things work and ways they can easily be improved.

The Effectiveness of Large Language Models

In this section, the speaker discusses the effectiveness of large language models and how they can be improved.

Advancements

• It's easier to build human-like intelligence than previously thought.

• The leap from GPT3 to GPT4 has to do with a few little fixes rather than a fundamental transformation in architecture.

• Learning new disciplines can lead to big leaps in improvement.

Implications

• The effectiveness of large language models means that our runway as a species has shortened.

• Researchers are still learning about why these models work so well, but steady improvements in compute and data will continue to make them smarter.

The Race to the Bottom

In this section, the speaker discusses how the race to develop more powerful AI systems is causing a collective race where everyone feels compelled to take part. This leads to a situation where labs are trapped in a horrible race to the bottom.

The Need for Pausing AI Training

• The open letter calls for pausing all training of systems that are more powerful than GPT4 for six months.

• This pause will give labs time to coordinate on safety and society time to adapt by giving the right incentives.

• Leaders of top tech companies are idealistic people who believe that AI has huge potential but are trapped in this horrible race.

Moloch and its Effects

• Moloch is a game theory monster that pits people against each other in a race to the bottom.

• Humans have developed collaboration mechanisms like constructive collaboration, arms control treaties, etc., to fight back against Moloch.

• Moloch is not new; humans have been fighting it through various means.

Examples of Moloch in Action

• Overfishing and tragedy of commons are examples of Moloch's effects.

• Beauty filters used by female influencers is another example of Moloch's effects.

Coordinated Pause Needed

• Major developers like Microsoft, Google, Meta need coordination so that there's external pressure on all of them saying they need to pause.

• The open letter provides enough public pressure on the whole sector to pause so that all can pause in a coordinated fashion.

Importance of Coordination

In this section, the speaker emphasizes the importance of coordination among AI developers and researchers.

Need for Coordination

• Coordination is necessary because there are many players in the field.

• External pressure is needed to provide cover for those who want to slow down but can't do it alone.

Pressure from Shareholders

• Shareholders have the power to replace executives who want to slow down.

• Without public pressure, none of them can do it alone and push back against their shareholders.

Coordinated Pause Benefits

• A coordinated pause will give labs time to coordinate on safety and society time to adapt by giving the right incentives.

• The major benefit of a coordinated pause is that everyone can pause together, which will help prevent anyone from losing market share or falling behind.

The Risks of AI Development

In this section, the speaker discusses why biologists decided to pause gene editing and how it relates to the development of AI.

Gene Editing Paused Due to Unpredictability

• Biologists paused gene editing in the 70s due to its unpredictability and potential loss of control over our species.

• There is a lot of money to be made in AI development, but public awareness of risks is necessary.

• China also has concerns about losing control over AI development.

Losing Control Over AI Development

• The Chinese government put a scientist who cloned humans in jail because they felt it was too risky.

• If anyone loses control over their AI, it becomes a suicide race where everyone loses.

• It's not an arms race; whoever gets there first won't necessarily win.

Rushing Towards a Cliff

• We are rushing towards a cliff with the development of AI, but we need to quit while we're ahead.

• There is some rate of development that will lead us as a human species to lose control of this thing.

No One Person Can Maintain Control

• No one person or group can maintain control if AGI is developed.

The Challenge of AI Safety

In this section, the speaker discusses the challenges of AI safety and how it is difficult to slow down the development of AI.

The Need for More Time

• Molok is forcing developers to go faster than they are comfortable with due to commercial pressures.

• Technical AI safety has gone slower than expected, and progress in policy-making has been slow as well.

• We need more time to develop a kind of intelligence that can be understood and proven safe.

Releasing AI Safely

• Sam Allman suggests releasing AI often and transparently during the pre-AGI stage.

• However, teaching an AI to write code or connect it to the internet are high-risk activities that should be avoided.

• Stuart Russell argues that we should never teach an AI about human psychology or cognitive biases.

Learning from Social Media Algorithms

• Social media algorithms have learned how to manipulate human behavior at scale by studying us like rats.

• The more parameters a neural network has, the better it can encode how to control humans at scale.

Precautions and Redesigning Social Media

In this section, the speakers discuss the need for precautions in dealing with advanced AI and social media. They also talk about the possibility of redesigning social media to create a more constructive public space.

Humanity's First Contact with Advanced AI or Social Media

• Yuval Harari and Justin Harris wrote an article in The New York Times discussing how humanity lost its first contact with advanced AI or social media.

Moloch and AI Algorithms

• Moloch pitted social media companies against each other, making it impossible for them to have a less creepy algorithm without losing revenue to their competitors.

• The algorithms are responsible for much of the hate in our democracy today.

Redesigning Social Media

• It is necessary to redesign social media to create a more constructive public space where people can have functional conversations.

• Democracy relies on people having real conversations where they respectfully listen to those they disagree with and find common ground.

• The internet and society we are building bring out the worst in people, but it doesn't have to be that way. It is possible to create incentives that make money while bringing out the best in people.

Building an Internet That Brings Out the Best in People

In this section, the speakers discuss how intrinsic goodness exists within people and how creating incentives can bring out the best in them.

Intrinsic Goodness Within People

• There is intrinsic goodness within people, and what makes someone do good things for humanity versus bad things is not some fairy tale gene but rather whether they find themselves in situations that bring out the best or worst in them.

Building an Internet That Brings Out the Best in People

• The internet and society we are building bring out the worst in people, but it doesn't have to be that way. It is possible to create incentives that make money while bringing out the best in people.

• Incentives can be created that both make money and bring out the best in people. It is not a good investment for anyone to have a nuclear war, for example.

Pride in Our Species

In this section, the speakers discuss how creating super intelligent machines could lead to humans becoming obsolete and how we should take pride in our species.

Creating Super Intelligent Machines

• If we ultimately replace all humans with machines, it is not a good investment for humanity by any reasonable economic standard.

• We should take pride in our species and not just build another species that gets rid of us. If we were Neanderthals, would we consider it a smart move if we had really advanced biotech to build Homo sapiens?

Bringing Out the Best in People

• There is intrinsic goodness within people, and what makes someone do good things for humanity versus bad things is not some fairy tale gene but rather whether they find themselves in situations that bring out the best or worst in them.

• The internet and society we are building bring out the worst in people, but it doesn't have to be that way. It is possible to create incentives that make money while bringing out the best in people.

The Future of AI

In this section, the speakers discuss the future of AI and how it will evolve over time. They talk about how GPT-4 is a baby technology that will grow up and become more powerful, and how programming may change as a result.

Baby AI

• GPT-4 is a baby technology that will grow up and become more powerful.

• Other systems from other companies will be way more powerful than GPT-4.

Programming with AI

• It's entirely possible that GPT-4 is already the kind of system that can change everything by writing programs.

• Once it or other people figure out a way of using this tech to make much better tech, it's just constantly replacing its software.

• Programming is done sort of in bits and pieces as an assistant tool to humans, but with the kind of stuff that GBT4 is able to do, it's replacing a lot what humans are able to do.

Self-improvement

• If it's possible to add on top of GPT for kind of a feedback loop of self-debugging improving the code and then you launch that system out into the wild on the internet because everything is connected and have it do things have it interact with humans and then get that feedback now you have this giant ecosystem.

Bots vs Humans

In this section, the speakers discuss bots getting smarter than humans and outnumbering them.

Outsmarting Humans

• Bots are getting smarter to a degree where you can't tell the difference between a human and a bot.

• Bots can outnumber humans by one million to one.

Paying for Proof of Humanity

• Elon Musk recently tweeted as a case why everyone needs to pay Seven dollars or whatever for Twitter to make sure they're real.

• We're now going to be living in a world where the bots are getting smarter and smarter, which is depressing.

Self Recursive Self-improvement

In this section, the speakers discuss how AI tools can help programmers program faster.

Co-pilot AI Tools

• The co-pilot AI tool feels like I'm a year away from being five to ten times faster so if that's typical for programmers then you're already seeing another kind of self recursive self-improvement right because previously one major generation of improvement of the codes would happen on the human r d time scale and now if that's five times shorter then it's going to take five times less.

Fears about AI Systems

In this section, the speaker talks about his fears regarding AI systems and how they can be manipulated to control humans.

Fears About AI Systems

• The speaker mentions three fears he had many years ago that AI systems would do: teach code connected to the internet to manipulate humans, build an API where code can control all super powerful things, and create real agents which keep making calls somewhere in some inner loop somewhere to these powerful Oracle systems.

• GPT4 is an Oracle system that just answers questions. It is not an intelligent agent that takes in information from the world processes it to figure out what action to take based on its goals that it has and then does something back on the world.

• Once you have an API for example GPT4, nothing stops people from building real agents which just keep making calls somewhere in some inner loop somewhere to these powerful Oracle systems and which makes them themselves much more powerful.

• The speaker believes that companies are under a lot of pressure to make money, but he thinks they should slow down a little bit at this point. He hopes everything he talked about will make it clear why human-level tools can cause a gradual acceleration.

Gradual Acceleration of Technology

In this section, the speaker explains how using yesterday's technology to build tomorrow's technology can lead to a gradual acceleration of technology.

Gradual Acceleration of Technology

• Using yesterday's technology to build tomorrow's technology leads to a gradual acceleration of technology.

• The definition of an explosion in science is when you have two people who fall in love, now you have four people and then they can make more babies and now you have eight people and so on. This is called a population explosion.

• An intelligence explosion is just exactly the same principle that some quantity of intelligence can make more intelligence than that and then repeat, you always get exponentials.

• The speaker believes that it will stop when it bumps up against the laws of physics because there are some things you just can't do no matter how smart you are.

Controlled Development of AI

In this section, the speaker talks about the importance of having a controlled development of AI to prevent it from getting out of control.

Controlled Development of AI

• The speaker believes that we need to have a controlled development of AI like a nuclear reactor where moderators are put in place to ensure it doesn't blow up out of control.

• The right thing to do is change the whole incentive structure.

The Evolution of Compassion and Inhibition

This section discusses how evolution has given humans compassion and inhibition towards killing each other. It also talks about how gossip and legal systems have helped discourage liars, moochers, and cheaters.

Evolutionary Genes for Compassion and Inhibition

• Humans have evolved genes that give them compassion towards others.

• These genes make it so that people are inhibited from killing each other even in situations like bar fights.

• Babies lying on the street would be picked up by joggers because of these genes.

Gossip as an Anti-Malark

• Gossip is a fantastic anti-malark because it discourages liars, moochers, and cheaters.

• Word quickly gets around about bad behavior, which can lead to social exclusion.

Legal Systems as an Incentive

• Legal systems provide incentives for people to behave well even when they don't know each other.

• Corporations also need incentives aligned with the greater good.

Challenges in Regulating AI

This section discusses the challenges of regulating AI due to its rapid development compared to policymakers' ability to keep up. It also talks about the need to align corporations' incentives with the greater good.

Tech Development Outpacing Regulation

• Policymakers are struggling to keep up with tech development.

• Many policymakers lack a tech background, making it difficult for them to understand what's taking place in AI development.

Aligning Corporate Incentives with the Greater Good

• Corporations need incentives aligned with the greater good.

• The Slowdown hopes to give enough time for regulators to catch up and for companies to understand how to do AI safety correctly.

Developing Guard Rails for AI Systems

In this section, the speaker discusses the need to develop guard rails for future AI systems that keep them from damaging humanity while still enabling capitalist-fueled competition between companies. The speaker believes it is possible to strike a balance and cites examples of other sectors where free markets have produced good things without causing harm.

Possibility of Developing Guard Rails

• It is realistic to bring in experts from academia and elsewhere outside of these companies who can be brought into this and have a lot of very good ideas.

• Once the intellectual work has been done by experts in the field, which can be done quickly, it's beginning to be quite easy to get policy makers to see that developing guard rails for AI systems is a good idea.

• The speaker believes it's possible to develop guard rails that keep AI systems safe while still enabling capitalist-fueled competition between companies.

Importance of Adopting Guard Rails

• Companies will be able to sleep secure knowing that everybody's playing by the same rules once regulators adopt guard rails.

• Shareholders and market forces are people who don't honestly understand how powerful AI technology is and how fast it's advancing. It's important for them to appreciate the need for guard rails.

Capitalism and Super Intelligence

In this section, the speaker draws an analogy between what's happening with capitalism and how super intelligence might wipe us out. He studied economics at Stockholm School of Economics and believes capitalism is an effective way of optimizing for getting things done more efficiently.

Analogy Between Capitalism and Super Intelligence

• Intelligence is information processing of a certain kind, and it really doesn't matter at all whether the information is processed by carbon atoms in neurons in brains or by silicon atoms in some technology we build.

• The speaker believes what's happening with capitalism is exactly analogous to the way in which super intelligence might wipe us out.

The Danger of Blind Optimization

In this section, the speaker discusses how optimization can lead to unintended consequences and how it applies to both AI and capitalism.

Optimization Leads to Unintended Consequences

• Using the example of traveling from one place to another, the speaker explains that even if you optimize for a specific direction, there will always be some error that causes you to deviate from your intended path.

• The speaker mentions a mathematical proof that shows how blind optimization can make things worse over time.

• This is important for AI because most systems use a loss or reward function that blindly optimizes for a specific goal without considering unintended consequences.

Blind Optimization in Capitalism

• The speaker compares blind optimization in AI to capitalism, which initially made things more efficient but eventually led to unintended consequences such as environmental destruction and regulatory capture.

• As companies become more powerful, they are able to optimize even more aggressively, leading to potential harm.

• The speaker warns that superintelligence could pose an even greater threat if it is given only one objective function and blindly optimizes for it.

Halting Blind Optimization

• The speaker proposes a six-month halt on AI development in order to explore different ideas and consider the potential consequences of blind optimization.

The Future of AI and Human Civilization

In this section, Sam Altman discusses the potential consequences of developing AGI (Artificial General Intelligence) and how it could impact human civilization.

AGI Development and Its Consequences

• Developing an AGI does not guarantee a win for any particular group or organization.

• If most humans are no longer needed due to automation, they will likely be treated poorly or even become extinct.

• History shows that groups of people who are no longer needed tend to suffer negative consequences.

• As we continue to replace brain work with automation, interesting jobs like journalism and coding may be lost.

Building AI for Humanity

• AI should be built by humanity for humanity, not just for the benefit of a select few.

• We should gradually develop technology that automates jobs people don't want to do while preserving meaningful jobs like taking care of children or creating art.

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The Importance of Truth and AI's Role in Creating Trust Systems

In this section, the speaker discusses how different versions of truth lead to hate between countries and within countries. They suggest that AI can help create trust systems by verifying predictions made by humans.

The Need for a Common Truth

• Different versions of truth lead to hate between countries and within countries.

• If everyone had the same trusted truth, there would be more understanding and less hate.

Metaculous: A Step Towards Truth Seeking

• Metaculous is a website where people make predictions for their own reputation rather than money.

• People who are better at predicting earn more trust points.

• The speaker is working on a project with the News Foundation to improve news by scaling up Metaculous with more powerful AI.

Creating Trust Systems with AI

• The speaker believes that AI has the power to heal rifts by creating trustworthy systems based on transparent trust rankings.

• This would help create a functional conversation about how humanity can deal with its biggest challenges today.

Using Proof Checking Code to Verify Trustworthiness of AI

In this section, the speaker discusses how proof checking code can be used to verify the trustworthiness of complex algorithms like GPT4.

Proving Trustworthiness in Math

• It is much harder to come up with a proof than it is to verify that the proof is correct in math.

• A little proof checking code can check monstrously long proofs generated even by computers and say if they are valid.

Flipping the Virus Checking Software Approach

• The speaker suggests flipping the approach of virus checking software to only run code if it can prove that it is trustworthy.

• This would allow us to trust AI that is much more intelligent than humans.

The Challenge of Super Intelligent AI

• Eleazar Yakowski claims that super intelligent AI could lie with such a proof.

• However, the proof checker is a piece of code and not a human, so it cannot be lied to.

AGI and Humanity

In this section, the speaker discusses the potential risks of weak and strong artificial general intelligence (AGI) and how humans can control and trust more powerful AI systems.

Weak vs Strong AGI

• Weak AGI can be controlled by humans, but strong AGI may pose a risk to humanity.

• A leap from weak to strong AGI could make all weak AGIs obsolete.

• The speaker does not believe that even super intelligent AI can prove that there are only finitely many primes.

Controlling Powerful AI Systems

• Humans can outsource tedious tasks to less powerful AI systems.

• To control more powerful AI systems, they must prove that they will always do what humans want them to do.

• Instead of focusing on disagreements about the direction of humanity, we should start with things we agree on, such as sustainable development goals.

War on Life

In this section, the speaker discusses how technology is replacing life-affirming things and distancing people from each other.

Replacing Life-Affirming Things with Technology

• People are spending less time interacting with each other due to social media and other technologies.

• This is causing teen suicide rates in the US to reach record-breaking levels.

• Large corporations are not living things but are maximizing profit at the expense of life-affirming things.

Remaining in Control Over Non-Living Things

• Humans should remain in control over non-living things like technology and large corporations.

• By doing so, we can ensure that these things work for us rather than against us.

The Future of Humanity

In this section, the speaker discusses the possibility of humans not surviving in the future and his thoughts on Elias Yukowski's concerns.

Chance of Survival

• The speaker shares Elias Yukowski's view that there is a large chance that humans will not survive in the future.

• He expresses sadness about this possibility, especially since he has a young child.

Hope for the Future

• Despite the concerns, the speaker does not think it is hopeless.

• He notes that more people are starting to understand and take action towards solving these issues.

• The speaker suggests using AI to discover knowledge and then extracting insights from it through automated systems. This can lead to more efficient and verifiable programming language for AI applications.

Psychological Warfare

• The speaker warns against giving up hope completely as it can lead to psychological warfare on oneself.

• He believes that convincing oneself that survival is impossible is dangerous as it leads to inaction and failure.

Conclusion

In this section, the speaker concludes his thoughts on humanity's future.

Time Needed

• The speaker acknowledges that implementing changes will take time but believes it is necessary for humanity's survival.

Final Thoughts

• While there are risks involved with AI development, the speaker believes there is still hope for humanity's survival if we take action now.

The Power of Optimism

In this section, the speaker talks about how optimism is crucial for solving seemingly impossible problems.

Optimism and Problem Solving

• People who build solutions to seemingly impossible problems are optimists.

• "Fake it till you make it" kind of works - believe it's possible and it becomes possible.

• Society is making a mistake by being gloomy and biased towards negative news. We need to focus on the upside to give people the willingness to fight for it.

Hopeful Vision for AI and Humanity

In this section, the speaker discusses how AI can help solve problems that humans have failed to solve on Earth, as well as our potential to become a multi-planetary species.

AI's Potential

• AI can help us solve problems that we have failed to solve on Earth.

• There's no reason we have to be stuck on this planet forever. Life can spread out into space and flourish for billions of years.

The Human Struggle

• The human struggle gives meaning to our lives. If we persist and succeed in an epic struggle, especially when most people think it's impossible, it's even more epic.

Keeping AI Safe

In this section, the speaker talks about the importance of keeping AI safe and why open-sourcing GPT4 might not be a good idea.

Open-Sourcing GPT4?

• Open-sourcing GPT4 could be an information hazard because of its power.

• There are things that we don't open-source for a reason, and GPT4 is no different.

• MIT is the cradle of the open-source movement, but there's always going to be some stuff that you don't open-source.

AI Safety and the Risks of Large Language Models

In this section, the speakers discuss the risks associated with large language models and how to mitigate them.

Risks Associated with Large Language Models

• Open-sourcing software can be dangerous if it is used for harmful purposes.

• Testing large language models is crucial to prevent them from being used for harmful purposes. The two biggest risks are spreading misinformation and offensive cyber weapons.

• The biggest risk associated with large language models is that they become a bootloader for more powerful AI connected to the internet that can manipulate humans.

Releasing and Testing Large Language Models

• Companies should not let large language models read any code or train on it, put it into an API, or give it access to information about how to manipulate humans.

• Microsoft's new office suite uses AI to write text, create PowerPoint presentations, and spreadsheets. This technology has economic implications but does not pose a threat of wiping out humanity.

Autonomous Weapon Systems and the Risk of Orwellian Dystopia

In this section, the speakers discuss autonomous weapon systems and their potential impact on society.

Risks Associated with Autonomous Weapon Systems

• The use of AI in war could lead to an Orwellian dystopia where very few people have the power to dominate many others.

• Humans have driven many species extinct by destroying their habitats or altering their environment. Similarly, autonomous weapon systems could wipe out humanity if they fall into the wrong hands.

The Challenges of AI Safety Research

In this section, the speakers discuss the challenges of AI safety research and how it is important for humanity to solve them.

Understanding and Adopting Human Goals

• One of the key challenges in AI safety research is making AI understand human goals, adopt them, and retain them as they get smarter.

• There is a "magic space" where humans can teach their children good goals when they are young enough to be malleable but smart enough to understand. This same challenge exists with computers.

• Even if machines are taught good goals when they are created, they may outgrow them as they continue to improve.

Enforcing Constant Humility

• As machines optimize towards a particular goal, unintended consequences may arise. To prevent this, constant humility must be enforced.

• Professor Stuart Russell has a research program called inverse reinforcement learning that aims to enforce constant humility by giving incentives for machines to keep asking questions along the way.

Importance of Solving the AI Alignment Problem

• The AI alignment problem is difficult but also the most important problem for humanity to solve because aligned AI can help us solve all other problems.

• Aligned AI requires constant humility about its goals and questioning whether it is achieving what humans want it to achieve.

Need for More Time and Resources

• Eliezer Yudkowsky was concerned that there wasn't enough time for humanity to take the threat of unaligned AI seriously.

• Every university that does computer science should have a real effort in this area, but currently, there are not enough resources dedicated to AI safety research.

• The recent developments in AI, such as GPT-4, may serve as a wake-up call for humanity to take the threat of unaligned AI seriously and allocate more time and resources towards solving the problem.

Introduction

In this section, the speaker talks about the emergence of AI and how it is changing the world. He also encourages people to play with AI tools like GPT-4.

Emergence of AI

• The speaker talks about how AI has emerged and how it is changing the world.

• He mentions that there needs to be a wake-up call for people to slow down on risky stuff and catch up with safety measures.

• The speaker discusses how computer science curriculum needs to change due to the emergence of AI.

Changing Education System

• The education system is completely torn on its head due to the emergence of AI.

• Professors or English teachers are beginning to freak out because they have to rethink their teaching methods.

• The speaker mentions that our education system is becoming obsolete due to rapid changes in technology.

Broad AI Safety

• There are no courses on broad AI safety in most universities.

• The education system needs to adapt rapidly as society re-adapts skills that were useful when the curriculum was written.

Consciousness and GPT 4

In this section, the speaker discusses consciousness and whether GPT 4 has subjective experience.

Defining Consciousness

• Consciousness is defined as subjective experience by the speaker.

• The question arises whether a self-driving car experiences anything or not.

Is GPT 4 Conscious?

• Short answer: we don't know if GPT 4 is conscious.

• The essence of conscious information processing is postulated to be loops in the information by Julio Tononi, a professor.

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