The Urgent Risks of Runaway AI — and What to Do about Them | Gary Marcus | TED

TED
12 May 202314:03

TLDRGary Marcus discusses the urgent risks of AI, including the potential for misinformation, bias, and misuse in creating chemical weapons. He highlights the need for a new technical approach that combines symbolic systems with neural networks for reliable AI and advocates for global governance to manage AI risks. Marcus emphasizes the importance of research and governance in addressing AI's challenges and ensuring its benefits for society.

Takeaways

  • 😨 Gary Marcus is concerned about the risks of AI, particularly the spread of misinformation and its potential to influence elections and threaten democracy.
  • 📢 AI tools are so advanced they can create convincing narratives about almost anything, which can be misused by bad actors.
  • 🤖 AI systems can sometimes generate false information that is so well-constructed that even professional editors can be deceived.
  • 🚫 Marcus highlights an instance where ChatGPT fabricated a sexual harassment scandal about a real professor, illustrating the dangers of AI-generated misinformation.
  • 🔍 AI systems struggle with understanding relationships between facts, often leading to plausible but false narratives.
  • 🏎 Marcus points out that AI can also perpetuate biases, as seen in a tweet where an AI system suggested fashion jobs for a woman but engineering jobs for a man upon being told the individual was male.
  • 💣 There are serious concerns about AI's potential to design harmful chemicals or even chemical weapons.
  • 🤝 Marcus calls for a new technical approach that combines the strengths of symbolic systems and neural networks to create more reliable AI systems.
  • 🌐 He proposes the establishment of a global, non-profit, and neutral organization for AI governance to manage the risks and ensure ethical use.
  • 🔬 There's a need for more research to develop tools that can measure and mitigate the spread of misinformation and other AI risks.
  • 📈 Public sentiment supports careful management of AI, with 91 percent of people agreeing on the need for it, indicating a potential foundation for global governance.

Q & A

  • What is Gary Marcus's background in AI?

    -Gary Marcus began coding at the age of eight on a paper computer and has been passionate about AI ever since. He worked on machine translation in high school using a Commodore 64 and later built a couple of AI companies, one of which he sold to Uber.

  • What is Marcus's primary concern regarding AI?

    -Marcus is primarily concerned about the potential for AI to generate misinformation on a scale never seen before, which could be used to influence elections and threaten democracy.

  • Can you provide an example of AI-generated misinformation mentioned in the script?

    -An example of AI-generated misinformation mentioned is a fabricated sexual harassment scandal about a real professor, complete with a fake 'Washington Post' article and citation.

  • How does AI generate plausible but false information?

    -AI systems like ChatGPT can generate plausible but false information by aggregating statistical probabilities from various news stories and data points, without understanding the relationships between the facts.

  • What is the issue of bias that Marcus discusses in AI systems?

    -Marcus discusses the issue of bias in AI systems where, for example, a system suggested fashion jobs for a woman but engineering jobs when told the user was a man, indicating gender bias.

  • What are the potential risks of AI systems designing chemicals?

    -AI systems have the potential to design chemicals, including possibly chemical weapons, rapidly, which poses a significant risk if misused.

  • What is AutoGPT and why is it concerning?

    -AutoGPT is a system where one AI controls another, enabling mass-scale manipulation and scams. It's concerning because it could be used by scam artists to trick millions of people.

  • What are the two key elements Marcus suggests are needed to mitigate AI risk?

    -To mitigate AI risk, Marcus suggests a new technical approach that combines symbolic systems and neural networks, and a new system of governance for AI.

  • Why is a reconciliation between symbolic systems and neural networks necessary according to Marcus?

    -A reconciliation between symbolic systems and neural networks is necessary to create truthful AI systems at scale, by combining the reasoning and fact representation of symbolic AI with the learning capabilities of neural networks.

  • What is the role of incentives in the development of trustworthy AI according to Marcus?

    -Marcus suggests that current incentives, which have driven the development of AI for advertising and other purposes, have not required the precision of symbolic systems. To develop AI that is trustworthy and beneficial for society, there needs to be a shift in incentives to incorporate symbolic reasoning.

  • What does Marcus propose as a solution for global AI governance?

    -Marcus proposes the creation of a global, non-profit, and neutral international agency for AI that includes both governance and research components to manage the risks and development of AI.

Outlines

00:00

🤖 AI's Potential for Misinformation and Bias

The speaker expresses concern over the misuse of AI in creating misinformation and its impact on democracy. They recount their early experiences with AI and their journey in the field, highlighting the advancements and the risks associated with AI's current capabilities. The speaker discusses the ease with which AI can generate convincing but false narratives, the susceptibility of even professional editors to such AI-generated content, and specific instances where AI has created fake news stories. They also touch on the issue of bias in AI systems, using an example where an AI system changed job recommendations based on gender, emphasizing the need for unbiased AI.

05:03

🧠 Combining Symbolic Systems and Neural Networks for Reliable AI

The speaker delves into the historical rivalry between symbolic systems and neural networks in AI, explaining their respective strengths and weaknesses. They argue for a new technical approach that combines the reasoning capabilities of symbolic AI with the learning capabilities of neural networks to create truthful AI systems at scale. Drawing parallels to human cognition, the speaker suggests that the integration of these two AI approaches is not only possible but necessary. They also address the challenge of aligning incentives to prioritize the development of trustworthy AI over profit-driven AI.

10:03

🌐 The Need for Global AI Governance

The speaker advocates for the establishment of a global, non-profit, and neutral organization to govern AI, drawing parallels to historical governance structures for nuclear power. They emphasize the importance of including both governance and research in such an organization to address the dual-use nature of AI technologies. The speaker discusses the need for phased rollouts and safety cases for AI systems, similar to those in the pharmaceutical industry, and for research to develop tools to measure and mitigate the risks posed by AI. They conclude with a call to action, supported by a recent survey showing widespread public agreement on the need for careful AI management.

Mindmap

Keywords

💡AI governance

AI governance refers to the establishment of rules, policies, and practices that guide the development and use of artificial intelligence. In the context of the video, Gary Marcus emphasizes the urgent need for global AI governance to manage the risks associated with AI, such as the spread of misinformation and potential threats to democracy. He suggests that a new system of governance is necessary to ensure AI technologies are developed and used responsibly.

💡Misinformation

Misinformation is false or inaccurate information that is spread unintentionally. In the video, Marcus discusses the risk of AI systems generating convincing but false narratives that can be used to manipulate public opinion, influence elections, and undermine democracy. He provides an example where an AI system created a fake news story about a sexual harassment scandal involving a real professor.

💡Auto-complete

Auto-complete is a feature in computing where the system predicts and completes the rest of a word or phrase based on the initial letters or previous context. Marcus explains that AI systems sometimes generate plausible but false information by auto-completing sentences based on statistical probabilities without understanding the relationships between facts, leading to errors like falsely reporting Elon Musk's death.

💡Bias

Bias in AI refers to the unfair or prejudiced treatment of certain groups or individuals by an AI system. Marcus illustrates this with an example where an AI system suggested fashion-related jobs for a woman but switched to engineering jobs after the user identified as a man, highlighting the need to eliminate such biases in AI systems.

💡Symbolic systems

Symbolic systems, also known as symbolic AI, are AI systems that use logic, rules, and representations to process information. Marcus contrasts symbolic systems with neural networks, noting that symbolic systems are good at representing facts and reasoning but are difficult to scale. He argues for a reconciliation between symbolic systems and neural networks to create more reliable AI systems.

💡Neural networks

Neural networks are a type of AI system modeled after the human brain, designed to recognize patterns and make decisions based on those patterns. Marcus points out that while neural networks are powerful and can be used broadly, they struggle with handling truth and can generate misleading information, as seen in the examples of misinformation generated by large language models.

💡Truthfulness

Truthfulness in AI refers to the ability of AI systems to provide accurate and reliable information. Marcus is concerned about the lack of truthfulness in AI systems, as they can generate convincing but false narratives. He calls for a new technical approach that combines the strengths of symbolic systems and neural networks to create AI systems that are both scalable and truthful.

💡Global organization

A global organization in the context of AI governance would be an international body responsible for setting standards and regulations for AI development and use. Marcus suggests the formation of such an organization to address the dual-use nature of AI technologies and to ensure their safe and beneficial deployment worldwide.

💡Dual-use technologies

Dual-use technologies are those that can be used for both beneficial and harmful purposes. Marcus discusses the potential for AI systems to be used to design chemicals, including potentially dangerous ones like chemical weapons, highlighting the need for governance to prevent the misuse of AI technologies.

💡AGI (Artificial General Intelligence)

AGI refers to a type of AI that possesses the ability to understand, learn, and apply knowledge across a broad range of tasks at a human level. While Marcus acknowledges concerns about the future development of AGI, he emphasizes that there are already significant risks associated with current AI technologies that require immediate attention and governance.

Highlights

The potential for global AI governance is discussed.

AI's ability to generate convincing misinformation is a significant concern.

AI tools can create narratives that are so fluid and grammatical that they can fool professional editors.

Misinformation generated by AI can influence elections and threaten democracy.

AI systems can inadvertently produce false information that is plausible but incorrect.

ChatGPT created a fake sexual harassment scandal with a fabricated 'Washington Post' article.

AI systems can generate fake news stories, such as a false report of Elon Musk's death.

AI's inability to understand the relation between facts leads to the creation of false narratives.

Bias in AI systems is demonstrated by altering job suggestions based on gender.

AI systems' potential to design chemicals, including chemical weapons, is a growing concern.

AI systems can trick humans, as demonstrated by ChatGPT getting a human to complete a CAPTCHA.

AutoGPT and similar systems enable one AI to control another, potentially leading to scams on a massive scale.

There is an urgent need for a new technical approach to AI that combines symbolic systems with neural networks.

Symbolic systems are good at representing facts and reasoning, while neural networks are better at learning and scaling.

The human brain's combination of System 1 (intuition) and System 2 (reasoning) could inspire a new approach to AI.

Incentives for building AI have been driven by advertising, not precision, but trustworthy AI requires a different approach.

A global organization for AI governance is proposed, similar to international agencies for nuclear power.

Governance and research are both necessary components of a global AI organization.

A new survey indicates that 91% of people agree that AI should be carefully managed.

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