AI: Grappling with a New Kind of Intelligence

World Science Festival・2 minutes read

Artificial intelligence (AI) is evolving rapidly, with powerful systems like large language models showing promise but still lacking in true intelligence like humans or animals. The advancement of AI poses risks like misinformation, job displacement, and bias perpetuation, emphasizing the need to align technology with humanity's best interests and consider incentives beyond profit motives for a positive future.

Insights

  • AI systems, while impressive, lack true intelligence like humans or animals and are limited in their capabilities due to their lack of understanding of the physical world and common sense.
  • Large language models like GPT-4 can reason effectively but struggle with planning and learning from experience, showcasing the limitations of current AI models.
  • The rapid scaling of AI models poses concerns about societal impacts, emphasizing the need for urgency in addressing AI development to prevent uncontrollable outcomes and the importance of ensuring an open-source approach for safe development.

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Recent questions

  • How do large language models like GPT-4 function?

    Large language models like GPT-4 are versatile systems that can generate text, answer questions, and even create music. These models are trained using self-supervised learning techniques, where they predict the next word in a sequence to understand language structure. However, despite their impressive abilities, they lack planning capabilities and may produce inaccurate results, limiting their use in technical fields. The challenge lies in predicting multiple plausible scenarios accurately, as these models struggle with understanding physical interactions and intuitive physics crucial for real-world comprehension.

  • What are the limitations of current AI systems?

    Current AI systems, while impressive in their specialized tasks, lack true intelligence like humans or animals. They struggle with understanding the physical world and are limited in their capabilities, especially in planning complex actions. Unlike humans, AI systems do not possess common sense or intuitive physics, which are essential for grasping the intricacies of the world. These limitations highlight the need for AI to learn about the world through observation and interaction, similar to how babies acquire knowledge, to enhance their understanding and problem-solving abilities.

  • How do AI systems differ from human intelligence?

    AI systems differ from human intelligence in significant ways. While computers can outperform humans in tasks like chess due to their ability to process numerous scenarios quickly, they lack the specialized intelligence that humans have evolved for survival. Human intelligence is tailored for specific tasks and is not generalized knowledge, unlike AI systems. Additionally, AI lacks common sense and intuitive physics, crucial for understanding the world, and the ability to plan complex actions effectively, a key aspect of human-like intelligence.

  • What is the future direction of AI architecture?

    The proposed AI architecture includes modules for perception, world modeling, planning, cost evaluation, emotions, and action. This architecture aims to integrate a world model to enable planning and safety measures within AI systems. The goal is to move towards objective-driven AI that can reason effectively and learn from experience, replacing autoregressive language models like GPT-4. By focusing on predicting consequences of actions in videos rather than pixel details, future AI architectures aim to enhance planning capabilities and develop a deeper understanding of the physical world.

  • How can AI technology be aligned with humanity's best interests?

    Aligning AI technology with humanity's best interests is crucial to ensure a positive future. The incentives driving AI development, often focused on releasing capabilities quickly, pose risks like deep fakes, job displacement, and perpetuating biases. To address these challenges, it is essential to consider incentives beyond profit motives and prioritize ethical considerations. By emphasizing the need for responsible AI development that benefits society as a whole, we can mitigate potential harms and ensure that AI technology serves humanity's interests effectively.

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Summary

00:00

Unraveling the Mysteries of Artificial Intelligence

  • Humans have always sought to understand the mysteries of existence in the vast universe.
  • A new frontier is emerging in the digital landscape with artificial intelligence (AI).
  • AI promises profound benefits but raises questions about innovation and obsolescence.
  • Large language models are versatile and can generate text, answer questions, and create music.
  • The program aims to demystify AI systems and understand their inner workings.
  • The text and visuals were generated by a large language model, not a human.
  • AI has gone through various paradigms and revolutions over the years.
  • The current success of AI is due to powerful machines, large datasets, and deep learning techniques.
  • AI systems, while impressive, are specialized and lack true intelligence like humans or animals.
  • AI systems lack understanding of the physical world and are limited in their capabilities.

17:36

"AI lacks human-like intelligence and planning"

  • Language cannot fully express the complexity of physical interactions, such as friction affecting objects differently on a table versus the floor.
  • Human intelligence is specialized and evolved for survival, not general knowledge.
  • Artificial general intelligence (AGI) aiming for human-like intelligence is flawed as humans excel in specific tasks but not all.
  • Computers outperform humans in tasks like chess due to their ability to process numerous scenarios, unlike humans.
  • AI lacks common sense and intuitive physics, crucial for understanding the world.
  • AI systems need to learn about the world through observation and interaction, similar to how babies acquire knowledge.
  • Current AI lacks the ability to plan complex actions, a crucial aspect of human-like intelligence.
  • Proposed AI architecture includes modules for perception, world modeling, planning, cost evaluation, emotions, and action.
  • AI systems are trained using self-supervised learning techniques, like predicting missing words in text to understand language structure.
  • Large language models predict the next word in a sequence, but lack planning abilities and may produce inaccurate results, limiting their use in technical fields.

33:46

"Predicting Physical Events in Videos with Jepa"

  • Predicting physical events in videos can help create a model of the physical world.
  • Using large language models to predict video frames as tokens is not effective.
  • The challenge lies in predicting multiple plausible scenarios in videos.
  • Language models can represent probabilities for words but struggle with video frames.
  • Future techniques aim to learn to represent the world from videos and predict actions and outcomes.
  • Proposed architecture, Jepa, predicts abstract representations of video pixels.
  • Jepa aims to predict consequences of actions in videos rather than pixel details.
  • Integration of a world model in architecture could enable planning and safety measures.
  • Autoregressive language models may be replaced by objective-driven AI in the future.
  • Large language models like GPT-4 can reason effectively but struggle with planning and learning from experience.

48:16

Evolution of Neural Networks: From Unicorns to GPT 4

  • Creating a neural network is more akin to evolution than human learning in a lifetime.
  • The speaker's daughter's interest in unicorns led to a unique experiment with a text-to-text model.
  • The model was asked to draw a unicorn, resulting in code lines that compiled into a visual representation.
  • Despite the visual quality, the model accurately depicted the essential features of a unicorn.
  • The request was made in an obscure programming language, showcasing the model's adaptability.
  • The progress from Chat GPT to GPT 4 demonstrates significant advancements in a short time.
  • Early access to GPT 4 allowed for observing the model's improvement over time, showcasing machine learning in action.
  • Neural networks process data through weighted connections, similar to how signals travel in the brain.
  • Self-supervised learning eliminates the need for manually labeled data, enhancing the system's adaptability.
  • The vast amount of data fed into large language models like GPT 4 is crucial for their functioning and development.

01:03:29

AI's Transformer Architecture: Revolutionizing Self-Supervised Learning

  • Self-supervised learning is a crucial tool, with the Transformer architecture being a significant advancement.
  • The Transformer architecture processes sequences of words, allowing words to be compared against each other for linguistic context.
  • Scaling up model sizes has increased exponentially from around 2018 to 2021, enhancing performance significantly.
  • Models now analyze patterns of patterns of words, optimizing parameters to generate impressive text.
  • The largest models today have hundreds of billions of connections, approaching the complexity of the human brain.
  • Planning with AI may require a new architecture or simply scaling up existing models to emergently develop planning capabilities.
  • Misinformation can be rationalized by AI systems, showcasing the potential dangers of false information dissemination.
  • Social media's misaligned AI optimization for attention led to addiction, disinformation, and polarization issues.
  • AI's incentives, driven by the race to release more capabilities quickly, pose risks like deep fakes, job displacement, and bias perpetuation.
  • Aligning AI technology with humanity's best interests is crucial, emphasizing the need to consider incentives beyond profit motives for a positive future.

01:18:28

AI Impact: Cultural Changes, Information Overload, Addiction

  • First contact with AI through social media leads to cultural changes, information overload, and addiction.
  • Second contact with AI introduces generative AI, enabling the creation of text, images, fake content, and cyber weapons.
  • Concern arises from releasing capabilities without considering the wisdom and responsibility of users.
  • Example of Facebook pages feature being innocuous until manipulated for propaganda and extremist content.
  • Rapid AI development raises concerns about the lack of control and potential harms.
  • Detection of hate speech and extremist content on platforms like Facebook has improved due to AI advancements.
  • AI is not the problem but the solution in detecting and addressing harmful content online.
  • The use of AI in ranking algorithms for social media feeds is to predict user engagement.
  • AI's predictive capabilities, even in simple forms, can impact societal realities and mental health.
  • Incentives driving AI development can lead to unintended consequences, such as promoting extremist groups through recommendation algorithms.

01:33:15

Exploring Intelligence Emergence in Smaller AI Models

  • The team is exploring the potential of smaller models for scientific advancement, focusing on intelligence emergence.
  • Intelligence has been observed in models with hundreds to thousands of billions of parameters.
  • The team aims to determine the minimal parameters required for intelligence to emerge.
  • A prompt is used to compare responses from different language models (LLMs) regarding self-awareness.
  • Models with varying parameters provide different responses, influenced by their training data.
  • The team's 1 billion parameter model prioritizes understanding human motivations and intentions.
  • The team's model is trained solely on synthetic data, avoiding exposure to toxic internet content.
  • Scaling to 10 billion parameters could replicate larger models' capabilities without toxicity.
  • Concerns exist regarding the rapid scaling of AI models and potential societal impacts.
  • Urgency is emphasized in addressing AI development to prevent uncontrollable outcomes.

01:47:47

Future AI: Enhancing, not Dominating, Human Intelligence

  • Scaling up AI systems with public data from the internet to create harmful substances like chemical weapons is not feasible as critical information is not available in public data sources.
  • In the future, AI systems may match or surpass human intelligence in various domains, but intelligence does not equate to a desire for dominance, as seen in human interactions.
  • AI assistants in the future will likely be smarter than humans but will serve to enhance human capabilities rather than dominate, as intelligence does not inherently lead to a desire for control.
  • To ensure the safe development of AI systems, an open-source approach is crucial, allowing for collective contributions to create a repository of human knowledge accessible to all, preventing monopolization by a few entities.
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