Yann LeCun, Meta's chief AI scientist, made this statement to emphasize that current large language models (LLMs), such as ChatGPT, are not sufficient for achieving human-level intelligence. His point is that while LLMs excel at processing and generating text based on patterns from vast amounts of data, they lack the deeper, general intelligence that humans possess. Human-level intelligence involves not just understanding language, but also reasoning, perception, and the ability to interact with the physical world, all of which require much more than pattern recognition from data.

Yann LeCun, Meta's chief AI scientist, made this statement to emphasize that current large language models (LLMs), such as ChatGPT, are not sufficient for achieving human-level intelligence. His point is that while LLMs excel at processing and generating text based on patterns from vast amounts of data, they lack the deeper, general intelligence that humans possess. Human-level intelligence involves not just understanding language, but also reasoning, perception, and the ability to interact with the physical world, all of which require much more than pattern recognition from data.

LeCun argues that to achieve true human-level intelligence, AI must go beyond LLMs and develop more sophisticated models that can understand the world in a more fundamental way—reasoning about cause and effect, learning from fewer examples, and interacting with their environment in a meaningful way. He suggests that LLMs, while impressive, are only one piece of the puzzle and are not the path to replicating the full range of human cognitive abilities.

Before AI experts, particularly those working on advancing artificial intelligence and machine learning, several key priorities are typically considered crucial for the development of the field. These priorities focus on addressing the limitations of current systems, ensuring ethical development, and expanding the capabilities of AI. Here are some of the main priorities:

1. Achieving General Artificial Intelligence (AGI): One of the primary goals is to move beyond narrow AI, which excels at specific tasks (e.g., LLMs for language), and towards AGI, which can perform a wide variety of tasks with human-like cognitive abilities, including reasoning, problem-solving, and learning in diverse domains.

2. Ethics and Responsible AI: Ensuring that AI systems are developed in an ethical and responsible way is a top priority. This includes mitigating biases in AI models, ensuring transparency, and preventing AI from being used in harmful or malicious ways. It also involves building systems that are fair, accountable, and respectful of privacy and human rights.

3. Safety and Alignment with Human Values: AI safety, especially in the context of AGI, involves creating systems that are aligned with human values and goals. Experts focus on ensuring that AI behaves predictably and safely, even as it becomes more autonomous, to avoid unintended consequences or risks.

4. Improving AI’s Explainability and Interpretability: Current AI models, especially deep learning systems, are often seen as "black boxes" because their decision-making processes are not easily interpretable. Developing AI systems that can provide clear explanations for their actions and decisions is critical for trust, accountability, and regulatory compliance.

5. Data Efficiency and Generalization: Current AI models, especially LLMs, require vast amounts of data to learn effectively. A major priority is to develop models that can learn more efficiently, with less data, and generalize better across different tasks, similar to how humans can learn from limited experiences and apply knowledge across various contexts.

6. Sustainability and Computational Efficiency: Large AI models require significant computational resources, which raises concerns about environmental impact and scalability. Reducing the energy consumption of AI models and making them more computationally efficient is an ongoing priority for researchers.

7. Human-AI Collaboration: Another important area is designing AI systems that can work alongside humans effectively, enhancing human decision-making, creativity, and productivity. This involves building systems that can understand human intentions and provide meaningful assistance without replacing human judgment.

8. Robustness and Security: AI systems need to be robust and secure, especially when deployed in critical applications such as healthcare, finance, and autonomous vehicles. Researchers prioritize building AI models that are resistant to adversarial attacks, errors, and failures, ensuring that they perform reliably in real-world situations.

9. Policy and Regulation: Developing frameworks and guidelines for the safe and fair use of AI is essential. Experts prioritize working with policymakers, governments, and international organizations to establish regulations that promote innovation while safeguarding society from potential AI-related risks.

10. Advancing AI in Multimodal and Embodied Intelligence: AI that can process and understand multiple types of input (e.g., text, images, sound, etc.) and interact with the physical world (embodied AI) is a growing area of interest. This type of intelligence is seen as crucial for creating AI systems that can operate in real-world environments and assist humans in complex tasks.

By addressing these priorities, AI experts aim to develop more advanced, safe, and beneficial AI systems that can meaningfully contribute to society while minimizing risks and ethical concerns.