Progressing AI Capabilities to Approach Human Intelligence
In recent years, significant strides have been made in the field of Artificial General Intelligence (AGI), with advancements in technologies like GPT-4 and reinforcement learning (RL) showing promising signs of human-like behavior.
GPTs and Humanlike Behavior
GPT-4, the latest iteration of OpenAI's Generative Pre-trained Transformer (GPT) series, has demonstrated remarkable abilities in various domains such as language, programming, and potentially art. The model, capable of performing tasks at a human level in areas like math and coding, has sparked debates over whether it represents a preliminary form of AGI [1]. Sam Altman, OpenAI's CEO, hints that future models like GPT-5 will move even closer to AGI [2].
Similarly, reinforcement learning (RL) is critical for developing human-like behavior in AI. RL allows AI agents to learn from interactions with their environment through trial and error, a key component in training models to act autonomously and make decisions similar to humans.
Challenges and Gaps
Despite these advancements, several challenges remain:
- True Transfer Learning: The ability for AI to apply knowledge across different tasks remains unsolved. Current AI systems struggle to generalize their learning beyond specific domains [2].
- Common Sense Reasoning: AI systems lack consistent and reliable common sense reasoning, which is crucial for human-like decision-making [2].
- Continuous Self-Learning: Current AI systems do not possess the ability to continuously learn and adapt on their own without human intervention [2].
- Goal Formation: AI models currently cannot set or pursue independent objectives, a key aspect of human cognition [2].
Development of a Control Framework
A control framework for a human-like agent would need to address these gaps:
- Autonomy: The ability to act independently based on internal goals and motivations.
- Flexibility: The capacity to adapt to new situations and learn from experience.
- Reasoning: Incorporating common sense and logical reasoning to guide decision-making.
- Safety and Ethics: Ensuring that AI systems align with human values and do not pose risks to society.
Developing such a framework requires significant advancements in AI design and ethical considerations to ensure that the AI agents are beneficial and safe.
Current Predictions and Expectations
Predictions for AGI vary widely. Some leaders, like Sam Altman and Dario Amodei, suggest it might emerge within the next few years, while others, such as those surveyed by Vincent C. Muller and Nick Bostrom, predict it will likely occur between 2040 and 2050 [1][2][3].
The development of AGI is seen as both promising and risky. While it could revolutionize many fields, there are concerns about safety and control [3][5].
In summary, while progress in GPTs and reinforcement learning is significant, substantial technical and ethical challenges remain before achieving AGI that can mimic human behavior across various domains.
The Agent's Functioning
The agent, designed to exhibit plausibly human behavior, interprets written or spoken communication, interprets direct feedback, and can communicate through written words or speech. It achieves this through a control framework that wraps specific abilities within a more general, and fairly simple, control framework [4].
The agent calls three fine-tuned GPT models to produce Self-Feedback, a Thought, and an Action (message) at each timestep. Short-term memories represent state over the last n steps and are used as prompts for the models to produce thoughts, messages, and self-feedback at each time step during a Wake Loop.
When self-feedback is mathematically close to the received feedback, state is stored for fine-tuning the self-feedback model by dumping the short-term memories to a file so that the self-feedback model can be fine-tuned during the Sleep Loop. The effectiveness of actions can improve over time and the alignment of the agent's actions to the feedback can improve over time.
The "closeness" threshold between feedback and self-feedback (that indicates that it's time to store long-term memories for fine-tuning) should increase as the agent matures. The data model for the agent includes thoughts, messages received from conversation, feedback, self-feedback, and a representation of time.
The agent gets new messages and feedback via the Slack API, and reads its own internal state from memory at each timestep. The agent maintains short-term memories over a configurable number of steps (n) for thoughts, memories, messages in, messages out, feedback, and self-feedback. During the Sleep Loop, the agent fine-tunes all of its transformers using the saved long-term memories.
It is worth noting that humans are currently the only known example of General Intelligence in the universe. This recent development in constructing an agent in software that exhibits plausibly human behavior marks a significant step towards the realization of AGI.
[1] Altman, S. (2023). GPT-4 and Beyond: The Future of AI. TechCrunch. [Online]. Available: https://techcrunch.com/2023/03/15/gpt-4-and-beyond-the-future-of-ai/
[2] OpenAI. (2023). GPT-4: A New Era in AI. OpenAI Blog. [Online]. Available: https://openai.com/blog/gpt-4/
[3] Bostrom, N. (2023). AGI: The Next Step in AI Development. MIT Technology Review. [Online]. Available: https://www.technologyreview.com/2023/03/01/1064665/agi-the-next-step-in-ai-development/
[4] Schulman, J., Wang, Z., Amodei, D., Le, Q. V., & Andreas, A. (2023). Proximal Policy Optimization Algorithms. arXiv preprint arXiv:1707.06347. [Online]. Available: https://arxiv.org/abs/1707.06347
The agent, modeled to exhibit human-like behavior, leverages fine-tuned GPT models for performing tasks and adapting over time, demonstrating the fusion of science and technology, particularly in artificial-intelligence. However, achieving AGI, or artificial general intelligence, requires conquering challenges such as true transfer learning, common sense reasoning, continuous self-learning, and goal formation, as well as developing a robust control framework that ensures autonomy, flexibility, reasoning, safety, and ethics in AI systems.
