AI Won't Be AGI, Until It Can At Least Do This (plus 6 key ways LLMs are being upgraded)
TLDRThe video explores the limitations of current AI, highlighting its inability to perform abstract reasoning tasks outside its training data, contrasting it with the debate on whether AI is overhyped or underhyped. It critiques overpromising in AI marketing and the rise of AI-generated content, which can lead to misinformation. However, it also presents six evidence-based pathways for improving large language models, including compositionality, verifiers, active inference, and combining neural networks with symbolic systems, suggesting a future where AI could become more capable without necessarily reaching AGI status.
Takeaways
- 🧠 Current AI, including GPT-4, lacks general intelligence and struggles with abstract reasoning not present in its training data.
- 🔍 The debate over AI being overhyped or underhyped is misguided; the reality is more nuanced, with evidence suggesting AI has room for significant improvement.
- 📉 Overpromising and underdelivering are prevalent issues in the AI industry, with high-profile examples like Google's AI Overview feature needing to be rolled back due to inaccuracies.
- 🤖 AI-generated content, or 'slop', is a growing concern, leading to a lack of trust in online information and a potential 'tragedy of the commons' scenario.
- 📚 There is a call for more evidence-based approaches to improving AI, focusing on six detailed pathways to develop more powerful and useful models.
- 🔄 The idea that simply scaling up models with more data and parameters will achieve AGI is overly simplistic and unlikely to resolve core issues with current AI.
- 🔢 There is potential in training AI on reasoning procedures or 'programs' to improve performance on certain benchmarks, but this doesn't equate to general intelligence.
- 📈 Some studies suggest that with enough scale, AI models can show improvements on rare concepts, but this is not a complete solution for achieving AGI.
- 🔬 Innovative approaches such as compositionality, verifiers, and active inference are being explored to enhance AI's reasoning capabilities.
- 🤝 Combining neural networks with traditional symbolic systems could offer a more holistic approach to improving AI's ability to plan and reason.
- 📝 The concept of 'tacit data'—the unwritten knowledge and intuition of experts—presents a significant opportunity for AI improvement if effectively captured and utilized.
Q & A
What is the main argument presented in the video regarding current language models and their limitations?
-The video argues that current language models, such as GPT-4, are not truly intelligent or generalizable because they cannot solve abstract reasoning challenges that were not present in their training data sets. They are not capable of artificial general intelligence (AGI) as they lack the ability to generalize from what they have seen to solve novel challenges.
Why is the video critical of the current state of AI and its applications?
-The video criticizes the overhyping of AI capabilities, the issues of AI-generated 'slop' leading to a lack of trust in digital content, and the privacy concerns arising from AI tools like Microsoft Recall. It also points out the discrepancy between the promises of AI capabilities and their actual performance.
What are the six key ways mentioned in the video that large language models (LLMs) are being upgraded to become more powerful and useful?
-The video does not explicitly list the six ways in the provided transcript. However, it discusses concepts such as compositionality, verifiers and Monte Carlo tree search, active inference, combining LLMs with traditional symbolic systems, joint training on specialized knowledge, and the potential of training on tacit data.
What is the 'tragedy of the commons' referred to in the context of AI-generated content?
-The 'tragedy of the commons' in this context refers to the overuse or misuse of shared resources, leading to a degradation of quality. Here, it refers to the proliferation of AI-generated content that lacks originality and authenticity, which can lead to a decrease in the overall quality and trustworthiness of online content.
How does the video address the debate about whether AI is overhyped or underhyped?
-The video acknowledges the debate and aims to provide evidence-based insights into the capabilities and limitations of current AI systems. It suggests that the truth lies somewhere in between the extremes of the debate, highlighting both the hype and the genuine progress being made in the field.
What is the role of 'hallucinations' in AI as discussed in the video?
-In the context of the video, 'hallucinations' refer to the AI's tendency to generate content that is not grounded in factual accuracy or the reality of its training data. This can be seen as both a creative advantage and a source of misinformation, depending on the application.
What is the significance of the 'Abstract Reasoning Challenge' mentioned in the video?
-The 'Abstract Reasoning Challenge' is significant because it highlights the limitations of current LLMs in solving problems that require generalization and reasoning beyond their training data. It serves as an example of a task that models like GPT-4 fail at, demonstrating their lack of true intelligence or AGI capabilities.
How does the video discuss the potential of using AI in scientific discoveries or discovery tasks?
-The video suggests that AI, particularly through the use of verifiers and simulations, can propose multiple solutions to complex problems, which can then be tested for validity. This iterative process can potentially lead to scientific discoveries or advancements in various fields.
What is the potential impact of training AI on tacit data, as mentioned in the video?
-Training AI on tacit data, which includes the unwritten knowledge and intuition of experts, could significantly improve AI's reasoning capabilities. However, this process relies on experts making their thought processes explicit, which is a challenging and time-consuming task.
What are some of the ethical concerns raised by the video regarding the use of AI?
-The video raises concerns about privacy breaches, the spread of misinformation through AI-generated content, and the potential for AI to be used in ways that undermine trust in digital interactions, such as deepfakes and AI-generated 'slop'.
Outlines
🧠 AI's Limitations and the Challenge of General Intelligence
The paragraph discusses the inability of current language models, like GPT-4, to perform abstract reasoning tasks not present in their training data. It highlights the difference between these models and artificial general intelligence (AGI), emphasizing the gap between current AI capabilities and true general intelligence. The script also touches on the hype surrounding AI and the debate over whether AI is overhyped or if AGI is near. It mentions the 'Ark AGI Challenge' and the issues with overpromising and underdelivering in AI, such as Google's AI Overview feature and Apple's admitted inaccuracies in their AI systems.
📉 The 'Dodgy' Side of AI: Overhype and Misuse
This section critiques the current AI landscape, focusing on the overhyping of AI capabilities and the misuse of AI-generated content. It points out the increase in AI-generated 'slop' on platforms like LinkedIn, leading to a lack of trust in online content. The paragraph also addresses concerns about AI-generated misinformation, privacy breaches, and the academic misuse of AI to write papers. It calls for a balanced view of AI's potential and current limitations, rather than falling into extreme opinions on its capabilities.
🔬 The Potential of Neural Networks Beyond LLMs
The script shifts focus to the broader applications of neural networks, beyond just large language models (LLMs). It discusses the use of GANs (Generative Adversarial Networks) in predicting the effects of chemicals on mice, potentially reducing the need for animal testing. It also mentions the use of convolutional neural networks in medical applications, such as the Brainix stroke system, which has improved patient outcomes. The paragraph emphasizes the diversity of neural network applications and their potential to contribute positively to various fields.
🔍 The Current State of LLMs and the Path to AGI
This paragraph delves into the current state of large language models, their limitations in reasoning, and the ongoing efforts to improve them. It explains that LLMs struggle with tasks not present in their training data and cannot generalize from past experiences to novel situations. The script introduces the concept of compositionality, where models could potentially combine simple reasoning blocks to solve more complex problems. It also discusses the potential of training strategies that diversify beyond just adding more data.
🛠 Enhancing LLMs with Verifiers and Active Inference
The script explores methods to enhance LLMs, such as using verifiers to identify faulty reasoning steps and Monte Carlo tree search to improve mathematical reasoning. It also discusses the concept of active inference, where models are fine-tuned with synthetic examples to teach them new programs on the fly. The paragraph highlights the potential of these approaches to improve the reasoning capabilities of LLMs and bring them closer to AGI.
🤖 Combining LLMs with Symbolic Systems for Enhanced Reasoning
This section discusses the potential of combining LLMs with traditional symbolic systems to improve reasoning and planning capabilities. It suggests that LLMs can act as idea generators, with symbolic systems verifying and refining those ideas. The script references a study where this approach significantly improved performance on reasoning challenges, indicating a promising path for developing more capable AI systems.
📚 The Importance of Tacit Knowledge in Advancing AI
The final paragraph emphasizes the role of tacit knowledge in human reasoning and the potential for AI to learn from this unwritten knowledge. It suggests that making explicit the implicit reasoning processes of experts could significantly improve AI capabilities. The script also touches on the efforts of organizations like OpenAI to capture this tacit knowledge and the potential for a combination of approaches to eventually achieve AGI.
Mindmap
Keywords
💡AGI
💡GPT-4
💡Abstract Reasoning
💡LLMs
💡Hallucinations in AI
💡AI Overhyping
💡Compositionality
💡Verifiers
💡Monte Carlo Tree Search
💡Tacit Data
💡Symbolic Systems
Highlights
AI models like GPT-4 struggle with abstract reasoning challenges not present in their training data.
Current AI is not AGI due to its inability to generalize from training data to novel situations.
The debate on whether AI is overhyped or underhyped is addressed with evidence from various papers and reports.
AI's shortcomings, such as delayed releases and overpromising, are discussed, including privacy concerns with features like Microsoft Recall.
The potential of AI in medical fields, like the Brainix stroke system, is highlighted, showing the positive impact on patient recovery.
Large language models (LLMs) can sometimes appear smart due to their ability to recall reasoning procedures from training data.
The importance of compositionality in improving LLMs is presented, with a study showing how smaller models can mimic human generalization.
Verifiers and Monte Carlo tree search are introduced as methods to enhance mathematical reasoning in LLMs.
Active inference and test-time fine-tuning are discussed as ways to teach LLMs new programs on the fly.
Combining LLMs with traditional symbolic systems is suggested as a hybrid approach to improve reasoning capabilities.
Jointly training LLMs with specialized algorithms is proposed to improve their knowledge and reasoning.
The concept of tacit data in human reasoning is explored as a potential area for improving AI.
The video argues that AGI is not imminent but also that AI's potential should not be dismissed as mere hype.
AI's current challenges, such as the tragedy of the commons from AI-generated content, are critiqued.
The potential for AI to assist in scientific discoveries and planning is discussed, emphasizing the need for continued research and development.
The video concludes by suggesting that a combination of approaches may be necessary to achieve AGI.