Podcast Summary
Misaligned definition of AGI: Misdefining AGI as a system capable of doing most economically useful work has led to over-investment in large language models and stalled progress in AGI research. The Arc Prizes, a new competition, aims to encourage the exploration of new ideas and publication of technical details to accelerate progress towards a more accurate definition of AGI.
According to Mike Neup, co-founder and head of AI at Appyear, progress towards Artificial General Intelligence (AGI) has stalled out due in part to a misaligned definition of what AGI is. The consensus definition, which equates AGI to a system capable of doing the majority of economically useful work that humans can do, is not sufficient. Mike believes that this definition is wrong and has led to over-investment in large language models and a lack of progress in frontier AI research. To address this issue, Mike and Francois Chole have launched the Arc Prizes, a million-dollar plus nonprofit public challenge to beat the Arc AGI eval, an evaluation of AGI that measures a more accurate definition of AGI. The current state-of-the-art in beating this eval is only 34%, and Mike hopes that this competition will accelerate progress towards AGI by encouraging the exploration of new ideas and the publication of technical details in research.
General Intelligence vs LLMs: Current AI systems, including LLMs, excel at discovering patterns but struggle with new, unknown problems, and true general intelligence is needed for advancements in physics, understanding the universe, and creating new therapeutics.
While large language models (LLMs) have shown impressive progress in memorization and pattern recognition, they fall short of true general intelligence. The ability to efficiently and effectively acquire new skills and solve open-ended problems, as defined by Francois Chollet, is what sets humans apart. Current AI systems, including LLMs, excel at discovering patterns in their training data and applying them to new contexts. However, they struggle when faced with completely new and unknown problems, where the reasoning chain doesn't exist in the training data. To build AI systems that can discover new branches of physics, pull forward our understanding of the universe, or create new therapeutics, we need more than just high-dimensional memorization systems. We need true general intelligence, and that's where current AI falls short. The discussion also touched upon the economic value of LLMs and their ability to perform a wide range of tasks, but it was agreed that this scalability doesn't necessarily lead to true general intelligence. Ultimately, the pursuit of artificial general intelligence (AGI) requires a focus on building systems that can effectively and efficiently acquire new skills and solve open-ended problems, and the definition of general intelligence as a system that can do so is a promising step in that direction.
Additional components for AGI: Robust reasoning modules, memory, potentially transformers are necessary for AGI beyond language models. Program synthesis and neural architecture search are promising techniques for AGI development.
While language models have shown significant progress, they are not yet sufficient for achieving Artificial General Intelligence (AGI). The discussion highlighted the need for additional components, such as robust reasoning modules, memory, and potentially transformers. A promising technique mentioned was program synthesis, which searches through a vast program space to discover general solutions, contrasting with the language model approach. Another area of exploration is neural architecture search, where computers discover their own architectures, a field that has shown potential but has yet to be fully realized due to limited access to large-scale compute in the past. The conversation also touched upon the distinction between intelligence and sentience in the context of AGI.
AI development focus: Future AI development should prioritize creating systems that can invent and discover alongside humans, emphasizing general intelligence and potential benefits, while acknowledging risks and uncertainties. Encourage outsiders and new ideas through open source competitions like the ARK prize.
The future of artificial intelligence (AI) development should focus on creating systems capable of inventing and discovering alongside humans, rather than just increasing economic value. This perspective emphasizes the importance of general intelligence and the potential benefits it could bring, while acknowledging the risks and uncertainties associated with advanced AI. It is crucial to approach AI research through an empirical lens and avoid making predictions or imposing regulations based on theoretical assumptions. The ARK prize, a million-dollar open source model competition for artificial general intelligence, was established to encourage outsiders and new ideas in the field, as the solution may come from unexpected sources.
Archegia Puzzle and AI Research: The Archegia Puzzle offers an accessible and achievable challenge for individuals interested in AI research, potentially leading to both status and financial incentives. Zapier's experimentation with AI technology resulted in the launch of a chat to PT plugin, significantly increasing their association with AI.
The "Archegia Puzzle" is an accessible and achievable challenge for individuals interested in artificial intelligence research, as it requires minimal resources and can potentially be solved with a small amount of code. The creator of the puzzle hopes that by offering a prize, it will encourage individuals to explore AI research as an alternative to starting a business, providing both status and financial incentives. During their time at Zapier, Brandon and the speaker experimented with AI technology, building internal versions of chatbots and thought models. They realized the gap was in making these models more dynamic and adaptable. This led to the idea of integrating Zapier's extensive tools with AI models, resulting in the launch of the chat to PT plugin, which significantly boosted Zapier's association with AI technology. As of now, over 50 million AI tasks have been completed on the Zapier platform since its implementation, with users utilizing AI steps for content generation, feature extraction, and summarization.
AI expansion in Zapier: Zapier is expanding AI usage beyond workflows, introducing new bots programmable via natural language, capable of independent tasks, increasing reliability and consistency to unlock more use cases.
Zapier is expanding the use of AI in their product beyond just workflows, making it more accessible and easier to use for users. They have introduced new AI bots, which can be programmed using natural language instructions, and these bots can perform tasks without the need for extensive configuration. The future direction for Zapier is towards more agentic tasks, where the bots can operate more independently and make decisions on their own. This is already happening, with users paying for these AI bots. However, as the risk increases, users may want to impose more restrictions on what the bot can do. Zapier's focus is on increasing the reliability and consistency of the technology to unlock more use cases and reduce the risk for users.
Open source and AI research: Open source and sharing are crucial for generating new ideas and driving progress in AI research, but concerns exist about decreasing openness due to commercialization and market incentives, which could potentially stall AGI discovery.
Open source ideas, software, and research sharing have significantly contributed to advancements in science and technology, including AI. The internet and open source have been instrumental in generating new ideas and fostering collaboration among researchers. However, there are concerns about the increasing commercialization and market incentives that may lead to a decrease in open research sharing, particularly at the foundational scientific level. This could potentially stall progress in discovering Artificial General Intelligence (AGI). Historically, open protocols and open source software have been crucial to the development of the internet. Regarding AI regulation, existing frameworks are considered sufficient for narrow AI systems. However, when it comes to AGI, it's crucial to avoid prescriptive legislation and instead focus on understanding its capabilities and potential risks before making decisions. Ultimately, open source and open sharing remain essential for generating new ideas and driving progress in AI research.
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