Sachin Adela argues that the definition of AGI (Artificial General Intelligence) is problematic because the concept of "cognitive labor" is not static. He explains that the cognitive labor of today may be automated, but new forms of cognitive labor will emerge. This means that the tasks that an AGI system would need to automate are constantly evolving, making a fixed definition of AGI challenging.

Adela emphasizes that we should not conflate "knowledge worker" with "knowledge work." The knowledge work of today may be automated, but this will lead to the creation of new, higher-level cognitive tasks. For example, an AI agent could automate the task of email triage, freeing up the human to focus on more complex work, such as reviewing important drafts.

Adela argues that the definition of AGI should not be based on the ability to automate all cognitive tasks, as this is a moving target. Instead, he suggests that a more meaningful benchmark for AGI would be the ability to drive significant economic growth, such as 10% year-over-year. This would indicate that the AGI system is providing real-world value to customers and businesses, rather than just achieving benchmark performance on specific tasks.

Sachin Adela argues that the traditional definition of AGI, as the ability to automate all cognitive labor, is flawed. He believes that cognitive labor is not a static concept, but rather a dynamic one that is constantly evolving.

Adela suggests that as new tasks and workflows emerge, the definition of cognitive labor changes. He argues that the true measure of AGI should not be the ability to automate existing cognitive tasks, but rather the ability to drive significant economic growth (e.g., 10% year-over-year growth).

Adela criticizes the current focus on benchmark hacking, where AI companies aim to outperform humans on specific tasks. Instead, he believes the focus should be on delivering real-world value to customers and businesses. He argues that the true test of AGI should be its ability to drive substantial economic growth, rather than just automating existing cognitive tasks.

Sachin Adela argues that the current definitions of AGI (Artificial General Intelligence) are often vague and do not capture the true value that AI systems should provide. He emphasizes that cognitive labor is not a static concept, and the tasks that can be automated are constantly evolving. Therefore, the definition of AGI should not be solely focused on automating all cognitive tasks, but rather on delivering tangible economic growth and value to customers.

Adela suggests that a more meaningful benchmark for AGI would be achieving 10% year-over-year economic growth. This metric would ensure that AI investments are translating into real-world impact, rather than just benchmark hacking or achieving narrow AI feats. He cautions that companies should be wary of overspending on AI infrastructure without having a clear path to delivering value to customers.

The key points made by Adela are:

• The definition of AGI should be based on the ability to drive significant economic growth, not just the automation of cognitive tasks.
• Companies should focus on delivering value to customers, rather than chasing narrow AI benchmarks.
• There is a risk of overspending on AI infrastructure without a clear understanding of how to translate that investment into real-world demand.
• The evolution of cognitive labor means that the tasks that can be automated are constantly changing, and the definition of AGI should reflect this dynamic nature.

Overall, Adela's perspective emphasizes the importance of aligning AI investments with tangible economic and customer-centric outcomes, rather than relying on vague definitions or benchmark-driven progress.

Several prominent CEOs and AI experts have shared their views on the timeline for achieving Artificial General Intelligence (AGI). Here's a summary of their perspectives:

Demis Hassabis, CEO of DeepMind

Hassabis believes that AGI, defined as an AI system capable of exhibiting all the cognitive capabilities of humans, is still "a handful of years away." He notes that current AI systems are quite capable in some areas but still lack key attributes like robust reasoning, hierarchical planning, and long-term memory. Hassabis thinks it will take several more major innovations to reach the level of AGI.

Dario Amodei, Co-founder of Anthropic

Amodei is more optimistic, estimating that AGI could be achieved by 2026 or 2027 if current trends in AI progress continue. He acknowledges that there are many potential roadblocks, but a straight-line extrapolation of recent advancements suggests AGI may be reached within the next 5-6 years.

Andrew Ng, AI Scientist

Ng takes a more cautious view, stating that the "standard definition of AGI" - an AI system that can perform any intellectual task a human can - is likely "many decades away." He notes that some companies are using non-standard definitions of AGI to claim shorter timelines, but under the traditional definition, AGI remains a distant goal.

Yann LeCun, Chief AI Scientist at Meta

LeCun believes we are missing a key component to achieve true AGI, which can match human-level intelligence across a wide range of tasks. He points out that current AI systems still struggle with basic capabilities that even young children can perform, like clearing a dinner table or learning to drive a car. LeCun suggests we may see progress in Artificial Superintelligence (ASI) in specific domains before reaching the level of general human-like intelligence.

In summary, the timeline for AGI remains highly debated, with some experts predicting it within the next 5-10 years, while others believe the traditional definition of AGI is still decades away. The path to achieving AGI appears to require significant breakthroughs beyond the current state of the art in AI.

According to Demis Hassabis, the CEO of DeepMind, there are still several key attributes that current AI systems are missing in order to achieve true Artificial General Intelligence (AGI):

• Reasoning: Current AI systems are still surprisingly weak and flawed when it comes to higher-order reasoning capabilities.

• Hierarchical Planning: The ability to plan complex, multi-step actions in a hierarchical manner is still lacking in today's AI.

• Long-Term Memory: AI systems struggle with maintaining consistent, long-term memory and knowledge, unlike the human mind.

• Creativity and Invention: A critical component of AGI would be the ability to invent new hypotheses, conjectures and ideas, not just prove existing ones. Current AI is far from this level of creative capability.

Hassabis notes that while today's AI systems are becoming increasingly capable at specific tasks, they still lack the consistent, robust behavior across a wide range of cognitive abilities that would be required for true AGI. He estimates that we are still 3-5 years away from achieving the key component breakthroughs needed to move towards AGI, rather than the much shorter timelines proposed by some other AI companies and researchers.

While the promise of Artificial General Intelligence (AGI) has captured the imagination of many, it is important to recognize that the path to achieving true AGI may be more challenging and longer than some have suggested. As the transcript highlights, there are differing views on the timeline and definition of AGI, with some experts arguing that we may see the emergence of specialized Artificial Superintelligence (ASI) systems before we reach the level of AGI.

The key point made in the transcript is that for AGI, an entire system needs to work exceptionally well across a wide range of tasks that humans can perform. This is a significantly more difficult challenge than achieving superhuman performance in a narrow, well-defined domain, which is what we have already seen with systems like AlphaFold and AlphaZero.

The transcript cites the Google DeepMind paper, which highlights that while we have already achieved level 5 superhuman AI in certain narrow domains, we are still far from achieving competent AGI that can match 50% of skilled adults. This suggests that the path to AGI may be longer and more arduous than some have claimed.

Furthermore, the transcript notes that companies like OpenAI have indicated a shift in focus towards developing Superintelligence, rather than solely pursuing AGI. This suggests that the near-term focus may be on creating specialized AI systems that can outperform humans in specific tasks, rather than attempting to create a generalized system that can match human-level intelligence across the board.

In conclusion, while the promise of AGI remains compelling, the transcript highlights the potential for the emergence of specialized ASI systems before the realization of true AGI. This perspective provides a more nuanced understanding of the current state of AI development and the challenges that lie ahead in achieving the ambitious goal of creating artificial general intelligence.

Sachin Adela's perspective on the definition of AGI is that it is a vague and constantly changing concept. He argues that cognitive labor is not static, and the tasks that need to be automated are constantly evolving. Therefore, the definition of AGI should not be based on automating all cognitive labor, as that target is always shifting.

Adela proposes a different metric for evaluating AGI - the ability to drive 10% annual economic growth. He believes this is a more meaningful benchmark than simply automating a wide range of tasks. Adela suggests that companies should focus on delivering real-world value to customers, rather than engaging in "benchmark hacking" to demonstrate their AI's capabilities.

The views of other industry leaders, such as Demi Hassabis and Yann LeCun, suggest that AGI is still many years away, with significant technical hurdles to overcome. They emphasize the need for breakthroughs in areas like reasoning, long-term memory, and the ability to invent new hypotheses, which current AI systems lack.

While some companies, like OpenAI, claim to be close to achieving AGI, others, like Andrew Ng, believe the standard definition of AGI is still decades away. This divergence in opinions may be influenced by the incentives and investment strategies of the companies involved.

Ultimately, the path to AGI remains uncertain, and the industry is grappling with the challenges of defining and measuring progress towards this ambitious goal. Adela's perspective highlights the need to focus on delivering tangible value, rather than chasing vague benchmarks, as the AI field continues to evolve.