The biggest issue I think is agency. In 2024 large improvements will be made to memory (a lot is happening in this regard). I agree that GPT-4 already has a lot of capability. Especially with fine-tuning it should do well on a lot of individual tasks relevant to AI development.
But the executive function is probably still lacking in 2024. Combining the tasks to a whole job will be challenging. Improving data is agency intensive (less intelligence intensive). You need to contact organizations, scrape the web, sift through the data etc. Also it would need to order the training run, get the compute for inference time, pay the bills etc. These require more agency than intelligence.
However, humans can help with the planning etc. And GPT-5 will probably boost productivity of AI developers.
note: depending on your definition of intelligence, agency or the executive function would/should be part of intelligence.
The biggest issue I think is agency. In 2024 large improvements will be made to memory (a lot is happening in this regard). I agree that GPT-4 already has a lot of capability. Especially with fine-tuning it should do well on a lot of individual tasks relevant to AI development.
But the executive function is probably still lacking in 2024. Combining the tasks to a whole job will be challenging. Improving data is agency intensive (less intelligence intensive). You need to contact organizations, scrape the web, sift through the data etc. Also it would need to order the training run, get the compute for inference time, pay the bills etc. These require more agency than intelligence.
Absolutely. Even with GPT-4′s constrained “short term memory”, it is remarkably proficient at managing sizable tasks using external systems like AutoGPT or Baby AGI that take on the role of extensive “planning” on behalf of GPT-4. Such tools equip GPT-4 with the capacity to contemplate and evaluate ideas—facets akin to “planning” and “agency”—and subsequently execute individual tasks derived from the plan through separate prompts.
This strategy could allow even GPT-4 to undertake larger responsibilities such as conducting scientific experiments or coding full-scale applications, not just snippets of code. If future iterations like GPT-5 or later were to incorporate a much larger token window (i.e., “short-term memory”), they might be able to execute tasks, while also keeping the larger scale planning in memory at the same time? Thus reducing the reliance on external systems for planning and agency.
However, humans can help with the planning etc. And GPT-5 will probably boost productivity of AI developers.
Note: depending on your definition of intelligence, agency or the executive function would/should be part of intelligence.
Agreed. Though, communication speed is a significant concern. AI-to-Human interaction is inherently slower than AI-to-AI or even AI-to-Self, due to factors such as the need to translate actions and decisions into human-understandable language, and the overall pace of Human cognition and response.
To optimize GPT-5′s ability in solving complex issues quickly, it may be necessary to minimize Human involvement in the process. The role of Humans could then be restricted to evaluating and validating the final outcome, thus not slowing down the ideation or resolution process? Though, depending on the size of the token window, GPT-5 might not have the ability to do the planning and execution at the same time. It might require GPT-6 or subsequent versions to get to that point.
Agree, human in the loop systems are very valuable and probably temporary. HITL systems provide valuable data for training allowing the next step. AI alone is indeed much faster and cheaper.
“Q: How do you see planning in AI systems? How advanced are AI right now at planning?
A: I don’t know it’s hard to judge we don’t have a metric for like how well agents are at planning but I think if you start asking the right questions for step by step thinking and processing, it’s really good.”
Agency is advancing pretty fast. Hard to tell how hard this problem is. But there is a lot of overhang. We are not seeing gpt-4 at its maximum potential.
Agency is advancing pretty fast. Hard to tell how hard this problem is. But there is a lot of overhang. We are not seeing gpt-4 at its maximum potential.
Yes, agreed. And, it is very likely that the next iteration (E.g. GPT-5) will have many more “emergent behaviors”. Which might include a marked increase in “agency”, planning, fossball, who knows…
People are finding ways to push the boundaries of the capabilities GPT-4 and are quite succesful at that (in reasoning, agency etc). These algorithmic improvements will probably also work on gpt5.
A lot of infrastructure built for gpt4 will also work on gpt5 (like plug-ins). We do not need to build new plug-ins for gpt5, we just swap the underlying foundational model (greatly increasing the adoption of gpt5 compared to gpt4).
This also works for agency shells like autogpt. Autogpt is independant of foundational model (works with gpt3.5, gpt4 and also gpt5). By the time gpt5 is released these agency shells will be greatly improved and we just have to swap out the underlying engine to get al lot more oomph from that.
Same for memory models like vector databases.
I think the infrastructure part will be a big difference. A year from now we will have a lot of applications, use cases, experience, better prompts etc. That could make the impact and speed of deployment of gpt5 (or Gemini) a lot bigger/faster than gpt4.
The biggest issue I think is agency. In 2024 large improvements will be made to memory (a lot is happening in this regard). I agree that GPT-4 already has a lot of capability. Especially with fine-tuning it should do well on a lot of individual tasks relevant to AI development.
But the executive function is probably still lacking in 2024. Combining the tasks to a whole job will be challenging. Improving data is agency intensive (less intelligence intensive). You need to contact organizations, scrape the web, sift through the data etc. Also it would need to order the training run, get the compute for inference time, pay the bills etc. These require more agency than intelligence.
However, humans can help with the planning etc. And GPT-5 will probably boost productivity of AI developers.
note: depending on your definition of intelligence, agency or the executive function would/should be part of intelligence.
Absolutely. Even with GPT-4′s constrained “short term memory”, it is remarkably proficient at managing sizable tasks using external systems like AutoGPT or Baby AGI that take on the role of extensive “planning” on behalf of GPT-4. Such tools equip GPT-4 with the capacity to contemplate and evaluate ideas—facets akin to “planning” and “agency”—and subsequently execute individual tasks derived from the plan through separate prompts.
This strategy could allow even GPT-4 to undertake larger responsibilities such as conducting scientific experiments or coding full-scale applications, not just snippets of code. If future iterations like GPT-5 or later were to incorporate a much larger token window (i.e., “short-term memory”), they might be able to execute tasks, while also keeping the larger scale planning in memory at the same time? Thus reducing the reliance on external systems for planning and agency.
Agreed. Though, communication speed is a significant concern. AI-to-Human interaction is inherently slower than AI-to-AI or even AI-to-Self, due to factors such as the need to translate actions and decisions into human-understandable language, and the overall pace of Human cognition and response.
To optimize GPT-5′s ability in solving complex issues quickly, it may be necessary to minimize Human involvement in the process. The role of Humans could then be restricted to evaluating and validating the final outcome, thus not slowing down the ideation or resolution process? Though, depending on the size of the token window, GPT-5 might not have the ability to do the planning and execution at the same time. It might require GPT-6 or subsequent versions to get to that point.
Agree, human in the loop systems are very valuable and probably temporary. HITL systems provide valuable data for training allowing the next step. AI alone is indeed much faster and cheaper.
“Q: How do you see planning in AI systems? How advanced are AI right now at planning?
A: I don’t know it’s hard to judge we don’t have a metric for like how well agents are at planning but I think if you start asking the right questions for step by step thinking and processing, it’s really good.”
Agency is advancing pretty fast. Hard to tell how hard this problem is. But there is a lot of overhang. We are not seeing gpt-4 at its maximum potential.
Yes, agreed. And, it is very likely that the next iteration (E.g. GPT-5) will have many more “emergent behaviors”. Which might include a marked increase in “agency”, planning, fossball, who knows…
People are finding ways to push the boundaries of the capabilities GPT-4 and are quite succesful at that (in reasoning, agency etc). These algorithmic improvements will probably also work on gpt5.
A lot of infrastructure built for gpt4 will also work on gpt5 (like plug-ins). We do not need to build new plug-ins for gpt5, we just swap the underlying foundational model (greatly increasing the adoption of gpt5 compared to gpt4).
This also works for agency shells like autogpt. Autogpt is independant of foundational model (works with gpt3.5, gpt4 and also gpt5). By the time gpt5 is released these agency shells will be greatly improved and we just have to swap out the underlying engine to get al lot more oomph from that.
Same for memory models like vector databases.
I think the infrastructure part will be a big difference. A year from now we will have a lot of applications, use cases, experience, better prompts etc. That could make the impact and speed of deployment of gpt5 (or Gemini) a lot bigger/faster than gpt4.