OpenAI CEO Sam Altman is currently on a global tour, recently stopping in Berlin to discuss AI and its potential future. During a panel discussion at TU Berlin, Altman posed a question to the audience: “How many of you think you’re still going to be smarter than GPT-5?”
He followed this by stating, “I don’t think I’m going to be smarter than GPT-5. And I don’t feel sad about it because I think it just means that we’ll be able to use it to do incredible things… we want to enable researchers to do things they couldn’t do before. This is the long history of humanity.”
Altman’s remarks hint that OpenAI is in the final stages of developing the GPT-5 model, with early evaluations suggesting significantly enhanced intelligence.
Despite this, Altman clarified in a recent Reddit AMA that there’s no firm release date for GPT-5. However, he has been more forthcoming about the model in recent interviews and press briefings.
Currently, OpenAI is prioritizing its o-series reasoning models. The o3 model achieved a breakthrough by solving the ARC-AGI benchmark, although at a higher computational cost. Additionally, the Deep Research agent, powered by the o3 model, scored 26.6% on Humanity’s Last Exam using web search and Python tools, surpassing other models that achieved around 9.4% without web search.
Interestingly, during his tour in Japan, Altman expressed OpenAI’s ambition to merge the GPT-series and o-series models into a unified AGI (Artificial General Intelligence) model. The ultimate goal is an inference-scaled model exhibiting significantly improved capabilities and intelligence.
Integrating GPT and o-Series Models for AGI
The most effective way to achieve AGI, as suggested by Altman, is to integrate the strengths of both the GPT and o-series models. This involves creating a unified architecture that leverages the natural language processing capabilities of the GPT series with the advanced reasoning abilities of the o-series.
The Significance of the o3 Model
The o3 model’s success in cracking the ARC-AGI benchmark highlights its potential for advanced reasoning.
Step 1: Understand the ARC-AGI benchmark.
This benchmark is designed to test a system’s ability to reason and generalize from a small number of examples.
Step 2: Analyze the o3 model’s architecture.
Examine how the model is structured to solve complex reasoning problems.
# Example (Hypothetical) of o3 model architecture
class O3Model(nn.Module):
def __init__(self, layers, embedding_dim):
super().__init__()
self.embedding = nn.Embedding(vocab_size, embedding_dim)
self.reasoning_layers = nn.ModuleList([ReasoningLayer(embedding_dim) for _ in range(layers)])
def forward(self, input):
embedded = self.embedding(input)
for layer in self.reasoning_layers:
embedded = layer(embedded)
return embedded
Step 3: Evaluate the trade-offs.
Consider the higher computational cost associated with the o3 model.
The Potential of the Deep Research Agent
The Deep Research agent’s performance on Humanity’s Last Exam demonstrates its ability to utilize tools and information retrieval for problem-solving.
Step 1: Understand the task requirements.
Familiarize yourself with the nature of Humanity’s Last Exam and its challenges.
Step 2: Examine the agent’s architecture.
Analyze how the Deep Research agent integrates web search and Python tools.
# Example (Hypothetical) of Deep Research agent architecture
class DeepResearchAgent:
def __init__(self, search_tool, python_executor):
self.search_tool = search_tool
self.python_executor = python_executor
def solve(self, problem):
relevant_info = self.search_tool.search(problem)
code = self.generate_code(relevant_info, problem)
result = self.python_executor.execute(code)
return result
Step 3: Assess the impact.
Compare the agent’s performance to other models and highlight the significance of using external tools.
The convergence of the GPT and o-series models could pave the way for more capable and intelligent AI systems. This integration would likely lead to significant advancements in various fields, enabling researchers to tackle previously insurmountable challenges.