The new O1 model Has artificial intelligence reached the level of human thinking?

On Friday, OpenAI launched its latest innovation in generative AI , a large language model (o1) — previously known internally as Project Strawberry — that doesn’t just give you answers, it thinks deeply before it gives them to you.

Imagine having a personal assistant like a detective who is able to think complex logically and solve multi-step problems, in the same way that the human mind does. This is exactly what the O1 model offers you.

But how does the new (o1) model think before answering, and can the quality of the response of artificial intelligence models be improved by developing their thinking capabilities, and has artificial intelligence actually reached the level of human thinking?

First, what is the new (o1) model from (OpenAI)?

OpenAI designed the new O1 model to excel at complex reasoning tasks. Unlike previous versions, like GPT-4 , which were trained to simulate patterns from their data, O1 is trained using a new approach called reinforcement learning, which allows it to learn through trial and error. It solves problems step by step, evaluates its results, and adjusts its behavior based on the rewards and punishments it receives. This approach brings it closer to human thinking, as it constantly learns and improves.

The company said that the (o1) model is designed to spend more time thinking before responding to user inquiries, and using this model, OpenAI tools are supposed to be able to solve problems that require multiple steps to solve, including complex math and programming questions.

The o1 comes in two versions: o1-preview and o1-mini, each with its own unique features, allowing users to choose the version that best suits their needs based on budget and performance requirements. The o1-preview offers superior reasoning and understanding capabilities, while the o1-mini offers a lower-cost alternative with slightly lower performance.

The company has paid great attention to the safety and ethics of the model, as both models have undergone rigorous testing to verify that they do not produce harmful or biased content, making them safe tools for users and developers alike.

Second, how does the new (o1) model think before answering?

The (o1) model significantly outperforms previous models, such as ( GPT-4 ) and ( GPT-4o ), in handling complex reasoning tasks with ease. These tasks require the AI system to go beyond simply understanding the literal meaning of text or data, and to apply complex logical rules, draw new conclusions, and solve problems that require critical and creative thinking.

This requires the model to perform several steps, including:

1- Deep analysis of information:

Understanding context: The O1 model does not just understand individual words, but tries to understand the overall meaning of the sentence and the context in which it was said.
Extracting relevant information: The model uses complex algorithms to extract the most important information from the text, and discard irrelevant information.
Connecting ideas: The model connects different ideas together to build a comprehensive understanding of the topic.

2- Logical reasoning:

Applying rules: The (o1) model uses complex logical rules to reach new conclusions based on available information.
Causal thinking: The model attempts to understand the relationship between causes and effects, and to predict what might happen in the future.
Problem solving: The model uses a range of strategies to solve complex problems, such as breaking the problem into smaller parts or finding alternative solutions.
Continuous learning: The model learns from its mistakes and adjusts its behavior to improve its performance in the future. It can also adapt to new environments and new information, making it more flexible.

The work of the (o1) model can easily be likened to that of a detective trying to solve a mystery. It collects evidence (information), analyzes it (using logical rules), and reaches conclusions (the answer). What makes it special is its ability to do this quickly and with high accuracy.

To illustrate further, let's take an example: If you ask model (o1) a question like: (Why is the sky blue?), it will not limit itself to providing a simple answer like: Because light is refracted in the atmosphere, but it will analyze the question more deeply, link this question to its knowledge about light, colors, and the atmosphere, and then provide a comprehensive and profound answer.

These capabilities make the model an indispensable tool in fields such as scientific research, quantum physics, mathematics, and healthcare. With enhanced critical thinking, the model can perform tasks such as creating complex mathematical formulas, explaining complex scientific data, and even helping discover new treatments for diseases.

Third, what are the capabilities of OpenAI o1 models?

OpenAI confirmed that the o1 models demonstrated during tests a superior ability to think logically and solve complex problems with performance similar to the way PhD students think, in solving tasks in the fields of physics, chemistry and biological sciences.

The new models achieved impressive results in mathematics and programming. In the International Mathematical Olympiad (IMO) test, the GPT-4o model was able to solve only 13% of the total mathematical problems, while the new (o1) models reached 83% of correct solutions to the test problems.

However, the new models have proven that they do not have the same advantages as the previous (OpenAI) models. For example, GPT-4o offers superior advantages in browsing the Internet for information and analyzing files attached to user commands from images and different types of documents, but the (o1) models do not provide this level of versatility in performance. Therefore, in most aspects of using ChatGPT , the GPT-4o family will be more capable of meeting users' needs in the near term.

OpenAI said it is currently looking into adding web browsing, file analysis, and other features to the o1 models to expand their reach to all users.

Fourth, can the quality of AI models’ response be improved by developing their thinking capabilities?

Yes, the quality of response of AI models can be improved by developing their thinking capabilities, and this step is one of the most important goals that researchers seek to achieve in the field of artificial intelligence.

This can be achieved through a set of techniques and methods, including: enhancing logical reasoning capabilities, improving natural language understanding, developing continuous learning mechanisms, and integrating human knowledge into models, such as moral values and scientific facts.

Fifth, has artificial intelligence really reached the level of human thinking?

This question is frequently asked in our current era with the rapid developments in the field of artificial intelligence. The short answer is: No, artificial intelligence has not fully reached the level of human thinking and it is expected that it will never reach that level!

The main reason for this is that AI does not have the self-awareness or emotions that distinguish humans, it operates on the basis of data and software, and does not have personal experiences or awareness of its existence.

While AI can generate creative texts or works of art, it relies entirely on the data it was trained on, still lacks the ability to think outside the box and produce entirely new ideas, and still struggles to understand the metaphorical meanings and social context of words.