o3 instead of o2 AI model? – 12 Days of OpenAI: Sam Altman reveals o3 and o3 Mini – The surprising reason behind the missing o2 model

At the “12 Days of OpenAI” event, OpenAI introduced two groundbreaking AI models: o3 and o3 Mini. These models represent the next generation in the development of powerful AI systems and follow the previously introduced model o1. With unprecedented advances in various performance areas, they mark a significant milestone in AI development.

Revolutionary performance from o3

o3 was specifically developed to overcome the challenges of demanding benchmarks and sets new standards in the world of artificial intelligence:

mathematics

The o3 model achieved remarkable results at the American Invitational Mathematics Examination (AIME) 2024, one of the most demanding mathematics olympiads in the United States. With a success rate of 96.7%, o3 demonstrates how efficiently AI can solve complex mathematical problems that remain challenging for many people.

programming

In the world of programming, o3 has also proven itself to be outstanding. On the Codeforces platform, which is known for its challenging programming competitions, o3 achieved a rating of 2727 points. This performance even exceeded that of OpenAI's Chief Scientist, highlighting the model's ability to efficiently handle complex code problems.

Scientific questions

What is particularly impressive is o3's ability to answer scientific questions at a level that corresponds to that of experts with a doctorate. In the GPT Diamond Benchmark, a PhD-level test of scientific understanding, o3 achieved an outstanding score of 87.7%. This puts the model well above the average human expert.

AGI benchmark

Another crucial measure of AI performance is the ARC (Abstraction and Reasoning Corpus) benchmark, which is often considered a test for artificial general intelligence (AGI). Here o3 achieved impressive results with a performance of 75.7% at normal and 87.5% at increased computing power. This underlines the progress towards universally applicable AI.

o3 Mini: Efficiency redefined

In parallel to the full version, OpenAI has developed a mini version of the o3 model, which serves as a cost-effective alternative for various applications. This model offers excellent value for money and is aimed at companies and developers looking for a powerful but affordable AI solution.

Features of o3 Mini

• Three Speed ​​Levels: With low, medium and high modes, o3 Mini offers flexible options to meet different needs in terms of speed and cost.
• Impressive performance: Even at medium speed, o3 Mini outperforms the previous o1 model, enabling more efficient results.
• Cost efficiency: Thanks to optimized resource management, o3 Mini is not only faster, but also significantly cheaper to use.
• Advanced API features: The model supports APIs for function calls and structured outputs, making it easier to integrate o3 Mini into existing systems.

The availability of o3 Mini from January 2025 promises to further lower the barriers to entry for powerful AI and revolutionize a wide range of applications.

Safety and responsibility

OpenAI attaches great importance to the security and integrity of its models. To ensure that o3 and o3 Mini can be used responsibly, an extensive security process has been implemented:

• External testing: OpenAI invited researchers and institutions to test the models before their release. This application process is intended to uncover and optimize possible weak points.
• Application deadline: Interested parties can apply for early access until January 10, 2025 to test the model in real scenarios.
• Phased release: The market launch will take place in stages: o3 Mini will be available at the end of January 2025, followed by the full version of o3 a short time later.

Limits and perspectives

Despite the impressive progress, it is important to emphasize that o3 does not yet represent artificial general intelligence (AGI). Although the model excels at complex tasks, there are still areas where it fails due to human intelligence. For example, tests show that o3 still has weaknesses in seemingly simple tasks such as understanding contexts or certain logical conclusions. This illustrates that the development of AGI remains one of the greatest challenges in AI research.

What is Artificial General Intelligence (AGI)?

Artificial general intelligence (AGI) is a hypothetical form of artificial intelligence that would be able to understand or learn any intellectual task that a human can perform. AGI aims to mimic the cognitive abilities of the human brain and would not be limited to specific task areas.

Key features of AGI

• Universal applicability in various areas
• Ability to learn and adapt
• Ability to retain and apply knowledge
• Language comprehension and production
• Autonomous planning and decision making
• Problem-solving skills in unknown situations

Difference to current AI

Unlike existing AI systems that are specialized for specific tasks, AGI would be able to independently acquire new skills and transfer them to different contexts. While current AI technologies work within given parameters, AGI strives for a form of self-control and an appropriate level of self-understanding.

Potential areas of application

AGI could be used in numerous areas including:

• Medical diagnosis and treatment
• Scientific research
• Autonomous driving
• Financial analysis
• Education
• Fighting crime
• Industrial optimization

It is important to emphasize that AGI currently remains a theoretical concept and research goal. The development of a full AGI with human-like capabilities has not yet been achieved.

Nevertheless, o3 and o3 Mini mark a decisive advance in the development of powerful AI models. Their introduction is expected to have a significant impact on various industries, from science to software development to industrial automation.

Potential Applications

The versatility of the o3 models opens doors for a variety of applications:

• Education: With the ability to solve complex mathematical and scientific problems, o3 models could be used as virtual tutors or teaching assistants.
• Software development: Developers could benefit from advanced coding capabilities that not only detect errors but also suggest optimized solutions.
• Medicine: By analyzing scientific data at an expert level, o3 models could help improve medical diagnoses or develop new treatment methods.
• Enterprise applications: From automated reports to data-driven decisions, companies could significantly increase the efficiency of their operations.

o3 and o3 Mini represent a new era in AI development. With their impressive performance, flexibility and cost-effectiveness, they offer solutions to some of the most complex challenges in the world today. At the same time, OpenAI underlines the importance of using these technologies responsibly. Although the road to AGI is still long, these models mark another significant step in that direction. The coming months and years promise exciting developments that have the potential to fundamentally change our understanding and use of AI.

Sam Altman on the surprising reason behind the missing o2 model

OpenAI's decision to skip the name "o2" for their new AI model and go straight to "o3" actually has several reasons beyond Sam Altman's humorous explanation.

Official reason

Sam Altman, CEO of OpenAI, gave two main reasons for naming it “o3”:

1. Respect for Telefónica: This refers to the British telecommunications provider O2, which is part of the Telefónica group.
2. OpenAI's “tradition” of being “very bad” at naming.

This statement contains a mixture of diplomatic consideration and self-deprecating humor.

Background and speculation

However, there is evidence that the decision is more complex:

Legal concerns

Insiders report that OpenAI had concerns that the name “o2” could lead to conflicts with the telecommunications provider O2. This suggests possible legal or trademark considerations.

Marketing strategy considerations

Critical observers suspect that OpenAI did not want to inadvertently advertise O2. This theory seems plausible since large technology companies are often very careful with naming to avoid unwanted associations.

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Presentation of the new OpenAI models o3 and o3 Mini

At the “12 Days of OpenAI” event, OpenAI once again caused a stir and raised the expectations of many AI enthusiasts. With the presentation of the two new models o3 and o3 Mini, the developers have clearly shown that they want to further expand their commitment to innovation and progress. The previously introduced o1 model had already caused a sensation, but now the new versions are going even further. The following information describes in detail what performance improvements can be expected, how o3 compares to previous models, what the mini version is all about and what significance this development has for the long-term path towards true artificial general intelligence (AGI) has. Although, according to experts, o3 does not yet represent AGI, it already offers exciting glimpses into a future in which AI systems could take on an even wider range of tasks. In the following, all aspects will be examined comprehensively in order to draw as clear a picture as possible of the new possibilities and the associated challenges.

Revolutionary advances in the o3 model

“OpenAI takes artificial intelligence to the next level.” These were the words that introduced the presentation of the o3 models at the event. At first glance, the published figures seem astonishing. For example, the new o3 model shone at the American Mathematics Olympiad AIME 2024 with a solution competence of 96.7 percent. This value illustrates how much AI systems have developed in recent years. Particularly in mathematical disciplines, competitive tasks are considered extremely demanding because they require logical thinking, creativity and often a high level of abstract problem solving. The fact that an AI model almost always delivers correct answers shows how well neural networks have proven themselves in complex thought processes.

Advanced performance in programming

What is also striking is that o3 achieved a rating of 2727 in programming tasks on the Codeforces platform. “This result even exceeded our own Chief Scientist,” said an OpenAI team member. The importance of this level of performance becomes particularly clear when you consider that Codeforces is a very competitive environment. Programmers from all over the world meet here to solve complex tasks and develop algorithms in real time. The high rating from o3 could have far-reaching consequences for everyday work in software development in the near future. On the one hand, automated code generation could be created that requires less human intervention. On the other hand, the model could test, optimize or even develop existing programs completely independently.

Scientific competence at the highest level

However, the performance of the o3 model is not only limited to the areas of mathematics and programming. Another highlight are the results on scientific questions at PhD level. According to internal information, o3 achieved a full 87.7 percent in the GPT Diamond Benchmark, significantly exceeding the average value of specialists with a doctorate. “We want our models to not only handle special tasks, but also to demonstrate broad scientific competence,” emphasizes a spokesman for OpenAI. This goal is within reach with the new model. The ability to analyze scientific papers, summarize studies and explore complex research topics could make the work of universities and research institutions enormously easier. Such support is easy to imagine, especially in times of ever-increasing amounts of data and publications.

How close is o3 to artificial general intelligence?

The question that looms over all of these aspects is: How far is o3 already on the path to artificial general intelligence? Although the system achieves an impressive 75.7 percent in normal mode and even 87.5 percent with increased computing power in the ARC benchmark, a common test for progress towards AGI, it is clear: “We are still a long way from a real one AGI to speak.” Despite these admissions, the results can be viewed as very promising. For many researchers, the ARC benchmark is a milestone that tests AI systems for their ability to think laterally and solve cross-context tasks. A value of over 80 percent is significant in this regard and indicates that AI is developing more and more towards more comprehensive intelligence.

Security and responsibility in development

How to deal with these new possibilities was also discussed at the “12 Days of OpenAI” event. “We have to take responsibility. AI is a tool that, on the one hand, allows us enormous progress, but on the other hand must be checked for misuse or sources of error,” said a presentation. These concerns are incorporated into the security process for o3. Before the final version is made available to the public, external researchers can apply until January 10th to gain early access and put the model through its paces. The aim of this procedure is to identify and eliminate possible vulnerabilities, security gaps or ethical risks at an early stage.

The Mini Version: A New Chapter for AI Democratization

The mini version of o3, which is scheduled to be released at the end of January 2025, is also eagerly awaited. The developers have high hopes for this model as it is specifically aimed at use cases where cost efficiency is a priority. “Not every company needs the full computing power of our largest models. “It is often more important that the model runs smoothly in constrained environments without requiring significant financial resources,” explained a senior team member.

Key technical data of o3 Mini

The key technical data of o3 Mini sound promising: It supports three speed levels (low, medium, high), with the middle level already promising significantly better performance than the previous o1 model. In addition, the lowest level requires significantly fewer computing resources and therefore also offers smaller companies or individual developers the opportunity to access a high level of AI. It has also been officially confirmed that o3 Mini will provide key API features, including function calls and structured output. This ensures easier integration into existing system landscapes.

Cost efficiency as the key to further distribution

The cost factor plays an important role, especially in times of rapid technological development. The more accessible high-performance AI becomes, the faster application scenarios will spread across various industries. In particular, start-ups that rely on AI services but only have limited funds available could benefit from o3 Mini. “We wanted to build an AI system that could be scaled up and down. With o3 Mini, we have succeeded in offering a variant that does not skimp on performance or flexibility, but sets new standards in terms of efficiency,” say the developers.

High performance activities with o3

What is also exciting is the question of what specific applications the new AI models can be used for. At o3, the focus is clearly on high-performance activities: complex scientific analyses, in-depth research projects or innovative software developments. With its impressive ability to solve a wide range of programming tasks, o3 could become an indispensable helper for teams that develop sophisticated software systems or create mathematical forecasting models. Especially in research institutes, o3 could be used to evaluate large amounts of data, accelerate literature research and establish cross-connections between studies and specialist areas that would otherwise have remained undiscovered for a long time.

The versatility of the mini version: o3 Mini

On the other hand, the mini version arouses the curiosity of users who are interested in a quick but cost-effective solution. Small and medium-sized companies could benefit from o3 Mini by setting up automated customer services or chatbots without having to invest in huge data centers. Personalized recommendations in the e-commerce sector, the prediction of market trends in finance or intelligent process automation in industry could also be made significantly easier with o3 Mini. “We developed o3 Mini so that it can perform most tasks competently, even with lower resource consumption,” emphasizes the team.

Opportunities and risks: A critical look at the new models

However, while many see o3 and o3 Mini as a major breakthrough, others urge caution. Although milestones in AI inventions have been repeatedly achieved in recent years, there are also risks inherent in this rapid development. The potential manipulation of information, incorrect evaluations in critical areas such as medicine or justice and questions of data security are just some of the issues that companies like OpenAI have to face. For this reason, OpenAI relies on comprehensive security and ethics testing. The fact that external researchers are invited not only signals transparency, but is also intended to significantly increase the quality of the end products. “We want our models to be tested in a wide range of application scenarios before we release them generally. The security and trustworthiness of the results is our top priority,” it says.

Publication and next steps

The next significant step will be the release of o3 Mini at the end of January 2025. The full version of o3 will follow shortly afterwards, which promises not only higher performance but also further improvements in terms of the interpretability of the results. For many observers, this is an indicator that OpenAI is striving not only to increase pure computing power, but also to strengthen the transparency and traceability of AI decisions. Especially at the political level, the call for “explainable AI models” is increasing so that society can better understand how and why an AI comes to certain conclusions.

The path to general artificial intelligence (AGI)

Of course, the question remains as to when – or if – true artificial general intelligence will be achieved. Experts assume that this will require several fundamental breakthroughs in various areas of AI research. “We are noticing that our models are becoming extremely good at processing large amounts of data and solving specific problems. But when confronted with everyday tasks that people effortlessly solve in a split second, they often fail,” explained a lead researcher. This is often a so-called “common sense” problem, which in many cases cannot yet be satisfactorily imitated by AI systems. An example would be the intuitive perception of spatial relationships or the understanding of social norms and emotions.

The rapid development: From o1 to o3

Nevertheless, it is obvious how rapidly the scene is developing. There are only a few months between o1 and o3, but the jumps in performance, flexibility and efficiency are significant. Some even say that we are facing a kind of exponential acceleration: the better the AI ​​models become, the more they accelerate their own development, for example by being able to evaluate research results more quickly and generate new ideas in a shorter time.

Keep opportunities and risks in balance

As in many areas of technology, the balance between euphoria and caution is crucial here. On the one hand, there are the possibilities: An AI that reliably solves the most demanding mathematics tasks, writes highly optimized code, answers scientific questions at a doctoral level and takes the step towards AGI could trigger revolutions in medicine, science, industry and education. On the other hand, the risks should not be underestimated. Any wrong decisions or incorrect forecasts made by an inadequately tested AI could lead to significant damage, be it in economic areas or even in healthcare.

o3 on the way to everyday life

The new o3 and o3 Mini models impressively demonstrate how far AI research has come. “We are at a turning point where AI systems are no longer just expert tools but are moving into the mass market,” summarized an OpenAI employee. With the clever combination of high performance and (in the case of o3 Mini) better affordability, we are moving closer to a world where advanced AI could become an everyday tool. Experts make it clear that o3 is not yet an AGI and that in some areas it fails due to simple tasks that are natural for humans. But the new generation of models undoubtedly marks a breakthrough and could have taken an important step on the way to actual general intelligence. It now remains to be seen in which areas o3 and o3 Mini will ultimately be used and whether the vision of a mass-market, broadly applicable AI will come true in the near future. One thing is certain: the next few years will be crucial in determining whether this rapid progress continues and to what extent our society adapts to it.

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