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The spectacular advances in the field ofartificial intelligence, embodied by systems like ChatGPT And AlphaCode, raise fascinating questions. While these models are now capable of generate computer code, the idea that an AI could go further and create other AIs arouses both curiosity and debate. This theoretical possibility highlights recent technical advances, while raising ethical and control questions about the ability of these technologies to self-reproduce or evolve autonomously.
In the growing field of artificial intelligence (AI), systems like ChatGPT and AlphaCode don’t just code: they open the door to potentially designing other AIs. This text explores how these tools evolve from simple code to the possible creation of autonomous intelligence, the technical challenges they encounter and the ethical questions that arise. Particular attention is paid to how modern AIs train and improve, often with assistance from other AIs.
ChatGPT and AlphaCode: A New Era of Coding
ChatGPT And AlphaCode embody a major advancement in the use of AI to generate code. ChatGPT, for example, relies on a sophisticated neural architecture called Transformers, brought to the forefront by the groundbreaking paper « Attention is all you need » in 2017. This architecture enabled new forms of language modeling, and GPT-3, among others, pushed this capability to new levels never seen before.
AlphaCode, developed by DeepMind, outperforms ChatGPT in computer code generation. This system has demonstrated the ability to create code that rivals that produced by human developers in programming competitions. This raises the question of how far these AIs could go: could they not only code, but also design other AIs?
AIs Training AIs
The notion of AI creating other AIs isn’t just science fiction. Already, tech giants like OpenAI and Google are using AIs to perfect other AI models. Since 2018, OpenAI has used large language models (LLM) to generate text on an unprecedented scale, as proven by GPT-3. The collaboration of several AIs has made it possible to model human languages with increasing precision, while recognizing that these models remain limited by their inability to be perceptually adaptive.
Dynamic Improvement and Training
Recent advances in AI can be explained not only by the increasing size of models but also by innovative methodologies. These methodologies, in particular reinforcement through human feedback (RLAIF), have been crucial in adjusting AIs to human preferences. For example, InstructGPT enabled substantial improvements in text interactions in 2022.
However, this process is laborious and requires human intervention to annotate and provide constructive feedback on countless texts. Initiatives like AgentInstruct from Microsoft, which uses specialized agents as teachers for LLMs, mark a step towards more autonomous learning. Despite this, reliance on humans persists, largely due to the high costs of setting up these systems.
Current Limitations and the Future of Conscious AI
Despite advances, questions remain about the ability of AI to autonomously design another AI. The technological limitations of LLMs, such as the inability to plan and set independent goals, mean that fully autonomous AI remains a distant goal. Sam Altman, CEO of OpenAI, talks about the emergence of an artificial general intelligence potentially as early as 2025, but this idea is still controversial. Ethics also plays a central role. The ability of AI to self-improve its systems raises concerns about control and alignment of ethics with human values. This requires strict regulation and reflection on potential impacts. Innovators and developers will need to navigate the technical and ethical advances to ensure that the evolution of AI serves the best interests of humanity.
