Back to the Future: The Story of Generative AI (Episode 4)
Epoch 4 – Outlook: What’s Next?
Welcome to the final installment of our blog series on the history of generative artificial intelligence! So far, we have explored the journey from the earliest statistical models to neural networks and modern applications. But what does the future hold for us? In this concluding part, we will examine the upcoming challenges and opportunities for generative AI.
Interpolating vs. Extrapolating
A central aspect in the advancement of GenAI is the transition from interpolation to extrapolation. While today’s models like GPT-4o and DALL-E 3 deliver impressive performances within the trained data space (interpolation), the capability for extrapolation—creating content beyond the learned scope—is still in its infancy. The next generation of models might aim to surpass this boundary, generating even more creative and versatile content. Whether and how this will happen is currently a hotly debated topic. As of now, there are no concrete concepts on what this new generation of extrapolating models might look like.
Agents
Another exciting area is AI agents. These intelligent systems can operate autonomously, make decisions, and execute tasks without human intervention. This capability sets them apart from ChatGPT and other chatbots, which can “only” provide useful answers to queries. Such agents could, in the future, take on complex tasks in various fields such as medicine, finance, or customer service, far exceeding today’s capabilities.
Ethical and Legal Questions
The growing prevalence of GenAI also brings ethical and legal challenges. Addressing bias—prejudiced or discriminatory outcomes—remains a critical issue. Moreover, ethical standards and legal frameworks for the use of third-party GenAI and proprietary models must be developed to minimize misuse and negative impacts. Currently, intellectual property rights are a focal point. The verdicts in the legal battles between Stability AI and Getty Images, OpenAI and the New York Times, as well as Universal, Sony, and Warner against Suno and Udo, are eagerly anticipated.
From Model to System
A significant development is the shift from individual models to integrated systems. What does this mean in practice? Generative AI will be embedded into complex systems that close security gaps and enhance the reliability of applications. An example of this is that ChatGPT does not directly execute terminal commands but serves a custom API with predefined behavior. This integration allows the benefits of GenAI to be harnessed while minimizing potential risks.
Outlook and Conclusion
The future of generative artificial intelligence is both promising and challenging. The capability for extrapolation, the development of autonomous agents, and the integration of models into secure systems are just some of the exciting developments that await us. At the same time, we must continually address ethical and legal questions to ensure the responsible use of these powerful technologies.
Overall, the history of generative AI shows how far we have come—from the first statistical models to highly advanced, multimodal systems. But the journey is far from over. The next few years promise further significant leaps. It is up to all of us to translate technological advancements into societal progress.
This was the fourth and final part of our series on the history and future of generative artificial intelligence. We hope you enjoyed reading it as much as we enjoyed writing it. If you want to learn more about AI, you can find many more blog posts, whitepapers, and interviews on our website.
