2023-09-20 09:14:57
With GPT-4, OpenAI has introduced an intelligent chatbot that has caused a stir worldwide. This is the so-called GenAI (“generative artificial intelligence”) or generative AI, which consists of automated language processing and response generation. This technology helps in many projects in the real world and especially in the areas of customer service, marketing and sales, software development and research.
Generative AI thus opens up groundbreaking opportunities for companies. This includes integration with data streaming, the real-time data flow within organizations, to create real, production-ready applications with unprecedented capabilities.
From data factories to real-time training of language models
With event streaming, companies can seamlessly connect their internal data storage to GPT-4. This allows the AI system to not only respond precisely to customer requests, but also adapt the model’s behavior to specific business needs. This is made possible by “context windows”, a principle that allows GPT-4 to store information from previous parts of a chat and adapt its behavior accordingly to provide relevant and tailored answers.
Data streaming is already the foundation of many GenAI infrastructures and software products. Very different scenarios are conceivable: data streaming as a data factory for the entire ML infrastructure, pattern scoring with stream processing for real-time predictions, streaming data pipeline generation with text or voice input, or even real-time online training of large language models.
Current limits of generative AI in real time
As with any innovation, there are limits. The real-time generative AI approach poses two major challenges:
On the one hand, the response generation of GenAI infrastructures that support data streaming is limited by the width of the context windows. If these are not wide enough, not all user prompts can be answered. However, the outlook is positive as AI systems are constantly evolving and developers are continually working to expand the width of contextual windows. In the medium to long term, this restriction will no longer exist.
On the other hand, generative AI in real time is vulnerable to so-called “prompt injection attacks”, a new type of vulnerability in AI and ML (machine learning) systems, where contradictory prompts are strung together in such a way that the system is no longer able to do so to distinguish between them and respond appropriately. This allows attackers to insert commands into data fields under their control and force the machine to take unexpected actions. Although prompt injection is currently mostly used for positive purposes only, there are scenarios where malicious manipulation of the response could be used to bypass restrictions or as a fingerprinting technique to detect software, network protocols, operating systems, or hardware devices on the network .
Generative AI + data streaming: relevance and added value for companies
So it’s clear that significant progress still needs to be made before the potential of generative AI is fully realized. However, it can already be said that both the streaming models and the large language models will drive each other forward in their development. Because generative AI only adds value when it delivers accurate and up-to-date insights, and here real-time data clearly takes precedence over “slow” data.
Across all industries, early adopters of generative AI have emerged in data streaming technologies such as Apache Kafka and Apache Flink has already been proven that this innovation offers enormous added value for companies.
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