By Subbu Subramanian
ChatGPT grabbed major headlines and is expected to have sparked numerous debates. Based on its mega-success and viral hit, many would find it hard to believe that ChatGPT was launched in November 2022 with almost zero fanfare and with few expectations. Fast forward to today, despite being in its early stages, generative pre-trained transformer (GPT) technology has occupied boardroom agendas and household discussions worldwide.
ChatGPT and Generative AI: A New World of Innovation
Since its launch, the world may seem divided on its long-term impact. Still, it is hard to downplay the numerous advantages offered by GPT (generative pre-trained transformer) technology and (large language models) LLM-based AI for multiple sectors. Depending on how you explore and use GPTs, it can help businesses with existing time-consuming manual tasks, such as writing repetitive and easily repeatable code or searching through a plethora of Stack Overflow pages before discovering the solution.
The primary areas where GPT technology, like ChatGPT, shows immense potential are software development and delivery. By utilising data from software libraries, it can, for instance, assist DevOps and platform engineering teams in writing code snippets. It can also speed up the process by which teams fix issues with custom code by feeding root-cause context into a GPT, enhancing problem reports or alerts with this context, and using it as the foundation for automatically created remediation.
Establishing boundaries to safeguard privacy and intellectual property
As the potential of generative AI continues to unfold, it is crucial to establish robust guardrails to protect intellectual property and ensure data privacy. While ChatGPT offers tremendous benefits, it also poses challenges related to ownership of generated content and potential misuse. Site reliability engineers (SREs) and privacy teams must ensure these technologies have the proper controls in place when DevOps and platform engineering teams employ GPTs to speed up software development. This prevents these technologies from causing more issues than they intended to address.
Make sure that teams are aware of any copyrighted, trademarked, or patented content as well as other intellectual property (IP) rights on any code exchanged by and with GPTs and other generative AI. Organizations should also consider regional and national privacy and security legislation like GDPR or the upcoming European AI Act to ensure that GPT technologies do not lead to data breaches and fines.
Recognizing and Mitigating the Risks of GPTs and Generative AI
While generative AI brings does bring opportunities, it also carries certain risks that demand careful consideration. Organizations need to be especially aware of the vulnerability of the LLM-based generative AI that underpins ChatGPT and related technologies. Its accuracy and quality depend on the publicly accessible data and input it uses, which may be inaccurate or biased.
In continuation, when it comes to software development and delivery use cases, these sources could be code libraries that are legally protected and contain syntax problems or vulnerabilities placed by hackers to perpetuate weaknesses that lead to increased opportunities for exploitation. Because of this, engineering teams must verify the code they receive from GPTs to make sure it doesn’t jeopardise software security, compliance, or dependability.
In addition to the above, users need to master prompt engineering techniques too. Essentially, GPT tools provide general answers unless it has access to precise details for specific responses with a detailed root cause. Achieving this precision requires another type of artificial intelligence: Causal AI. By continuously observing relationships and dependencies across a technology ecosystem or throughout the development lifecycle, causal AI generates accurate insights in close to real-time. Thus, combining Casual AI and ChaGPT will lead to better results.
Increasing the impact of ChatGPT and generative AI
The impact and utility of ChatGPT and related technologies could be increased in the future by merging generative AI with causal AI. It could make them even more potent and open up new use cases for boosting productivity and efficiency. We may pair natural language queries with causal AI-powered answers to provide precise and understandable context. The GPT’s recommendations are improved by this exact input engineering, making them more accurate and practical for automation and remediation.
The dynamic duo of Generative AI and Causal AI
Technologies like ChatGPT and others don’t offer fixes on their own. The scope and accuracy of the data and context that organizations provide them with determine how effective their suggestions will be. To avoid receiving overly general or inappropriate responses, organizations will be in a far better position to make the most of generative AI by integrating it with Causal AI.A combined approach while addressing security and privacy concerns will not only allow GPTs to drive productivity but will also lay the foundation for the next phase of GPT-powered innovation.
The author is country director, India, Dynatrace