There is much debate about the potential positive or negative impact of generative AI on jobs and creativity. But the good news for software developers is that generative AI is taking on many mundane tasks and elevating its role to business consulting and customer experience.
Chris Casey, director and general manager of Worldwide Industry Technology Partnerships at Amazon Web Services, said: We recently asked Casey to share his thoughts on what a post-AI developer job might look like.
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Casey predicts that with the rise of generative AI tools, “we will need more highly skilled IT professionals to fill strategic business roles.” AI can “quickly see and resolve coding errors from AI tools, understand new application review and compliance processes, and interpret the massive amounts of data these tools communicate to the benefit of the organization. provide a function to use.
Generative AI “allows IT professionals to bring creative solutions to market faster,” says Casey. “We are reinventing the way developers build applications and deliver experiences to their customers. Generative AI has eliminated much of the manual writing of undifferentiated code, saving time and increasing productivity. allows developers to focus on the creative aspects of coding and designing solutions.”
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This is a “paradigm shift” in development work, Casey continues: Building the skills necessary to efficiently and effectively motivate a generative AI agent to produce desired outcomes, fine-tuning the large language model his transformers and base models to suit his specific use case. ”
Generative AI also represents the next generation of low-code and no-code environments, which means many AI-era developers will be based outside the IT realm, says Casey. increase. Generative AI “allows developers to quickly put concepts into the hands of an early user base to demonstrate proof of value and drive collaborative, user-driven experiences.”
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For example, “Generative AI empowers non-technical employees to build AI applications by eliminating the need to write undifferentiated technical code such as common workflows, schemas, data, APIs, identity access roles, etc. I will be able to,” he says.
It also makes sense to provide a better user experience. Generative AI can generate code suggestions in real time to predict the next line of code. This also positively impacts work such as UX and UX test engineers who write code that encourages generative AI agents to generate different versions of code. Near real-time he is UX,” he says Casey. “These engineers are able to interpret and act on the feedback they receive to continuously improve the customer experience.”
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Casey indicates that AWS aims to “democratize” machine learning and make it widely accessible. “We have invested in cost-effective and scalable infrastructure, including services like his Amazon Sagemaker that reduce the time to build, train, and deploy models,” he says. His new product, Amazon Bedrock, makes the underlying model accessible through an API.
