Every year, I take time to check the pulse of the IT career market. Yes, what companies are looking for as they hire entry level engineers and tech workers. For 2024, I am using three words from a recent IBM recruitment message as the summary: curiosity, individuality and possibility.
In a recent recruitment flyer, IBM wrote, “Are you an IT or Computer Science graduate, ready to lead in this new era of technology and solve some of the world’s most challenging problems? Then apply for the IBM Graduate Programme and join IBM as a graduate trainee…. “ IBM summarized what they needed around those three words.
Looking at the three words, the second one – individuality – is noticeable. Notice that being good as an individual is now highly valued because increasingly AI is now our team members at work. In other words, if you have a lot of team members as AIs, the challenge now is how you will bring the specific and individual human element into the team.
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Sure, this is not to say that Teamwork is not important, but the message is clear: what you can deliver in a team of AIs is as valuable as what you can deliver as a team member in a world of humans.
That also underscores my point that the career of the future is not just prompt engineering – helping machines understand humans – but how to help humans understand machines better, because overtime the machines will evolve!
Be curious, deepen your individual capacities and unlock possibilities. Happy New Year.
According to ChatGPT “…Prompt engineering is the process of designing effective and efficient prompts for generating high-quality responses from natural language processing models such as GPT-3. It involves the selection and combination of various text inputs that can help guide the model to produce specific outputs.
Prompt engineering requires a deep understanding of the underlying language model and the task at hand, as well as the ability to create prompts that are tailored to the specific needs of the user or application. The goal is to design prompts that provide sufficient context and direction to the model, while avoiding ambiguity or confusion that could result in incorrect or irrelevant responses.
Effective prompt engineering can significantly improve the accuracy and usefulness of natural language processing models, and is a critical component of many AI applications such as chatbots, language translation, and content generation…”
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