ChatGPT is the perfect combo of two AI concepts: chatbots and GPT3. Chatbots enable interaction through seemingly ‘intelligent’ conversation, while GPT3 produces output that appears to have ‘understood’ the question, the content and the context. When these two concepts are together it can produce the ‘uncanny valley’ effect, which prompts some pretty existential questions such as “is this a human or is this a computer” or “Is this a human-like computer”?
While that combination of tech is producing some noteworthy results – for example, it might be a helpful boost for your Tinder dating profile. On the flip side, it’s absolute rubbish at creating crosswords. Despite its gaps and shortcomings – ChatGPT is an amazing tool that can write poems, summarise documents, write software and help businesses with proposal writing.
But it’s just not ready to take your job.
When it comes to AI replacing actual-human-jobs, analysts predict that it will instead create new job opportunities. For example, the World Economic Forum predicts that although the shift to automation might mean the loss of some 75 million jobs, there’ll be 133 million new jobs created as a direct consequence of the added machine workforce.
A great example of this is bank tellers and ATMs. When ATMs became popular, it was predicted bank tellers would lose their jobs on account of using automated tellers. However, the dawn of ATMs lowered the costs of opening new offices, so more offices were opened up.
In turn, the new offices needed people to run them, so banks hired more people including bank tellers. However, by this point, their jobs had changed. They started focusing more on customer relations, as their routine tasks became automated.
These assistant capabilities can be helpful in cutting down your time in software development rather than using ChatGPT to do your whole job. Like any new technology, it’s important to experiment and see how you could possibly use it as a tool to boost your productivity and possibly reserve some time to learn some new skills.
*Please note, no AI was used to ideate, compile, or write this article – so it’s possible it took longer than it could have, or possibly less time than it might have. I’ll leave those existential questions to you.
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At Kiandra, we work closely with Product Owners to bridge the gap between their organisation’s needs and our delivery team’s technical expertise. This collaboration is crucial for keeping the project aligned to business goals, managing scope effectively, and ensuring value is delivered.
“How do we make sure our AI systems behave responsibly, not just accurately?” We get this question a lot. Usually after something has already gone a bit sideways. Here is the short answer: You build responsibility into AI from the very beginning. Guided by our B-Corp principles, we see responsible AI as a balance of purpose and effectiveness.
When working with clients in the earliest stages of a project, speed matters. The faster we can turn ideas into something visual, the sooner we can test assumptions, get feedback, and align on a direction. That’s where product ideation tools like Lovable come in.
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