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After delving into the realms of sports reports and movie assistants, our Generative AI Playground takes another turn. In this third article, we introduce you to our AI-powered quiz platform that transforms the traditional quiz format into an interactive, intelligent, and incredibly fun experience. The idea was to create a quiz that could be moderated by GPT, offering a dynamic way to generate questions, evaluate answers, and ensure the fun never stops. Even though this use case provides the ideal work break, it also illustrates how we can use this technology for other domains.

Last week, Google had some explaining to do after people discovered its Gemini AI tool generated racially diverse Nazis and other inaccuracies in historical image generation depictions. Many people asked themselves how this is possible, but actually, it’s not surprising this happened. Bias in AI is not new, and this was a (poor) attempt of Google to subvert long-standing racial and gender bias problems in AI, resulting in an overcorrection. Why does Google have to (over)correct bias? In this article, we’re discussing where bias in AI comes from and what we can do to correct it.

The first experiment in our Generative AI Playground was all about turning incomprehensible structured data into captivating, human-readable reports. In our second experiment, we wanted to explore the possibilities of function calling, a groundbreaking GPT feature we already discussed in our whitepaper, The Generative AI Playbook.

The rapid advancements in the field of generative AI have truly amazed us over the past year. As AI experts, it was a challenge to keep up with new technologies and iterations of them being released daily — let alone that we could build something each time to experiment with them. Of course, we wouldn’t be real experts if we didn’t try!

We have all seen the news of large organizations putting their trust in a new GPT-powered chatbot on their website and having, well, not-so-ideal responses. Remember DPD, who had to ‘fire’ their new chatbot for insulting the company? Or Chevrolet, which inadvertently sold cars for $1 via its chatbot? These incidents highlight the challenges of innovating with GPT-powered chatbots. And addressing these challenges? Exactly what we at Oswald aimed to solve.

Generative AI is very powerful but could be harmful if deployed wrongly. And while thousands have urged a six-month pause on the creation of ‘giant’ AIs, the fear of missing out is forcing companies big and small to promote reckless practices.

Data, the backbone of machine learning

You can compare machine learning without data to a car without fuel — you won’t get far if you don’t have any. So, whenever we start a new AI project, we first look at the data, as our systems are only as good as the data we use to train the model.

While looking for internships, I thought of searching for an AI-based project at De Cronos Groep or even Brainjar specifically since multiple friends had recommended an internship with them. So, when my time came to apply for internships, I did not hesitate to contact them and was very excited when they responded for an interview.

Landing an internship at the right company isn’t a given in most cases. In this blog, I will share how I experienced the search for one and how my first weeks at the internship went from very stressful to “better than expected”.

The impact of machine learning on the environment. Do you often reflect and think about it? Where does the responsibility of making green choices lie? Users, researchers, programmers, hardware developers, or somewhere else?