Default HubSpot Blog

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.

For De Warmste Week, VRT called on us to produce a highly interactive and unique experience to reward the campaigners.

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.

At Brainjar, we are real Apple fans. Most of us use a MacBook Pro, and when Apple released the new MacBooks with upgraded hardware (M2 Pro and M2 Max chips), we couldn’t wait to order them and test them out. Of course, as we are all AI engineers and data scientists, we couldn’t let the opportunity pass by to test the M2 chips and benchmark them. 

 

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.

The city of Sint-Niklaas has decided to work on digitizing its grants and subsidies to improve its services for its citizens and businesses. The old system of paper applications, processing, and verification was extremely time-consuming, making the subsidy granting process lengthy and intrusive. Digitizing the system with LB365 offers a more efficient and accurate way to process grants and subsidies.

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.