ChatGPT has generated an enormous amount of interest over the past several months, but now several more generative AI tools are being released (some in Beta) that will also grab attention.

Microsoft has made significant investments into generative AI with its investments in OpenAI, and it recently released it’s new Bing AI in Beta for journalists and other tech influencers.

The results have created quite a buzz.

Kevin Roose, a technology journalist for the New York Times and co-host with Casey Newton of the Hard Fork podcast, was one of the journalists invited out to Microsoft headquarters to test drive the new Bing AI. In many ways he was initially impressed as he describes in this Hard Fork episode with Newton. The Bing AI created a side-by-side display, with traditional Bing search results next to answers generated by the AI tool in a narrative format with some citations. Both Roose and Newton explained how this development could radically change the search landscape, with Google‘s domination of the business suddenly facing a serious threat. Bing AI was a potential game-changer.

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The image above was created using generative AI. More specifically, it was created using a tool called Dall-E from a company called OpenAI. It’s a text-to-image tool powered by generative AI. For this image I simply entered the following text – “castle on the edge of a cliff at sunset.” This is one of the four images the tool spit out. The implications for creative work going forward are obvious.

Image-to-text tools like Dall-E and Midjourney were already creating plenty of buzz in late 2022, but the release of ChatGPT on November 30, 2020 created a sensation. It was downloaded by millions within days and has sparked a frenzy around generative AI.

ChatGPT is a chatbot that can write essays, answer questions, write poetry and write code. It’s not always accurate, so it’s flawed, but the results can often be stunning. After playing around with the tool for a few minutes, it becomes clear how this will change the creative process forever.

What is Generative AI?

We’ve been hearing about artificial intelligence, or AI, for years. So why all the fuss now? This paper from Sequoia on generative AI summarizes it nicely in the first few paragraphs:

Humans are good at analyzing things. Machines are even better. Machines can analyze a set of data and find patterns in it for a multitude of use cases, whether it’s fraud or spam detection, forecasting the ETA of your delivery or predicting which TikTok video to show you next. They are getting smarter at these tasks. This is called “Analytical AI,” or traditional AI.

But humans are not only good at analyzing things—we are also good at creating. We write poetry, design products, make games and crank out code. Up until recently, machines had no chance of competing with humans at creative work—they were relegated to analysis and rote cognitive labor. But machines are just starting to get good at creating sensical and beautiful things. This new category is called “Generative AI,” meaning the machine is generating something new rather than analyzing something that already exists.

Generative AI tools are based on large language and large image models. The system learns what a cat looks like by processing millions of images tagged as a cat, and then is able to generate new images of a cat based on prompts. It also learns artistic styles based again on analyzing millions of images, so it can generate an image of a cat in the requested style of an artist or specific style such as impressionism. The same applies to text. You can ask ChatGPT to create a poem using the style of a particular poet, or a short story in the style of a particular writer. The possibilities are endless.

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