OpenAI, the research lab behind the viral ChatGPT chatbot, is in talks to sell existing shares in a tender offer that would value the company at around $29 billion, according to people familiar with the matter, making it one of the most valuable US Start-ups on paper despite generating little revenue according to the Wall Street Journal,
The technology, developed by OpenAI, has captured the imagination of more than a million users. From asking for cocktail recipes to penning a love song, users have been experimenting with ChatGPT’s instant conversational responses.
Since its launch in November last year, ChatGPT has become an extraordinary hit. Essentially a chatbot on “steroids”, the AI program can churn out answers to the biggest and smallest questions in life, and draw up college essays, fictional stories, haikus, and even job application letters. It does this by drawing on what it has learnt from a staggering amount of text on the internet, with careful guidance from human experts.
It is the potential that generative AI has in business that has got investors excited. According to data from PitchBook, generative AI investment increased by as much as 425% from 2020 to December 2022, reaching a total figure of $2.1bn last year – a particularly impressive feat considering a wider downturn in tech investment in 2022.
Many investors and analysts now predict a ChatGPT-inspired funding boom for generative AI companies. ChatGPT and other programmes like it have provided plenty of entertainment to users. But if generative AI is to truly revolutionise industries ranging from search to journalism to recruitment, there need to be real-world use cases for the technology to justify the hype.
UKTN has looked at some of the ways businesses are already using generative AI tools in their day-to-day.
Generative AI tech has actually been around and in use for some time. Nathan Benaich, general partner at London-based AI-focused VC firm Air Street Capital, mentions Grammarly as one well-known company that’s already put generative AI to use.
“It’s definitely not the first time that generative models have produced pretty astounding outputs,” he says. “You can see some work from DeepMind on short term weather predictions, which also uses generative models to roll out the future of what a certain number of hours of weather movements might entail.”
But how is generative AI different to machine learning models that we’ve seen in the past, and why are people getting excited by it?
Antoine Blondeau, managing partner at AI-focused fund Alpha Intelligence Capital, explains that previous applications of AI have been outperforming humans in two areas for some time: “perception” and “optimisation”.
An example of perception is facial recognition algorithms that can pick out every face from a given ethnicity from a crowd of thousands, in seconds — “the ability to extract patterns extremely quickly,” Blondeau says.
The significant step forward with ChatGPT lies in the extra training it received. The initial language model was fine-tuned by feeding it a vast number of questions and answers provided by human AI trainers. These were then incorporated into its dataset. This human-guided fine-tuning means ChatGPT is often highly impressive at working out what information a question is really after, gathering the right information, and framing a response in a natural manner.
(Sources: FT, UKTN, Sifted, Guardian)