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ChatGPT3 became the newest internet sensation last year when it allows users to generate text and answer complex questions in a manner that seems almost human. But, beyond the prowess of ChatGPT3, the underlying impact of the technology — generative AI — on business is only just coming into focus.
ChatGPT3, together with its image-generating cousin Dall-E, has the potential to revolutionize the way content is created, from blogs to white papers, student essays to business correspondence. It provides access to expert-level syntax and grammar to anyone who uses it. But this also raises some important ethical questions.
This is not the first time that technology has captured the attention of the public. IBM Watson made headlines in 2011 when it won the television game show Jeopardy! and Amazon’s
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Yet these technology solutions, initially hailed as game-changers, have not had the impact that many had expected. IBM Watson reached worldwide fame when triumphing at Jeopardy!, but it did not become the universal problem-solving engine prophesied by some experts. Yet machine learning has become the prevalent technology supporting most at scale AI since. Similarly, Alexa was initially understood as the revolutionary multi-purpose personal assistance in the home and did not fully materialize this promise while its underlying technology — deep learning neural networks — has had massive developments.
ChatGPT3, introduced in November 2022 by the private AI research institute, OpenAI, is the latest product built off the institute’s GPT3, the third iteration of its Generative Pretrained Transformer large language model. The core of the model is the transformer algorithm, introduced in 2017 by a Google Brain team in a paper titled “Attention is all you need.”
Transformers are a type of artificial neural network architecture that makes the use of self-attention mechanisms, which allow the model to process input sequences of variable length and to learn dependencies between input elements in a more flexible way compared to traditional recurrent neural networks (RNNs). This makes transformers particularly well-suited for handling long-range dependencies and for parallelizing the training process, which makes them faster and more efficient to train than RNNs.
By scaling the model and training it on every more data from the internet, OpenAI’s GPT3 produced surprising results, learning not only the structure of the English language but of coding languages that it encountered, such as HyperText Markup Language or HTML. Given a prompt, it could write coherent and cohesive text and even translate from English to HTML, allowing users to create web pages without knowing how to code.
The first vertical that OpenAI released based on GPT3 was Codex, which translates natural language into code (the basis for a coding autocompletion tool called GitHub CoPilot). ChatGPT3 is the latest GPT3 spinoff.
But, transformative as ChatGPT3 appears to be, it comes with caveats. ChatGPT3 generated content may be biased or based on incorrect sources (it for example told me Armenia was in the European Union). This raises the issue of how to verify the accuracy of the information it provides.
Another question is around intellectual property. ChatGPT3 can generate answers to questions based on a vast amount of data from various sources, but, unlike Alexa, it does not quote its sources. This raises the question of who should be granted intellectual property rights for the answers it provides.
It is not yet clear how much ChatGPT3 will disrupt the way the general public uses the internet or the way students write their research papers, but the underlying technology has the potential to disrupt industries and processes like never before.
Beyond ChatGPT3, Generative AI has already begun to disrupt large industries. In biopharma, Generative AI can generate millions of candidate molecules for a certain disease, then test their application, significantly speeding up R&D cycles. In the supply chain, it can optimize processes by generating scenarios and optimizing for specific constraints. In marketing, it can personalize experiences, content, and product recommendations. In finance, it can generate personalized investment recommendations, analyze market data, and generate and test different scenarios to propose new trading strategies.
Whatever the future of GPT3 will be, Generative AI will be a profound technological revolution that will have a tremendous impact on a wide range of industries and potentially contribute to some of the world’s more complex issues, such as education, health, and climate change.
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