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We are on the brink of a technological revolution not seen since the dotcom boom of the ’90s. Microsoft and Google racing to launch competitive products based on the technology that powers it. All that’s left is for smaller startups to rebrand themselves to join the hype and boom! We have entered a bubble.
Anyone who was around during the 2021 NFT golden era knows exactly where I’m going with this. The hype surrounding OpenAI’s generative AI chatbot, ChatGPT, is giving us all a dose of deja vu. Fortunately, there are important differences between the AI paradigm shift we are currently experiencing and the NFT bubble of a year and a half ago.
It is crucial to separate fact from fiction and ensure that AI innovators seize this moment to push the boundaries of the technology efficiently and ethically.
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The technology itself
While we can learn lessons from the 2021 NFT boom, from a strictly technology point of view, ChatGPT simply blows the Ethereum wallet on which you store NFT jpegs out of the water.
We are talking about a complex Language Learning Model (LLM) that processes huge amounts of text data and derives relationships between words in the text. Essentially, LLMs fill in the blank with the most statistically likely word given the surrounding context – and ChatGPT is doing this on an unprecedented scale to write poems, movies, and essays.
Conversely, NFTs are stored in blockchain-based wallets to represent the digital ownership of a particular asset – both digital and physical. This can be a painting, a car or a meme. So the “NFT technology” we’re talking about is really just code for “blockchain”.
That is not to downplay the potential of blockchain, and NFTs in particular, to solve the digital property problem. For example, a world in which musicians regain the ability to own and sell their music online sounds promising for creators who have come short of the democratization of information fueled by the internet. However, it does mean that its potential to radically transform industries has been vastly exaggerated by many of the companies selling themselves as “Metaverse” and “NFT” platforms. And it is certainly limited compared to AI’s potential.
After years of determination, blockchain enthusiasts are still trying to find a use case that will drive mass adoption. Sure, some average people invest in bitcoin and bought NFTs in 2021. But compare that to the number of offices that started using ChatGPT days after launch, and we have a clear winner.
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The challenges ahead
It is much more difficult to convincingly “fake” an AI company. The blockchain industry is so deliberately confusing that in 2021 companies tried to pass off digital art that wasn’t even blockchain-based as “NFTs,” and standard Play-to-Earn (P2E) games added “Metaverse” to their messaging .
That just won’t be a problem for AI. Instead, the AI industry faces more serious challenges. Businesses in virtually every industry will integrate and build on ChatGPT and other successful generative AI tools, finding new and interesting use cases for them.
For that to happen, AI innovators will need to track down ChatGPT’s shortcomings and leverage its strengths. Dr. Michal Tzuchman-Katz, Co-Founder and Chief Medical Officer at Kahun Medical, points out the improvements an AI model like ChatGPT would need to make a dent in healthcare and better serve doctors. The company built an AI tool that “thinks like a doctor” and provides doctors with a clinical intake before visiting patients.
While ChatGPT might be able to make textual interaction with patients run smoother, it can’t think clinically like Kahun, which consults its own database of peer-reviewed medical literature to produce answers and traces to the original sources.
ChatGPT, on the other hand, produces answers based on comparing the user’s input to the input of thousands of others and is not as transparent about the sources. This is also a problem for other sectors. There is talk of students using ChatGPT to write essays and answer homework questions. But professional journalists and authors will not be able to use the model beyond ideation and sketching if it cannot adequately cite its sources.
And then there’s the bias problem. Conservative commentators have enjoyed tweeting about examples of ChatGPT that show clear leftist bias. AI in a broader sense is also riddled with racial bias. Finding a solution to this will be one of the biggest challenges for AI innovators as they expand the use of the technology.
In terms of accuracy, of course, we can expect ChatGPT to improve quite quickly. The goal for future AI innovators is to participate in and improve the expansion. Adding a layer of transparency and addressing the issue of bias will be key to making sure it becomes more ethical and practical overall.
Related: How Will ChatGPT Change Education and Teaching?