By Don Gossen, CEO of Nevermined
Until very recently, artificial intelligence has been limited mainly to science fiction. However, with ChatGPT, the field of AI has burst into the public consciousness, offering a natural language model that has dazzled, surprised and awoken the world to the potential of this cutting-edge technology.
Along with ChatGPT, tools such as DALLE2 and Stable Diffusion are capturing the attention of industry leaders, influencers and politicians, garnering equal amounts praise and criticism.
Many are now asking questions about ownership, as well as the existential threat AIs pose to human creativity, and to work more generally. Will AI replace our ingenuity, or merely act as an extension of ourselves? And how will AI transact within the machine economy? There’s likely no single answer to these questions, but exploring the options will help prepare us for an almost certain inevitability: our coexistence with AI.
But first, a crash course to explain how these models came to be. Both Stable Diffusion and GPT-3 are deep learning models, each trained on a massive volume of information. Stable Diffusion was trained on LAION 5B, an open dataset representing more than five billion image-text pairs as inputs. Similarly, ChatGPT’s underlying model GPT-3, was trained on 500 billion pieces of text data scraped from the internet.
These massive training sets allow the AI to comprehend text and its corresponding sentiment, and to return responses often indistinguishable from human answers. ChatGPT has taken the world by storm, with people arguing that it will take over everything from internet search to repetitive work like that done in call centers.
From a content creation point of view, the introduction of AI to the creative process will almost certainly lead to an explosion in content volume. Whether the quality of that content matches its proliferation is yet to be seen.
Of primary concern at the moment is the infringement upon intellectual property rights. If a creator’s work can be recreated, or an entire academic paper written at the “touch of a button”, what does this mean for the state of creative endeavors? Can we enable AI creation while protecting the source of inspiration? We’re beginning to see the effects of this ambiguity, with Getty announcing it will take Stability AI, the company behind Stable Diffusion, to court in the UK for copyright infringement.
Perhaps we can enable creative expression while, at the same time, protecting the interests of artists and creators inspiring that expression?
Another consideration is that the AI, while comprehending the request and creating a relevant associated output, is not necessarily creative…at least not yet. Its response mechanism is tied to what it knows from its training set, so if the AI hasn’t seen something before, it cannot create it from scratch. There remains the need for human intervention, in the form of highly descriptive prompts as source input, as well as in form of source content from which to synthesize its response.
How this influences the creative industries is still to-be-determined, but in all likelihood, it will lead to the ability to create more content faster. Despite this, and in a world of potential infinite content, a piece’s relevance will remain its best measure of its success.
To control the progress of AI into the future, what the space needs going forward is less about regulation, and more about transparency and proper attribution. There needs to be a way to understand and track source training material used by the AI in creating its output. This way, genuine provenance can be established, and all parties’ contributions can be identified and acknowledged.
Which brings us to an opportunity: use one technology, blockchain, to assist with the growth of another, AI. Blockchains were created to address trust and governance issues. The fact that public blockchains are immutable and fully transparent for all to see provides the foundations for a trust-based AI asset registry.
Imagine if each image used in an AI’s source dataset had a corresponding NFT “tag” associating the asset creator’s wallet and usage conditions. For any derivative work created by an AI, it could associate the inputs used to create the output via a pipeline of on-chain transactions. Should that derivative work go on to be sold, the proper royalties could be passed on to each wallet associated with the source content NFTs, proportional to the contribution made to the AI’s output.
Let’s take this a step further. It can be demonstrated that AIs can leverage inputs not available within the original training dataset they were trained on. For example, imagine being able to input an entire company’s text-based collateral into an AI’s dataset; from annual reports to internal presentations to emails. Then imagine being able to use ChatGPT to accurately search this information with questions in natural language in real time. This is essentially a ChatGPT filter, and the potential business applications of this technique could be unparalleled for enterprise search.
In particular, one niche area of interest for this filtering technique is the medical and BioPharma research and informed consent arenas. Traditionally, informed consent has required intervention by humans, often doctors, in order to answer the questions of candidate research subjects about the program and the consent requirements.
The cost of this process can be immense, especially when doctors are involved. With an AI-enabled filter, all pertinent research and consent information could instead be inputted to the AI. The AI could then answer the candidate’s questions with comprehensive, natural responses without the need for human intervention. The cost displacement opportunities are huge.
Finally, there is the simple question of payments. How will AIs transact in an efficient manner? It seems fairly safe to say that AIs won’t be able to get a bank account, so how will they pay or be paid by people, services, systems, and even other AIs?
Again, we turn to blockchain as a solution. In the simplest form, AIs could transact via stablecoins or CBDC (Central Bank Digital Currencies). Alternatively, perhaps the AIs mint their own token to transact and reward activities? Combining AI and blockchain unlocks some exciting use cases that look light years ahead of the way we transact now.
The explosion of AI in the last 12 months is unparalleled. It has captured interest from those normally not influenced by bleeding edge technology. What is clear is that Pandora’s box has been opened. Determining how we utilize and interact with AI is more critical than ever, with transparency and proper attribution likely being a driving force in the development of AI going forward.
About the author:
Don Gossen, Founder and CEO at Nevermined AG
Data and Analytics expert with extensive global experience working on four different continents. Breadth of knowledge extends to every facet of Web3.0 and the Data and Analytics landscape, from large scale Big Data Estates to Digital Transformation and Web3.0 Ecosystem Development.
The views and opinions expressed herein are the views and opinions of the author and do not necessarily reflect those of Nasdaq, Inc.