Exploring the Growing Convergence
Between Blockchain and AI
And how it supports ethical AI initiatives for businesses
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How to Responsibly Harness AI’s Benefits.
When it comes to modern AI systems and AI risks – and the biggest barriers towards realizing a more responsible approach to AI – three key truths emerge:
- Generative AI is by far the most current popular application of AI, and drives the majority of current and near-term AI use cases.
- GenAI systems today are a black box when it comes to data. This non-deterministic approach relies on raking in a vast amount of inputs, synthesizing them and spitting them out on the other side with no context as to why or how it reached a given conclusion.
- AI systems are consuming a massive and ever-growing amount of data and GPU. Training and re-parameterizing AI systems under the current system is not going to be sustainable for most organizations over the long run, as AI margins inevitably tighten.
For all of its immense promise, AI – especially generative AI – isn’t going to be a feasible option for most businesses until they can more reliably audit and modify AI systems. For a more detailed overview of what the ideal risk management model for AI looks like, it’s worth looking at the NIST’s comprehensive standards framework, which has emerged as a guiding north for the entire industry.
Realizing these standards ultimately comes down to achieving greater transparency into AI training data; today, that environment is essentially a black box. When an AI hallucination occurs, it’s prohibitively difficult if not impossible to verify why and where it happened – which makes addressing the problem a non-starter.
Blockchain technology offers the most cost-effective and tamper-proof approach to realizing this critical level of visibility. When AI is augmented with blockchain, it’s possible to reconcile and track which data caused which outcomes, when and why. It also unlocks version control: when a given AI system falters, there’s currently no reliable method to “restore” a previous, working iteration. Think of this as akin to Google Docs’ beloved “restore previous version” feature, just on a much grander scale.
While the economics of such an approach are challenging in a traditional public blockchain environment, the advent of hybrid blockchains eradicates that concern. By hashing key data and storing it on-chain, organizations retain a tamper-proof methodology, while maintaining sensitive and/or extraneous data in more cost-effective private environments.
Here’s a more detailed look into how blockchain technology is increasingly being used to augment AI systems:
If you’re interested in learning more about Casper Labs’ specific efforts to address this challenge, you can view a webinar featuring IBM, watsonx and Casper Labs that previews Brave.AI, a new AI governance tool that offers unparalleled transparency and control for AI training data.
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Artificial intelligence has emerged as arguably the most exciting new technology on the market.
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Its future potential is nearly limitless, and it’s already impacting the ways we live and work today.
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However, current A.I. systems are limited by the “black box problem.” With few exceptions, A.I. systems are informed by completely opaque data sets.
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When an A.I. goes rogue or experiences hallucinations, we have little to no way of understanding how and where in the process it was fed data that led to an undesirable outcome.
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Understanding how blockchain compliments Artificial Intelligence is key to safely unlocking the next wave of innovation.
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So, what does this mean in practice?
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At its core, AI is a set of decision gates that assigns weights to the data that comes in and the analysis that goes out.
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Hence, the black box problem that currently plagues AIs.
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We often don't know why AIs weigh data the way they do, and it's prohibitively difficult, if not impossible, to retroactively analyze or audit training data.
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This puts considerable limits on AI’s near-term potential.
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When a blockchain is paired with an AI, it does 3 things uniquely better and more cost effective than any other technology.
- A blockchain provides a tamper proof, transparent ledger: anyone can view the specific data and its corresponding weights flowing in and out of the training environment.
- Blockchains are highly serialized and time stamped: if an AI demonstrates signs of hallucination or inherent biases, you can simply roll the AI system back to a recent iteration that lacked those issues, and subsequently diagnose where the problem data came from.
- A blockchain serves as a secure, decentralized, and externalized, system from the AI itself.
This means the blockchain can safely act as a place for multiple owners (or even the public commons) to co-own, govern, incentivize maintenance and training, or even shutdown the AI.
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Now that you understand how blockchains can provide a tamper proof ledger for training data, a serialized record of versions, and an externalized system of checks and balances, you might be wondering how to set such a pair up.
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Casper has developed the ideal hybrid blockchain for governing AI solutions.
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With Casper, you can spin up completely private blockchains with custom parameters while also offering a fully public environment and the security benefits that come with that.
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This allows you to realize the uniquely powerful security benefits of public blockchains, while maintaining requisite levels of data privacy and efficiency that come from private blockchains.
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If you’re looking to safely and efficiently explore the intersection of blockchain and Ai reach out to Casper Labs to see how we can configure the perfect solution for your vision.