June 24, 2023 - 6 min read
The synergies of Web3 and A.I. are set to supercharge a number of industries by introducing transformative new use cases.
Everyone seems to be thinking and talking about AI these days, but it’s not always clear what people mean when using this term. To be clear, AI should be understood as the leveraging of computers to recreate the problem-solving process that humans go through in order to efficiently interact with information and the world around them more broadly.
That is, AI can be expected to synthesize a variety of inputs and produce an output in response to certain stimuli, receive some sort of feedback through various mechanisms, and use that feedback to propagate new strategies for improved performance in future iterations. In this sense, AI learns from the world in a similar way that humans do.
AI is therefore useful for automating repetitive tasks which can be brutal and time-consuming for humans to carry out. It can also be useful for simulating potential outcomes during the decision-making process. Instead of paying a human worker to suffer through a long and arduous task, we can ask AI to do the brute force work.
At the same time, human workers are freed up to add creative flair and provide the feedback that AI needs to do its job properly. The convenience of a worldwide customer service agent that never sleeps paired with a competent human to manage its operations will make a formidable team in terms of productivity.
First of all, blockchain ledgers are inherently transparent. That is, the shared digital historical records of Web3 applications may offer provenance for the algorithms and metadata which powers AI. On the other hand, early AI models have received mixed reviews from users who feel that there are issues with bias or other limitations to the functionality which were made arbitrarily and behind the scenes.
This, of course, comes after decades of declining public trust in major institutions which often appear opaque and rife with conflicts of interest from the donors to the leadership stewarding them. Transparency and auditability will thus be key components for instilling confidence in outputs or suggestions that AI might provide for us.
AI derives much of its power from processing data at incredible speeds, and blockchains will provide the provenance and fidelity of the data its hosting. This combination will allow for the sort of qualities which will scale AI beyond language models.
For businesses, AI will be able to provide recommendations to improve efficiency and reduce waste. As it applies to supply chains, AI models paired with a blockchain can read overall inventories, make recommendations about popular or unpopular merchandise, conduct product safety recalls, and settle transactions in real time.
By utilizing smart contracts, thresholds can be set so that certain actions are taken once the conditions are met and transactions are triggered. The resulting reduction of ambiguity, waste, and disputes between transacting parties will be drastically reduced if not entirely eliminated. The cumulative boost of efficiency and cost savings should bolster GDP growth and global capital accumulation. Essentially, the world becomes collectively better off.
In addition to DeFi ecosystems, the use of AI can go even further than smart contracts and AMMs to automate services. Asking AI to achieve a specific on-chain goal might require a small fee to your AI agent so that it might analyze and implement a strategy to trade based on the prompts it’s given.
By having an AI helper with intimate knowledge of active wallets, balances, trading volume and so on, one could become as formidable as an entire department might be without the help of AI or a complete historical record of financial transactions to draw upon in real time.
Given the power of combining blockchain and AI, concerns have arisen regarding data privacy. In healthcare, for instance, blockchain-based AI systems could securely store patient data on-chain while AI algorithms can be used for predictive analytics and patient diagnosis. However, patients would be wondering if they can trust their data is secured.
Imagine that AI had access to your medical history and could predict that you might develop a genetic disorder based on family history or a number of different parameters showing up in your records?
AI could then recommend treatments like cancer screenings or other check-ups. Access to identifiable medical information could be safeguarded by individuals using their own public/private key pairs, which could be used to provide or revoke access to records for consultations.
The combination of AI and blockchain and AI in the pharmaceutical industry can play a role in fortifying logistics and other supply chain challenges. In other words, patient tracking, ordering drug refills, and predictive analysis will all be turbocharged when it can be fully automated through combining these high-octane, automation technologies.
Perhaps the most underappreciated potential benefits of combining AI and blockchain technology relate to education and knowledge-sharing. For instance, by implementing blockchains as record-keepers for educational progress and credentialing, AI can then be layered onto such a system in order to analyze and make recommendations. Of course, AI can also work as an agent to manage payments and customer service requests.
Academic experiences can be stored on blockchains, ensuring a secure and transparent record of every student’s learning journey, laying the groundwork for AI to help customize their education. AI could synthesize exam scores or even more granular data such as specific questions of a given exam, analyze a student’s strengths and weaknesses in order to better customize the experience.
AI could also aid teachers in scoring and evaluating the needs of students, freeing up educators to focus on the delivery of creative and compelling content rather than tedious administrative duties. This sort of data-driven and targeted approach to education will likewise reduce waste and student burnout. Thus, the case is convincing that such arrangements will foster more favorable outcomes for students by meeting them exactly where they’re at in their education.
Going even further, if educational materials are stored on public blockchains, then access to education will be easier than ever if one has access to reliable electricity and the Internet. This could result in the emergence of decentralized, P2P learning platforms which resemble social media websites but with sophisticated AI features.
This could be used to group students according to a variety of factors like geographical location, shared language, skill level or age, and even what level of social interaction desired by the students themselves. That is, while some students might prefer passive lectures that can be absorbed while doing the dishes as opposed to discussion-style tutorials which would require active participation or debate by participants.
It is no secret that artificial intelligence and artificial morality are wildly different concepts. Given that AI models will be designed with ambiguity regarding moral boundaries or codes of conduct, there must be a concerted effort to assemble and subsequently recommend frameworks aimed at reigning in the most dangerous risks. This is not to say that AI will necessarily present dangers to humanity, but that we should at least be having public discussions over the risks we’ll face and what measures can be taken to mitigate them.
For instance, AI models must be transparent, full stop. Failure to make them so will never garner trust; users must comprehend its strengths and limitations. Without knowledge of the algorithm’s ins and outs, users will question its potential for corruptibility or bias. Finally, AI models have to provide demonstrable proof to users that their personal data won’t be compromised.
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