Cutting Through The Web3 Hype: AI In The Decentralized Web

Innovation Cutting Through The Web3 Hype: AI In The Decentralized Web Jeff Wong Forbes Councils Member Forbes Technology Council COUNCIL POST Expertise from Forbes Councils members, operated under license. Opinions expressed are those of the author. | Membership (fee-based) Aug 4, 2022, 06:30am EDT | Share to Facebook Share to Twitter Share to Linkedin As Global Chief Innovation Officer at EY , Jeff Wong helps companies harness disruptive technology and prepare for the future of work.

getty AI is critical to the functioning of the current Web 2. 0. It allows internet users to find friends, news and products.

It underpins the core business model of Web 2. 0—targeted advertising. It also drives many of the dysfunctions of the current internet—misinformation, echo chambers and amplified extremist content.

Today, Web3 is a high-level concept that describes a future version of the internet in which power and benefits are more equitably shared by the virtue of decentralization. The core capabilities of the web will no longer be monopolized by a handful of large technology companies. In this decentralized world, users will own their data, privacy will be preserved, censorship will not exist and rewards will be shared equitably.

Under the current paradigm, the success of AI has been built upon the centralization of the web. Now, given the hype around Web3, what role will AI play in this new decentralized world? And how should we go about untangling AI’s centralization tendencies? Where We Are Today Huge advances have been made in using AI to understand the meaning of online content and combining that with understanding user needs and intent. Looking ahead requires a look at the critical challenges AI addresses today: discovery, matching and filtering.

MORE FOR YOU Google Issues Warning For 2 Billion Chrome Users Forget The MacBook Pro, Apple Has Bigger Plans Google Discounts Pixel 6, Nest & Pixel Buds In Limited-Time Sale Event Discovery: Discovering relevant content was easy when we only had three TV channels. The simplest model for addressing today’s major technical challenge of discovering relevant content is web search via search engines. But web search is hard because it’s not simply finding the objectively most relevant content for a search, but also matching that content to the individual and contextual needs of a given user.

When I search for “Odyssey” to help my daughter with her homework, I’m looking for something different than when I happen to have misspelled Odysee, the blockchain-based video distribution service. Matching: A generalization of discovery, matching is the challenge of pairing content to users, friends to each other or even advertisements to buyers. For example, in a social network, one must identify likely pairings between billions of users, resulting in millions and trillions of possible matches—only a tiny fraction of which might be relevant.

This not only requires complex algorithms and huge sets of training data—it also requires huge computational infrastructure. Filtering: Filtering is the challenge of identifying content that should be removed for a specific reason. It might be due to propaganda, illegal or lewd content, Covid misinformation or copyright theft, to name a few examples.

Open internet platforms that empower creators to upload content they generate create a huge filtering burden. On the other hand, users are rightly frustrated if their compliant content is mislabeled as restricted. Even distinguishing between illegal and legitimate videos is an incredibly hard challenge for AI, requiring enormous infrastructure, vast armies of human moderators, huge datasets and processes to rapidly remove offending content that falls through the cracks.

Though these applications have dominated the AI literature for more than a decade, the solutions are burdensome in terms of the expertise, infrastructure, data and expense required to effectively implement them. Early Capabilities While Web3 is principally a high-level concept today, there are some early capabilities that illustrate what it can achieve and the challenges to overcome, demonstrated in Odysee, NFT marketplaces and Friends with Benefits. • Odysee is a video-sharing service built on the LBRY file-sharing service, with both services leveraging blockchain capabilities.

The philosophy is that video files may be irreversibly placed on a blockchain and are then accessed via BitTorrent. There are many challenges resulting from this approach: How does illegal content get removed from this blockchain and will those that host the service be criminally liable for this content? • NFT marketplaces are currently the largest monetization opportunity for Web3 and suffer from the same challenges. As the number of NFTs increases and as the customer base broadens, the challenges of discovering content, matching customers to relevant content and filtering illegal or stolen content arise exactly as they do in traditional marketplaces.

• Friends with Benefits (FWB) is something of a Web3 analog of a social network. Specifically, it is a decentralized autonomous organization (DAO) and a selective social club. Seen by many as the future of social networks, these organizations face some of the same challenges that AI addresses in existing social networks: discovery, matching and filtering.

Which DAO do you join? How do you find DAOs that match your interests? This is a classic search/discovery problem. Overcoming Today’s Drivers In A Decentralized System Many of the issues addressed by AI in Web 2. 0 also arise in Web3.

However, the existing centralized approaches are considered to be the antithesis to the promise of Web3. The current high degree of centralization is driven by expensive talent, rapidly evolving technology and large data and infrastructure requirements. The question remains: Can these be overcome in a decentralized system? One commonly proposed solution is federated learning, where machine learning algorithms partition their learning across disparate (federated) datasets and infrastructure.

These techniques are complex and rapidly evolving. While they are technically interesting, it is not clear whether they meet users’ privacy expectations. The result is that the leaders in federated learning are the existing large technology companies.

The opportunity remains for the emergence of truly decentralized AI solutions. AI and machine learning have been driving forces behind the centralization of the web, and now the future of Web3 will first require figuring out how to decentralize machine learning. It’s up to tech leaders to determine how to achieve meaningful interoperability and effectively untangle AI’s centralization tendencies to create a more private, safe and equitable web.

The views reflected in this article are the views of the author and do not necessarily reflect the views of the global EY organization or its member firms. Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify? Follow me on Twitter or LinkedIn .

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