Why Decentralized AI Matters Part I: Economics and Enablers

Jesus Rodriguez
Apr 4, 2018 · 5 min read

The emerging field of decentralized artificial intelligence(AI) is becoming one of the most exciting technology trends of the last few months. A lot has been written about the potential value of the intersection of artificial intelligence(AI) and blockchain technologies and we, this year, we have even entire conferences dedicated to the subject of decentralized AI. However, I feel that a lot of the hype behind decentralized AI fails to highlight some of the key value propositions of the new technology movement that can make it one of the most foundational technology trends of this decade. If you believe in the idea that AI is going to become an increasingly influential factor in our daily lives, I believe decentralized AI will be an essential element to guide the impact that machine intelligence will have in future generations. Sounds dramatic? Let’s look at some of the economic dynamics behind decentralized AI to try to clarify our point.

These days, the notion of AI systems is intuitively linked to centralization. The first thing that comes to mind when we talk about AI are companies such as Amazon, ,Facebook or Google whose machine intelligence systems are becoming part of our daily lives. The increasingly rich data assets possessed by those companies have allowed them to capitalize first on the AI revolution and create an economic dynamic that is not always aligned with the end consumer. Even the technology and methodologies we used today for building AI systems assumes a centralization model at its core.

The lifecycle of a modern AI project assumes that you have a model and a massively large, high quality dataset that you can use to train it as well as a pool of data scientists that can constantly regularize and optimize the model in order to become more intelligent. In most AI scenarios, that entire cycle is performed by a single entity that has the resources to collect large datasets, create highly sophisticated AI models and run across expensive computing resources.

The irony of all this is that, when you look deeper, the economic incentives of the large party providing the AI models are not necessarily aligned with the value creation for consumers. From an economic standpoint, there can be many scenarios in which the ability of an AI agent to increase the value of the assets of its creator in the form or revenue, data or simple outcomes is not directly correlated with the ability of creating more value for consumers.

Centralized Intelligence vs. Federated Knowledge

The centralized nature of AI systems highly contrasts with the evolution of human intelligence. Knowledge exists completely scattered and federated across the world. Erudition is a novel goal in live but nobody can claim to posses all knowledge of a particular subject. Knowledge collaboration and federation is one of the key unique advantages that allow humans to evolve and dominate other species that were physically more powerful. And yet, AI remains increasingly centralized. In a world that is moving rapidly towards the creation of general AI and systems that can vastly superpass the level of intelligence of mankind, wouldn’t we want that knowledge and influence to be federated instead of controlled a few organizations?

The emergence of technologies such as mobile computing or internet of things(IOT) challenged the centralized notion of AI. Today, knowledge is constantly created in the edges and flows towards centralized hubs. The pendulum has to shift to a dynamic in which aspects such as the training, optimization, testing and knowledge creation of AI model becomes federated across many participants.

In order to decentralized AI models, we need to solve a few challenges:

a) The Privacy Problem: Can entities train a model without having to disclose their data.

b) The Influence Problem: Can third parties contribute to the behavior of knowledge of an AI model in a way that is quantitatively influential.

c) The Economic Problem: Can third parties be correctly incentivized to contribute to the knowledge and quality of an AI model.

d) The Transparency Problem: Can the activity of behavior of an AI model be transparently available to all parties without the need of trusting a centralized authority.

Centralized AI Today is Like Closed Source in the 1990s

Open source today is highly rewarded and the best and most efficient way to create software but that wasn’t always the case. For decades, large software companies preferred to embrace closed source delivery models in order to have an edge in terms of intellectual property(IP). Eventually, the economic dynamics proved that thousands of talented engineers regularly contributing to a project produce better code than a few engineers driven by corporate interests.

If we extrapolate the evolution of open source to the AI world, today we are somewhere in the 1990s in which the value creation of software was controlled and influenced by a few companies. What is worse, when comes to AI, we are not only talking about software or AI models, but also other expensive resources such as data science talent, data and computing power. In that world, decentralized AI is the new open source except that the impact in mankind can be order of magnitude more impactful to mankind.

In the next part of this article, we will discuss the technologies that are enabling decentralized AI architectures and some of the emerging players in the space.

DDI Recommended Reading:

Data Driven Investor

from confusion to clarity, not insanity

Jesus Rodriguez

Written by

Chief Scientist, Managing Partner at Invector Labs. CTO at IntoTheBlock. Angel Investor, Writer, Board Member of Several Software Companies

Data Driven Investor

from confusion to clarity, not insanity

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