“ ” What AI model maker would take the risk to make any powerful model if they could be responsible for anything someone might possibly do with it? This – author, futurist, engineer, and systems architect. I’ve had the pleasure of conversing with him a couple of times, first about the , which he leads, and then about . I’ve noticed some of Jeffries’ recent shares about OpenAI, AI doomers, and upcoming regulations, most notably the EU AI Act. question is posed by Dan Jeffries AI Infrastructure Alliance Generative AI I’ve and generally consider it and its tiered approach a step in the right direction. I also thought that introducing the notion of general-purpose AI systems powered by foundation models made sense. As far as I understood, uses of the system may or may not be regulated, depending on how they are categorized. covered the EU AI Act https://hackernoon.com/what-the-eu-ai-act-effect-means-for-the-bloc?embedable=true In addition, there are specific requirements for foundation models, including for things such as data governance and compute. That means and will probably converge toward compliance. foundation models can be evaluated In , Jeffries makes some good points about fearmongering and innovation. He also lays out a simple framework for regulation, which looks reasonable. It certainly made me think long and hard about finding the right balance between innovation and regulation in AI. this post How can we find the right balance between innovation and regulation in AI? I’ve laid out what i believe are some important questions to ask here are, along with answers i’ve arrived at. 1. If foundation models can be used in a number of settings, including in ones that would be regulated if dedicated models were developed for them, does it make sense to regulate foundation models, too? Probably yes, and we don’t need to include AI doomerism in this conversation. Let’s take surveillance, for example. I picked the example on purpose because you’d think surveillance would be regulated. It is not, and that’s a good point Jeffries makes – it probably should be. If surveillance is regulated, and foundation models can be used for that or any other regulated application, I don’t see why they should be exempt from regulation. However, there are lots of nuances here in defining “use.” See the next point. 2. What is the threshold that distinguishes a foundation model from a non-foundation one? Capabilities, but it’s not straightforward at all. I’ve seen ideas like , which I don’t think will work. using the amount of compute used to train models as a proxy for capabilities being floated If a model can be *directly* used to power regulated applications such as surveillance, then it should be subject to the same regulation dedicated models are. The key word here is *directly*. A surveillance system contains many components, software, and hardware, from cameras to databases and image recognition algorithms. None of these should or could be regulated in isolation. But if a model does have image recognition capabilities and can *directly* connect to a camera and a database and automate surveillance, then it makes sense to regulate it. Emphasis on *automate* here. See the next point. 3. If foundation models are to be regulated, what type of oversight should be applied to them, and how will it be enforced? High-risk systems, as defined in the EU AI Act, seem about right. There’s a syllogism to unpack here. The EU AI Act originally adopted the definition proposed by the OECD regarding what it applies to: “The OECD defines an Artificial Intelligence (AI) System as a machine-based system that can, for a given set of human-defined objectives, make predictions, recommendations, or decisions influencing real or virtual environments”. The , but the emphasis remains on decisions. That is the whole point of specifically regulating AI systems – the automation part. A camera or a database can be used by people to make decisions. An AI system can make decisions autonomously. OECD has issued a revised definition In the EU AI Act, : Unacceptable risk systems are banned entirely (although some exceptions apply), high-risk systems are subject to rules of traceability, transparency, and robustness, low-risk systems require transparency on the part of the supplier and minimal risk systems for which no requirements are set. AI systems are classified into 4 categories according to the perceived risk they pose https://linkeddataorchestration.com/2022/05/12/the-eu-ai-act-what-you-need-to-know/?embedable=true Requiring traceability, transparency, and robustness from foundation models seems reasonable. , so we have an idea of what this would look like in practice. Stanford researchers have evaluated foundation model providers like OpenAI and Google for their compliance with the EU AI Act It does not look like asking foundation model developers to provide this information would impose excessive overhead or stifle competition. But it does look like having them comply with these requirements would create a level playing field and benefit everyone using them directly or indirectly. 4. Should there be a distinction between regulating big tech models vs. open source models? Probably not, counter-intuitive as it may seem. First, because it’s hard to draw a line, case in point – does OpenAI count as big tech, for example? Second, because big tech, open source, and innovation are not mutually exclusive. Case in point – Meta, IBM, and the . newly announced AI Alliance Third, because . Case in point – and . open source is also a nuanced concept Llama AI model weights The question of whether governments should regulate open foundation models and how to do so is the key to finding the right balance between innovation and regulation in AI. Experts from industry, academia, and government convened to outline a path forward at a . Princeton-Stanford workshop on How to Promote Responsible Open Foundation Models This is the first in a series of short posts about important questions posed by people who think deeply and far about technology and its impact, focusing on AI in particular. The idea is to introduce a question and tease some answers as provided and/or inspired by the person who posed the question. Feedback welcome. Also published . here

The is an opinion piece based on the author’s POV and does not necessarily reflect the views of HackerNoon.

Hot off the press! This story contains factual information about a recent event.

Augmentastic || Augmented Reality

LinkedIn's Skills Graph: Paving the Way for the Skills-First Economy with AI and Ontology

The Year of the Graph Newsletter Vol. 25: The Fusion of Generative AI and Graph Technologies

George's got tech, data, and media, and he's not afraid to use them. 

Read My Stories

Register for the Pragmatic AI Training

Subscribe to the Orchestrate all the Things Newsletter

Subscribe to the Orchestrate all the Things Podcast

Too Long; Didn't Read

Make resilience your competitive advantage

Balancing AI Innovation & Regulation: Perspectives on Foundation Models and Responsible Development

About Author

Comments

TOPICS

THIS ARTICLE WAS FEATURED IN

Related Stories

Untitled Story

Spend Less on Ads, Get More SEO: A Smarter Growth Play

The Noonification: How Often Do NFTs Pass The Howey Test? (1/13/2023)

Darwin's Hybrid Intelligence to Align AI & Human Goals for Startups & VCs

The Noonification: White Man (11/26/2022)

The Noonification: The Metaverse is a Sh*tshow (11/2/2022)

Spend Less on Ads, Get More SEO: A Smarter Growth Play

The Noonification: How Often Do NFTs Pass The Howey Test? (1/13/2023)

Darwin's Hybrid Intelligence to Align AI & Human Goals for Startups & VCs

The Noonification: White Man (11/26/2022)

The Noonification: The Metaverse is a Sh*tshow (11/2/2022)

Light-Mode

Classic

Newspaper

Dark-Mode

Neon Noir

Minty

HN StartUps