The pivotal role the Icelandic Institute for Intelligent Machines (IIIM) played in Iceland’s AI readiness and IIIM’s contribution to Iceland’s post-crisis recovery, was recently detailed in a thorough report produced by the Canadian research outfit Small Globe.

Following the 2008 world-wide financial meltdown, Iceland needed innovative solutions to rebuild its economy. The establishment of IIIM in 2009 turned out to play an important part in that process. Leveraging artificial intelligence and robotics in economic revitalization is a strategy for long-term growth. 

Since its inception, IIIM has developed into a self-sustaining, world-renowned research facility that bridges the gap between academic research and industry-driven development. Many of its projects are open-source, allowing for wide-reaching impact across organizations and countries, which aligns with its mission to provide AI solutions for the betterment of society.

IIIM has also taken a leadership role in ethical AI development and advising European governments about AI strategy. Its Civilian AI Ethics Policy, introduced in 2015, underscores its commitment to ensuring that AI research and development are conducted responsibly, balancing both technical and ethical considerations.

The institute has demonstrated its value by providing AI-driven solutions tailored to Icelandic industries. One such achievement was the development of an AI tool aimed at tackling youth substance use, a pressing issue in Iceland. This highlights not only IIIM’s technical capabilities but also its commitment to applying technology to societal problems. The institute’s ability to work across sectors—from private enterprises to public institutions—has helped redefine the role of AI in the Icelandic economy, showing it as a tool for both innovation and societal progress.

IIIM’s influence now extends beyond Iceland, with nearly half of its research collaborations involving international partners. Through partnerships with universities such as Reykjavik University and the University of Camerino, IIIM has created numerous opportunities for young researchers. These collaborations have boosted Iceland’s global presence in AI research, attracting international talent and fostering the next generation of AI and robotics experts,  ensuring that Iceland remains connected to the broader European R&D community, securing its place in AI research for years to come.

In traditional image analytics, each frame – whether a photo or a frame from a video – is analyzed in a single pass. If more analysis is desired or needed, incorrect classifications were made, or important details missed, so be it. Not so in the revolutionary new computer vision system that Cisco, in collaboration with a number of academic and industry collaborators, plans to release as open-source software in the coming days.

One of these collaborators is IIIM, whose research on novel AI approaches has, for the past 3 years, been funded in part by Cisco Systems. The system, called Ethosight, uses reasoning to enhance the ability of traditional ANN-based large language models to dissect and classify objects and events in images and video, in realtime. Like a human looking for more clues about what is happening in a particular scenario, the system can improve the quality and depth of its analysis over time, the longer it looks at it, collecting more information about what may be going on. The system is possibly the first of its kind to demonstrate what has been called cumulative learning, that is, the ability to autonomously improve its knowledge about a particular thing over time. A preprint of a paper describing Ethosight has been published on ArXiV repository.

For Ethosight, the things it can address may for instance involve a variety of social situations, such as a child playing near a hot stove, or opening a closet where chemical are stored. According to the blog of Cisco’s Principal Engineer and the first author (see here) of the paper Hugo Latapie, Ethosight breaks away from the traditional limitations of AI systems being positioned “…not just as a real-time video analysis tool but as a vanguard in the continuous learning paradigm…”.

-RT

Resources

Ethosight ArXiV paper
Hugo Latapie’s Cisco blog
Paper on cumulative learning

by Helgi Páll Helgason

We now live in a world where generative AI can conjure photorealistic images of pretty much anything we can think of with results that are often indistinguishable from the real thing (this comes with its own set of problems but that’s a topic for another time). Then we have highly potent Large Language Models (LLMs) that can service very complex requests phrased in natural language, OpenAI’s GPT-4 reigning supreme at the moment. Consider that you can make absurd requests, such as…

The image was generated with Midjourney 5.1. The prompt used was simply “a man looking at the generative AI explosion”.

“Prove the Pythagorean theorem in a German poem and then list the elements in the periodic table in Chinese”

… and the model will usually generate a correct result from scratch in mere seconds. The same goes for more useful requests such as writing a piece of code and reviewing, rewriting or even generating written content on almost any topic. These examples just begin to scratch the surface of what is possible.

It is clear that LLMs at their present state of development can create significant business value already, but these models have limitations that are sometimes overlooked.

In the midst of the storm of progress and activity currently taking place with Generative AI, I’d like to stop for a moment to reflect, and offer a grounded and practical assessment.

LLMs are very large artificial neural networks. It is sometimes said that they simulate the inner workings of the human brain, but this is true to a far lesser extent than commonly perceived. Since neural networks were first introduced in the 80s, it has been well understood that they are function approximators. Even with the introduction of new features (e.g. attention) and new architectures (e.g. transformers) this fundamental nature remains unchanged. Although simplified, you can think of how they work as learning to map a set of training data points to their correct result values and then interpolating between these data points when given novel data. While often very effective, there is no guarantee that this will always produce correct results. An approximation of a function is not the same as the actual function. As statistician George Box famously said, “All models are wrong, but some are useful”.

Continue reading A Grounded Assessment of the Generative A.I. Explosion →