The AI act is the European Union-wide regulation of AI systems. It includes specific provisions for general-purpose AI models which however need to be further interpreted in terms of technical standards and state-of-art studies to ensure practical compliance solutions. This paper examines the AI act requirements for providers and deployers of general-purpose AI and further proposes uncertainty estimation as a suitable measure for legal compliance and quality assurance in training of such models. We argue that uncertainty estimation should be a required component for deploying models in the real world, and under the EU AI Act, it could fulfill several requirements for transparency, accuracy, and trustworthiness. However, generally using uncertainty estimation methods increases the amount of computation, producing a dilemma, as computation might go over the threshold ($10^{25}$ FLOPS) to classify the model as a systemic risk system which bears more regulatory burden.

These narratives distract from what we call the "pathologies of scale" that become more entrenched every day: large-scale AI models are still largely controlled by Big Tech firms because of the enormous computing and data resources they require, and also present well-documented concerns around discrimination, privacy and security vulnerabilities, and negative environmental impacts. Large-scale AI models like Large Language Models (LLMs) have received the most hype, and fear-mongering, over the past year. "Opinion You Can Have the Blue Pill or the Red Pill, and We're Out of Blue Pills." Greg Noone, "'Foundation models' may be the future of AI. They're also deeply flawed," Tech Monitor, November 11, 2021 (updated February 9, 2023); Dan McQuillan, "We Come to Bury ChatGPT, Not to Praise It," danmcquillan.org,

Fox News correspondent Mark Meredith has the latest on ChatGPT on'Special Report.' Geoffrey Hinton, a computer scientist who has been called "the godfather of artificial intelligence", says it is "not inconceivable" that AI may develop to the point where it poses a threat to humanity. The computer scientist sat down with CBS News this week about his predictions for the advancement of AI. He compared the invention of AI to electricity or the wheel. Hinton, who works at Google and the University of Toronto, said that the development of general purpose AI is progressing sooner than people may imagine.

Ten European software industry associations have called on the EU to scrap plans to include the regulation of general-purpose AI including natural language processing and chatbots in its new AI Act, describing it as a "fundamental departure from its original objective" and saying that it could stifle innovation and hit the open source community. The European Union AI Act aims to establish a framework to regulate the use of artificial intelligence, taking a "risk-based" approach to its use and establish a worldwide standard. The Act includes core provisions including tighter regulations in high-risk areas such as healthcare and transparency requirements, focusing on specific-purpose narrow AI. However, the group of industry associations, led by BSA, the software alliance, has published a joint statement urging EU institutions to reject recent additions to the Act that include regulation of general purpose AI and instead "maintain a risk-based approach". The objective of general purpose AI is to create machines that can reason and think like a human.

The United States is pushing for a narrower Artificial Intelligence definition, a broader exemption for general purpose AI and an individualised risk assessment in the AI Act, according to a document obtained by EURACTIV. The non-paper is dated October 2022 and was sent to targeted government officials in some EU capitals and the European Commission. It follows much of the ideas and wording of the initial feedback sent to EU lawmakers last March. "Many of our comments are prompted by our growing cooperation in this area under the U.S.-EU Trade and Technology Council (TTC) and concerns over whether the proposed Act will support or restrict continued cooperation," the document reads. The document is a reaction to the progress made by the Czech Presidency of the EU Council on the AI regulation last month.

Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! The AI space is laden with acronyms -- but arguably, one of the most-discussed right now is GPAI (general purpose AI). As anyone paying attention to the AI landscape is well-aware, this term could eventually define -- and regulate -- systems in the European Union's AI Act. But, since it was proposed in an amendment earlier this year, many question its specificity (or lack thereof) and implications.

If you read through the human history, especially the last 2,000 years, you will notice that famines, diseases and wars were the three most formidable challenges that we had to face (or as I call it -- the trifecta of human sufferings). Our response to the trifecta was mostly to seek safety and pray for our survival. History also tells us that when faced with external threats and no possible solutions in sight, we turned inwards and sought solace in numbers to overcome some serious challenges to our species' survival. Gradually, these cycles of sufferings led to emergence and prevalence of the unifying behavior of collaboration amongst humans. Tremendous efforts and resources, collaborated at extraordinary scale, were thrown at solving the trifecta of problems with some amount of success.

As I wrote in March, Google sees its cloud services as fueling the "democratization of AI" by abstracting many of the hard implementation details of building and using an AI software stack into cloud services. The early examples, such as Amazon AI, Azure Cognitive Services and Google Cloud Machine Learning Services are splendid examples of encapsulating sophisticated AI functions in a fairly straightforward service with an API wrapper. However, the high-level services mostly focus on the well-trodden paths of image and speech recognition; domains that have long catalyzed AI research. While such applications certainly have many uses in business, including for conversational interfaces as I detail here, they don't address the vast majority of business problems that could benefit from machine learning optimization and where applying AI still requires too much time and expertise. As the ARCHITECT blog rightly points out, AI research has often focused on games like Chess and Go, or handy add-ons to online consumer services like automatic image tagging and voice commands, not hard business problems.

The changes that artificial intelligence will bring to the technology landscape could pale in comparison to what it wreaks on global society. Humans need not be taken over by intelligent machines, as some doomsday soothsayers predict, to face a brave new world in which they must revolutionize the way they conduct their daily existence. From employment upheaval to environmental maintenance, people may face hard choices as they adapt to the widespread influence of artificial intelligence advances. Experts offer that artificial intelligence (AI) itself will undergo significant growing pains as it transitions from childhood into adolescence. Where ongoing research largely focuses on applied AI, it eventually will expand to cover a broader range of aspects.

Lately, there has been a lot of concern about the recent explosion of AI, and how it could reach the point of 1) being more intelligent than humans, and 2) that it could decide that it no longer needs us and could in fact, take over the Earth. Physicist Stephen Hawking famously told the BBC: "The development of full artificial intelligence could spell the end of the human race." Billionaire Elon Musk has said that he thinks AI is the "biggest existential threat" to the human race. Computers running the latest AI have already beaten humans at games ranging from Chess to Go to esports games (which is interesting, because this is a case where AI could be better than humans at playing games which were built as software from the ground up, unlike Chess and Go, which were developer before the computer age). AI has been making dramatic leaps over the past few years -- the question that Hawking and Musk are asking is: Could AI evolve to the point where it could replace humans? If this scenario sounds like science fiction, it's one that science fiction writers have posed again and again. One of the most popular is of course, Skynet, the intelligence that takes over in the Terminator universe and decides to wipe out most of humanity and enslave the rest (except for the resistance fighters, led by John Conner, but that involves a terminator travelling back in time, and time travel will be handled in another essay). In perhaps equally popular trilogy of the Matrix, super-intelligent machines take over the planet as well, but rather than killing humans they enslave them in a unique way. In order to ensure that electricity generated by the human brain can be put to use, the super-intelligent machines put humans in pods, keeping our minds busy playing a giant video game or simulation (i.e. the Matrix).