A robust nonproliferation regime has contained the spread of nuclear weapons to just nine states. Yet, emerging and disruptive technologies are reshaping the landscape of nuclear risks, presenting a critical juncture for decision makers. This article lays out the contours of an overlooked but intensifying technological arms race for nuclear (in)visibility, driven by the interplay between proliferation-enabling technologies (PETs) and detection-enhancing technologies (DETs). We argue that the strategic pattern of proliferation will be increasingly shaped by the innovation pace in these domains. Artificial intelligence (AI) introduces unprecedented complexity to this equation, as its rapid scaling and knowledge substitution capabilities accelerate PET development and challenge traditional monitoring and verification methods. To analyze this dynamic, we develop a formal model centered on a Relative Advantage Index (RAI), quantifying the shifting balance between PETs and DETs. Our model explores how asymmetric technological advancement, particularly logistic AI-driven PET growth versus stepwise DET improvements, expands the band of uncertainty surrounding proliferation detectability. Through replicable scenario-based simulations, we evaluate the impact of varying PET growth rates and DET investment strategies on cumulative nuclear breakout risk. We identify a strategic fork ahead, where detection may no longer suffice without broader PET governance. Governments and international organizations should accordingly invest in policies and tools agile enough to keep pace with tomorrow's technology.

To see what the future might look like it is often helpful to study our history. This is what I will do in this article. I retrace the brief history of computers and artificial intelligence to see what we can expect for the future. How rapidly the world has changed becomes clear by how even quite recent computer technology feels ancient to us today. Mobile phones in the '90s were big bricks with tiny green displays.

Artificial intelligence that surpasses our own intelligence sounds like the stuff from science fiction books or films. What do experts in the field of AI research think about such scenarios? Do they dismiss these ideas as fantasy, or are they taking such prospects seriously? A human-level AI would be a machine, or a network of machines, capable of carrying out the same range of tasks that we humans are capable of. It would be a machine that is "able to learn to do anything that a human can do," as Norvig and Russell put it in their textbook on AI.1 It would be able to choose actions that allow the machine to achieve its goals and then carry out those actions.

I remember the days before Google: Finding answers on the internet was tedious and clunky, I had to switch between search engines or run meta-searches to get workable results, and it still felt like I wasn't finding all the information that was out there. Then Google came along and everything changed – I felt like I had gained new super-powers. Using ChatGPT feels at least as transformative as switching from AltaVista to Google. After only a few days of working with ChatGPT, I feel like it has made me much more effective. It'd be hard to go back to pre-ChatGPT life.

Until recently, the conventional wisdom was that while AI was better than humans at data-driven decision making tasks, it was still inferior to humans for cognitive and creative ones. But in the past two years language-based AI has advanced by leaps and bounds, changing common notions of what this technology can do. The most visible advances have been in what's called "natural language processing" (NLP), the branch of AI focused on how computers can process language like humans do. It has been used to write an article for The Guardian, and AI-authored blog posts have gone viral -- feats that weren't possible a few years ago. AI even excels at cognitive tasks like programming where it is able to generate programs for simple video games from human instructions.

So you're interested in AI? Then join our online event, TNW2020, where you'll hear how artificial intelligence is transforming industries and businesses. The coronavirus pandemic has clearly accelerated our dependency on technology, online activities, and artificial intelligence. AI is particularly important for businesses as it enables personalized services on a massive scale, and customers are increasingly demanding it. However, not every company has the knowledge or the tools to implement AI, nor do they know what is required from them to become AI-driven. In this post, I will discuss what options these companies have.

Recently the concept of transformative AI (TAI) has begun to receive attention in the AI policy space. TAI is often framed as an alternative formulation to notions of strong AI (e.g. artificial general intelligence or superintelligence) and reflects increasing consensus that advanced AI which does not fit these definitions may nonetheless have extreme and long-lasting impacts on society. However, the term TAI is poorly defined and often used ambiguously. Some use the notion of TAI to describe levels of societal transformation associated with previous 'general purpose technologies' (GPTs) such as electricity or the internal combustion engine. Others use the term to refer to more drastic levels of transformation comparable to the agricultural or industrial revolutions. The notion has also been used much more loosely, with some implying that current AI systems are already having a transformative impact on society. This paper unpacks and analyses the notion of TAI, proposing a distinction between TAI and radically transformative AI (RTAI), roughly corresponding to societal change on the level of the agricultural or industrial revolutions. We describe some relevant dimensions associated with each and discuss what kinds of advances in capabilities they might require. We further consider the relationship between TAI and RTAI and whether we should necessarily expect a period of TAI to precede the emergence of RTAI. This analysis is important as it can help guide discussions among AI policy researchers about how to allocate resources towards mitigating the most extreme impacts of AI and it can bring attention to negative TAI scenarios that are currently neglected.

Transformative AI technologies have the potential to reshape critical aspects of society in the near future. However, in order to properly prepare policy initiatives for the arrival of such technologies accurate forecasts and timelines are necessary. A survey was administered to attendees of three AI conferences during the summer of 2018 (ICML, IJCAI and the HLAI conference). The survey included questions for estimating AI capabilities over the next decade, questions for forecasting five scenarios of transformative AI and questions concerning the impact of computational resources in AI research. Respondents indicated a median of 21.5% of human tasks (i.e., all tasks that humans are currently paid to do) can be feasibly automated now, and that this figure would rise to 40% in 5 years and 60% in 10 years. Median forecasts indicated a 50% probability of AI systems being capable of automating 90% of current human tasks in 25 years and 99% of current human tasks in 50 years. The conference of attendance was found to have a statistically significant impact on all forecasts, with attendees of HLAI providing more optimistic timelines with less uncertainty. These findings suggest that AI experts expect major advances in AI technology to continue over the next decade to a degree that will likely have profound transformative impacts on society.

As if Artificial Intelligence wasn't complicated enough, there's now yet another term bringing more complexity. Applications of AI can be generally summed up into one of three categories – DIY, faux, and transformative. The first two are most common, and fairly self explianatory. DIY AI gathers information for a user that then carries out an action themselves, for example picking a restaurant out of a list of suggestions, and Faux AI is a touted as Artificial Intelligence but isn't actually based on machine learning properties. Transformative AI, on the other hand, gathers, processes and acts on information without human intervention.

Artificial intelligence can be seen in various applications all over the world, but generally, these can all be divided into three very loose categories: Transformative AI, DIY (Do It Yourself) AI, and Faux AI. While the latter two are the most common of them, according to experts it's transformative AI that holds the most potential. AI applications that are used on a daily basis are primarily aimed at the accessing and processing of data. For example, Alexa will turn on your music, give you a rundown of what's happening in your day ahead, while advising on the weather outside. But these are all relatively simple tasks, and nothing too dramatic that will change the world anytime soon.