AI Fatalism Won't Help Us Deal With Its Actual Risks – Gizmodo

Over the past few months, artificial intelligence (AI) has entered the global conversation as a result of the widespread adoption of generative AI-based tools such as chatbots and automatic image generation programs. Prominent AI scientists and technologists have raised concerns about the hypothetical existential risks posed by these developments.
Having worked in AI for decades, this surge in popularity and the sensationalism that has followed have caught us by surprise. Our goal with this article is not to antagonise, but to balance the public perception which seems disproportionately dominated by fears of speculative AI-related existential threats.
It’s not our place to say one cannot, or should not, worry about the more exotic risks. As members of the European Laboratory for Learning and Intelligent Systems (ELLIS), a research-anchored organisation focused on machine learning, we do feel it is our place to put these risks into perspective, particularly in the context of governmental organisations contemplating regulatory actions with input from tech companies.
AI is a discipline within computer science or engineering that took shape in the 1950s. Its aspiration is to build intelligent computational systems, taking as a reference human intelligence. In the same way as human intelligence is complex and diverse, there are many areas within artificial intelligence that aim to emulate aspects of human intelligence, from perception to reasoning, planning and decision-making.
Depending on the level of competence, AI systems can be divided into three levels:
AI can be applied to any field from education to transportation, healthcare, law or manufacturing. Thus, it is profoundly changing all aspects of society. Even in its “narrow AI” form, it has a significant potential to generate sustainable economic growth and help us tackle the most pressing challenges of the 21st century, such as climate change, pandemics, and inequality.
The adoption of AI-based decision-making systems over the last decade on a wide range of domains, from social media to the labour market, also poses significant societal risks and challenges that need to be understood and addressed.
The recent emergence of highly capable large, generative pre-trained transformer (GPT) models exacerbates many of the existing challenges while creating new ones that deserve careful attention. The unprecedented scale and speed with which these tools have been adopted by hundreds of millions of people worldwide is placing further stress on our societal and regulatory systems.
There are some critically important challenges that should be our priority:
Unfortunately, rather than focusing on these tangible risks, the public conversation – most notably the recent open letters – has mainly focused on hypothetical existential risks of AI.
An existential risk refers to a potential event or scenario that represents a threat to the continued existence of humanity with consequences that could irreversibly damage or destroy human civilisation, and therefore lead to the extinction of our species. A global catastrophic event (such as an asteroid impact or a pandemic), the destruction of a livable planet (due to climate change, deforestation or depletion of critical resources like water and clean air), or a worldwide nuclear war are examples of existential risks.
Our world certainly faces a number of risks, and future developments are hard to predict. In the face of this uncertainty, we need to prioritise our efforts. The remote possibility of an uncontrolled super-intelligence thus needs to be viewed in context, and this includes the context of 3.6 billion people in the world who are highly vulnerable due to climate change; the roughly 1 billion people who live on less than 1 US dollar a day; or the 2 billion people who are affected by conflict. These are real human beings whose lives are in severe danger today, a danger certainly not caused by super AI.
Focusing on a hypothetical existential risk deviates our attention from the documented severe challenges that AI poses today, does not encompass the different perspectives of the broader research community, and contributes to unnecessary panic in the population.
Society would surely benefit from including the necessary diversity, complexity, and nuance of these issues, and from designing concrete and coordinated actionable solutions to address today’s AI challenges, including regulation. Addressing these challenges requires the collaboration and involvement of the most impacted sectors of society together with the necessary technical and governance expertise. It is time to act now with ambition and wisdom – and in cooperation.
Want to know more about AI, chatbots, and the future of machine learning? Check out our full coverage of artificial intelligence, or browse our guides to The Best Free AI Art Generators and Everything We Know About OpenAI’s ChatGPT.
The authors of this article are members of The European Lab for Learning & Intelligent Systems (ELLIS) Board.
Nuria Oliver, Directora de la Fundación ELLIS Alicante y profesora honoraria de la Universidad de Alicante, Universidad de Alicante; Bernhard Schölkopf, , Max Planck Institute for Intelligent Systems; Florence d’Alché-Buc, Professor, Télécom Paris – Institut Mines-Télécom; Nada Lavrač, PhD, Research Councillor at Department of Knowledge Technologies, Jožef Stefan Institute and Professor, University of Nova Gorica; Nicolò Cesa-Bianchi, Professor, University of Milan; Sepp Hochreiter, , Johannes Kepler University Linz, and Serge Belongie, Professor, University of Copenhagen
This article is republished from The Conversation under a Creative Commons license. Read the original article.