Forthcoming in Philipp Hacker, Andreas Engel, Sarah Hammer and Brent Mittelstadt (eds) The Oxford Handbook on the Foundations and Regulation of Generative AI (Oxford University Press) Abstract Foundation models and generative artificial intelligence (AI) exacerbate a core regulatory challenge associated with AI: its heterogeneity. By their very nature, foundation models and generative AI can perform multiple functions for their users, thus presenting a vast array of different risks. This multifunctionality means that prescriptive, one-size-fits-all regulation will not be a viable option. Even performance standards and ex post liability--regulatory approaches that usually afford flexibility--are unlikely to be strong candidates for responding to multifunctional AI's risks, given challenges in monitoring and enforcement. Regulators will do well instead to promote proactive risk management on the part of developers and users by using management-based regulation, an approach that has proven effective in other contexts of heterogeneity. Regulators will also need to maintain ongoing vigilance and agility. More than in other contexts, regulators of multifunctional AI will need sufficient resources, top human talent and leadership, and organizational cultures committed to regulatory excellence. Consider one of humanity's most primal of tools: the knife [30]. The knife is not a singular tool; rather, it comes in many different varieties that serve many functions, each of which can generate value for society. Knives are used in the kitchen to prepare delicious meals, and then they are used by diners to consume those same meals. Knives carve objects, cut rope, and open packages. They clear paths through forests and jungles, and they help in harvesting seasonal crops. Knives can be used, of course, to injure or kill people. But in the hands of surgeons, knives are routinely used to save lives. And even though knives take many different forms and are often designed for many different purposes--think of, for example, the many types and sizes of surgical scalpels, woodcarver's chisels, and kitchen implements, among others--knives designed for one purpose also can be adapted for different uses, as anyone who has used a dinner knife to open a postal letter can attest. Many knives, though, are deliberately intended to serve multiple functions, as is the case with a simple pocketknife or, even more emblematically, the classic Swiss army knife, some models of which boast a combination of more than 30 different tools in one. The proliferation of functions performed by different knives has led over the years to different forms and sources of rules governing their manufacture, sale, and deployment.

Modern neural networks (NNs), trained on extensive raw sentence data, construct distributed representations by compressing individual words into dense, continuous, high-dimensional vectors. These representations are specifically designed to capture the varied meanings, including ambiguity, of word occurrences within context. In this thesis, our objective is to examine the efficacy of distributed representations from NNs in encoding lexical meaning. Initially, we identify four sources of ambiguity - homonymy, polysemy, semantic roles, and multifunctionality - based on the relatedness and similarity of meanings influenced by context. Subsequently, we aim to evaluate these sources by collecting or constructing multilingual datasets, leveraging various language models, and employing linguistic analysis tools.

Multifunctional biological neural networks exploit multistability in order to perform multiple tasks without changing any network properties. Enabling artificial neural networks (ANNs) to obtain certain multistabilities in order to perform several tasks, where each task is related to a particular attractor in the network's state space, naturally has many benefits from a machine learning perspective. Given the association to multistability, in this paper we explore how the relationship between different attractors influences the ability of a reservoir computer (RC), which is a dynamical system in the form of an ANN, to achieve multifunctionality. We construct the `seeing double' problem to systematically study how a RC reconstructs a coexistence of attractors when there is an overlap between them. As the amount of overlap increases, we discover that for multifunctionality to occur, there is a critical dependence on a suitable choice of the spectral radius for the RC's internal network connections. A bifurcation analysis reveals how multifunctionality emerges and is destroyed as the RC enters a chaotic regime that can lead to chaotic itinerancy.

Multifunctionality describes the capacity for a neural network to perform multiple mutually exclusive tasks without altering its network connections; and is an emerging area of interest in the reservoir computing machine learning paradigm. Multifunctionality has been observed in the brains of humans and other animals: particularly, in the lateral horn of the fruit fly. In this work, we transplant the connectome of the fruit fly lateral horn to a reservoir computer (RC), and investigate the extent to which this 'fruit fly RC' (FFRC) exhibits multifunctionality using the 'seeing double' problem as a benchmark test. We furthermore explore the dynamics of how this FFRC achieves multifunctionality while varying the network's spectral radius. Compared to the widely-used Erd\"os-Renyi Reservoir Computer (ERRC), we report that the FFRC exhibits a greater capacity for multifunctionality; is multifunctional across a broader hyperparameter range; and solves the seeing double problem far beyond the previously observed spectral radius limit, wherein the ERRC's dynamics become chaotic.

A new kind of hollow, pea-sized robot can roll, flip and jump to navigate its surroundings. It can transition from dry surfaces to pools of liquid with ease, making it fully amphibious. Its ability to use different types of motion in multiple environments--while carrying a cargo--sets it apart from other wee machines, most of which can only move in a single way. The new bot's versatility also makes it uniquely adept at working its way through, over and around obstacles. One day its small size and multifunctionality might let it navigate the complex environment of a human body and deliver a targeted payload of medicine to a patient in need.

GO has been diagnostics and drug discovery. In this paper, we further extensively used to compute the similarity between genes our previous study on gene-disease relationship (details in section 3) [19, 20]. In this work, we use the specifically with the multifunctional genes. We investigate functional annotations of a gene from the Gene Ontology the multifunctional gene-disease relationship based on the Annotation (GOA) databases to compute the shortest published molecular function annotations of genes from distance (path length) between the Molecular Function the Gene Ontology which is the most comprehensive (mf) GO terms annotating the gene.