Experts researching advancements in artificial intelligence are now warning that AI models could create the next "enhanced pathogens capable of causing major epidemics or even pandemics." The declaration was made in a paper published in the journal Science by co-authors from Johns Hopkins University, Stanford University and Fordham University, who say that AI models are being "trained on or [are] capable of meaningfully manipulating substantial quantities of biological data, from speeding up drug and vaccine design to improving crop yields." "But as with any powerful new technology, such biological models will also pose considerable risks. Because of their general-purpose nature, the same biological model able to design a benign viral vector to deliver gene therapy could be used to design a more pathogenic virus capable of evading vaccine-induced immunity," researchers wrote in their abstract. "Voluntary commitments among developers to evaluate biological models' potential dangerous capabilities are meaningful and important but cannot stand alone," the paper continued. "We propose that national governments, including the United States, pass legislation and set mandatory rules that will prevent advanced biological models from substantially contributing to large-scale dangers, such as the creation of novel or enhanced pathogens capable of causing major epidemics or even pandemics."

This paper presents a new version of an oscillating error classifier that has added fixed value ranges through bands, for each column or feature of the input dataset. An earlier version of the classifier added branches [8] to a categorical classification technique that allows the error update to be independent for each column value and can therefore oscillate around the desired output, reducing to some minimum. Because that classifier works off averaged values, it may be the case that some data can be classified directly, without it having to be sorted by weight sets, for example. The averaged value is simply 1 value for a whole range of actual input values and so maybe a value band can represent that range as a fixed set of boundaries. It may also be possible to construct these fixed boundaries for single dimensions, when much more complex hypercubes are not required. It is shown that some of the data can in fact be correctly classified through using fixed value ranges only, while the rest can be classified by using the classifiers. With the idea of these fixed bands that do not process very much, plus the more complex classifiers, the paper also presents the whole system in terms of a biological model of neurons and neuron links.

In this paper, we describe an interactive environment for the representation, interpretation, and revision of explanatory biological models. We illustrate our approach on the systems biology of aging, a complex topic that involves many interacting components, and discuss our experiences using this environment to codify an informal model of aging. We close by discussing related efforts and directions for future research.