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    • Exploring Artificial Intelligence Futures

    Shahar Avin

    An Anthology of Global Risk（2024）

    Centre for the Study of Existential Risk

    Cited 18|Views154
    Abstract
    This chapter presents a survey of the different methods available for the exploration of Artificial Intelligence futures, from fictional narratives to integrative, interdisciplinary, and participatory methods of exploring AI futures. Examining these different methods and tools, this chapter provides the advantages and limitations of each one, recognising that although no tool can reliably predict the future of AI, they are useful in reducing possible future surprises and creating a platform which enables conversations about what we want the future to look like. The author suggests a collaboration between all future AI narratives, that attention should be equalised between different tools and methods, calling for a more balanced portfolio when it comes to exploring AI futures.
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    Key words
    Predictive Analytics,Business Intelligence,Analytics,Sustainability,Big Data
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    • Pretraining has recently greatly promoted the development of natural language processing (NLP)
    • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
    • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
    • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
    • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
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