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AI in Industry Today

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Artificial Intelligence for Industries of the Future

Part of the book series: Future of Business and Finance ((FBF))

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Abstract

This chapter provides an extensive dive into AI research and innovation in industry today. We begin by considering the most important source of AI innovation outside of academia: Big Tech research labs. While it is not always evident who should, and should not, be included under the umbrella of “Big Tech,” some candidates are fairly apparent, including Apple, Meta, and Alphabet. These companies and their products have had an outsize impact on smartphones (and other similar devices, as well as enabling services such as digital payment systems and media), social media, and Web search. At the same time, it has become evident over the last several years that companies not traditionally or currently classed as Big Tech companies, such as Tesla, but also large defense companies, such as Lockheed Martin, have also made important innovations in various subfields of AI. We briefly comment on these, before switching our discussion to the Chinese “Big Tech.” Finally, we comment on the important and disruptive role that small- and medium-sized enterprises, including startups, play in fostering and commercializing innovation in emerging technologies such as AI. We close the chapter with a case study on neural language models that have revolutionized applications in natural language processing and potential ethical concerns.

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Notes

  1. 1.

    Needless to say, Netflix has been unceremoniously dropped from the club in this new acronym.

  2. 2.

    Indeed, in early 2019, Apple, Amazon, Google, and Zigbee Alliance announced a partnership to make smart home products more compatible, not dissimilar to how standards are in place (sometimes due to regulation) for other longstanding technologies, such as cars and refrigerators.

  3. 3.

    Similar to the Google search engine, and other such systems, all the details behind the architecture of such complex products and services are not known and must be extrapolated given information such as publications, patents, public interviews with (and presentations by) Alexa researchers, and journalistic reporting, as well as our current knowledge about the state-of-the-art in fields such as natural language processing.

  4. 4.

    Discerning technical readers will recognize that this is just one of several graph-theoretic fields that are prominent in AI, where graphs have always played an important role. Within our own group, and others, these fields can often intersect and may also involve other disciplines. A particularly good example is network science, which is mature enough by now that several books have been written on it [58, 63]. Similar to KGs, network science can also be applied to many structured problems and domains [34, 38, 39, 47, 74].

  5. 5.

    In a sign of where the future was eventually headed, Netflix’s initial foray into the subscription-based content model (1999) far predates the streaming, although subscriptions underpin the revenue of many streaming services today. The subscription model was discontinued only a few months after start, but was (obviously) brought back later.

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    Mayank Kejriwal

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Kejriwal, M. (2023). AI in Industry Today. In: Artificial Intelligence for Industries of the Future. Future of Business and Finance. Springer, Cham. https://doi.org/10.1007/978-3-031-19039-1_3

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