=Paper=
{{Paper
|id=Vol-3862/keynote
|storemode=property
|title=7 Pillars for the Future of AI (abstract)
|pdfUrl=https://ceur-ws.org/Vol-3862/keynote.pdf
|volume=Vol-3862
|authors=Erik Cambria
|dblpUrl=https://dblp.org/rec/conf/lacatoda/Cambria24
}}
==7 Pillars for the Future of AI (abstract)==
<pdf width="1500px">https://ceur-ws.org/Vol-3862/keynote.pdf</pdf>
<pre>
               LaCATODA 2024 Invited Talk

               Erik Cambria

               7 Pillars for the Future of AI

               Abstract

               In recent years, AI research has showcased tremendous potential to impact positively
               humanity and society. Although AI frequently outperforms humans in tasks related to
               classification and pattern recognition, it continues to face challenges when dealing
               with complex tasks such as intuitive decision-making, sense disambiguation, sarcasm
               detection, and narrative understanding, as these require advanced kinds of reasoning,
               e.g., commonsense reasoning and causal reasoning, which have not been emulated
               satisfactorily yet. The Seven Pillars for the future of AI address these shortcomings
               and pave the way for more efficient, scalable, safe and trustworthy AI systems.

               Invited Speaker’s Bio

               Erik Cambria is a Professor at Nanyang Technological University, where he also holds
               the appointment of Provost Chair in Computer Science and Engineering, and
               Founder of several AI companies, such as SenticNet, offering B2B sentiment analysis
               services, and finaXai, providing fully explainable financial insights. Prior to moving to
               Singapore, he worked at Microsoft Research Asia (Beijing) and HP Labs India
               (Bangalore), after earning his PhD through a joint program between the University of
               Stirling (UK) and MIT Media Lab (USA). Today, his research focuses on
               neurosymbolic AI for interpretable, trustworthy, and explainable affective computing
               in domains like social media monitoring, financial forecasting, and AI for social good.
               He is ranked in Clarivate's Highly Cited Researchers List of World's Top 1%
               Scientists, is recipient of many awards, e.g., IEEE Outstanding Early Career, was
               listed among the AI's 10 to Watch, and was featured in Forbes as one of the 5 People
               Building Our AI Future. He is an IEEE Fellow, Associate Editor of various top-tier AI
               journals, e.g., Information Fusion and IEEE Transactions on Affective Computing,
               and is involved in several international conferences as keynote speaker, program chair
               and committee member.




CEUR
                  ceur-ws.org
Workshop      ISSN 1613-0073
Proceedings

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</pre>