AI, energy and geopolitics: Leadership's triple transition challenge
AI's growth, changes to the energy system and geopolitical fragmentation have all come at once. For leaders, thats both a challenge and an opportunity.
Mark Esposito, Professor of Strategy and Technology Policy, Faculty Fellow at the Center for Emerging Markets, and Affiliate Scientist of the Global Resilience Institute at Northeastern University, is a globally recognized scholar, educator, and practitioner at the forefront of technology, economics, and public policy. He specializes in the Fourth Industrial Revolution and artificial intelligence, with expertise spanning academia, international organizations, and the private sector.
Mark Esposito equally holds appointments at Harvard University, where his affiliations span the Berkman Klein Center for Internet and Society at Harvard Law School, the Center for International Development at Harvard Kennedy School, and the Institute for Quantitative Social Science. He is also a Senior Associate at the University of Cambridge, an institution with which he has maintained deep ties since he joined as inaugural fellow the Circular Economy Research Center at Cambridge Judge Business School in 2016, a role he held through 2020.
Mark is closely connected to policy and innovation initiatives worldwide. He is a member of the World Economic Forum's Global AI Alliance, Fostering Converging Technologies group and Next Frontier of Operations, Centre for Advanced Manufacturing and Supply Chains. He has been a Fellow at the Mohammed Bin Rashid School of Government in Dubai since 2017. He has advised government agencies across the Gulf Cooperation Council and Eurasia, and for over a decade co-led the Institute's Council on Microeconomics of Competitiveness at Harvard Business School under Professor Michael Porter. An accomplished entrepreneur, he co-founded Nexus FrontierTech, an influential machine learning research firm, as well as The Chart ThinkTank and the AI Native Foundation. He currently serves as Chief Economist of micro1, a Silicon Valley AI lab developing human intelligence platforms for the training of frontier AI models.
He holds a doctoral degree from École des Ponts ParisTech in France, the world's oldest civil engineering school.
AI's growth, changes to the energy system and geopolitical fragmentation have all come at once. For leaders, thats both a challenge and an opportunity.
随着劳动力市场更加紧张、申请人数突破纪录,企业正将AI招聘纳入实践。AI主导的面试可以为被忽视的候选人提供更多机会、减少招聘人员偏见、提高效率,并使拒录变得更加公平。但这些好处也依赖于谨慎的AI风险管理和治理。
Las entrevistas guiadas por IA pueden ampliar las oportunidades para los candidatos pasados por alto, reducir el sesgo de reclutamiento y aumentar la eficiencia.
AI-led interviews can expand opportunities for overlooked candidates, reduce recruitment bias, increase efficiency and make rejection more constructive.
La competencia estratégica en materia de IA está marcada por el aumento de las barreras comerciales, ambiciones rivales y disputas por controlar los datos y su infraestructura.
Strategic competition over AI is marked by rising trade barriers, competing ambitions and a scramble to secure control over data and its infrastructure.
Los bienes comunes digitales son clave para compartir conocimientos y navegar las complejidades del siglo XXI. Este proceso proceso que puede mejorarse con blockchain.
The digital commons are essential for pooling knowledge to tackle 21st-century challenges, a process that blockchain technology can further enhance.
La gobernanza algorítmica abarca las normas y prácticas para la construcción y el uso de algoritmos integrados en tecnologías de IA. Pero, ¿cómo deben aplicarse?
Algorithmic governance covers the rules and practices for the construction and use of algorithms embedded in AI technologies. But how should these be applied?
Generative AI has sped up content creation, but businesses must find new ways to engage with the technology to improve its trustworthiness and reliability.
Technology rules are increasingly fragmented across regions, but agile governance can create a nimbler and more adaptive approach to regulation.
自疫情爆发以来,我们了解到不同的复苏曲线:Z型复苏(乐观:衰退,反弹至危机前的增长态势)、V型复苏(乐观:急剧下降,迅速复苏)、U型复苏(有点悲观:介于衰退与复苏之间的时期)、W型复苏(悲观:复苏,第二次下降)与L型复苏(最悲观:持续低迷)。
New research from JP Morgan suggests the recovery from the COVID-19 pandemic might be K-shaped - with implications for SMEs in particular.
Our adoption of AI technology has accelerated during the pandemic. It's time for a deeper conversation on what we need from AI in order to respond to future crises.











