Tag: Machine Learning

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Improving Alpha: Global AI Investment & ROI: Navigating US, China, and Asian Markets

Improving Alpha: Global AI Investment & ROI: Navigating US, China, and Asian Markets

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How do the United States and Asia approach the expansion of artificial intelligence for the future? On one side of the world, you have frontier model development requiring massive compute and content ingestion, while the other side is looking at their internal strengths and figuring out where AI can have the greatest impact. As investors, how can we figure out a true ROI to place our bets on across regions of the world?

In this latest episode of the Improving Alpha podcast, host Michael Oliver Weinberg is joined by Farhan Ahmad, Former CEO of PayNet Malaysia, Three Time Entrepreneur, and MIT CSAIL Board Member, to discuss the nuanced approach of Asian AI model development, the concept of interoperability vs isolated development, ongoing AI adoption and improved social outcomes,  and how the United States and Europe plan to stay engaged in this innovation.

Key highlights include:

  • How is AI impacting small- to medium-sized businesses, and is there a valuable investor story to uncover?
  • Can the US effectively compete with China when it comes to robotics, manufacturing, and autonomous vehicles? Will a new age of US manufacturing come into play if we can lower labor costs across the country?
  • What sort of impact does the Chinese government have in mandating the development and rollout of AI across schools, banks, and other businesses? Is there a comparable national-level strategy for that rollout in the US?
  • Does a national security threat exist in Chinese exports to the US that have embedded AI components?
  • How does Malaysia’s ILMU differ from foundation models being deployed in the US and mainland China? Will smaller models be able to catch up with larger LLM models through distillation, and how do real world examples figure into these equations?
  • How can the lessons at PayNet on shared rails and interoperability be applied to cross-border cooperation, especially when looking at Singapore and Vietnam?
  • And more.

Resources:

Connect with Farhan Ahmad:

Connect with Michael Oliver Weinberg: 

About Our Guest:

Farhan Ahmad is a three-time entrepreneur, technology executive, and former CEO of PayNet Malaysia, the country’s national payments network. Throughout his career, he has founded and successfully exited three technology companies and held leadership roles across banking, financial technology, and digital payments in both the United States and Europe. While leading PayNet, he helped expand one of the world’s most advanced real-time payment networks, significantly increasing its scale and strengthening cross-border payment interoperability across Asia. Farhan also serves on the board of the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL), where he contributes to discussions at the intersection of artificial intelligence, technology, and innovation. His work focuses on AI adoption, financial infrastructure, digital transformation, and the practical application of emerging technologies to solve real-world challenges.

The information covered and posted represents the views and opinions of the guest and does not necessarily represent the views or opinions of Vidrio Financial, and/or our host, Michael Oliver Weinberg. The Content has been made available for informational and educational purposes only. The Content is not intended to be a substitute for professional investing advice. Always seek the advice of your financial advisor or other qualified financial service provider with any questions you may have regarding your investment planning.

The release date may not correspond to the recording date.

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Improving Alpha: Chris Wiggins Exploring and Exploiting the Value of Artificial Intelligence

Improving Alpha: Chris Wiggins Exploring and Exploiting the Value of Artificial Intelligence

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Allocators face a barrage of ideas and debates around the use of artificial intelligence and large language models, better known as LLMs. How can huge amounts of investor data be transformed and enriched to solve challenges in asset allocations and improve an investment team’s strategy? Better yet, have we accumulated enough ‘scar tissue’ in artificial intelligence to understand what AI gets wrong and is easily corrected, versus what is instantaneously correct and becomes actionable intelligence?

In our latest episode of the Improving Alpha podcast, host Michael Oliver Weinberg sits down with Chris H. Wiggins, Chief Data Scientist, The New York Times and Associate Professor of Applied Mathematics, Columbia University, to dive deeper into the fundamentals of data science, dashboarding, and KPIs.

Further highlights from Michael’s discussion with Chris include: 

  • in a crowded field of AI adopters, where does the advantage lie today? With the new disruptors in a given industry or the incumbents?
  • what are the forces behind data science and how does the framework of descriptive data sets, predictive models, and prescriptive solutions help inform those wishing to leverage AI or LLMs?
  • when adding a data scientist to an investment team, what should the hiring organization be on the lookout for, before extending an offer?
  • the ‘shocking’ acceptance of the AI problem.
  • AI’s impact on the labor force in the next few years.
  • from an investment play is it better to buy into the AI craze and the deep learning networks, or is the real ‘gold rush’ centered around the requirements necessary to expand artificial intelligence usage via data centers, people who train the models, and the data contained in these models? 
  • Listen now to find out more. 

Resources:

Connect with Chris Wiggins:

Connect with Michael Oliver Weinberg: 

About Our Guest:

Chris Wiggins is an associate professor of applied mathematics at Columbia University and the Chief Data Scientist at the New York Times. At Columbia he is a founding member of the Department of Systems Biology, the executive committee of the Data Science Institute (http://datascience.columbia.edu/), and the Institute’s education and entrepreneurship committees. He is also an affiliate of Columbia’s Department of Statistics and a founding member of Columbia’s Center for Computational Biology and Bioinformatics (C2B2). He is a co-founder and co-organizer of hackNY (http://hackNY.org), a nonprofit that since 2010 has organized once-a-semester student hackathons and the hackNY Fellows Program, a structured summer internship at NYC startups. Prior to joining the faculty at Columbia he was a Courant Instructor at NYU (1998-2001) and earned his PhD at Princeton University (1993-1998) in theoretical physics. In 2014 he was elected Fellow of the American Physical Society and is a recipient of Columbia’s Avanessians Diversity Award.

The information covered and posted represents the views and opinions of the guest and does not necessarily represent the views or opinions of Vidrio Financial, and/or our host, Michael Oliver Weinberg. The Content has been made available for informational and educational purposes only. The Content is not intended to be a substitute for professional investing advice. Always seek the advice of your financial advisor or other qualified financial service provider with any questions you may have regarding your investment planning.

The release date may not correspond to the recording date.