The amazing power of “machine eyes”

Link: https://erictopol.substack.com/p/the-amazing-power-of-machine-eyes

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Today’s report on AI of retinal vessel images to help predict the risk of heart attack and stroke, from over 65,000 UK Biobank participants, reinforces a growing body of evidence that deep neural networks can be trained to “interpret” medical images far beyond what was anticipated. Add that finding to last week’s multinational study of deep learning of retinal photos to detect Alzheimer’s disease with good accuracy. In this post I am going to briefly review what has already been gleaned from 2 classic medical images—the retina and the electrocardiogram (ECG)—as representative for the exciting capability of machine vision to “see” well beyond human limits. Obviously, machines aren’t really seeing or interpreting and don’t have eyes in the human sense, but they sure can be trained from hundreds of thousand (or millions) of images to come up with outputs that are extraordinary. I hope when you’ve read this you’ll agree this is a particularly striking advance, which has not yet been actualized in medical practice, but has enormous potential.

Author(s): Eric Topol

Publication Date: 4 Oct 2022

Publication Site: Eric Topol’s substack, Ground Truths

Deep Learning for Liability-Driven Investment

Link: https://www.soa.org/sections/investment/investment-newsletter/2022/february/rr-2022-02-shang/

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This article summarizes key points from the recently published research paper “Deep Learning for Liability-Driven Investment,” which was sponsored by the Committee on Finance Research of the Society of Actuaries. The paper applies reinforcement learning and deep learning techniques to liability-driven investment (LDI). The full paper is available at https://www.soa.org/globalassets/assets/files/resources/research-report/2021/liability-driven-investment.pdf.

LDI is a key investment approach adopted by insurance companies and defined benefit (DB) pension funds. However, the complex structure of the liability portfolio and the volatile nature of capital markets make strategic asset allocation very challenging. On one hand, the optimization of a dynamic asset allocation strategy is difficult to achieve with dynamic programming, whose assumption as to liability evolution is often too simplified. On the other hand, using a grid-searching approach to find the best asset allocation or path to such an allocation is too computationally intensive, even if one restricts the choices to just a few asset classes.

Artificial intelligence is a promising approach for addressing these challenges. Using deep learning models and reinforcement learning (RL) to construct a framework for learning the optimal dynamic strategic asset allocation plan for LDI, one can design a stochastic experimental framework of the economic system as shown in Figure 1. In this framework, the program can identify appropriate strategy candidates by testing varying asset allocation strategies over time.

Author(s): Kailan Shang

Publication Date: February 2022

Publication Site: Risks & Rewards, SOA