On October 14, 2021, the U.S. Food and Drug Administration ("FDA" or the "Agency") held a virtual workshop entitled, Transparency of Artificial Intelligence ("AI")/Machine Learning ("ML")-enabled Medical Devices. The workshop builds upon previous Agency efforts in the AI/ML space. Back in 2019, FDA issued a discussion paper and request for feedback called, Proposed Regulatory Framework for Modifications to AI/ML-Based Software as a Medical Device ("SaMD"). To support continued framework development and to increase collaboration and innovation between key stakeholders and specialists, FDA created the Digital Health Center of Excellence in 2020. And, in January 2021, FDA published an AI/ML Action Plan, based, in part, on stakeholder feedback to the 2019 discussion paper.
Increasing availability of machine learning (ML) frameworks and tools, as well as their promise to improve solutions to data-driven decision problems, has resulted in popularity of using ML techniques in software systems. However, end-to-end development of ML-enabled systems, as well as their seamless deployment and operations, remain a challenge. One reason is that development and deployment of ML-enabled systems involves three distinct workflows, perspectives, and roles, which include data science, software engineering, and operations. These three distinct perspectives, when misaligned due to incorrect assumptions, cause ML mismatches which can result in failed systems. We conducted an interview and survey study where we collected and validated common types of mismatches that occur in end-to-end development of ML-enabled systems. Our analysis shows that how each role prioritizes the importance of relevant mismatches varies, potentially contributing to these mismatched assumptions. In addition, the mismatch categories we identified can be specified as machine readable descriptors contributing to improved ML-enabled system development. In this paper, we report our findings and their implications for improving end-to-end ML-enabled system development.
The above pitch confused detecting an attack with detecting an intrusion. An attack may not be successful; an intrusion is. Suppose you detected five new attacks, but only one was a real intrusion. Wouldn't you want to focus on the one successful intrusion, not the four failed attacks? ML-enabled security may not be robust, meaning that it works well on one data set (more often than not, the vendor's), but not on another (your real network).
When the COVID-19 outbreak became a global pandemic, financial-markets volatility hit its highest level in more than a decade, amid pervasive uncertainty over the long-term economic impact. Calm has returned to markets in recent months, but volatility continues to trend above its long-term average. Amid persistent uncertainty, financial institutions are seeking to develop more advanced quantitative capabilities to support faster and more accurate decision making. As financial markets gyrated in recent months, banks faced particular problems calculating value at risk (VAR) across asset classes. Many institutions experienced elevated levels of VAR back-testing exceptions, leading to higher regulatory-capital multipliers.
If you recently purchased an Amazon Echo, Echo Dot, or Amazon Tap you might be left wondering "Well, now what?" Nothing to fear, my friend. There's a lot to get through in order to get a smart assistant to do the heavy lifting of your home automation. And while we've gone over the seemingly endless list of everything that works with Alexa, there are some fundamental settings you might want to familiarize yourself with first. Below you'll find a step-by-step guide on how to manage your new Alexa-enabled device. The first thing you'll see when you get into the Settings tab of the Alexa mobile app or at alexa.amazon.com is a list of some pretty generic settings.