Good piece with link to detailed Forrester study:
AI Accountability: Proceed at Your Own Risk
A new report suggests that to improve AI accountability, enterprises should tackle third-party risk head-on.
A report issued by technology research firm Forrester: AI Aspirants: Caveat Emptor , highlights the growing need for third-party accountability in artificial intelligence tools.
The report found that a lack of accountability in AI can result in regulatory fines, brand damage, and lost customers, all of which can be avoided by performing third-party due diligence and adhering to emerging best practices for responsible AI development and deployment.
The risks of getting AI wrong are real and, unfortunately, they're not always directly within the enterprise's control, the report observed. "Risk assessment in the AI context is complicated by a vast supply chain of components with potentially nonlinear and untraceable effects on the output of the AI system," it stated.
Most enterprises partner with third parties to create and deploy AI systems because they don’t have the necessary technology and skills in house to perform these tasks on their own, said report author Brandon Purcell, a Forrester principal analyst who covers customer analytics and artificial intelligence issues. "Problems can occur when enterprises fail to fully understand the many moving pieces that make up the AI supply chain. Incorrectly labeled data or incomplete data can lead to harmful bias, compliance issues, and even safety issues in the case of autonomous vehicles and robotics," Purcell noted. .... "
Showing posts with label Deployment. Show all posts
Showing posts with label Deployment. Show all posts
Tuesday, September 08, 2020
Monday, May 25, 2020
Evolution of Distributed Systems on Kubernetes
Ultimately in delivery, workflow design is key, here a presentation on the topic.
Kubernetes is an open-source container-orchestration system for automating application deployment, scaling, and management ...
The Evolution of Distributed Systems on Kubernetes
Bilgin Ibryam takes us on a journey exploring Kubernetes primitives, design patterns and new workload types.
Bio
Bilgin Ibryam is a product manager and a former architect at Red Hat. In his day-to-day job, he works with customers from all sizes and locations, helping them to be successful with adoption of emerging technologies through proven and repeatable patterns and practises. His current interests include enterprise blockchains, cloud-native data and serverless.
About the conference
Software is changing the world. QCon empowers software development by facilitating the spread of knowledge and innovation in the developer community. A practitioner-driven conference, QCon is designed for technical team leads, architects, engineering directors, and project managers who influence innovation in the ... "
Kubernetes is an open-source container-orchestration system for automating application deployment, scaling, and management ...
The Evolution of Distributed Systems on Kubernetes
Bilgin Ibryam takes us on a journey exploring Kubernetes primitives, design patterns and new workload types.
Bio
Bilgin Ibryam is a product manager and a former architect at Red Hat. In his day-to-day job, he works with customers from all sizes and locations, helping them to be successful with adoption of emerging technologies through proven and repeatable patterns and practises. His current interests include enterprise blockchains, cloud-native data and serverless.
About the conference
Software is changing the world. QCon empowers software development by facilitating the spread of knowledge and innovation in the developer community. A practitioner-driven conference, QCon is designed for technical team leads, architects, engineering directors, and project managers who influence innovation in the ... "
Sunday, March 08, 2020
MIT Center for Deployable Machine Learning
Was just reminded of CDML, recently established. Would further continue to like to see more about the practical deployment of related and integrated processes for practical decision making.
The MIT Center for Deployable Machine Learning (CDML) works towards creating AI systems that are robust, reliable and safe for real-world deployment.
Our Mission
The impressive—often "super-human"—performance of state-of-the-art learning systems creates a major expectation that broad deployment of machine learning will revolutionize almost every aspect of our lives. However, fulfilling this expectation requires ML that is robust to a variety of random and adversarial corruptions, provides reliable decision-making, and is understandable and easy to work with for humans, even if they have no ML expertise. The goal of the MIT Center for Deployable Machine Learning (CDML) is to bring together the broad expertise and focused effort needed to build ML systems that are safe, robust, and reliable enough to be confidently and responsibly deployed in the real world. ....
Further an article in MIT News on this:
“Doing machine learning the right way”
Professor Aleksander Madry strives to build machine-learning models that are more reliable, understandable, and robust.
By Rob Matheson ....
The MIT Center for Deployable Machine Learning (CDML) works towards creating AI systems that are robust, reliable and safe for real-world deployment.
Our Mission
The impressive—often "super-human"—performance of state-of-the-art learning systems creates a major expectation that broad deployment of machine learning will revolutionize almost every aspect of our lives. However, fulfilling this expectation requires ML that is robust to a variety of random and adversarial corruptions, provides reliable decision-making, and is understandable and easy to work with for humans, even if they have no ML expertise. The goal of the MIT Center for Deployable Machine Learning (CDML) is to bring together the broad expertise and focused effort needed to build ML systems that are safe, robust, and reliable enough to be confidently and responsibly deployed in the real world. ....
Further an article in MIT News on this:
“Doing machine learning the right way”
Professor Aleksander Madry strives to build machine-learning models that are more reliable, understandable, and robust.
By Rob Matheson ....
Sunday, January 05, 2020
Dependability in Edge Computing
Quite a good pece, broad and deep, on the topic of edge computing. Scale, Security and standardization in Deployment. Below the abstract, more about the specific insights covered are at the link, full access requires sign into the latest CACM issue.
Dependability in Edge Computing
By Saurabh Bagchi, Muhammad-Bilal Siddiqui, Paul Wood, Heng Zhang
Communications of the ACM, January 2020, Vol. 63 No. 1, Pages 58-66
10.1145/3362068
Edge computing is the practice of placing computing resources at the edges of the Internet in close proximity to devices and information sources. This, much like a cache on a CPU, increases bandwidth and reduces latency for applications but at a potential cost of dependability and capacity. This is because these edge devices are often not as well maintained, dependable, powerful, or robust as centralized server-class cloud resources.a
This article explores dependability and deployment challenges in the field of edge computing, what aspects are solvable with today's technology, and what aspects call for new solutions. The first issue addressed is failures—both hard (crash, hang, and so on) and soft (performance-related)—and real-time constraint violation. In this domain, edge computing bolsters real-time system capacity through reduced end-to-end latency. However, much like cache misses, overloaded or malfunctioning edge computers can drive latency beyond tolerable limits. Second, decentralized management and device tampering can lead to chain of trust and security or privacy violations. Authentication, access control, and distributed intrusion detection techniques have to be extended from current cloud deployments and need to be customized for the edge ecosystem. The third issue deals with handling multi-tenancy in the typically resource-constrained edge devices and the need for standardization to allow for interoperability across vendor products. ... "
Dependability in Edge Computing
By Saurabh Bagchi, Muhammad-Bilal Siddiqui, Paul Wood, Heng Zhang
Communications of the ACM, January 2020, Vol. 63 No. 1, Pages 58-66
10.1145/3362068
Edge computing is the practice of placing computing resources at the edges of the Internet in close proximity to devices and information sources. This, much like a cache on a CPU, increases bandwidth and reduces latency for applications but at a potential cost of dependability and capacity. This is because these edge devices are often not as well maintained, dependable, powerful, or robust as centralized server-class cloud resources.a
This article explores dependability and deployment challenges in the field of edge computing, what aspects are solvable with today's technology, and what aspects call for new solutions. The first issue addressed is failures—both hard (crash, hang, and so on) and soft (performance-related)—and real-time constraint violation. In this domain, edge computing bolsters real-time system capacity through reduced end-to-end latency. However, much like cache misses, overloaded or malfunctioning edge computers can drive latency beyond tolerable limits. Second, decentralized management and device tampering can lead to chain of trust and security or privacy violations. Authentication, access control, and distributed intrusion detection techniques have to be extended from current cloud deployments and need to be customized for the edge ecosystem. The third issue deals with handling multi-tenancy in the typically resource-constrained edge devices and the need for standardization to allow for interoperability across vendor products. ... "
Saturday, May 18, 2019
Ethically Deploying AI
Thoughtful piece, making the distinction between creating an AI and deploying it. Deploying means how it operates in current and predicted future context. Often with statistical outcomes. But then any augmentation of humans has this conundrum. As I suggested in a conversation ... the invention of binoculars could be said to make long distance facial recognition much easier, faster .... deployable ... so should we restrict their use? And how? Deployment is a slippery place, where lots about context and culture and other technologies come into play.
" ... In this episode of the McKinsey Podcast, Simon London speaks with MGI partner Michael Chui and McKinsey partner Chris Wigley about how companies can ethically deploy artificial intelligence. .."
" ... In this episode of the McKinsey Podcast, Simon London speaks with MGI partner Michael Chui and McKinsey partner Chris Wigley about how companies can ethically deploy artificial intelligence. .."
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