Public consultation on joint ISO standard 80000 that defines quantities and units for space, time, thermodynamics, light, radiation and even the characteristic numbers for each of the foregoing closes October 11th.
Since so much of what we do in standards setting is built upon a foundation of a shared understanding and agreement of the meaning of words (no less so than in technical standard setting) that time is well spent reflecting upon the origin of the nouns and verbs of that we use every day. Best practice cannot be discovered, much less promulgated, without its understanding secured with common language.
Title: IEEE P3119 – Standard for the Procurement of Artificial Intelligence and Automated Decision Systems
Scope: The IEEE P3119 standard establishes a uniform set of definitions and a process model for procuring Artificial Intelligence (AI) and Automated Decision Systems (ADS). It covers government procurement, in-house development, and hybrid public-private development of AI/ADS. The standard redefines traditional procurement stages—problem definition, planning, solicitation, critical evaluation (e.g., impact assessments), and contract execution—using an IEEE Ethically Aligned Design (EAD) foundation and a participatory approach to address socio-technical and responsible innovation considerations. It focuses on mitigating unique AI risks compared to traditional technologies and applies to commercial AI products and services procured through formal contracts.
Purpose: The purpose of IEEE P3119 is to help government entities, policymakers, and technologists make transparent, accountable, and responsible choices in procuring AI/ADS. It provides a framework to strengthen procurement processes, ensuring due diligence, transparency about risks, and alignment with public interest. The standard aims to minimize AI-related risks (e.g., bias, ethical concerns) while maximizing benefits, complementing existing procurement practices and shaping the market for responsible AI solutions. It supports agencies in critically evaluating AI tools, assessing vendor transparency, and integrating ethical considerations into procurement.
Developmental Timelines:
September 23, 2021: The IEEE Standards Association (SA) Standards Board approved the project and established the IEEE P3119 Working Group. The Project Authorization Request (PAR) was created to define the scope.
2021–Ongoing: Development continues, with no final publication date confirmed in available sources. As of July 18, 2024, the standard was still in progress, focusing on detailed process recommendations.
The standard is being developed as a voluntary socio-technical standard, with plans to test it against existing regulations (e.g., via regulatory sandboxes).
By Whom:
Working Group Chair: Gisele Waters, Ph.D., Director of Service Development and Operations at Design Run Group, co-founder of the AI Procurement Lab, and a human-centered design researcher focused on risk mitigation for vulnerable populations.
Working Group Vice Chair: Cari Miller, co-founder of the AI Procurement Lab and the Center for Inclusive Change, an AI governance leader and risk expert.
IEEE P3119 Working Group: Comprises a global network of IEEE SA volunteers from diverse industries, collaborating to develop standards addressing market needs and societal benefits. The group integrates expertise from government workers, policymakers, and technologists.
Inspiration: The standard was inspired by the AI and Procurement: A Primer report from the New York University Center for Responsible AI.
The IEEE P3119 standard is a collaborative effort to address the unique challenges of AI procurement, emphasizing ethical and responsible innovation for public benefit
Title: IEEE P3120 – Standard for Quantum Computing Architecture
Scope: The IEEE P3120 standard defines a general architecture for quantum computers, focusing on the structure and organization of quantum computing systems. It covers the overall system architecture, including quantum hardware components (e.g., qubits, quantum gates), control systems, interfaces with classical computing systems, and software layers for programming and operation. The standard aims to provide a framework for designing interoperable and scalable quantum computing systems, addressing both hardware and software considerations for quantum and hybrid quantum-classical architectures.
Purpose: The purpose of IEEE P3120 is to establish a standardized framework to guide the design, development, and integration of quantum computing systems. It seeks to ensure consistency, interoperability, and scalability across quantum computing platforms, facilitating innovation and collaboration in the quantum computing ecosystem. By providing clear architectural guidelines, the standard supports developers, researchers, and industry stakeholders in building reliable and efficient quantum computers, bridging the gap between theoretical quantum computing and practical implementation.
Developmental Timelines:
September 21, 2023: The IEEE Standards Association (SA) Standards Board approved the Project Authorization Request (PAR) for P3120, initiating the project under the IEEE Computer Society’s Microprocessor Standards Committee (C/MSC).
2023–Ongoing: Development is in progress, with no confirmed publication date in available sources. As a standards development project, it involves iterative drafting, review, and consensus-building, typical of IEEE processes, which can span several years.
The standard is being developed as a voluntary standard, with potential for testing and refinement through industry and academic collaboration.
By Whom:
Sponsor: IEEE Computer Society, specifically the Microprocessor Standards Committee (C/MSC), which oversees standards related to microprocessor and computing architectures.
Working Group: The IEEE P3120 Working Group consists of volunteers from academia, industry, and research institutions with expertise in quantum computing, computer architecture, and related fields. Specific chairs or members are not detailed in available sources, but IEEE SA working groups typically include global experts from relevant domains.
Stakeholders: The development involves contributions from quantum computing researchers, hardware manufacturers, software developers, and standardization experts to ensure a comprehensive and practical standard.
The IEEE P3120 standard is a critical step toward formalizing quantum computing architectures, aiming to support the growing quantum technology industry with a robust and interoperable framework.
Come see ‘Pygmalion’ this weekend at Arno Gustin Hall on campus! University of Mary students bring this classic to life in a riveting weekend of performances. 🎬
Julia is a programming language that has gained popularity in the field of artificial intelligence (AI) and scientific computing for several reasons.
High Performance: Julia is designed to be a high-performance language, often compared to languages like C and Fortran. It achieves this performance through just-in-time (JIT) compilation, allowing it to execute code at speeds close to statically compiled languages. This makes Julia well-suited for computationally intensive AI tasks such as numerical simulations and deep learning.
Ease of Use: Julia is designed with a clean and expressive syntax that is easy to read and write. It feels similar to other high-level languages like Python, making it accessible to developers with a background in Python or other scripting languages.
Multiple Dispatch: Julia’s multiple dispatch system allows functions to be specialized on the types of all their arguments, leading to more generic and efficient code. This feature is particularly useful when dealing with complex data types and polymorphic behavior, which is common in AI and scientific computing.
Rich Ecosystem: Julia has a growing ecosystem of packages and libraries for AI and scientific computing. Libraries like Flux.jl for deep learning, MLJ.jl for machine learning, and DifferentialEquations.jl for solving differential equations make it a powerful choice for AI researchers and practitioners.
Interoperability: Julia offers excellent interoperability with other languages, such as Python, C, and Fortran. This means you can leverage existing code written in these languages and seamlessly integrate it into your Julia AI projects.
Open Source: Julia is an open-source language, which means it is freely available and has an active community of developers and users. This makes it easy to find resources, documentation, and community support for your AI projects.
Parallel and Distributed Computing: Julia has built-in support for parallel and distributed computing, making it well-suited for tasks that require scaling across multiple cores or distributed computing clusters. This is beneficial for large-scale AI projects and simulations.
Interactive Development: Julia’s REPL (Read-Eval-Print Loop) and notebook support make it an excellent choice for interactive data analysis and experimentation, which are common in AI research and development.
While Julia has many advantages for AI applications, it’s important to note that its popularity and ecosystem continue to grow, so some specialized AI libraries or tools may still be more mature in other languages like Python. Therefore, the choice of programming language should also consider the specific requirements and constraints of your AI project, as well as the availability of libraries and expertise in your development team.
ABSTRACT. Many optimization problems in power transmission networks can be formulated as polynomial problems with complex variables. A polynomial optimization problem with complex variables consists in optimizing a real-valued polynomial whose variables and coefficients are complex numbers subject to some complex polynomial equality or inequality constraints. These problems are usually directly expressed with real variables. In this work, we propose a Julia module allowing the representation of polynomial problems in their original complex formulation. This module is applied to power system optimization and its generic design enables the description of several variants of power system problems. Results for the Optimal Power Flow in Alternating Current problem and for the Preventive-Security Constrained Optimal Power Flow problem are presented.
“Eco-friendly”, “Green”, “Bio”… Companies are increasingly using those tags as a signal to consumers of their environmental awareness. Yet also on the rise is a public concern about potential corporate lies in this subject, a phenomena labelled as “greenwashing”.
According to IESE professor Pascual Berrone, “many companies highlight one green positive aspect of their product or service, and hide the true impact that its production has on the environment”. With more and more NGO’s act as public watchdogs, “the consequences of getting caught can be, in terms of reputation but also economically, severe”, he says.
Most educational settlements are not overloaded by signage by design but distracted management (overlapping temporary signs, inconsistent styles) or large footprints supports the perception. Today at the usual hour we explore the literature covering exterior and interior signage with emphases on coherence and necessity.
Signage must align with the educational institution’s brand identity, including logos, colors, and typography (e.g., Helvetica font is often specified, as seen in some university standards).
Corporate logos are typically prohibited on primary exterior signage to maintain institutional focus.
Compliance with Local Zoning and Building Codes
Signs must adhere to municipal zoning regulations, which dictate size, height, placement, and illumination (e.g., NYC Building Code Appendix H or similar local codes).
Permits may be required, and signage must not obstruct traffic visibility or pedestrian pathways.
ADA Accessibility Requirements
Exterior signs identifying permanent spaces (e.g., entrances or exits) must meet Americans with Disabilities Act (ADA) standards, including visual character requirements (legible fonts, sufficient contrast).
Tactile signs with Braille are required at specific locations like exit stairways or discharge points, per the U.S. Access Board guidelines, though not all exterior signs need to be tactile.
Wayfinding and Identification Functionality
Signs should clearly identify buildings, provide directional guidance, and include essential information (e.g., building names, departments, or campus districts).
Placement is typically near main entrances, limited to one per building unless otherwise justified.
Material and Durability Standards
Materials must be weather-resistant and durable (e.g., extruded or cast aluminum with finishes like natural or dark bronze, avoiding plastic in some cases).
Maintenance considerations ensure longevity and legibility over time.
Size and Placement Restrictions
Size is often regulated (e.g., no larger than necessary for legibility, with some institutions capping temporary signs at 32 square feet).
Placement avoids upper building portions unless in urban settings or campus peripheries, ensuring aesthetic harmony.
Approval and Review Processes
Exterior signage often requires review by a campus design or sign committee (e.g., a university’s Design Review Board).
For partnerships or donor-funded buildings, a Memorandum of Understanding (MOU) may govern signage rights and standards.
Safety and Visibility Standards
Signs must not create hazards (e.g., minimum clearance of 7.5 feet above walkways, no sharp edges).
Illumination, if allowed, must comply with safety codes and enhance visibility without causing glare or distraction.
Temporary Signage Regulations
Temporary signs (e.g., banners or construction signs) have time limits (e.g., 30-90 days per year) and must be approved, with size and frequency restrictions. The National Electrical Code Article 590 covers temporary wiring for festoon illumination and defines “temporary” as 90 days.
National Institutes of Health: Moral grandstanding in public discourse
In Irish author Jonathan Swift’s 1726 satire — “Gulliver’s Travels” — Lagado is the capital of Balnibarbi whose king had invested a great fortune on building an “Academy of Projectors” so that it shall contribute to the nation’s development through research.
Gulliver describes pointless experiments conducted there — trying to change human excretion back into food, trying to extract sunbeams out of cucumbers, teaching mathematics to pupils by writing propositions on wafers and consuming them.
“Gulliver’s Travels” 1939 Production | (Max Fleischer (1883 – 1972)
“None are so blind as those who refuse to see” is a proverbial expression that has been used by many authors and public figures throughout history. The exact origin of the phrase is unknown, but it has been attributed to various sources, including the Bible, where Jesus says, “For judgment I am come into this world, that they which see not might see; and that they which see might be made blind” (John 9:39, King James Version).
The phrase has also been attributed to Jonathan Swift, an Irish author and satirist, who wrote in his 1738 work,
“Polite Conversation”: “Blind, sir? I see every day where Lord M– goes upon the bench without his bag, and you tell me he is not blind?”.
However, it is possible that the phrase existed prior to Swift and was simply popularized by him.
New update alert! The 2022 update to the Trademark Assignment Dataset is now available online. Find 1.29 million trademark assignments, involving 2.28 million unique trademark properties issued by the USPTO between March 1952 and January 2023: https://t.co/njrDAbSpwBpic.twitter.com/GkAXrHoQ9T
