At RE•WORK, we are strong advocates for supporting women working towards advancing technology, so ahead of the upcoming Toronto AI Summit, on November 9-10, we set out to highlight inspirational women who are working at the forefront of AI developments, and who deserve recognition for their achievements. While we set out to create a list of just 20 – we couldn't narrow it down, as there are so many inspiring and prominent females in this space! Hear from many of them at our Toronto AI Summit, and more at our Women in AI Reception, both being held in Toronto next month. Help us to continue highlighting leading women in AI by nominating your influential woman for our next edition. RE•WORK holds Women in AI events, podcasts, and blogs. Get in touch if you'd like to collaborate or support our initiatives! Doina Precup is a researcher living in Montreal, Canada.

Explainable artificial intelligence (XAI) twin systems will be a fundamental enabler of zero-touch network and service management (ZSM) for sixth-generation (6G) wireless networks. A reliable XAI twin system for ZSM requires two composites: an extreme analytical ability for discretizing the physical behavior of the Internet of Everything (IoE) and rigorous methods for characterizing the reasoning of such behavior. In this paper, a novel neuro-symbolic explainable artificial intelligence twin framework is proposed to enable trustworthy ZSM for a wireless IoE. The physical space of the XAI twin executes a neural-network-driven multivariate regression to capture the time-dependent wireless IoE environment while determining unconscious decisions of IoE service aggregation. Subsequently, the virtual space of the XAI twin constructs a directed acyclic graph (DAG)-based Bayesian network that can infer a symbolic reasoning score over unconscious decisions through a first-order probabilistic language model. Furthermore, a Bayesian multi-arm bandits-based learning problem is proposed for reducing the gap between the expected explained score and the current obtained score of the proposed neuro-symbolic XAI twin. To address the challenges of extensible, modular, and stateless management functions in ZSM, the proposed neuro-symbolic XAI twin framework consists of two learning systems: 1) an implicit learner that acts as an unconscious learner in physical space, and 2) an explicit leaner that can exploit symbolic reasoning based on implicit learner decisions and prior evidence. Experimental results show that the proposed neuro-symbolic XAI twin can achieve around 96.26% accuracy while guaranteeing from 18% to 44% more trust score in terms of reasoning and closed-loop automation.

Mark Minevich is a highly regarded and trusted Digital Cognitive AI Strategist, Artificial Intelligence expert, Global Social Innovation and Technology Executive, UN Advisor, Leading Author and Columnist, Private Investor/Venture Capitalist, and the principal founder and President of Going Global Ventures. He is an award-winning technology executive and has published two books and over 40 articles on AI, Industry 4.0, IoT. Mark is newly appointed Chairman of the Executive committee of AI for Good Foundation. Mark is a Chief Digital Strategist at the International Research Centre for AI, under the auspices of UNESCO. Mark is Sr. Advisor to Boston Consulting Group Boston Consulting Group. Currently, he serves as the strategic advisor and Global ambassador to the CEO and Chairman of New York based Amelia/ IPsoft Inc. Mark collaborates and advises large global enterprises both in the US and Japan (Hitachi). Mark is Advisory Partner to Canadian Growth Investments and Business Advisor to Infosec Global.   What is the definition of leadership for you? As a leader, should one create more followers or leaders? I believe that a leader should create more leaders, not followers. A leader is someone who inspires and motivates others to achieve their goals. A leader also has […]

What do you think about driverless cars? Would you ride in one? Do you think they are the way of the future? In "Stuck on the Streets of San Francisco in a Driverless Car," the Times technology reporter Cade Metz went for a ride in the back seat of an experimental autonomous vehicle and wrote about his experience: It was about 9 p.m. on a cool Tuesday evening in San Francisco this month when I hailed a car outside a restaurant a few blocks from Golden Gate Park. A few minutes later, as I waited at a stoplight, a white Mercedes pulled up next to me.

As chief data officer of payroll and benefits management company ADP, Jack Berkowitz has three primary responsibilities. One is to oversee the organization's data overall, ensuring that functions like data governance, security, and analytics, are running well. Another is to build ADP's data products, such as people analytics and benchmark tools. But the responsibility that's of most interest to Me, Myself, and AI hosts Sam Ransbotham and Shervin Khodabandeh is Jack's oversight of the organization's use of artificial intelligence. In this episode of the podcast, Jack describes how focusing on the outcomes the organization wants to achieve leads to better processes and results. He also dives into the topic of AI ethics and outlines how other organizations might consider assembling an AI ethics board. Jack Berkowitz is chief data officer at ADP, where he leads the company's data security and governance, data platforms, and analytics/machine learning operations. His role also involves partnering with stakeholders to develop new data initiatives to improve clients' experience and ADP's competitive position. Berkowitz joined ADP in 2018 as the senior vice president of product development for the DataCloud people analytics and compensation benchmarking solution.

A new chatbot start-up from two top artificial intelligence talents lets anyone strike up a conversation with impersonations of Donald Trump, Elon Musk, Albert Einstein and Sherlock Holmes. Registered users type in messages and get responses. They can also create a chatbot of their own on Character.ai, "There were reports of possible voter fraud and I wanted an investigation," the Trump bot said. The start-up's two founders helped create Google's artificial intelligence project LaMDA, which Google keeps closely guarded while it develops safeguards against social risks.

The field of artificial intelligence has never lacked for hype. Back in 1965, AI pioneer Herb Simon declared, "Machines will be capable, within 20 years, of doing any work a man can do." That hasn't happened -- but there certainly have been noteworthy advances, especially with the rise of "deep learning" systems, in which programs plow through massive data sets looking for patterns, and then try to make predictions. Perhaps most famously, AIs that use deep learning can now beat the best human Go players (some years after computers bested humans at chess and Jeopardy). Mastering language has proven tougher, but a program called GPT-3, developed by OpenAI, can produce human-like text, including poetry and prose, in response to prompts.

Sitting on a mattress in an art gallery turned bunker in Kharkiv, with Russian munitions "howling and thumping" overhead, Dariia Selishcheva began making a video game. Jauntily titled What's Up in a Kharkiv Bomb Shelter, it was an attempt at self-distraction that evolved into a work of journalistic "autofiction". It offers a brief, vivid portrait of life under bombardment in the early months of Russia's unprovoked invasion of Ukraine, based closely on conversations with Selishcheva's neighbours in the shelter and correspondence with friends hiding elsewhere. "My goal was to provide an opportunity for ordinary people's voices to be heard, to capture a fragment of life in a shelter," Selishcheva says. "I wanted everyone to know about their lives and thoughts."

Recent years have seen advances on principles and guidance relating to accountable and ethical use of artificial intelligence (AI) spring up around the globe. Specifically, Data Privacy, Accountability, Interpretability, Robustness, and Reasoning have been broadly recognized as fundamental principles of using machine learning (ML) technologies on decision-critical and/or privacy-sensitive applications. On the other hand, in tremendous real-world applications, data itself can be well represented as various structured formalisms, such as graph-structured data (e.g., networks), grid-structured data (e.g., images), sequential data (e.g., text), etc. By exploiting the inherently structured knowledge, one can design plausible approaches to identify and use more relevant variables to make reliable decisions, thereby facilitating real-world deployments.

I did a startup in 1998 by applying AI to the lead generation and qualification problem. It was early. The data was not yet rich enough. Now, the data is there. Can the problem finally be solved at the right level of sophistication? Sramana Mitra: Let's go to the very beginning of your journey. Where were you born and raised? Jonathan Spier: I'm a California guy raised in San Diego. I came up here to go to school at Berkeley. I was never able to escape again. Sramana Mitra: What did you do after Berkeley? Jonathan Spier: I went briefly into consulting and then I landed at a company called Ariba. I was the number 85 employee. Within a few years, we were 3,500 people. It was a fun place to be. Sramana Mitra: We have the Ariba case study. Keith Krach was on the series. Jonathan Spier: He was a great leader. That whole team was amazing. I was the youngest person they hired. It was a really senior team they had by the time I joined. I got pretty much hooked on growth