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

Inappropriate design and deployment of machine learning (ML) systems leads to negative downstream social and ethical impact -- described here as social and ethical risks -- for users, society and the environment. Despite the growing need to regulate ML systems, current processes for assessing and mitigating risks are disjointed and inconsistent. We interviewed 30 industry practitioners on their current social and ethical risk management practices, and collected their first reactions on adapting safety engineering frameworks into their practice -- namely, System Theoretic Process Analysis (STPA) and Failure Mode and Effects Analysis (FMEA). Our findings suggest STPA/FMEA can provide appropriate structure toward social and ethical risk assessment and mitigation processes. However, we also find nontrivial challenges in integrating such frameworks in the fast-paced culture of the ML industry. We call on the ML research community to strengthen existing frameworks and assess their efficacy, ensuring that ML systems are safer for all people.

Heart rate variability is an indicator of heart health. Mohammad Moshawrab's research on this topic received the best paper award at the 19th International Conference on Mobile Systems and Persuasive Computing (MobiSPC), held August 9-11 in Niagara Falls, Canada. Like the other papers accepted by MobiSPC 2022, " Cardiovascular Events Prediction using Artificial Intelligence Models and Heart Rate Variability"is published by Elsevier Science in the online open access Procedia Computer Science series. Mohammad Moshawrab is a doctoral student in engineering at the Université du Québec à Rimouski (UQAR), which welcomes about 6,700 students each year, including nearly 600 international students from more than 45 countries. His doctorate in engineering aims to train specialists capable of designing and carrying out independently a research program to advance the state of knowledge in the engineering of physical systems and industrial processes.

Assistant Professor Jia Deng has been selected for a 2018 Sloan Research Fellowship by the Alfred P. Sloan Foundation for his work in computer vision and machine learning. Prof. Deng directs the Michigan Vision & Learning Lab. His research seeks to enable computers to see and think like humans. In 2015, Prof. Deng was awarded a Google Faculty Research Award for his work on large-scale image understanding. He aimed to advance image understanding in terms of recognizing the relationships present between multiple entities in images. The development of such an image understanding system would enable image retrieval for complex or arbitrary queries, such as "is there a person standing on a red chair and fixing the light?"