Steven Adler used to lead product safety at OpenAI. On this week's episode of, he talks about what AI users should know about their bots. When the history of AI is written, Steven Adler may just end up being its Paul Revere--or at least, one of them--when it comes to safety. Last month Adler, who spent four years in various safety roles at OpenAI, wrote a piece for The New York Times with a rather alarming title: "I Led Product Safety at OpenAI. In it, he laid out the problems OpenAI faced when it came to allowing users to have erotic conversations with chatbots while also protecting them from any impacts those interactions could have on their mental health. "Nobody wanted to be the morality police, but we lacked ways to measure and manage erotic usage carefully," he wrote. "We decided AI-powered erotica would have to wait." Adler wrote his op-ed because OpenAI CEO Sam Altman had recently announced that the company would soon allow " erotica for verified adults ."

Product recalls provide valuable insights into potential risks and hazards within the engineering design process, yet their full potential remains underutilized. In this study, we curate data from the United States Consumer Product Safety Commission (CPSC) recalls database to develop a multimodal dataset, RECALL-MM, that informs data-driven risk assessment using historical information, and augment it using generative methods. Patterns in the dataset highlight specific areas where improved safety measures could have significant impact. We extend our analysis by demonstrating interactive clustering maps that embed all recalls into a shared latent space based on recall descriptions and product names. Leveraging these data-driven tools, we explore three case studies to demonstrate the dataset's utility in identifying product risks and guiding safer design decisions. The first two case studies illustrate how designers can visualize patterns across recalled products and situate new product ideas within the broader recall landscape to proactively anticipate hazards. In the third case study, we extend our approach by employing a large language model (LLM) to predict potential hazards based solely on product images. This demonstrates the model's ability to leverage visual context to identify risk factors, revealing strong alignment with historical recall data across many hazard categories. However, the analysis also highlights areas where hazard prediction remains challenging, underscoring the importance of risk awareness throughout the design process. Collectively, this work aims to bridge the gap between historical recall data and future product safety, presenting a scalable, data-driven approach to safer engineering design.

Ensuring safety of the products offered to the customers is of paramount importance to any e- commerce platform. Despite stringent quality and safety checking of products listed on these platforms, occasionally customers might receive a product that can pose a safety issue arising out of its use. In this paper, we present an innovative mechanism of how a large scale multinational e-commerce platform, Zalando, uses Natural Language Processing techniques to assist timely investigation of the potentially unsafe products mined directly from customer written claims in unstructured plain text. We systematically describe the types of safety issues that concern Zalando customers. We demonstrate how we map this core business problem into a supervised text classification problem with highly imbalanced, noisy, multilingual data in a AI-in-the-loop setup with a focus on Key Performance Indicator (KPI) driven evaluation. Finally, we present detailed ablation studies to show a comprehensive comparison between different classification techniques. We conclude the work with how this NLP model was deployed.

On March 2, 2021, at a virtual forum attended by stakeholders across the entire industry, the Consumer Product Safety Commission (CPSC) reminded us all that it has the last say on regulating AI and machine learning consumer product safety. The CPSC defines AI as "any method for programming computers or products to enable them to carry out tasks or behaviors that would require intelligence if performed by humans" and machine learning as "an iterative process of applying models or algorithms to data sets to learn and detect patterns and/or perform tasks, such as prediction or decision making that can approximate some aspects of intelligence."3 To inform the ongoing discussion on how to regulate AI, machine learning, and related technologies, the CPSC provides the following list of considerations: Do AI and machine learning affect consumer product safety? Do AI and machine learning affect consumer product safety? UL 4600 Standard for Safety for the Evaluation of Autonomous Products covers "fully autonomous systems that move such as self-driving cars along with applications in mining, agriculture, maintenance, and other vehicles including lightweight unmanned aerial vehicles."5

American households are increasingly connected internally through the use of artificially intelligent appliances.1 But who regulates the safety of those dishwashers, microwaves, refrigerators, and vacuums powered by artificial intelligence (AI)? On March 2, 2021, at a virtual forum attended by stakeholders across the entire industry, the Consumer Product Safety Commission (CPSC) reminded us all that it has the last say on regulating AI and machine learning consumer product safety. The CPSC is an independent agency comprised of five commissioners who are nominated by the president and confirmed by the Senate to serve staggered seven-year terms. With the Biden administration's shift away from the deregulation agenda of the prior administration and three potential opportunities to staff the commission, consumer product manufacturers, distributors, and retailers should expect increased scrutiny and enforcement.2

Job Description The successful candidate will own all aspects of regulatory compliance processes/practices programs (including driving continuous improvement) within Intel's Internet of Things (IoT) Group. Product Regulatory Engr will support growth through expanded footprint in key verticals such as transportation, industrial/energy, retail, home/buildings etc through development and implementation of product regulatory test plans to ensure that the system development platforms (or products) meet regulatory requirements for countries where the products/platforms will be shipped. Responsibilities include: supporting design teams on product safety, functional safety, connectivity (WiFi-BT, Zigbee, Cellular, RFID, NFC), and EMC related issues.; Minimum QualificationsBS in Electrical Engineering, Physics or related field.• 3 years with regulatory certifications in product safety, EMC and/or RF/wireless• 3 years Rf/ Wireless regulatory certifications – FCC, PTCRB, experience with any of the carriers – ATT, Verizon, Nokia, Siemens, etc• 3 years EMC regulatory certification – FCC, CE, CISPRPreferred qualifications: – MS or PhD in electrical engineering or physics or related field preferred.- Unrestricted right to work in the US without sponsorship- Global product regulatory knowledge; inclusive of wireless safety EMC- Experience with wireless RF test methods and equipment- Experience in the design of wireless/RF systems including antennas is highly desired.-

Artificial intelligence (AI) has permeated every aspect of our lives. From major advancements in medicine to transforming the way business is conducted, machine learning shows great potential to improve the quality of our lives in a variety of ways. A behind-the-scenes look into different companies' AI ventures shows that certain aspects of machine learning even aid in making the world a safer place. Manufacturing problems can have serious negative impacts on a business. From cars to home appliances, product safety is of utmost importance when it comes to customer satisfaction.

Twitter is what's happening in the world and what people are talking about right now. Given the real-time nature of this platform, discovery or helping users find what they want at the right time is a key challenge. The Recommendations team builds infrastructure to address these for many products like who-to-follow, push & email recommendations, etc. In this talk, we will discuss some of these problems, the interesting challenges they pose at scale, and dive into specific applications.

Robots are the technology of the future. But the current legal system is incapable of handling them. This generic statement is often the premise for considerations about the possibility of awarding rights (and liabilities) to these machines at some, less-than clearly identified, point in time. Discussing the adequacy of existing regulation in accommodating new technologies is certainly necessary, but the ontological approach is incorrect. The recent Resolution of the European Parliament (henceforth Resolution) has great political relevance and strategic importance in the development of a European Robotic Industry.