He is an experienced engineer, programmer, and entrepreneur specializing in the integration of machine vision, robotics, and other automation technologies, with an extensive career in the industry. He was the founder, owner, and principal engineer for two successful vision systems integration firms. Prior to joining Landing AI he was Principal Vision Systems Architect with Integro Technologies, responsible for application evaluation and design of complex automated imaging solutions for inspection, metrology, and robotic guidance. Previously, he had served as Staff Engineer for Intelligent Robotics/Machine Vision at FANUC America Corporation. Mr. Dechow is a recipient of the A3 Automated Imaging Achievement Award honoring industry leaders for outstanding career contributions in industrial and/or scientific imaging.

Michael Stefferson received his PhD in Physics from the University of Colorado before deciding to make the jump into machine learning (ML). He spent the last several years as a Machine Learning Engineer at Manifold, where he first started working on projects in the healthcare industry. Recently, Stefferson joined the team at Cerebral as a Staff Machine Learning Engineer and hopes to leverage data to make clinical improvements for patients that will improve their lives in meaningful ways. Here, he talks about use cases, best practices, and what he has learned along his journey into the field of ML. What is your background and how did you first get into machine learning?

HPCwire presents our interview with Bronson Messer, distinguished scientist and director of Science at the Oak Ridge Leadership Computing Facility (OLCF), ORNL, and an HPCwire 2022 Person to Watch. Messer recaps ORNL's journey to exascale and sheds light on how all the pieces line up to support the all-important science. Also covered are the role of the Exascale Computing Project, insights into architectural directions and evolving HPC-AI synergies. This interview was conducted by email earlier this year. Bronson, congratulations on being named a 2022 HPCwire Person to Watch! Can you give us a summary overview of your responsibilities at Oak Ridge Leadership Computing Facility and what your position entails?

In this edition of our Engineer Innovation podcast, we hear from Chad Jackson at Lifecycle Insights in discussion with Siemens Digital Industries Software AI expert, Justin Hodges as they explore the role of machine learning for simulation engineers. Justin breaks down what can seem a daunting area into the key benefits, real-life application examples and most importantly how you can adopt the methodology to see rewards for your simulation projects. Whilst the clearest benefit is the time saved, not only for the simulation process but potentially across the entire design-cycle, that time can then be used to determine even better outcomes and improvements for future product configurations. Chad and Justin explore some real-life examples before diving into how to roll out this methodology in your organization. Ginni Saraswati: Welcome to the Engineer Innovation podcast.

Government agencies collect a lot of data, and have access to even more of it in their archives. The trick has always been trying to tap into that store of information to improve decision-making, which is a major focus in government these days. The President's Management Agenda, for example, emphasizes the importance of data-driven decision-making to improve federal services. The volume of data that most agencies are working with is such that humans can't easily tap into it for help with that decision-making. And even if they can perform searches into that data, the process is slow.

I am a "hollow vessel waiting to be filled with insights" [5]. I had one simple goal but no objective to achieve: attempt to study BlenderBot 3, Meta's "improved" artificial intelligence (AI) chatbot [7][9], inside its personalized context by interviewing it. I have no intention of learning about its pain points. None of the elements of body language come to play since such an endeavor is impossible. Having worked with many large language models (LLM) in my time, I have personally seen how they could potentially turn into raucous ideological donnybrooks [11].

In her new novel, Tomorrow, and Tomorrow, and Tomorrow, Gabrielle Zevin presents playing video games and understanding each other as kindred activities. "There is no more intimate act than play," states one character states during a fictional interview with Kotaku, "even sex." For those who will struggle to square this conviction with the image of teenagers screaming into their mics as they firebomb enemy soldiers, the book acts as a kind of corrective. To go even deeper, WIRED spoke with Zevin over Zoom about her book, the public perception of gamers, and the problematic brilliance of Metal Gear Solid. This interview has been edited for length and clarity.

It's the early 1990s, and you – a college dropout – have been tasked with babysitting your chronically disappointed father's launderette business. It is not an exciting job. You pick up rubbish, you unclog the toilet, you load laundry into machines and take it out again. But in the back room, there's a small collection of arcade machines to help customers while away the time as their shirts dry, and there's enough money in their coin hoppers to buy a whole new cabinet. And so you begin the slow process of secretly transforming your father's business into a thriving arcade, reinvesting the cash you make from washing people's dirty underwear into buying more video games.

With deep learning (DL) outperforming conventional methods for different tasks, much effort has been devoted to utilizing DL in various domains. Researchers and developers in the traffic domain have also designed and improved DL models for forecasting tasks such as estimation of traffic speed and time of arrival. However, there exist many challenges in analyzing DL models due to the black-box property of DL models and complexity of traffic data (i.e., spatio-temporal dependencies). Collaborating with domain experts, we design a visual analytics system, AttnAnalyzer, that enables users to explore how DL models make predictions by allowing effective spatio-temporal dependency analysis. The system incorporates dynamic time warping (DTW) and Granger causality tests for computational spatio-temporal dependency analysis while providing map, table, line chart, and pixel views to assist user to perform dependency and model behavior analysis. For the evaluation, we present three case studies showing how AttnAnalyzer can effectively explore model behaviors and improve model performance in two different road networks. We also provide domain expert feedback.

I believe that anyone who has seen the movie "Artificial Intelligence" was deeply impressed by the cute-looking, kind and soft-hearted robot, David, who longed for the love of human mother Monica. David was a robot made by a robot company that could love people. He replaced Monica's son Henry, who is terminally ill and falls into a vegetative state. When Henry wakes up, David is faced with the situation of being destroyed. He turns into a real human boy, and seeks to gain the love of his mother, Monica.