We present AutoDOViz, an interactive user interface for automated decision optimization (AutoDO) using reinforcement learning (RL). Decision optimization (DO) has classically being practiced by dedicated DO researchers where experts need to spend long periods of time fine tuning a solution through trial-and-error. AutoML pipeline search has sought to make it easier for a data scientist to find the best machine learning pipeline by leveraging automation to search and tune the solution. More recently, these advances have been applied to the domain of AutoDO, with a similar goal to find the best reinforcement learning pipeline through algorithm selection and parameter tuning. However, Decision Optimization requires significantly more complex problem specification when compared to an ML problem. AutoDOViz seeks to lower the barrier of entry for data scientists in problem specification for reinforcement learning problems, leverage the benefits of AutoDO algorithms for RL pipeline search and finally, create visualizations and policy insights in order to facilitate the typical interactive nature when communicating problem formulation and solution proposals between DO experts and domain experts. In this paper, we report our findings from semi-structured expert interviews with DO practitioners as well as business consultants, leading to design requirements for human-centered automation for DO with RL. We evaluate a system implementation with data scientists and find that they are significantly more open to engage in DO after using our proposed solution. AutoDOViz further increases trust in RL agent models and makes the automated training and evaluation process more comprehensible. As shown for other automation in ML tasks, we also conclude automation of RL for DO can benefit from user and vice-versa when the interface promotes human-in-the-loop.

Amid a recent explosion of rapid and thrilling advances in consumer-facing artificial intelligence applications, the AI community made up of industry experts, academics and folks who are just plain interested in the tech are looking forward to AI Week. The international event begins Monday, hosted by The Blavatnik Interdisciplinary Cyber Research Center and The Yuval Ne'eman Workshop for Science, Technology & Security, in cooperation with TAD Center for Artificial Intelligence and Data Science at Tel Aviv University. There, the AI community will gather to discuss the technology's development, potential future application and inherent ethical quandaries, steering the ship of artificial intelligence into the new year by answering the industry's current burning questions, such as where the next breakthroughs will be, how the working class will be impacted by these tools and what kind of fine-tuning is required for current applications. To answer these questions and set the stage for AI Week, The Jerusalem Post spoke with Nadav Cohen, one of the event's many keynote speakers. Cohen is a professor of computer science, a deep learning researcher and the chief scientist at Imubit, which implements deep learning for optimizing manufacturing processes, enabling real time control of large manufacturing facilities and making them run optimally, which is good for both profit and sustainability. It seems as though, in 2023, every Tom, Dick and Harry has their eyes on AI and its development thanks to the meteoric popularity and widespread usage of generative AI platforms like ChatGPT and DALL-E.

As we continue to make strides in the field of Artificial Intelligence (AI), one concept that has been gaining momentum is Artificial General Intelligence (AGI). Unlike traditional AI systems, AGI aims to replicate the human-like ability to learn, reason and adapt in any given situation. In other words, AGI seeks to create a machine that can perform any intellectual task that a human can do. This level of sophistication is still far from being achieved, but experts predict that once we create AGI, it will be a major turning point in human history, with implications far beyond what we can currently imagine. In this article, we'll dive into what experts are saying about AGI and what it could mean for the future of humanity.

The first time Captain Kirk had a conversation with the ship's computer was in 1966 during Episode 13 of Season 1 in the classic Star Trek series. Calling it a "conversation" is quite generous, for it was really a series of stiff questions from Kirk, each prompting an even stiffer response from the computer. There was no conversational back-and-forth, no questions from the AI asking for elaboration or context. And yet, for the last 57 years, computer scientists have not been able to exceed this stilted 1960s vision of human-machine dialog. Even platforms like Siri and Alexa, created by some of the world's largest companies at great expense have not allowed for anything that feels like real-time natural conversation.

AI tools have allowed researchers to solve complex mathematical problems.Credit: Fadel Senna/AFP/Getty As interest in chatbots spreads like wildfire, mathematicians are beginning to explore how artificial intelligence (AI) could help them to do their work. Whether it's assisting with verifying human-written work or suggesting new ways to solve difficult problems, automation is beginning to change the field in ways that go beyond mere calculation, researchers say. "We're looking at a very specific question: will machines change math?" says Andrew Granville, a number theorist at the University of Montreal in Canada. A workshop at the University of California, Los Angeles (UCLA), this week explored this question, aiming to build bridges between mathematicians and computer scientists. "Most mathematicians are completely unaware of these opportunities," says one of the event's organizers, Marijn Heule, a computer scientist at Carnegie Mellon University in Pittsburgh, Pennsylvania.

Last year, my regular co-authors – John Morris (Auburn), Marty Mayer (UNC Pembroke), Rob Kenter (Center for Policing Equity) – published a little book on policymaking. The book took a year or so to gestate, as books will, but the dictates of academia also point to a real advantage of doing one: books spawn articles and articles spawn fame and glory for you. Sure enough, after the book hit the stands, we jumped into extending, updating and expanding its reach. We recently presented one of these at the Southern Political Science Association's annual meetings and another is in the works. To pick up sorely needed extra fuel, we added another author, Joe Aistrup (also Auburn) to the mix.

Generative Artificial Intelligence (AI) models such as OpenAI's ChatGPT have the potential to revolutionize Statistical Process Control (SPC) practice, learning, and research. However, these tools are in the early stages of development and can be easily misused or misunderstood. In this paper, we give an overview of the development of Generative AI. Specifically, we explore ChatGPT's ability to provide code, explain basic concepts, and create knowledge related to SPC practice, learning, and research. By investigating responses to structured prompts, we highlight the benefits and limitations of the results. Our study indicates that the current version of ChatGPT performs well for structured tasks, such as translating code from one language to another and explaining well-known concepts but struggles with more nuanced tasks, such as explaining less widely known terms and creating code from scratch. We find that using new AI tools may help practitioners, educators, and researchers to be more efficient and productive. However, in their current stages of development, some results are misleading and wrong. Overall, the use of generative AI models in SPC must be properly validated and used in conjunction with other methods to ensure accurate results.

Existing research on human-AI collaborative decision-making focuses mainly on the interaction between AI and individual decision-makers. There is a limited understanding of how AI may perform in group decision-making. This paper presents a wizard-of-oz study in which two participants and an AI form a committee to rank three English essays. One novelty of our study is that we adopt a speculative design by endowing AI equal power to humans in group decision-making.We enable the AI to discuss and vote equally with other human members. We find that although the voice of AI is considered valuable, AI still plays a secondary role in the group because it cannot fully follow the dynamics of the discussion and make progressive contributions. Moreover, the divergent opinions of our participants regarding an "equal AI" shed light on the possible future of human-AI relations.

Last week, both Microsoft and Google announced that they would incorporate AI programs similar to ChatGPT into their search engines--bids to transform how we find information online into a conversation with an omniscient chatbot. One problem: These language models are notorious mythomaniacs. In a promotional video, Google's Bard chatbot made a glaring error about astronomy--misstating by well over a decade when the first photo of a planet outside our solar system was captured--that caused its parent company's stock to slide as much as 9 percent. The live demo of the new Bing, which incorporates a more advanced version of ChatGPT, was riddled with embarrassing inaccuracies too. Even as the past few months would have many believe that artificial intelligence is finally living up to its name, fundamental limits to this technology suggest that this month's announcements might actually lie somewhere between the Google Glass meltdown and an iPhone update--at worst science-fictional hype, at best an incremental improvement accompanied by a maelstrom of bugs. The trouble arises when we treat chatbots not just as search bots, but as having something like a brain--when companies and users trust programs like ChatGPT to analyze their finances, plan travel and meals, or provide even basic information.

Safety and security are everyone's number one priority. Cyberguy lists some of the best tech to keep you safe at home or on the go. Artificial intelligence software is growing quickly in popularity, especially among tech companies. This has led to many wondering if this is the end of human interaction as we know it, and some are fearful that these AI robots could begin taking away jobs from you and me. CLICK TO GET KURT'S CYBERGUY NEWSLETTER WITH QUICK TIPS, TECH REVIEWS, SECURITY ALERTS AND EASY HOW-TO'S TO MAKE YOU SMARTER AI is the computer systems simulation of human intelligence processes, which include learning, reasoning, and self-correction.